>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Confidential computing is a technology that aims to enhance data privacy and security by providing encrypted computation on sensitive data and isolating data from apps in a fenced off enclave during processing. The concept of confidential computing is gaining popularity, especially in the cloud computing space where sensitive data is often stored and of course processed. However, there are some who view confidential computing as an unnecessary technology in a marketing ploy by cloud providers aimed at calming customers who are cloud phobic. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this Breaking Analysis, we revisit the notion of confidential computing, and to do so, we'll invite two Google experts to the show, but before we get there, let's summarize briefly. There's not a ton of ETR data on the topic of confidential computing. I mean, it's a technology that's deeply embedded into silicon and computing architectures. But at the highest level, security remains the number one priority being addressed by IT decision makers in the coming year as shown here. And this data is pretty much across the board by industry, by region, by size of company. I mean we dug into it and the only slight deviation from the mean is in financial services. The second and third most cited priorities, cloud migration and analytics, are noticeably closer to cybersecurity in financial services than in other sectors, likely because financial services has always been hyper security conscious, but security is still a clear number one priority in that sector. The idea behind confidential computing is to better address threat models for data in execution. Protecting data at rest and data and transit have long been a focus of security approaches, but more recently, silicon manufacturers have introduced architectures that separate data and applications from the host system. Arm, Intel, AMD, Nvidia and other suppliers are all on board, as are the big cloud players. Now the argument against confidential computing is that it narrowly focuses on memory encryption and it doesn't solve the biggest problems in security. Multiple system images updates different services and the entire code flow aren't directly addressed by memory encryption, rather to truly attack these problems, many believe that OSs need to be re-engineered with the attacker and hacker in mind. There are so many variables and at the end of the day, critics say the emphasis on confidential computing made by cloud providers is overstated and largely hype. This tweet from security researcher Rodrigo Branco sums up the sentiment of many skeptics. He says, "Confidential computing is mostly a marketing campaign for memory encryption. It's not driving the industry towards the hard open problems. It is selling an illusion." Okay. Nonetheless, encrypting data in use and fencing off key components of the system isn't a bad thing, especially if it comes with the package essentially for free. There has been a lack of standardization and interoperability between different confidential computing approaches. But the confidential computing consortium was established in 2019 ostensibly to accelerate the market and influence standards. Notably, AWS is not part of the consortium, likely because the politics of the consortium were probably a conundrum for AWS because the base technology defined by the the consortium is seen as limiting by AWS. This is my guess, not AWS's words, and but I think joining the consortium would validate a definition which AWS isn't aligned with. And two, it's got a lead with this Annapurna acquisition. This was way ahead with Arm integration and so it probably doesn't feel the need to validate its competitors. Anyway, one of the premier members of the confidential computing consortium is Google, along with many high profile names including Arm, Intel, Meta, Red Hat, Microsoft, and others. And we're pleased to welcome two experts on confidential computing from Google to unpack the topic, Nelly Porter is head of product for GCP confidential computing and encryption, and Dr. Patricia Florissi is the technical director for the office of the CTO at Google Cloud. Welcome Nelly and Patricia, great to have you. >> Great to be here. >> Thank you so much for having us. >> You're very welcome. Nelly, why don't you start and then Patricia, you can weigh in. Just tell the audience a little bit about each of your roles at Google Cloud. >> So I'll start, I'm owning a lot of interesting activities in Google and again security or infrastructure securities that I usually own. And we are talking about encryption and when encryption and confidential computing is a part of portfolio in additional areas that I contribute together with my team to Google and our customers is secure software supply chain. Because you need to trust your software. Is it operate in your confidential environment to have end-to-end story about if you believe that your software and your environment doing what you expect, it's my role. >> Got it. Okay. Patricia? >> Well, I am a technical director in the office of the CTO, OCTO for short, in Google Cloud. And we are a global team. We include former CTOs like myself and senior technologists from large corporations, institutions and a lot of success, we're startups as well. And we have two main goals. First, we walk side by side with some of our largest, more strategic or most strategical customers and we help them solve complex engineering technical problems. And second, we are devise Google and Google Cloud engineering and product management and tech on there, on emerging trends and technologies to guide the trajectory of our business. We are unique group, I think, because we have created this collaborative culture with our customers. And within OCTO, I spend a lot of time collaborating with customers and the industry at large on technologies that can address privacy, security, and sovereignty of data in general. >> Excellent. Thank you for that both of you. Let's get into it. So Nelly, what is confidential computing? From Google's perspective, how do you define it? >> Confidential computing is a tool and it's still one of the tools in our toolbox. And confidential computing is a way how we would help our customers to complete this very interesting end-to-end lifecycle of the data. And when customers bring in the data to cloud and want to protect it as they ingest it to the cloud, they protect it at rest when they store data in the cloud. But what was missing for many, many years is ability for us to continue protecting data and workloads of our customers when they running them. And again, because data is not brought to cloud to have huge graveyard, we need to ensure that this data is actually indexed. Again, there is some insights driven and drawn from this data. You have to process this data and confidential computing here to help. Now we have end to end protection of our customer's data when they bring the workloads and data to cloud, thanks to confidential computing. >> Thank you for that. Okay, we're going to get into the architecture a bit, but before we do, Patricia, why do you think this topic of confidential computing is such an important technology? Can you explain, do you think it's transformative for customers and if so, why? >> Yeah, I would maybe like to use one thought, one way, one intuition behind why confidential commuting matters, because at the end of the day, it reduces more and more the customer's thresh boundaries and the attack surface. That's about reducing that periphery, the boundary in which the customer needs to mind about trust and safety. And in a way, is a natural progression that you're using encryption to secure and protect the data. In the same way that we are encrypting data in transit and at rest, now we are also encrypting data while in use. And among other beneficials, I would say one of the most transformative ones is that organizations will be able to collaborate with each other and retain the confidentiality of the data. And that is across industry, even though it's highly focused on, I wouldn't say highly focused, but very beneficial for highly regulated industries. It applies to all of industries. And if you look at financing for example, where bankers are trying to detect fraud, and specifically double finance where you are, a customer is actually trying to get a finance on an asset, let's say a boat or a house, and then it goes to another bank and gets another finance on that asset. Now bankers would be able to collaborate and detect fraud while preserving confidentiality and privacy of the data. >> Interesting. And I want to understand that a little bit more but I'm going to push you a little bit on this, Nelly, if I can because there's a narrative out there that says confidential computing is a marketing ploy, I talked about this upfront, by cloud providers that are just trying to placate people that are scared of the cloud. And I'm presuming you don't agree with that, but I'd like you to weigh in here. The argument is confidential computing is just memory encryption and it doesn't address many other problems. It is over hyped by cloud providers. What do you say to that line of thinking? >> I absolutely disagree, as you can imagine, with this statement, but the most importantly is we mixing multiple concepts, I guess. And exactly as Patricia said, we need to look at the end-to-end story, not again the mechanism how confidential computing trying to again, execute and protect a customer's data and why it's so critically important because what confidential computing was able to do, it's in addition to isolate our tenants in multi-tenant environments the cloud covering to offer additional stronger isolation. They called it cryptographic isolation. It's why customers will have more trust to customers and to other customers, the tenant that's running on the same host but also us because they don't need to worry about against threats and more malicious attempts to penetrate the environment. So what confidential computing is helping us to offer our customers, stronger isolation between tenants in this multi-tenant environment, but also incredibly important, stronger isolation of our customers, so tenants from us. We also writing code, we also software providers will also make mistakes or have some zero days. Sometimes again us introduced, sometimes introduced by our adversaries. But what I'm trying to say by creating this cryptographic layer of isolation between us and our tenants and amongst those tenants, we're really providing meaningful security to our customers and eliminate some of the worries that they have running on multi-tenant spaces or even collaborating to gather this very sensitive data knowing that this particular protection is available to them. >> Okay, thank you. Appreciate that. And I think malicious code is often a threat model missed in these narratives. Operator access, yeah, maybe I trust my clouds provider, but if I can fence off your access even better, I'll sleep better at night. Separating a code from the data, everybody's, Arm, Intel, AMD, Nvidia, others, they're all doing it. I wonder if, Nelly, if we could stay with you and bring up the slide on the architecture. What's architecturally different with confidential computing versus how operating systems and VMs have worked traditionally. We're showing a slide here with some VMs, maybe you could take us through that. >> Absolutely. And Dave, the whole idea for Google and now industry way of dealing with confidential computing is to ensure that three main property is actually preserved. Customers don't need to change the code. They can operate on those VMs exactly as they would with normal non-confidential VMs, but to give them this opportunity of lift and shift or no changing their apps and performing and having very, very, very low latency and scale as any cloud can, something that Google actually pioneer in confidential computing. I think we need to open and explain how this magic was actually done. And as I said, it's again the whole entire system have to change to be able to provide this magic. And I would start with we have this concept of root of trust and root of trust where we will ensure that this machine, when the whole entire post has integrity guarantee, means nobody changing my code on the most low level of system. And we introduce this in 2017 called Titan. It was our specific ASIC, specific, again, inch by inch system on every single motherboard that we have that ensures that your low level former, your actually system code, your kernel, the most powerful system is actually proper configured and not changed, not tampered. We do it for everybody, confidential computing included. But for confidential computing, what we have to change, we bring in AMD, or again, future silicon vendors and we have to trust their former, their way to deal with our confidential environments. And that's why we have obligation to validate integrity, not only our software and our former but also former and software of our vendors, silicon vendors. So we actually, when we booting this machine, as you can see, we validate that integrity of all of the system is in place. It means nobody touching, nobody changing, nobody modifying it. But then we have this concept of AMD secure processor, it's special ASICs, best specific things that generate a key for every single VM that our customers will run or every single node in Kubernetes or every single worker thread in our Hadoop or Spark capability. We offer all of that. And those keys are not available to us. It's the best keys ever in encryption space because when we are talking about encryption, the first question that I'm receiving all the time, where's the key, who will have access to the key? Because if you have access to the key then it doesn't matter if you encrypted or not. So, but the case in confidential computing provides so revolutionary technology, us cloud providers, who don't have access to the keys. They sitting in the hardware and they head to memory controller. And it means when hypervisors that also know about these wonderful things saying I need to get access to the memories that this particular VM trying to get access to, they do not decrypt the data, they don't have access to the key because those keys are random, ephemeral and per VM, but the most importantly, in hardware not exportable. And it means now you would be able to have this very interesting role that customers or cloud providers will not be able to get access to your memory. And what we do, again, as you can see our customers don't need to change their applications, their VMs are running exactly as it should run and what you're running in VM, you actually see your memory in clear, it's not encrypted, but God forbid is trying somebody to do it outside of my confidential box. No, no, no, no, no, they would not be able to do it. Now you'll see cyber and it's exactly what combination of these multiple hardware pieces and software pieces have to do. So OS is also modified. And OS is modified such way to provide integrity. It means even OS that you're running in your VM box is not modifiable and you, as customer, can verify. But the most interesting thing, I guess, how to ensure the super performance of this environment because you can imagine, Dave, that encrypting and it's additional performance, additional time, additional latency. So we were able to mitigate all of that by providing incredibly interesting capability in the OS itself. So our customers will get no changes needed, fantastic performance and scales as they would expect from cloud providers like Google. >> Okay, thank you. Excellent. Appreciate that explanation. So, again, the narrative on this as well, you've already given me guarantees as a cloud provider that you don't have access to my data, but this gives another level of assurance, key management as they say is key. Now humans aren't managing the keys, the machines are managing them. So Patricia, my question to you is, in addition to, let's go pre confidential computing days, what are the sort of new guarantees that these hardware-based technologies are going to provide to customers? >> So if I am a customer, I am saying I now have full guarantee of confidentiality and integrity of the data and of the code. So if you look at code and data confidentiality, the customer cares and they want to know whether their systems are protected from outside or unauthorized access, and that recovered with Nelly, that it is. Confidential computing actually ensures that the applications and data internals remain secret, right? The code is actually looking at the data, the only the memory is decrypting the data with a key that is ephemeral and per VM and generated on demand. Then you have the second point where you have code and data integrity, and now customers want to know whether their data was corrupted, tampered with or impacted by outside actors. And what confidential computing ensures is that application internals are not tampered with. So the application, the workload as we call it, that is processing the data, it's also, it has not been tampered and preserves integrity. I would also say that this is all verifiable. So you have attestation and these attestation actually generates a log trail and the log trail guarantees that, provides a proof that it was preserved. And I think that the offer's also a guarantee of what we call ceiling, this idea that the secrets have been preserved and not tampered with, confidentiality and integrity of code and data. >> Got it. Okay, thank you. Nelly, you mentioned, I think I heard you say that the applications, it's transparent, you don't have to change the application, it just comes for free essentially. And we showed some various parts of the stack before. I'm curious as to what's affected, but really more importantly, what is specifically Google's value add? How do partners participate in this, the ecosystem, or maybe said another way, how does Google ensure the compatibility of confidential computing with existing systems and applications? >> And a fantastic question by the way. And it's very difficult and definitely complicated world because to be able to provide these guarantees, actually a lot of work was done by community. Google is very much operate in open, so again, our operating system, we working with operating system repository OSs, OS vendors to ensure that all capabilities that we need is part of the kernels, are part of the releases and it's available for customers to understand and even explore if they have fun to explore a lot of code. We have also modified together with our silicon vendors a kernel, host kernel to support this capability and it means working this community to ensure that all of those patches are there. We also worked with every single silicon vendor as you've seen, and that's what I probably feel that Google contributed quite a bit in this whole, we moved our industry, our community, our vendors to understand the value of easy to use confidential computing or removing barriers. And now I don't know if you noticed, Intel is pulling the lead and also announcing their trusted domain extension, very similar architecture. And no surprise, it's, again, a lot of work done with our partners to, again, convince, work with them and make this capability available. The same with Arm this year, actually last year, Arm announced their future design for confidential computing. It's called Confidential Computing Architecture. And it's also influenced very heavily with similar ideas by Google and industry overall. So it's a lot of work in confidential computing consortiums that we are doing, for example, simply to mention, to ensure interop, as you mentioned, between different confidential environments of cloud providers. They want to ensure that they can attest to each other because when you're communicating with different environments, you need to trust them. And if it's running on different cloud providers, you need to ensure that you can trust your receiver when you are sharing your sensitive data workloads or secret with them. So we coming as a community and we have this attestation sig, the, again, the community based systems that we want to build and influence and work with Arm and every other cloud providers to ensure that we can interrupt and it means it doesn't matter where confidential workloads will be hosted, but they can exchange the data in secure, verifiable and controlled by customers way. And to do it, we need to continue what we are doing, working open, again, and contribute with our ideas and ideas of our partners to this role to become what we see confidential computing has to become, it has to become utility. It doesn't need to be so special, but it's what we want it to become. >> Let's talk about, thank you for that explanation. Let's talk about data sovereignty because when you think about data sharing, you think about data sharing across the ecosystem and different regions and then of course data sovereignty comes up. Typically public policy lags, the technology industry and sometimes is problematic. I know there's a lot of discussions about exceptions, but Patricia, we have a graphic on data sovereignty. I'm interested in how confidential computing ensures that data sovereignty and privacy edicts are adhered to, even if they're out of alignment maybe with the pace of technology. One of the frequent examples is when you delete data, can you actually prove that data is deleted with a hundred percent certainty? You got to prove that and a lot of other issues. So looking at this slide, maybe you could take us through your thinking on data sovereignty. >> Perfect. So for us, data sovereignty is only one of the three pillars of digital sovereignty. And I don't want to give the impression that confidential computing addresses it all. That's why we want to step back and say, hey, digital sovereignty includes data sovereignty where we are giving you full control and ownership of the location, encryption and access to your data. Operational sovereignty where the goal is to give our Google Cloud customers full visibility and control over the provider operations, right? So if there are any updates on hardware, software stack, any operations, there is full transparency, full visibility. And then the third pillar is around software sovereignty where the customer wants to ensure that they can run their workloads without dependency on the provider's software. So they have sometimes is often referred as survivability, that you can actually survive if you are untethered to the cloud and that you can use open source. Now let's take a deep dive on data sovereignty, which by the way is one of my favorite topics. And we typically focus on saying, hey, we need to care about data residency. We care where the data resides because where the data is at rest or in processing, it typically abides to the jurisdiction, the regulations of the jurisdiction where the data resides. And others say, hey, let's focus on data protection. We want to ensure the confidentiality and integrity and availability of the data, which confidential computing is at the heart of that data protection. But it is yet another element that people typically don't talk about when talking about data sovereignty, which is the element of user control. And here, Dave, is about what happens to the data when I give you access to my data. And this reminds me of security two decades ago, even a decade ago, where we started the security movement by putting firewall protections and login accesses. But once you were in, you were able to do everything you wanted with the data. An insider had access to all the infrastructure, the data and the code. And that's similar because with data sovereignty we care about whether it resides, where, who is operating on the data. But the moment that the data is being processed, I need to trust that the processing of the data will abide by user control, by the policies that I put in place of how my data is going to be used. And if you look at a lot of the regulation today and a lot of the initiatives around the International Data Space Association, IDSA, and Gaia-X, there is a movement of saying the two parties, the provider of the data and the receiver of the data are going to agree on a contract that describes what my data can be used for. The challenge is to ensure that once the data crosses boundaries, that the data will be used for the purposes that it was intended and specified in the contract. And if you actually bring together, and this is the exciting part, confidential computing together with policy enforcement, now the policy enforcement can guarantee that the data is only processed within the confines of a confidential computing environment, that the workload is cryptographically verified that there is the workload that was meant to process the data and that the data will be only used when abiding to the confidentiality and integrity safety of the confidential computing environment. And that's why we believe confidential computing is one necessary and essential technology that will allow us to ensure data sovereignty, especially when it comes to user control. >> Thank you for that. I mean it was a deep dive, I mean brief, but really detailed. So I appreciate that, especially the verification of the enforcement. Last question, I met you two because as part of my year end prediction post, you guys sent in some predictions and I wasn't able to get to them in the predictions post. So I'm thrilled that you were able to make the time to come on the program. How widespread do you think the adoption of confidential computing will be in 23 and what's the maturity curve look like, this decade in your opinion? Maybe each of you could give us a brief answer. >> So my prediction in five, seven years, as I started, it'll become utility. It'll become TLS as of, again, 10 years ago we couldn't believe that websites will have certificates and we will support encrypted traffic. Now we do and it's become ubiquity. It's exactly where confidential computing is getting and heading, I don't know we deserve yet. It'll take a few years of maturity for us, but we will be there. >> Thank you. And Patricia, what's your prediction? >> I will double that and say, hey, in the future, in the very near future, you will not be able to afford not having it. I believe as digital sovereignty becomes evermore top of mind with sovereign states and also for multi national organizations and for organizations that want to collaborate with each other, confidential computing will become the norm. It'll become the default, if I say, mode of operation. I like to compare that today is inconceivable. If we talk to the young technologists, it's inconceivable to think that at some point in history, and I happen to be alive that we had data at rest that was not encrypted, data in transit that was not encrypted, and I think that will be inconceivable at some point in the near future that to have unencrypted data while in use. >> And plus I think the beauty of the this industry is because there's so much competition, this essentially comes for free. I want to thank you both for spending some time on Breaking Analysis. There's so much more we could cover. I hope you'll come back to share the progress that you're making in this area and we can double click on some of these topics. Really appreciate your time. >> Anytime. >> Thank you so much. >> In summary, while confidential computing is being touted by the cloud players as a promising technology for enhancing data privacy and security, there are also those, as we said, who remain skeptical. The truth probably lies somewhere in between and it will depend on the specific implementation and the use case as to how effective confidential computing will be. Look, as with any new tech, it's important to carefully evaluate the potential benefits, the drawbacks, and make informed decisions based on the specific requirements in the situation and the constraints of each individual customer. But the bottom line is silicon manufacturers are working with cloud providers and other system companies to include confidential computing into their architectures. Competition, in our view, will moderate price hikes. And at the end of the day, this is under the covers technology that essentially will come for free. So we'll take it. I want to thank our guests today, Nelly and Patricia from Google, and thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well out of our Boston studio, Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor-in-chief over at siliconangle.com. Does some great editing for us, thank you all. Remember all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com where you can get all the news. If you want to get in touch, you can email me at david.vellante@siliconangle.com or dm me @DVellante. And you can also comment on my LinkedIn post. Definitely you want to check out etr.ai for the best survey data in the enterprise tech business. I know we didn't hit on a lot today, but there's some amazing data and it's always being updated, so check that out. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (upbeat music)

>> Welcome Nelly and Patricia, great to have you. >> Great to be here. >> Thank you so much for having us. >> You're very welcome. Nelly, why don't you start, and then Patricia you can weigh in. Just tell the audience a little bit about each of your roles at Google Cloud. >> So I'll start, I'm honing a lot of interesting activities in Google and again, security or infrastructure securities that I usually hone, and we're talking about encryption, Antware encryption, and confidential computing is a part of portfolio. In additional areas that I contribute to get with my team to Google and our customers is secure software supply chain. Because you need to trust your software. Is it operating your confidential environment to have end to end story about if you believe that your software and your environment doing what you expect, it's my role. >> Got it, okay. Patricia? >> Well I am a technical director in the office of the CTO, OCTO for short, in Google Cloud. And we are a global team. We include former CTOs like myself and senior technologies from large corporations, institutions, and a lot of success for startups as well. And we have two main goals. First, we work side by side with some of our largest, more strategic or most strategic customers and we help them solve complex engineering technical problems. And second, we are device Google and Google Cloud engineering and product management on emerging trends in technologies to guide the trajectory of our business. We are unique group, I think, because we have created this collaborative culture with our customers. And within OCTO I spend a lot of time collaborating with customers in the industry at large on technologies that can address privacy, security, and sovereignty of data in general. >> Excellent, thank you for that both of you. Let's get into it. So Nelly, what is confidential computing from Google's perspective? How do you define it? >> Confidential computing is a tool. And it's one of the tools in our toolbox. And confidential computing is a way how would help our customers to complete this very interesting end to end lifecycle of their data. And when customers bring in the data to Cloud and want to protect it, as they ingest it to the Cloud, they protect it address when they store data in the Cloud. But what was missing for many, many years is ability for us to continue protecting data and workloads of our customers when they running them. And again, because data is not brought to Cloud to have huge graveyard, we need to ensure that this data is actually indexed. Again there is some insights driven and drawn from this data. You have to process this data and confidential computing here to help. Now we have end to end protection of our customer's data when they bring the workloads and data to Cloud, thanks to confidential computing. >> Thank you for that. Okay, we're going to get into the architecture a bit but before we do Patricia, why do you think this topic of confidential computing is such an important technology? Can you explain, do you think it's transformative for customers and if so, why? >> Yeah, I would maybe like to use one thought, one way, one intuition behind why confidential matters. Because at the end of the day it reduces more and more the customers thrush boundaries and the attack surface, that's about reducing that periphery, the boundary, in which the customer needs to mind about trust and safety. And in a way is a natural progression that you're using encryption to secure and protect data in the same way that we are encrypting data in transit and at rest. Now we are also encrypting data while in use. And among other beneficial I would say one of the most transformative ones is that organizations will be able to collaborate with each other and retain the confidentiality of the data. And that is across industry. Even though it's highly focused on, I wouldn't say highly focused, but very beneficial for highly regulated industries. It applies to all of industries. And if you look at financing for example, where bankers are trying to detect fraud and specifically double finance where you are a customer is actually trying to get a finance on an asset, let's say a boat or a house and then it goes to another bank and gets another finance on that asset. Now bankers would be able to collaborate and detect fraud while preserving confidentiality and privacy of the of the data. >> Interesting, and I want to understand that a little bit more but I'm going to push you a little bit on this, Nelly, if I can, because there's a narrative out there that says confidential computing is a marketing ploy. I talked about this upfront, by Cloud providers that are just trying to placate people that are scared of the Cloud. And I'm presuming you don't agree with that but I'd like you to weigh in here. The argument is confidential computing is just memory encryption, it doesn't address many other problems, it is overhyped by Cloud providers. What do you say to that line of thinking? >> I absolutely disagree as you can imagine, it's a crazy statement. But the most importantly is we mixing multiple concepts I guess. And exactly as Patricia said, we need to look at the end-to-end story not again the mechanism of how confidential computing trying to again execute and protect customer's data, and why it's so critically important. Because what confidential computing was able to do it's in addition to isolate our tenants in multi-tenant environments the Cloud over. To offer additional stronger isolation, we called it cryptographic isolation. It's why customers will have more trust to customers and to other customers, the tenants that's running on the same host but also us, because they don't need to worry about against threats and more malicious attempts to penetrate the environment. So what confidential computing is helping us to offer our customers, stronger isolation between tenants in this multi-tenant environment but also incredibly important, stronger isolation of our customers. So tenants from us, we also writing code, we also software providers will also make mistakes or have some zero days sometimes again us introduced, sometimes introduced by our adversaries. But what I'm trying to say by creating this cryptographic layer of isolation between us and our tenants, and amongst those tenants, they're really providing meaningful security to our customers and eliminate some of the worries that they have running on multi-tenant spaces or even collaborating together this very sensitive data, knowing that this particular protection is available to them. >> Okay, thank you, appreciate that. And I, you know, I think malicious code is often a threat model missed in these narratives. You know, operator access, yeah, could maybe I trust my Clouds provider, but if I can fence off your access even better I'll sleep better at night. Separating a code from the data, everybody's arm Intel, AM, Invidia, others, they're all doing it. I wonder if Nell, if we could stay with you and bring up the slide on the architecture. What's architecturally different with confidential computing versus how operating systems and VMs have worked traditionally? We're showing a slide here with some VMs, maybe you could take us through that. >> Absolutely, and Dave, the whole idea for Google and industry way of dealing with confidential computing is to ensure as it's three main property is actually preserved. Customers don't need to change the code. They can operate in those VMs exactly as they would with normal non-confidential VMs. But to give them this opportunity of lift and shift or no changing their apps and performing and having very, very, very low latency and scale as any Cloud can, something that Google actually pioneered in confidential computing. I think we need to open and explain how this magic was actually done. And as I said, it's again the whole entire system have to change to be able to provide this magic. And I would start with we have this concept of root of trust and root of trust where we will ensure that this machine, the whole entire post has integrity guarantee, means nobody changing my code on the most low level of system. And we introduce this in 2017 code Titan. Those our specific ASIC specific, again inch by inch system on every single motherboard that we have, that ensures that your low level former, your actually system code, your kernel, the most powerful system, is actually proper configured and not changed, not tempered. We do it for everybody, confidential computing concluded. But for confidential computing what we have to change we bring in a MD again, future silicon vendors, and we have to trust their former, their way to deal with our confidential environments. And that's why we have obligation to validate integrity not only our software and our firmware but also firmware and software of our vendors, silicon vendors. So we actually, when we booting this machine as you can see, we validate that integrity of all of this system is in place. It means nobody touching, nobody changing, nobody modifying it. But then we have this concept of the secure processor. It's special Asics best, specific things that generate a key for every single VM that our customers will run or every single node in Kubernetes, or every single worker thread in our Spark capability. We offer all of that, and those keys are not available to us. It's the best keys ever in encryption space. Because when we are talking about encryption the first question that I'm receiving all the time, where's the key, who will have access to the key? Because if you have access to the key then it doesn't matter if you encrypt it enough. But the case in confidential computing quite so revolutionary technology, ask Cloud providers who don't have access to the keys. They're sitting in the hardware and they fed to memory controller. And it means when Hypervisors that also know about these wonderful things, saying I need to get access to the memories that this particular VM I'm trying to get access to. They do not encrypt the data, they don't have access to the key. Because those keys are random, ephemeral and VM, but the most importantly in hardware not exportable. And it means now you will be able to have this very interesting role that customers all Cloud providers, will not be able to get access to your memory. And what we do, again, as you can see our customers don't need to change their applications. Their VMs are running exactly as it should run. And what you're running in VM you actually see your memory in clear, it's not encrypted. But God forbid is trying somebody to do it outside of my confidential box. No, no, no, no, no, you will not be able to do it. Now you'll see cybernet. And it's exactly what combination of these multiple hardware pieces and software pieces have to do. So OS is also modified, and OS is modified such way to provide integrity. It means even OS that you're running in UVM bucks is not modifiable and you as customer can verify. But the most interesting thing I guess how to ensure the super performance of this environment because you can imagine, Dave, that's increasing it's additional performance, additional time, additional latency. So we're able to mitigate all of that by providing incredibly interesting capability in the OS itself. So our customers will get no changes needed, fantastic performance, and scales as they would expect from Cloud providers like Google. >> Okay, thank you. Excellent, appreciate that explanation. So you know again, the narrative on this is, well you know you've already given me guarantees as a Cloud provider that you don't have access to my data but this gives another level of assurance. Key management as they say is key. Now you're not, humans aren't managing the keys the machines are managing them. So Patricia, my question to you is in addition to, you know, let's go pre-confidential computing days what are the sort of new guarantees that these hardware-based technologies are going to provide to customers? >> So if I am a customer, I am saying I now have full guarantee of confidentiality and integrity of the data and of the code. So if you look at code and data confidentiality the customer cares then they want to know whether their systems are protected from outside or unauthorized access. And that we covered with Nelly that it is. Confidential computing actually ensures that the applications and data antennas remain secret, right? The code is actually looking at the data only the memory is decrypting the data with a key that is ephemeral, and per VM, and generated on demand. Then you have the second point where you have code and data integrity and now customers want to know whether their data was corrupted, tempered, with or impacted by outside actors. And what confidential computing insures is that application internals are not tampered with. So the application, the workload as we call it, that is processing the data it's also it has not been tempered and preserves integrity. I would also say that this is all verifiable. So you have attestation, and this attestation actually generates a log trail and the log trail guarantees that provides a proof that it was preserved. And I think that the offers also a guarantee of what we call ceiling, this idea that the secrets have been preserved and not tempered with. Confidentiality and integrity of code and data. >> Got it, okay, thank you. You know, Nelly, you mentioned, I think I heard you say that the applications, it's transparent,you don't have to change the application it just comes for free essentially. And I'm, we showed some various parts of the stack before. I'm curious as to what's affected but really more importantly what is specifically Google's value add? You know, how do partners, you know, participate in this? The ecosystem or maybe said another way how does Google ensure the compatibility of confidential computing with existing systems and applications? >> And a fantastic question by the way. And it's very difficult and definitely complicated world because to be able to provide these guarantees actually a lot of works was done by community. Google is very much operate and open. So again, our operating system we working in this operating system repository OS vendors to ensure that all capabilities that we need is part of their kernels, are part of their releases, and it's available for customers to understand and even explore if they have fun to explore a lot of code. We have also modified together with our silicon vendors, kernel, host kernel, to support this capability and it means working this community to ensure that all of those patches are there. We also worked with every single silicon vendor as you've seen, and that's what I probably feel that Google contributed quite a bit in this role. We moved our industry, our community, our vendors to understand the value of easy to use confidential computing or removing barriers. And now I don't know if you noticed Intel is pulling the lead and also announcing the trusted domain extension very similar architecture and no surprise, it's again a lot of work done with our partners to again, convince, work with them, and make this capability available. The same with ARM this year, actually last year, ARM unknowns are future design for confidential computing. It's called confidential computing architecture. And it's also influenced very heavily with similar ideas by Google and industry overall. So it's a lot of work in confidential computing consortiums that we are doing. For example, simply to mention to ensure interop, as you mentioned, between different confidential environments of Cloud providers. We want to ensure that they can attest to each other. Because when you're communicating with different environments, you need to trust them. And if it's running on different Cloud providers you need to ensure that you can trust your receiver when you are sharing your sensitive data workloads or secret with them. So we coming as a community and we have this at the station, the community based systems that we want to build and influence and work with ARM and every other Cloud providers to ensure that they can interrupt. And it means it doesn't matter where confidential workloads will be hosted but they can exchange the data in secure, verifiable, and controlled by customers way. And to do it, we need to continue what we are doing. Working open again and contribute with our ideas and ideas of our partners to this role to become what we see confidential computing has to become, it has to become utility. It doesn't need to be so special but it's what what we've wanted to become. >> Let's talk about, thank you for that explanation. Let talk about data sovereignty, because when you think about data sharing you think about data sharing across, you know, the ecosystem and different regions and then of course data sovereignty comes up. Typically public policy lags, you know, the technology industry and sometimes is problematic. I know, you know, there's a lot of discussions about exceptions, but Patricia, we have a graphic on data sovereignty. I'm interested in how confidential computing ensures that data sovereignty and privacy edicts are adhered to even if they're out of alignment maybe with the pace of technology. One of the frequent examples is when you you know, when you delete data, can you actually prove the data is deleted with a hundred percent certainty? You got to prove that and a lot of other issues. So looking at this slide, maybe you could take us through your thinking on data sovereignty. >> Perfect, so for us, data sovereignty is only one of the three pillars of digital sovereignty. And I don't want to give the impression that confidential computing addresses at all. That's why we want to step back and say, hey, digital sovereignty includes data sovereignty where we are giving you full control and ownership of the location, encryption, and access to your data. Operational sovereignty where the goal is to give our Google Cloud customers full visibility and control over the provider operations, right? So if there are any updates on hardware, software, stack, any operations, that is full transparency, full visibility. And then the third pillar is around software sovereignty where the customer wants to ensure that they can run their workloads without dependency on the provider's software. So they have sometimes is often referred as survivability that you can actually survive if you are untethered to the Cloud and that you can use open source. Now let's take a deep dive on data sovereignty, which by the way is one of my favorite topics. And we typically focus on saying, hey, we need to care about data residency. We care where the data resides because where the data is at rest or in processing it typically abides to the jurisdiction, the regulations of the jurisdiction where the data resides. And others say, hey, let's focus on data protection. We want to ensure the confidentiality and integrity and availability of the data which confidential computing is at the heart of that data protection. But it is yet another element that people typically don't talk about when talking about data sovereignty, which is the element of user control. And here Dave, is about what happens to the data when I give you access to my data. And this reminds me of security two decades ago, even a decade ago, where we started the security movement by putting firewall protections and login accesses. But once you were in, you were able to do everything you wanted with the data, an insider had access to all the infrastructure, the data, and the code. And that's similar because with data sovereignty we care about whether it resides, who is operating on the data. But the moment that the data is being processed, I need to trust that the processing of the data will abide by user control, by the policies that I put in place of how my data is going to be used. And if you look at a lot of the regulation today and a lot of the initiatives around the International Data Space Association, IDSA, and Gaia X, there is a movement of saying the two parties, the provider of the data and the receiver of the data going to agree on a contract that describes what my data can be used for. The challenge is to ensure that once the data crosses boundaries, that the data will be used for the purposes that it was intended and specified in the contract. And if you actually bring together, and this is the exciting part, confidential computing together with policy enforcement. Now the policy enforcement can guarantee that the data is only processed within the confines of a confidential computing environment. That the workload is cryptographically verified that there is the workload that was meant to process the data and that the data will be only used when abiding to the confidentiality and integrity, safety of the confidential computing environment. And that's why we believe confidential computing is one, necessary and essential technology that will allow us to ensure data sovereignty especially when it comes to user control. >> Thank you for that. I mean it was a deep dive, I mean brief, but really detailed, so I appreciate that, especially the verification of the enforcement. Last question, I met you two because as part of my year end prediction post you guys sent in some predictions, and I wasn't able to get to them in the predictions post. So I'm thrilled that you were able to make the time to come on the program. How widespread do you think the adoption of confidential computing will be in '23 and what's the maturity curve look like, you know, this decade in, in your opinion? Maybe each of you could give us a brief answer. >> So my prediction in five, seven years as I started, it'll become utility. It'll become TLS. As of, again, 10 years ago we couldn't believe that websites will have certificates and we will support encrypted traffic. Now we do, and it's become ubiquity. It's exactly where our confidential computing is heading and heading, I don't know if we are there yet yet. It'll take a few years of maturity for us, but we'll do that. >> Thank you, and Patricia, what's your prediction? >> I would double that and say, hey, in the future, in the very near future you will not be able to afford not having it. I believe as digital sovereignty becomes ever more top of mind with sovereign states and also for multinational organizations and for organizations that want to collaborate with each other, confidential computing will become the norm. It'll become the default, If I say mode of operation, I like to compare that, today is inconceivable if we talk to the young technologists. It's inconceivable to think that at some point in history and I happen to be alive that we had data at address that was not encrypted. Data in transit, that was not encrypted. And I think that we will be inconceivable at some point in the near future that to have unencrypted data while we use. >> You know, and plus, I think the beauty of the this industry is because there's so much competition this essentially comes for free. I want to thank you both for spending some time on Breaking Analysis. There's so much more we could cover. I hope you'll come back to share the progress that you're making in this area and we can double click on some of these topics. Really appreciate your time. >> Anytime. >> Thank you so much.

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Confidential computing is a technology that aims to enhance data privacy and security, by providing encrypted computation on sensitive data and isolating data, and apps that are fenced off enclave during processing. The concept of, I got to start over. I fucked that up, I'm sorry. That's not right, what I said was not right. On Dave in five, four, three. Confidential computing is a technology that aims to enhance data privacy and security by providing encrypted computation on sensitive data, isolating data from apps and a fenced off enclave during processing. The concept of confidential computing is gaining popularity, especially in the cloud computing space, where sensitive data is often stored and of course processed. However, there are some who view confidential computing as an unnecessary technology in a marketing ploy by cloud providers aimed at calming customers who are cloud phobic. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we revisit the notion of confidential computing, and to do so, we'll invite two Google experts to the show. But before we get there, let's summarize briefly. There's not a ton of ETR data on the topic of confidential computing, I mean, it's a technology that's deeply embedded into silicon and computing architectures. But at the highest level, security remains the number one priority being addressed by IT decision makers in the coming year as shown here. And this data is pretty much across the board by industry, by region, by size of company. I mean we dug into it and the only slight deviation from the mean is in financial services. The second and third most cited priorities, cloud migration and analytics are noticeably closer to cybersecurity in financial services than in other sectors, likely because financial services has always been hyper security conscious, but security is still a clear number one priority in that sector. The idea behind confidential computing is to better address threat models for data in execution. Protecting data at rest and data in transit have long been a focus of security approaches, but more recently, silicon manufacturers have introduced architectures that separate data and applications from the host system, ARM, Intel, AMD, Nvidia and other suppliers are all on board, as are the big cloud players. Now, the argument against confidential computing is that it narrowly focuses on memory encryption and it doesn't solve the biggest problems in security. Multiple system images, updates, different services and the entire code flow aren't directly addressed by memory encryption. Rather to truly attack these problems, many believe that OSs need to be re-engineered with the attacker and hacker in mind. There are so many variables and at the end of the day, critics say the emphasis on confidential computing made by cloud providers is overstated and largely hype. This tweet from security researcher Rodrigo Bronco, sums up the sentiment of many skeptics. He says, "Confidential computing is mostly a marketing campaign from memory encryption. It's not driving the industry towards the hard open problems. It is selling an illusion." Okay. Nonetheless, encrypting data in use and fencing off key components of the system isn't a bad thing, especially if it comes with the package essentially for free. There has been a lack of standardization and interoperability between different confidential computing approaches. But the confidential computing consortium was established in 2019 ostensibly to accelerate the market and influence standards. Notably, AWS is not part of the consortium, likely because the politics of the consortium were probably a conundrum for AWS because the base technology defined by the consortium is seen as limiting by AWS. This is my guess, not AWS' words. But I think joining the consortium would validate a definition which AWS isn't aligned with. And two, it's got to lead with this Annapurna acquisition. It was way ahead with ARM integration, and so it's probably doesn't feel the need to validate its competitors. Anyway, one of the premier members of the confidential computing consortium is Google, along with many high profile names, including Aem, Intel, Meta, Red Hat, Microsoft, and others. And we're pleased to welcome two experts on confidential computing from Google to unpack the topic. Nelly Porter is Head of Product for GCP Confidential Computing and Encryption and Dr. Patricia Florissi is the Technical Director for the Office of the CTO at Google Cloud. Welcome Nelly and Patricia, great to have you. >> Great to be here. >> Thank you so much for having us. >> You're very welcome. Nelly, why don't you start and then Patricia, you can weigh in. Just tell the audience a little bit about each of your roles at Google Cloud. >> So I'll start, I'm owning a lot of interesting activities in Google and again, security or infrastructure securities that I usually own. And we are talking about encryption, end-to-end encryption, and confidential computing is a part of portfolio. Additional areas that I contribute to get with my team to Google and our customers is secure software supply chain because you need to trust your software. Is it operate in your confidential environment to have end-to-end security, about if you believe that your software and your environment doing what you expect, it's my role. >> Got it. Okay, Patricia? >> Well, I am a Technical Director in the Office of the CTO, OCTO for short in Google Cloud. And we are a global team, we include former CTOs like myself and senior technologies from large corporations, institutions and a lot of success for startups as well. And we have two main goals, first, we walk side by side with some of our largest, more strategic or most strategical customers and we help them solve complex engineering technical problems. And second, we advice Google and Google Cloud Engineering, product management on emerging trends and technologies to guide the trajectory of our business. We are unique group, I think, because we have created this collaborative culture with our customers. And within OCTO I spend a lot of time collaborating with customers in the industry at large on technologies that can address privacy, security, and sovereignty of data in general. >> Excellent. Thank you for that both of you. Let's get into it. So Nelly, what is confidential computing from Google's perspective? How do you define it? >> Confidential computing is a tool and one of the tools in our toolbox. And confidential computing is a way how we would help our customers to complete this very interesting end-to-end lifecycle of the data. And when customers bring in the data to cloud and want to protect it as they ingest it to the cloud, they protect it at rest when they store data in the cloud. But what was missing for many, many years is ability for us to continue protecting data and workloads of our customers when they run them. And again, because data is not brought to cloud to have huge graveyard, we need to ensure that this data is actually indexed. Again, there is some insights driven and drawn from this data. You have to process this data and confidential computing here to help. Now we have end-to-end protection of our customer's data when they bring the workloads and data to cloud thanks to confidential computing. >> Thank you for that. Okay, we're going to get into the architecture a bit, but before we do Patricia, why do you think this topic of confidential computing is such an important technology? Can you explain? Do you think it's transformative for customers and if so, why? >> Yeah, I would maybe like to use one thought, one way, one intuition behind why confidential computing matters because at the end of the day, it reduces more and more the customer's thrush boundaries and the attack surface. That's about reducing that periphery, the boundary in which the customer needs to mind about trust and safety. And in a way is a natural progression that you're using encryption to secure and protect data in the same way that we are encrypting data in transit and at rest. Now, we are also encrypting data while in the use. And among other beneficials, I would say one of the most transformative ones is that organizations will be able to collaborate with each other and retain the confidentiality of the data. And that is across industry, even though it's highly focused on, I wouldn't say highly focused but very beneficial for highly regulated industries, it applies to all of industries. And if you look at financing for example, where bankers are trying to detect fraud and specifically double finance where a customer is actually trying to get a finance on an asset, let's say a boat or a house, and then it goes to another bank and gets another finance on that asset. Now bankers would be able to collaborate and detect fraud while preserving confidentiality and privacy of the data. >> Interesting and I want to understand that a little bit more but I got to push you a little bit on this, Nellie if I can, because there's a narrative out there that says confidential computing is a marketing ploy I talked about this up front, by cloud providers that are just trying to placate people that are scared of the cloud. And I'm presuming you don't agree with that, but I'd like you to weigh in here. The argument is confidential computing is just memory encryption, it doesn't address many other problems. It is over hyped by cloud providers. What do you say to that line of thinking? >> I absolutely disagree as you can imagine Dave, with this statement. But the most importantly is we mixing a multiple concepts I guess, and exactly as Patricia said, we need to look at the end-to-end story, not again, is a mechanism. How confidential computing trying to execute and protect customer's data and why it's so critically important. Because what confidential computing was able to do, it's in addition to isolate our tenants in multi-tenant environments the cloud offering to offer additional stronger isolation, they called it cryptographic isolation. It's why customers will have more trust to customers and to other customers, the tenants running on the same host but also us because they don't need to worry about against rats and more malicious attempts to penetrate the environment. So what confidential computing is helping us to offer our customers stronger isolation between tenants in this multi-tenant environment, but also incredibly important, stronger isolation of our customers to tenants from us. We also writing code, we also software providers, we also make mistakes or have some zero days. Sometimes again us introduce, sometimes introduced by our adversaries. But what I'm trying to say by creating this cryptographic layer of isolation between us and our tenants and among those tenants, we really providing meaningful security to our customers and eliminate some of the worries that they have running on multi-tenant spaces or even collaborating together with very sensitive data knowing that this particular protection is available to them. >> Okay, thank you. Appreciate that. And I think malicious code is often a threat model missed in these narratives. You know, operator access. Yeah, maybe I trust my cloud's provider, but if I can fence off your access even better, I'll sleep better at night separating a code from the data. Everybody's ARM, Intel, AMD, Nvidia and others, they're all doing it. I wonder if Nell, if we could stay with you and bring up the slide on the architecture. What's architecturally different with confidential computing versus how operating systems and VMs have worked traditionally? We're showing a slide here with some VMs, maybe you could take us through that. >> Absolutely, and Dave, the whole idea for Google and now industry way of dealing with confidential computing is to ensure that three main property is actually preserved. Customers don't need to change the code. They can operate in those VMs exactly as they would with normal non-confidential VMs. But to give them this opportunity of lift and shift though, no changing the apps and performing and having very, very, very low latency and scale as any cloud can, some things that Google actually pioneer in confidential computing. I think we need to open and explain how this magic was actually done, and as I said, it's again the whole entire system have to change to be able to provide this magic. And I would start with we have this concept of root of trust and root of trust where we will ensure that this machine within the whole entire host has integrity guarantee, means nobody changing my code on the most low level of system, and we introduce this in 2017 called Titan. So our specific ASIC, specific inch by inch system on every single motherboard that we have that ensures that your low level former, your actually system code, your kernel, the most powerful system is actually proper configured and not changed, not tempered. We do it for everybody, confidential computing included, but for confidential computing is what we have to change, we bring in AMD or future silicon vendors and we have to trust their former, their way to deal with our confidential environments. And that's why we have obligation to validate intelligent not only our software and our former but also former and software of our vendors, silicon vendors. So we actually, when we booting this machine as you can see, we validate that integrity of all of this system is in place. It means nobody touching, nobody changing, nobody modifying it. But then we have this concept of AMD Secure Processor, it's special ASIC best specific things that generate a key for every single VM that our customers will run or every single node in Kubernetes or every single worker thread in our Hadoop spark capability. We offer all of that and those keys are not available to us. It's the best case ever in encryption space because when we are talking about encryption, the first question that I'm receiving all the time, "Where's the key? Who will have access to the key?" because if you have access to the key then it doesn't matter if you encrypted or not. So, but the case in confidential computing why it's so revolutionary technology, us cloud providers who don't have access to the keys, they're sitting in the hardware and they fed to memory controller. And it means when hypervisors that also know about this wonderful things saying I need to get access to the memories, that this particular VM I'm trying to get access to. They do not decrypt the data, they don't have access to the key because those keys are random, ephemeral and per VM, but most importantly in hardware not exportable. And it means now you will be able to have this very interesting world that customers or cloud providers will not be able to get access to your memory. And what we do, again as you can see, our customers don't need to change their applications. Their VMs are running exactly as it should run. And what you've running in VM, you actually see your memory clear, it's not encrypted. But God forbid is trying somebody to do it outside of my confidential box, no, no, no, no, no, you will now be able to do it. Now, you'll see cyber test and it's exactly what combination of these multiple hardware pieces and software pieces have to do. So OS is also modified and OS is modified such way to provide integrity. It means even OS that you're running in your VM box is not modifiable and you as customer can verify. But the most interesting thing I guess how to ensure the super performance of this environment because you can imagine Dave, that's increasing and it's additional performance, additional time, additional latency. So we're able to mitigate all of that by providing incredibly interesting capability in the OS itself. So our customers will get no changes needed, fantastic performance and scales as they would expect from cloud providers like Google. >> Okay, thank you. Excellent, appreciate that explanation. So you know again, the narrative on this is, well, you've already given me guarantees as a cloud provider that you don't have access to my data, but this gives another level of assurance, key management as they say is key. Now humans aren't managing the keys, the machines are managing them. So Patricia, my question to you is in addition to, let's go pre-confidential computing days, what are the sort of new guarantees that these hardware based technologies are going to provide to customers? >> So if I am a customer, I am saying I now have full guarantee of confidentiality and integrity of the data and of the code. So if you look at code and data confidentiality, the customer cares and they want to know whether their systems are protected from outside or unauthorized access, and that we covered with Nelly that it is. Confidential computing actually ensures that the applications and data antennas remain secret. The code is actually looking at the data, only the memory is decrypting the data with a key that is ephemeral, and per VM, and generated on demand. Then you have the second point where you have code and data integrity and now customers want to know whether their data was corrupted, tempered with or impacted by outside actors. And what confidential computing ensures is that application internals are not tempered with. So the application, the workload as we call it, that is processing the data is also has not been tempered and preserves integrity. I would also say that this is all verifiable, so you have attestation and this attestation actually generates a log trail and the log trail guarantees that provides a proof that it was preserved. And I think that the offers also a guarantee of what we call sealing, this idea that the secrets have been preserved and not tempered with, confidentiality and integrity of code and data. >> Got it. Okay, thank you. Nelly, you mentioned, I think I heard you say that the applications is transparent, you don't have to change the application, it just comes for free essentially. And we showed some various parts of the stack before, I'm curious as to what's affected, but really more importantly, what is specifically Google's value add? How do partners participate in this, the ecosystem or maybe said another way, how does Google ensure the compatibility of confidential computing with existing systems and applications? >> And a fantastic question by the way, and it's very difficult and definitely complicated world because to be able to provide these guarantees, actually a lot of work was done by community. Google is very much operate and open. So again our operating system, we working this operating system repository OS is OS vendors to ensure that all capabilities that we need is part of the kernels are part of the releases and it's available for customers to understand and even explore if they have fun to explore a lot of code. We have also modified together with our silicon vendors kernel, host kernel to support this capability and it means working this community to ensure that all of those pages are there. We also worked with every single silicon vendor as you've seen, and it's what I probably feel that Google contributed quite a bit in this world. We moved our industry, our community, our vendors to understand the value of easy to use confidential computing or removing barriers. And now I don't know if you noticed Intel is following the lead and also announcing a trusted domain extension, very similar architecture and no surprise, it's a lot of work done with our partners to convince work with them and make this capability available. The same with ARM this year, actually last year, ARM announced future design for confidential computing, it's called confidential computing architecture. And it's also influenced very heavily with similar ideas by Google and industry overall. So it's a lot of work in confidential computing consortiums that we are doing, for example, simply to mention, to ensure interop as you mentioned, between different confidential environments of cloud providers. They want to ensure that they can attest to each other because when you're communicating with different environments, you need to trust them. And if it's running on different cloud providers, you need to ensure that you can trust your receiver when you sharing your sensitive data workloads or secret with them. So we coming as a community and we have this at Station Sig, the community-based systems that we want to build, and influence, and work with ARM and every other cloud providers to ensure that they can interop. And it means it doesn't matter where confidential workloads will be hosted, but they can exchange the data in secure, verifiable and controlled by customers really. And to do it, we need to continue what we are doing, working open and contribute with our ideas and ideas of our partners to this role to become what we see confidential computing has to become, it has to become utility. It doesn't need to be so special, but it's what what we've wanted to become. >> Let's talk about, thank you for that explanation. Let's talk about data sovereignty because when you think about data sharing, you think about data sharing across the ecosystem in different regions and then of course data sovereignty comes up, typically public policy, lags, the technology industry and sometimes it's problematic. I know there's a lot of discussions about exceptions but Patricia, we have a graphic on data sovereignty. I'm interested in how confidential computing ensures that data sovereignty and privacy edicts are adhered to, even if they're out of alignment maybe with the pace of technology. One of the frequent examples is when you delete data, can you actually prove the data is deleted with a hundred percent certainty, you got to prove that and a lot of other issues. So looking at this slide, maybe you could take us through your thinking on data sovereignty. >> Perfect. So for us, data sovereignty is only one of the three pillars of digital sovereignty. And I don't want to give the impression that confidential computing addresses it at all, that's why we want to step back and say, hey, digital sovereignty includes data sovereignty where we are giving you full control and ownership of the location, encryption and access to your data. Operational sovereignty where the goal is to give our Google Cloud customers full visibility and control over the provider operations, right? So if there are any updates on hardware, software stack, any operations, there is full transparency, full visibility. And then the third pillar is around software sovereignty, where the customer wants to ensure that they can run their workloads without dependency on the provider's software. So they have sometimes is often referred as survivability that you can actually survive if you are untethered to the cloud and that you can use open source. Now, let's take a deep dive on data sovereignty, which by the way is one of my favorite topics. And we typically focus on saying, hey, we need to care about data residency. We care where the data resides because where the data is at rest or in processing need to typically abides to the jurisdiction, the regulations of the jurisdiction where the data resides. And others say, hey, let's focus on data protection, we want to ensure the confidentiality, and integrity, and availability of the data, which confidential computing is at the heart of that data protection. But it is yet another element that people typically don't talk about when talking about data sovereignty, which is the element of user control. And here Dave, is about what happens to the data when I give you access to my data, and this reminds me of security two decades ago, even a decade ago, where we started the security movement by putting firewall protections and logging accesses. But once you were in, you were able to do everything you wanted with the data. An insider had access to all the infrastructure, the data, and the code. And that's similar because with data sovereignty, we care about whether it resides, who is operating on the data, but the moment that the data is being processed, I need to trust that the processing of the data we abide by user's control, by the policies that I put in place of how my data is going to be used. And if you look at a lot of the regulation today and a lot of the initiatives around the International Data Space Association, IDSA and Gaia-X, there is a movement of saying the two parties, the provider of the data and the receiver of the data going to agree on a contract that describes what my data can be used for. The challenge is to ensure that once the data crosses boundaries, that the data will be used for the purposes that it was intended and specified in the contract. And if you actually bring together, and this is the exciting part, confidential computing together with policy enforcement. Now, the policy enforcement can guarantee that the data is only processed within the confines of a confidential computing environment, that the workload is in cryptographically verified that there is the workload that was meant to process the data and that the data will be only used when abiding to the confidentiality and integrity safety of the confidential computing environment. And that's why we believe confidential computing is one necessary and essential technology that will allow us to ensure data sovereignty, especially when it comes to user's control. >> Thank you for that. I mean it was a deep dive, I mean brief, but really detailed. So I appreciate that, especially the verification of the enforcement. Last question, I met you two because as part of my year-end prediction post, you guys sent in some predictions and I wasn't able to get to them in the predictions post, so I'm thrilled that you were able to make the time to come on the program. How widespread do you think the adoption of confidential computing will be in '23 and what's the maturity curve look like this decade in your opinion? Maybe each of you could give us a brief answer. >> So my prediction in five, seven years as I started, it will become utility, it will become TLS. As of freakin' 10 years ago, we couldn't believe that websites will have certificates and we will support encrypted traffic. Now we do, and it's become ubiquity. It's exactly where our confidential computing is heeding and heading, I don't know we deserve yet. It'll take a few years of maturity for us, but we'll do that. >> Thank you. And Patricia, what's your prediction? >> I would double that and say, hey, in the very near future, you will not be able to afford not having it. I believe as digital sovereignty becomes ever more top of mind with sovereign states and also for multinational organizations, and for organizations that want to collaborate with each other, confidential computing will become the norm, it will become the default, if I say mode of operation. I like to compare that today is inconceivable if we talk to the young technologists, it's inconceivable to think that at some point in history and I happen to be alive, that we had data at rest that was non-encrypted, data in transit that was not encrypted. And I think that we'll be inconceivable at some point in the near future that to have unencrypted data while we use. >> You know, and plus I think the beauty of the this industry is because there's so much competition, this essentially comes for free. I want to thank you both for spending some time on Breaking Analysis, there's so much more we could cover. I hope you'll come back to share the progress that you're making in this area and we can double click on some of these topics. Really appreciate your time. >> Anytime. >> Thank you so much, yeah. >> In summary, while confidential computing is being touted by the cloud players as a promising technology for enhancing data privacy and security, there are also those as we said, who remain skeptical. The truth probably lies somewhere in between and it will depend on the specific implementation and the use case as to how effective confidential computing will be. Look as with any new tech, it's important to carefully evaluate the potential benefits, the drawbacks, and make informed decisions based on the specific requirements in the situation and the constraints of each individual customer. But the bottom line is silicon manufacturers are working with cloud providers and other system companies to include confidential computing into their architectures. Competition in our view will moderate price hikes and at the end of the day, this is under-the-covers technology that essentially will come for free, so we'll take it. I want to thank our guests today, Nelly and Patricia from Google. And thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well out of our Boston studio. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters, and Rob Hoof is our editor-in-chief over at siliconangle.com, does some great editing for us. Thank you all. Remember all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com where you can get all the news. If you want to get in touch, you can email me at david.vellante@siliconangle.com or DM me at D Vellante, and you can also comment on my LinkedIn post. Definitely you want to check out etr.ai for the best survey data in the enterprise tech business. I know we didn't hit on a lot today, but there's some amazing data and it's always being updated, so check that out. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (subtle music)

>>Hello everyone. Welcome back to the Cube's Live coverage. I'm John Fur, host of the Cube. We got two sets here, three sets total. Another one in the executive center. It's our 10th year covering AWS Reinvent. I remember 2013 like it was yesterday. You know, now it's a massive of people buying out restaurants. 35,000 people now it's 55,000, soon to be 70,000 back. Great event. Continuing to set the standard in the industry. We had an amazing guest here, Leah Bibo, vice President of Product Marketing. She's in charge of the messaging, the product, overseeing how these products gonna market. Leah, great to see you. Thanks for joining me on the Cube today. >>Absolutely. It's great to be here. It's also my 10 reinvent, so it's, it's been a wild ride. >>Absolutely. Yeah. You and I were talking before we came on camera, how much we love products and yes, this is a product-centric company, has been from day one and you know, over the years watching the announcements, the tsunami of announcements, just all the innovation that's come out from AWS over the years has been staggering to say the least. Everyone always jokes about, oh my God, 5,000 new announcements, over 200 services you're managing and you're marketing them. It's pretty crazy right now. And Adam, as he comes on, as I called them, the solutions CEO on my piece I wrote on Friday, we're in an era where solutions, the products are enabling more solutions. Unpack the messaging around this cuz this is really big moment for aws. >>Absolutely. Well, I'll say first of all that we are a customer focused company that happens to be really good at innovating incredible products and services for our customers. So today the, the energy in the room and what Adam talked about, I think is focused on a few great things for customers that are really important for transformation. So we talked a lot about best price performance for workloads and we talked about extreme workloads, but if you think about the work that we've been doing to innovate on the silicon side, we're really talking about with Graviton all your workloads and getting really great price performance for all of them. You know, we came out with graviton three 25% faster than graviton two, also 60% more energy efficient. We talked about something that is emerging that I think is gonna be really big, which is simulation and really the ability to model these complex worlds and all the little interactions, which I think, you know, in the future as we have more complex environments like 3D simulation is gonna be a bigger part of every, every business's >>Business. You know, just as an aside, we were talking on the analyst segment that speeds and feeds are back and the old days and the data center days was like, we don't wanna talk about speeds and feeds about solutions and you know, the outcomes when you get the cloud, it was like, okay, get the workloads over there, but people want faster and lower cost performance workloads gotta be running at at high performance. And, and there's a real discussion around those. Let's unpack security data performance. What, what does that mean for customers? Because again, I get the workloads run fast. That's great. What else is behind the curtain, so to speak from a customer standpoint? >>Absolutely. Well I think if you're gonna move all your workloads to the cloud, you know, security is a really big area that's important. It's important to every one of our enterprise companies customers. Actually it's important to all of our customers and we've been working, you know, since the beginning of AWS to really create and build the most secure global infrastructure. And you know, as our customers have moved mission critical workloads, we've built out a lot more capabilities and now we have a whole portfolio of security services. And what we announced today is kind of game changing. The service called Security Lake, which brings together, you know, an ecosystem of security data in a format that's open. So you can share data between all of these sources and it's gonna give folks the opportunity to really be able to analyze data, find threats faster, and just kind of know their security posture. And I think, you know, as we talked about today, you don't wanna think about the cloud as unfathomable, the unfathomable, you really need to know that security. And I think that like a lot of things we discussed, security is a data opportunity, right? And I think we, we had a section on on data, but really if you look at the keynote across security, across solutions, across the purpose built things we made, it's all, it all comes down to data and it's really the, the transformational element that our customers >>Are. I mean the data secured is very integral part good call out there. And I, I wanna just double down on that real quick because I remember in 2014 I interviewed Steven Schmidt when he was the CSOs and back then in 2014, if you remember the conversation was this, the clouds not secure, gotta be on premises. Now in today's keynote, Adam says, and he laid out the whole global security footprint. There's a lot going on that Amazon has now become more secure than on-prem. He actually made that statement. So, and then plus you got thousands of security partners, third party partners, you got the open cyber security framework which you guys co-found with all the other, so you got securities not as a team sport, this is what they, they said yes, yes. What does that mean for customers? Because now this is a big deal. >>Well I think for customers, I mean it means nothing but goodness, right? But all of these thousands of security partners have really innovated and created solutions that our customers are using. But they all have different types of data in different silos. And to really get a full picture bringing all that data together is really important. And it's not easy today. You know, log data from different sources, data from detection services and really what customers want is an easier way to get it all together. Which is why we have the open OCS F and really analyze using the tools of their choice. And whether that's AWS tools for analytics or it's tools from our partners, customers need to be able to make that choice so that they can feel like their applications and their workloads are the most secure on aws. >>You know, I've been very impressed with guard duty and I've been following Merit Bear's blogs on online. She's in the security team, she's amazing. Shout out to her. She's been pushing guard duty for a long time now there's big news around guard duty. So you got EKS protection, you know, at Coan this was the biggest cloud native issue, the runtime of Kubernetes and inside the container and outside the container detection of threats, right? As a real software supply chain concern. How are you guys marketing that? This is a huge announcement. EKS protection I know is very nuanced but it's pretty big deal. >>It is a big deal. It is a big deal. And guard duty has been kind of like a quiet service that maybe you don't hear a lot about, but has been really, really popular with our customers. Adam mentioned that 85% of, you know, our top 2000 customers are using guard duty today. And it was a big moment. We launched EKS protection, you know, a little bit earlier and the customer uptake on that has been really incredible. And it is because you can protect your Kubernetes cluster, which is really important because so many customers are, you know, part of their migration to the cloud is containers. Yeah. And so we're pretty excited that now we can answer that question of what's going on inside the container. And so you have both, yeah, right. You know that your Kubernetes pluses are good and you know what's going on inside the container and it's just more threats that you can detect and protect >>Yourself from. You know, as an aside, I'm sure you're watching this, but you know, we go to a lot of events, you know, the C I C D pipeline as developers are getting higher velocity coding, it has moved in because of DevOps on the cloud into the C I C D pipeline. So you're seeing that developer takes some of those IT roles in the coding workflow, hence the, the shift left and or container security, which you guys now, now and are driving towards. But the security and the data teams are emerging as a very key element inside the organizational structure. When I sat down with Adam, one of the things he was very adamant about in my conversation was not just digital transformation, business transformation, structural organizational moves are making where it's not a department anymore, it is the company, a technology is the company when you transform. Absolutely. So digital is the process, business is the outcome. This is a really huge message. What's your reaction to that? What's, what can you share extra cuz that's, this is a big part of the thing. He hit it right outta the gate on the front end of the keynote. >>Absolutely. Absolutely. I mean I think, you know, companies have been migrating to the cloud for a while, but I think that this time that we're going through has really accelerated that migration And as part of that, you know, digital transformation has become real for a lot of companies. And it is true what Adam said there is technology transformation involved, there's data transformation involved, but it, it is transforming businesses. And I think if you look at some of the things that Adam talked about, you know, aws, supply chain, security Lake, aws clean rooms, and Omic, aws, omic, you know, those are all examples of data and the ability to work with data transforming different lines of business within a company, transforming horizontal processes like contact centers and like supply chain and also, you know, going into vertical specific solutions. So what it means is that as technology becomes more pervasive, as data becomes more pervasive, businesses are transforming and that means that a lot more people are going to use the cloud and interact with the cloud and they might not want to or be able to kind of use our building blocks. And so what's really exciting that what we're able to do is make cloud more accessible to lines of business folks to analysts, to security folks. So >>It's, yeah, and that's, and that's why I was calling my this this new trend I see as Amazon Classic, my words, not your words, I call the, hey there was classic cloud and then you got the next gen clown, the new next generation. And I was talking with Adrian Cockcroft, former aws, so he's now retired, he's gonna come on later today. He and I were talking, he use this thing of you got a bag of Legos aka primitives or a toy that's been assembled for you glued together, ones out of the box, but they're not mutually exclusive. You can build a durable application and foundation with the building blocks more durable. You can manage it, refine it, but you got the solution that breaks. You don't have as much flexibility but you gotta replace it. That's okay too. So like this is now kind of a new portfolio approach to the cloud. It's very interesting and I think, I think, I think that's what I took away from the keynote is that you can have both. >>Yes, absolutely. You can do both. I mean, we're gonna go full throttle on releasing innovations and pushing the envelope on compute and storage and databases and our core services because they matter. And having, you know, the choice to choose from a wide range of options. I mean that's what, that's what customers need. You know, if you're gonna run hpc, you're gonna run machine learning and you're gonna run your SAP applications or your Windows applications, you need choice of what you know, specific type of instance and compute capabilities. You need to get the price performance. It's, it's definitely not a one size fits all. It's a 600 instance type. Size fits all maybe. >>Exactly. And you got a lot of instance and we'll get to that in a second. Yeah, I love the themes. I love this keynote themes you had like at first space, but I get the whole data, then you look at it, you can look at it differently. Really good metaphor, the ocean one I love with the security because he mentioned you can have the confidence to explore go deep snorkeling versus scuba and knowing how much oxygen you have. I mean, so really cool metaphor made me think very provocative. So again, this is kind of why people go to AWS because you now have these, these abilities to do things differently, depend on the context of what products you're working with. Yes. Explain why that was the core theme. Was there any rationale behind that? Was it just how you guys saw it? I mean that was pretty clever. >>Well, I think that, you know, we're, we're talking about environments and I think in this world, you know, there's uncertainty in a lot of places and we really feel like all of us need to be prepared for different types of environments. And so we wanted to explore what that could look like. And I think, you know, we're fascinated by space and the vastness and it is very much like the world of data. I don't know about you, but I actually scuba dive. So I love the depths of the ocean. I loved working on that part. There's extremes, extreme workloads like hpc, extreme workloads like machine learning with the growing models and there's an imagination, which is also one of my favorite areas to explore. >>Yeah. And you use the Antarctica one for about the whole environment and extreme conditions. That's good in the performance. And I love that piece of it. And I want to get into the, some of the things I love the speeds and fee. I think the, the big innovation with the silicon we've been covering as, you know, like a blanket. The, he's got the GRAVITON three 25% faster than GRAVITON two, the C seven GN network intense workloads. This is kind of a big deal. I mean this is one of those things where it might not get picked up in the major press, but the network use cases are significant. Nira has been successful. Share your thoughts on these kinds of innovations because they look kind of small, but they're not, they're >>Big, they're not small for sure, especially at the scale that our customers are, are, are running their applications. Like every little optimization that you can get really makes a huge difference. And I think it's exciting. I mean you hit on, you kind of hit on it when we've been working on silicon for a while now we know that, you know, if we're gonna keep pushing the element, the envelope in these areas, we had to, we had to go down to the silicon. And I think that Nitro has really been what's kind of been a breakthrough for us. You know, reinventing that virtualization layer, offloading security and storage and networking to special purpose chips. And I think that it's not just in the area of network optimization, right? You saw training optimized instances and inference optimized instances and HPC optimized instances. So yeah, we are kind of looking at all the extremes of, of what customers want to do. >>I know you can't talk about the future, but I can almost connect the dots as you're talking. It's like, hmm, specialized instances, specialized chips, maybe programmability of workload, smart intelligence, generative AI, weaving in there. A lot of kind of cool things I can see around the corner around generative AI automation. Hey, go to this instance with that go here. This is kind of what I see kind of coming around the corner. >>And we have some of that with our instance optimizers, our cost optimizer products where, you know, we wanna help customers find the best instance for their workload, get the best utilization they possibly can, you know, cut costs, but still have the great performance. So I don't, I don't know about your future, John, it sounds great, but we have, you know, we're taking steps in that direction today. >>Still look in this code that's gonna be on this code. Okay. Any, okay, I wanna give you one final question. Well, well two questions. One was a comment Adam made, I'd love to get your reaction if you want to tighten your bell, come to the cloud. I thought that was a very interesting nuance. A lot of economic pressure. Cloud is an opportunity to get agile, time to value faster. We had Zs carve cube analyst who's with us earlier said, the more you spend on the cloud, the more you save. That was his line, which I thought was very smart. Spending more doesn't mean you're gonna lose money, means you can save money too. So a lot of cost optimization discussions. Absolutely. Hey, your belt come to the cloud. What does he mean by that? >>Well I think that in, in times where, you know, there's uncertainty and economic conditions, it is, it's really, you know, you sometimes wanna pull back kind of, you know, batten down the hatches. But the cloud really, and we saw this with C you know, if you, if you move to the cloud, not only can you cut costs, but you put yourself in this position where you can continue to innovate and you can be agile and you can be prepared for whatever environment you're in so that you know when things go back or you have a customer needs that and innovation that goes off like you, you can accelerate back up really, really quickly. And I think we talked about Airbnb, that example of how, you know, in, in that really tough time of covid when travel industry wasn't happening so much, you know, they were able to scale back and save money. And then at the same time when, you know, Airbnb's kind of once again travel came back, they were in a position to really, really quickly change with the, the customer needs. >>You know, Lee, it's always great talking with you. You got a lot of energy, you're so smart and we both love products and you're leading the product marketing. We have an Instagram challenge here on the cube. I'm gonna put you on the spot here. Oh my gosh. It's called Instagram. We called a bumper sticker section. We used to call it what's the bumper sticker for reinvent. But we kind of modernized that. If you were gonna do an Instagram reel right now, what would be the Instagram reel for reinvent Keynote day one. As we look for, we got Verner, we'll probably talk about productivity with developers. What's the Instagram reel for reinvent? >>Wow. That means I have to get short with it, right? I am, I'm not always, that's still wrong answer. Yeah, well I think, you know, this is really big day one, so it's excitement, it's, we're glad to be here. We have a lot coming for you. We're super excited. And if you think about it, it's price, performance, it's data, it's security and it's solutions for purpose-built use cases. >>Great job. Congratulations. I love the message. I love how you guys had the theme. I thought it was great. And it's great to see Amazon continue to innovate with, with the, with the, with the innovation on the product side. But as we get into transformation, starting to see these solutions and the ecosystem is thriving and looking forward to hearing the, the new partner, chief Aruba tomorrow. Absolutely. See what she's got a new plan apparently unveiling. So exciting. Everyone's pretty excited. Thanks for coming >>On. Great. Great. Thanks for having >>Me. All right. Leah, here in the cube. You are the cube, the leader in tech coverage. I'm John Fur, your host. More live coverage after the short break. We'll be right back here. Day two of the cube, day one of reinvent. Lot of great action. Three, four days of wall to wall coverage. We'll be right back.

(upbeat music) (logo shimmers) >> Good afternoon everyone, and welcome back to AWS re Invent 2022. We are live here from the show floor in Las Vegas, Nevada, we're theCUBE, my name is Savannah Peterson, joined by John Furrier, John, are you excited for the next segment? >> I love the innovation story, this next segment's going to be really interesting, an example of ecosystem innovation in action, it'll be great. >> Yeah, our next guests are actually award-winning, I am very excited about that, please welcome Alan and Becky from IBM. Thank you both so much for being here, how's the show going for ya? Becky you got a, just a platinum smile, I'm going to go to you first, how's the show so far? >> No, it's going great. There's lots of buzz, lots of excitement this year, of course, three times the number of people, but it's fantastic. >> Three times the number of people- >> (indistinct) for last year. >> That is so exciting, so what is that... Do you know what the total is then? >> I think it's over 55,000. >> Ooh, loving that. >> John: A lot. >> It's a lot, you can tell by the hallways- >> Becky: It's a lot. >> John: It's crowded, right. >> Yeah, you can tell by just the energy and the, honestly the heat in here right now is pretty good. Alan, how are you feeling on the show floor this year? >> Awesome, awesome, we're meeting a lot of partners, talking to a lot of clients. We're really kind of showing them what the new IBM, AWS relationship is all about, so, beautiful time to be here. >> Well Alan, why don't you tell us what that partnership is about, to start us off? >> Sure, sure. So the partnership started with the relationship in our consulting services, and Becky's going to talk more about that, right? And it grew, this year it grew into the IBM software realm where we signed an agreement with AWS around May timeframe this year. >> I love it, so, like you said, you're just getting started- >> Just getting started. >> This is the beginning of something magic. >> We're just scratching the surface with this right? >> Savannah: Yeah. >> But it represents a huge move for IBM to meet our clients where they are, right? Meet 'em where they are with IBM technology, enterprise technology they're used to, but with the look and feel and usage model that they're used to with AWS. >> Absolutely and so to build on that, you know, we're really excited to be an AWS Premier Consulting Partner. We've had this relationship for a little over five years with AWS, I'd say it's really gone up a notch over the last year or two as we've been working more and more closely, doubling down on our investments, doubling down on our certifications, we've got over 15,000 people certified now, almost 16,000 actually- >> Savannah: Wow. >> 14 competencies, 16 service deliveries and counting. We cover a mass of information and services from Data Analytics, IoT, AI, all the way to Modernization, SAP, Security Services, right. So it's pretty comprehensive relationship, but in addition to the fantastic clients that we both share, we're doing some really great things around joint industry solutions, which I'll talk about in a few minutes and some of those are being launched at the conference this year, so that's even better. But the most exciting thing to me right now is that we just found out that we won the Global Innovator Partner of the Year award, and a LATAM Partner of the Year award. >> Savannah: Wow. >> John: That's (indistinct) >> So, super excited for IBM Consulting to win this, we're honored and it's just a great, exciting part to the conference. >> The news coming out of this event, we know tomorrow's going to be the big keynote for the new Head of the ecosystem, Ruba. We're hearing that it's going to be all about the ecosystem, enabling value creation, enabling new kinds of solutions. We heard from the CEO of AWS, this nextGen environment's upon us, it's very solution-oriented- >> Becky: Absolutely. >> A lot of technology, it's not an either or, it's an and equation, this is a huge new shift, I won't say shift, a continuation for AWS, and you guys, we've been covering, so you got the and situation going on... Innovation solutions and innovation technology and customers can choose, build a foundation or have it out of the box. What's your reaction to that? Do you think it's going to go well for AWS and IBM? >> I think it fits well into our partnership, right? The the thing you mentioned that I gravitate to the most is the customer gets to choose and the thing that's been most amazing about the partnership, both of these companies are maniacally focused on the customer, right? And so we've seen that come about as we work on ways the customer to access our technology, consume the technology, right? We've sold software on-prem to customers before, right, now we're going to be selling SaaS on AWS because we had customers that were on AWS, we're making it so that they can more easily purchase it by being in the marketplace, making it so they can draw down their committed spin with AWS, their customers like that a lot- [John] Yeah. >> Right. We've even gone further to enable our distributor network and our resellers, 'cause a lot of our customers have those relationships, so they can buy through them. And recently we've enabled the customer to leverage their EDP, their committed spend with AWS against IBM's ELA and structure, right, so you kind of get a double commit value from a customer point of view, so the amazing part is just been all about the customers. >> Well, that's interesting, you got the technology relationship with AWS, you mentioned how they're engaging with the software consumption in marketplace, licensed deals, there's all kinds of new business model innovations on top of the consumption and building. Then you got the consulting piece, which is again, a big part of, Adam calls it "Business transformation," which is the result of digital transformation. So digital transformation is the process, the outcome is the business transformation, that's kind of where it all kind of connects. Becky, what's your thoughts on the Amazon consulting relationships? Obviously the awards are great but- >> They are, no- >> What's the next step? Where does it go from here? >> I think the best way for me to describe it is to give you some rapid flyer client examples, you know, real customer stories and I think that's where it really, rubber meets the road, right? So one of the most recent examples are IBM CEO Arvind Krishna, in his three key results actually mentioned one of our big clients with AWS which is the Department of Veterans Affairs in the US and is an AI solution that's helped automate claims processing. So the veterans are trying to get their benefits, they submit the claims, snail mail, phone calls, you know, some in person, some over email- >> Savannah: Oh, it gives me all the feels hearing you talk about this- >> It's a process that used to take 25 to 30 days depending on the complexity of the claims, we've gotten it down with AWS down to within 24 hours we can get the veterans what they need really quickly so, I mean, that's just huge. And it's an exciting story that includes data analytics, AI and automation, so that's just one example. You know, we've got examples around SAP where we've developed a next generation SAP for HANA Platform for Phillips Carbon Black hosted on AWS, right? For them, it created an integrated, scalable, digital business, that cut out a hundred percent the capital cost from on-prem solutions. We've got security solutions around architectures for telecommunications advisors and of course we have lots of examples of migration and modernization and moving workloads using Red Hat to do that. So there's a lot of great client examples, so to me, this is the heart of what we do, like you said, both companies are really focused on clients, Amazon's customer-obsessed, and doing what we can for our clients together is where we get the impact. >> Yeah, that's one of the things that, it sounds kind of cliche, "Oh we're going to work backwards from the customer," I know Amazon says that, they do, you guys are also very customer-focused but the customers are changing. So I'd love to get your reaction because we're now in that cloud 2.0, I call that 2.0 or you got the Amazon Classic, my word, and then Next Gen Cloud coming, the customers are different, they're transforming because IT's not a department anymore, it's in the DevOps pipeline. The developers are driving a lot of IT but security and on DataOps, it's the structural change happening at the customer, how do you guys see that at IBM? I know we cover a lot of Red Hat and Arvind talks to us all the time, meeting the customer where they are, where are they? Where are the customers? Can you share your perspective on where they are? >> It's an astute observation, right, the customer is changing. We have both of those sets of customers, right, we still have the traditional customer, our relationship with Central IT, right, and driving governance and all of those things. But the folks that are innovating many times they're in the line of business, they're discovering solutions, they're building new things. And so we need our offerings to be available to them. We need them to understand how to use them and be convenient for these guys and take them through that process. So that change in the customer is one that we are embracing by making our offerings easy to consume, easy to use, and easy to build into solutions and then easy to parlay into what central IT needs to do for governance, compliance, and these types of things, it's becoming our new bread and butter. >> And what's really cool is- >> Is that easy button- >> We've been talking about- >> It's the easy button. >> The easy button a lot on the show this week and if you just, you just described it it's exactly what people want, go on Becky. >> Sorry about that, I was going to say, the cool part is that we're co-creating these things with our clients. So we're using things like the Amazon Working Backward that you just mentioned.` We're using the IBM garage methodology to get innovative to do design working, design thinking workshops, and think about where is that end user?, Where is that stakeholder? Where are they, they thinking, feeling, doing, saying how do we make the easier? How do we get the easy button for them so that they can have the right solutions for their businesses. We work mostly with lines of business in my part of the organization, and they're hungry for that. >> You know, we had a quote on theCUBE yesterday, Savannah remember one of our guests said, you know, back in the, you know, 1990s or two 2000s, if you had four production apps, it was considered complex >> Savannah: Yeah. >> You know, now you got hundreds of workloads, thousands of workloads, so, you know, this end-to-end vision that we heard that's playing out is getting more complex, but the easy button is where these abstraction layers and technology could come in. So it's getting more complex because there's more stuff but it's getting easier because- >> Savannah: What is the magnitude? >> You can make it easier. This is a dynamic, share your thoughts on that. >> It's getting more complex because our clients need to move faster, right, they need to be more agile, right, so not only are there thousands of applications there are hundreds of thousands microservices that are composing those applications. So they need capabilities that help them not just build but govern that structure and put the right compliance over that structure. So this relationship- >> Savannah: Lines of governance, yeah- >> This relationship we built with AWS is in our key areas, it's a strategic move, not a small thing for us, it covers things like automation and integration where you need to build that way. It covers things like data and AI where you need to do the analytics, even things like sustainability where we're totally aligned with what AWS is talking about and trying to do, right, so it's really a good match made there. >> John: It really sounds awesome. >> Yeah, it's clear. I want to dig in a little bit, I love the term, and I saw it in my, it stuck out to me in the notes right away, getting ready for you all, "maniacal", maniacal about the customer, maniacal about the community, I think that's really clear when we're talking about 24 days to 24 hours, like the veteran example that you gave right there, which I genuinely felt in my heart. These are the types of collaborations that really impact people's lives, tell me about some of the other trends or maybe a couple other examples you might have because I think sometimes when our head's in the clouds, we talk a lot about the tech and the functionality, we forget it's touching every single person walking around us, probably in a different way right now than we may even be aware- >> I think one of the things that's been, and our clients have been asking us for, is to help coming into this new era, right, so we've come out of a pandemic where a lot of them had to do some really, really basic quick decisions. Okay, "Contact Center, everyone work from home now." Okay, how do we do that? Okay, so we cobbled something together, now we're back, so what do we do? How do we create digital transformation around that so that we are going forward in a really positive way that works for our clients or for our contact center reps who are maybe used to working from home now versus what our clients need, the response times they need, and AWS has all the technology that we're working with like Amazon Connect to be able to pull those things together with some of our software like Watson Assistant. So those types of solutions are coming together out of that need and now we're moving into the trend where economy's getting tougher, right? More cost cutting potentially is coming, right, better efficiencies, how do we leverage our solutions and help our clients and customers do that? So I think that's what the customer obsession's about, is making sure we really understand where their pain points are, and not just solve them but maybe get rid of 'em. >> John: Yeah, great one. >> Yeah. And not developing in a silo, I mean, it's a classic subway problem, you got to be communicating with your community if you want to continue to serve them. And IBM's been serving their community for a very long time, which is super impressive, do you think they're ready for the challenge? >> Let's do it. >> So we have a new thing on theCUBE. >> Becky: Oh boy. >> We didn't warn you about this, but here we go. Although you told, Alan, you've mentioned you're feeling very cool with the microphone on, so I feel like, I'm going to put you in the hot seat first on this one. Not that I don't think Becky's going to smash it, but I feel like you're channeling the power of the microphone. New challenges, treat it like a 32nd Instagram reel-style story, a hot take, your thought leadership, money clip, you know, this is your moment. What is the biggest takeaway, most important thing happening at the show this year? >> Most important thing happening at the show? Well, I'm glad you mentioned it that way, because earlier you said we may have to sing (presenters and guests all laughing) >> So this is much better than- >> That's actually part of the close. >> John: Hey, hey. >> Don't worry, don't worry, I haven't forgotten that, it's your Instagram reel, go. (Savannah laughs) >> Original audio happening here on theCUBE, courtesy of Alan and IBM, I am so here for it. >> So what my takeaway and what I would like for the audience to take away, out of this conversation especially, but even broadly, the IBM AWS relationship is really like a landmark type of relationship, right? It's one of the biggest that we've established on both sides, right- >> Savannah: It seems huge, okay you are too monolith in the world of companies, like, yeah- >> Becky: Totally. >> It's huge. And it represents a strategic change on both sides, right? With that customer- >> Savannah: Fundamentally- >> In the middle right? >> Savannah: Yeah. >> So we're seeing things like, you know, AWS is working with us to make sure we're building products the way that a AWS client likes to consume them, right, so that we have the right integration, so they get that right look and feel, but they still get the enterprise level capabilities they're used to from IBM, right? So the big takeaway I like for people to take, is this is a new IBM, it's a new AWS and IBM relationship, and so expect more of that goodness, more of those new things coming out of it. [John] Excellent, wow. >> That was great, well done, you nailed it. and you're going to finish with some acapella, right? (Alan laughs) >> You got a pitch pipe ready? (everyone laughs) >> All right Becky, what about you? Give us your hot take. >> Well, so for me, the biggest takeaway is just the way this relationship has grown so much, so, like you said, it's the new IBM it's the new AWS, we were here last year, we had some good things, this year we're back at the show with joint solutions, have been jointly funded and co-created by AWS and IBM. This is huge, this is a really big opportunity and a really big deal that these two companies have come together, identified joint customer needs and we're going after 'em together and we're putting 'em in the booth. >> Savannah: So cool. And there's things like smart edge for welding solutions that are out there. >> Savannah: Yes. >> You know, I talked about, and it's, you know you wouldn't think, "Okay, well what's that?" There's a lot to that, a lot of saving when you look at how you do welding and if you apply things like visual AI and auditory AI to make sure a weld is good. I mean, I think these are, these things are cool, I geek out on these things- >> John: Every vertical. >> I'm geeking out with you right now, just geeking- >> Yeah, yeah, yeah, so- >> Every vertical is infected. >> They are and it's so impactful to have AWS just in lockstep with us, doing these solutions, it's so different from, you know, you kind of create something that you think your customers like and then you put it out there. >> Yeah, versus this moment. >> Yeah, they're better together. >> It's strategic partnership- >> It's truly a strategic partnership. and we're really bringing that this year to reinvent and so I'm super excited about that. >> Congratulations. >> Wow, well, congratulations again on your awards, on your new partnership, I can't wait to hear, I mean, we're seven months in, eight months in to this this SaaS side of the partnership, can't wait to see what we're going to be talking about next year when we have you back on theCUBE. >> I know. >> and maybe again in between now and then. Alan, Becky, thank you both so much for being here, this was truly a joy and I'm sure you gave folks a taste of the new IBM, practicing what you preach. >> John: Great momentum. >> And I'm just, I'm so impressed with the two companies collaborating, for those of us OGs in tech, the big companies never collaborated before- >> Yeah. >> John: Yeah. Joint, co-created solutions. >> And you have friction between products and everything else. I mean's it's really, co-collaboration is, it's a big theme for us at all the shows we've been doing this year but it's just nice to see it in practice too, it's an entirely different thing, so well done. >> Well it's what gets me out of the bed in the morning. >> All right, congratulations. >> Very clearly, your energy is contagious and I love it and yeah, this has been great. Thank all of you at home or at work or on the International Space Station or wherever you might be tuning in from today for joining us, here in Las Vegas at AWS re Invent where we are live from the show floor, wall-to-wall coverage for three days with John Furrier. My name is Savannah Peterson, we're theCUBE, the source for high tech coverage. (cheerful upbeat music)

>>Hello >>Everyone and welcome back to Dallas where we're live from, Super computing. My name is Savannah Peterson, joined with my co-host David, and we have a rocket of a show for you this afternoon. The doctors are in the house and we are joined by nasa, ladies and gentlemen. So excited. Please welcome Dr. Dan Duffy and Dr. Bill Putman. Thank you so much for being here, guys. I know this is kind of last minute. How's it to be on the show floor? What's it like being NASA here? >>What's exciting? We haven't, we haven't been here for three years, so this is actually really exciting to come back and see everybody, to see the showroom floor, see the innovations that have happened over the last three years. It's pretty exciting. >>Yeah, it's great. And, and so, because your jobs are so cool, and I don't wanna even remotely give even too little of the picture or, or not do it justice, could you give the audience a little bit of background on what you do as I think you have one of the coolest jobs ever. YouTube bill. >>I, I appreciate that. I, I, I run high Performance Computing Center at NASA Goddard for science. It's high performance information technology. So we do everything from networking to security, to high performance computing, to data sciences, artificial intelligence and machine learning is huge for us now. Yeah, large amounts of data, big data sets, but we also do scientific visualizations and then cloud and commercial cloud computing, as well as on premises cloud computing. And quite frankly, we support a lot of what Bill and his team does. >>Bill, why don't you tell us what your team >>Does? Yeah, so I'm a, I'm an earth scientist. I work as the associate chief at the global modeling assimilation office. And our job is to really, you know, maximize the use of all the observations that NASA takes from space and build that into a coherent, consistent physical system of the earth. Right? And we're focused on utilizing the HC that, that Dan and the folks at the nccs provide to us, to the best of our abilities to integrate those observations, you know, on time scales from hours, days to, to seasonal to to monthly time scales. That's, that's the essence of our focus at the GMA o >>Casual modeling, all of NASA's earth data. That, that in itself as a sentence is pretty wild. I imagine you're dealing with a ton of data. >>Oh, massive amounts of data. Yes, >>Probably, I mean, as much as one probably could, now that I'm thinking about it. I mean, and especially with how far things have to travel. Bill, sticking with you, just to open us up, what technology here excites you the most about the future and that will make your job easier? Let's put it that way. >>To me, it's the accelerator technologies, right? So there's the limited, the limiting factor for, for us as scientists is how fast we can get an answer. And if we can get our answer faster through accelerated technologies, you know, with the support of the, of the nccs and the computing centers, but also the software engineers enabling that for us, then we can do more, right. And push the questions even further, you know, so once we've gotten fast enough to do what we want to do, there's always something next that we wanna look for. So, >>I mean, at nasa you have to exercise such patience, whether that be data, coming back, images from a rover, doesn't matter what it is. Sometimes there's a lot of time, days, hours, years, depending on the situation. Right? I really, I really admire that. What about you, Dan? What's got you really excited about the future here? So >>Bill talked about the, the accelerated technology, which is absolutely true and, and, and is needed to get us not to only to the point where we have the compute resources to do the simulations that Bill wants to do, and also do it in a energy efficient way. But it's really the software frameworks that go around that and the software frameworks, the technology that dealing with how to use those in an energy efficient and and most efficient way is extremely important. And that's some of the, you know, that's what I'm really here to try to understand better about is how can I support these scientists with not just the hardware, but the software frameworks by which they can be successful. >>Yeah. We've, we've had a lot of kind of philosophical discussion about this, the difference between the quantitative increases in power in computing that we're seeing versus the question of whether or not we need truly qualitative changes moving forward. Where do you see the limits of, of, of, you know, if you, if you're looking at the ability to gather more data and process more data more quickly, what you can do with that data changes when you're getting updates every second versus every month seems pretty obvious. Is there a, is there, but is there, is there a near term target that you have specifically where once you reach that target, if you weren't thinking ahead of that target, you'd kind of be going, Okay, well we solved that problem, we're getting the data in so fast that you can, you can ask me, what is the temperature in this area? And you can go, Oh, well, huh, an hour ago the data said this. Beyond that, do you need a qualitative change in our ability to process information and tease insight into out of chaos? Or do you just need more quantity to be able to get to the point where you can do things like predict weather six months in advance? What are, what are your thoughts on that? Yeah, >>It's an interesting question, right? And, and you ended it with predicting whether six months in advance, and actually I was thinking the other way, right? I was thinking going to finer and finer scales and shorter time scales when you talk about having data more frequently, right? So one of the things that I'm excited about as a modeler is going to hire resolution and representing smaller scale processes at nasa, we're, we're interested in observations that are global. So our models are global and we'd like to push those to as fine a resolution as possible to do things like severe storm predictions and so forth. So the faster we can get the data, the more data we can have, and that area would improve our ability to do that as well. So, >>And your background is in meteorology, right? >>Yes, I'm a meteorologist. >>Excellent. Okay. Yeah, yeah, >>Yeah. So, so I have to ask a question, and I'm sure all the audience cares about this. And I went through this when I was talking about the ghost satellites as well. What, what is it about weather that makes it so hard to predict? >>Oh, it's the classic chaos problem. The, the butterfly effects problem, and it's just true. You know, you always hear the story of a butterfly in Africa flaps, its rings and wings, and the weather changes in, in New York City, and it's just, computers are an excellent example of that, right? So we have a model of the earth, we can run it two times in a row and get the exact same answer, but if we flip a bit somewhere, then the answer changes 10 days later significantly. So it's a, it's a really interesting problem. So, >>Yeah. So do you have any issue with the fact that your colleague believes that butterflies are responsible for weather? No, I does that, does that, is it responsible for climate? Does that bother you at all? >>No, it doesn't. As a matter of fact, they actually run those butterfly like experi experiments within the systems where they do actually flip some bits and see what the uncertainties are that happen out 7, 8, 9 days out in advance to understand exactly what he's saying, to understand the uncertainties, but also the sensitivity with respect to the observations that they're taking. So >>Yeah, it's fascinating. It is. >>That is fascinating. Sticking with you for a second, Dan. So you're at the Center for Climate Simulation. Is that the center that's gonna help us navigate what happens over the next decade? >>Okay, so I, no one center is gonna help us navigate what's gonna happen over the next decade or the next 50 or a hundred years, right. It's gonna be everybody together. And I think NASA's role in that is really to pioneer the, the, the models that that bill and others are doing to understand what's gonna happen in not just the seasonal sub, but we also work with G, which is the God Institute for Space Studies. Yeah. Which does the decatal and, and the century long studies. Our, our job is to really help that research, understand what's happening with the client, but then feed that back into what observations we need to make next in order to better understand and better quantify the risks that we have to better quantify the mitigations that we can make to understand how and, and, and affect how the climate is gonna go for the future. So that's really what we trying to do. We're trying to do that research to understand the climate, understand what mitigations we can have, but also feedback into what observations we can make for the future. >>Yeah. And and what's the partnership ecosystem around that? You mentioned that it's gonna take all of us, I assume you work with a lot of >>Partners, Probably both of you. I mean, obviously the, the, the federal agencies work huge amounts together. Nasa, Noah is our huge partnerships. Sgs, a huge partnerships doe we've talked to doe several times this, so this, this this week already. So there's huge partnerships that go across the federal agency. We, we work also with Europeans as much as we can given the, the, the, you know, sort of the barriers of the countries and the financials. But we do collaborate as much as we can with, And the nice thing about NASA, I would say is the, all the observations that we take are public, they're paid for by the public. They're public, everybody can down them, anybody can down around the world. So that's also, and they're global measurements as Bill said, they're not just regional. >>Do you have, do you have specific, when you think about improving your ability to gain insights from data that that's being gathered? Yeah. Do you set out specific milestones that you're looking for? Like, you know, I hope by June of next year we will have achieved a place where we are able to accomplish X. Yeah. Do you, do you, Yeah. Bill, do you put, what, >>What milestones do we have here? So, yeah, I mean, do you have >>Yeah. Are, are you, are you sort of kept track of that way? Do you think of things like that? Like very specific things? Or is it just so fluid that as long as you're making progress towards the future, you feel okay? >>No, I would say we absolutely have milestones that we like to keep in track, especially from the modeling side of things, right? So whether it's observations that exist now that we want to use in our system, milestones to getting those observations integrated in, but also thinking even further ahead to the observations that we don't have yet. So we can use the models that we have today to simulate those kind of observations that we might want in the future that can help us do things that we can do right now. So those missions are, are aided by the work that we do at the GBO and, and the nccs, but, >>Okay, so if we, if we extrapolate really to the, to the what if future is really trying to understand the entire earth system as best as we can. So all the observations coming in, like you said, in in near real time, feeding that into an earth system model and to be able to predict short term, midterm or even long term predictions with, with some degree of certainty. And that may be things like climate change or it may be even more important, shorter term effects of, of severe weather. Yeah. Which is very important. And so we are trying to work towards that high resolution, immediate impact model that we can, that we can, you know, really share with the world and share those results as best, as best we can. >>Yeah. I, I have a quick, I have a quick follow up on that. I I bet we both did. >>So, so if you think about AI and ml, artificial intelligence and machine learning, something that, you know, people, people talk about a lot. Yeah. There's the concept of teaching a machine to go look for things, call it machine learning. A lot of it's machine teaching we're saying, you know, hit, you know, hit the rack on this side with a stick or the other side with the stick to get it to, to kind of go back and forth. Do you think that humans will be able to guide these systems moving forward enough to tease out the insights that we want? Or do you think we're gonna have to rely on what people think of as artificial intelligence to be able to go in with this massive amount of information with an almost infinite amount of variables and have the AI figure out that, you know what, it was the butterfly, It really was the butterfly. We all did models with it, but, but you understand the nuance that I'm saying. It's like we, we, we think we know what all the variables are and that it's chaotic because there's so many variables and there's so much data, but maybe there's something we're not taking into >>A account. Yeah, I I, I'm, I'm, I'm sure that's absolutely the case. And I'll, I'll start and let Bill, Bill jump in here. Yeah, there's a lot of nuances with a aiml. And so the, the, the, the real approach to get to where we want to be with this earth system model approach is a combination of both AI ML train models as best as we can and as unbiased way as we can. And there's a, there's a big conversation we have around that, but also with a physics or physical based model as well, Those two combined with the humans or the experts in the loop, we're not just gonna ask the artificial intelligence to predict anything and everything. The experts need to be in the loop to guide the training in as best as we, as, as we can in an unbiased, equitable way, but also interpret the results and not just give over to the ai. But that's the combination of that earth system model that we really wanna see. The future's a combination of AI l with physics based, >>But there's, there's a, there's an obvious place for a AI and ML in the modeling world that is in the parameterizations of the estimations that we have to do in our systems, right? So when we think about the earth system and modeling the earth system, there are many things like the equations of motions and thermodynamics that have fixed equations that we know how to solve on a computer. But there's a lot of things that happen physically in the atmosphere that we don't have equations for, and we have to estimate them. And machine learning through the use of high resolution models or observations in training the models to understand and, and represent that, yeah, that that's the place where it's really useful >>For us. There's so many factors, but >>We have to, but we have to make sure that we have the physics in that machine learning in those, in those training. So physics informed training isn't very important. So we're not just gonna go and let a model go off and do whatever it wants. It has to be constrained within physical constraints that the, that the experts know. >>Yeah. And with the wild amount of variables that affect our, our earth, quite frankly. Yeah, yeah. Which is geez. Which is insane. My god. So what's, what, what technology or what advancement needs to happen for your jobs to get easier, faster for our ability to predict to be even more successful than it is currently? >>You know, I think for me, the vision that I have for the future is that at some point, you know, all data is centrally located, essentially shared. We have our applications are then services that sit around all that data. I don't have to sit as a user and worry about, oh, is this all this data in place before I run my application? It's already there, it's already ready for me. My service is prepared and I just launch it out on that service. But that coupled with the performance that I need to get the result that I want in time. And I don't know when that's gonna happen, but at some point it might, you know, I don't know rooting for you, but that's, >>So there are, there are a lot of technologies we can talk about. What I'd like to mention is, is open science. So NASA is really trying to make a push and transformation towards open science. 2023 is gonna be the year of open science for nasa. And what does that mean? It means a lot of what Bill just said is that we have equity and fairness and accessibility and you can find the data, it's findability, it's fair data, you know, a fair findability accessibility reproducibility, and I forget what the eye stands for, but these are, these are tools and, and, and things that we need to, as, as a computing centers and including all the HC centers here, as well as the scientists need to support, to be as transparent as possible with the data sets and the, and the research that we're doing. And that's where I think is gonna be the best thing is if we can get this data out there that anybody can use in an equitable way and as transparent as possible, that's gonna eliminate, in my opinion, the bias over time because mistakes will be found and mistakes will be corrected over time. >>I love that. Yeah. The open source science end of this. No, it's great. And the more people that have access people I find in the academic world, especially people don't know what's going on in the private sector and vice versa. And so I love that you just brought that up. Closing question for you, because I suspect there might be some members of our audience who maybe have fantasized about working at nasa. You've both been working there for over a decade. Is it as cool as we all think of it? It is on the outside. >>I mean, it's, it's definitely pretty cool. >>You don't have to be modest about it, you know, >>I mean, just being at Goddard and being at the center where they build the James web web telescope and you can go to that clean room and see it, it's just fascinating. So it, it's really an amazing opportunity. >>Yeah. So NASA Goddard as a, as a center has, you know, information technologist, It has engineers, it has scientists, it has support staff, support team members. We have built more things, more instruments that have flown in this space than any other place in the world. The James Lab, we were part of that, part of a huge group of people that worked on James. We and James, we came through and was assembled in our, our, our clean room. It's one of the biggest clean rooms in, in, in the world. And we all took opportunities to go over and take selfies with this as they put those loveness mirrors on them. Yeah, it was awesome. It was amazing. And to see what the James we has done in such a short amount of time, the successes that they've gone through is just incredible. Now, I'm not a, I'm not a part of the James web team, but to be a, to be at the same center, to to listen to scientists like Bill talk about their work, to listen to scientists that, that talk about James, we, that's what's inspiring. And, and we get that all the time. >>And to have the opportunity to work with the astronauts that service the, the Hubble Telescope, you know, these things are, >>That's literally giving me goosebumps right now. I'm sitting over >>Here just, just an amazing opportunity. And woo. >>Well, Dan, Bill, thank you both so much for being on the show. I know it was a bit last minute, but I can guarantee we all got a lot out of it. David and I both, I know I speak for us in the whole cube audience, so thank you. We'll have you, anytime you wanna come talk science on the cube. Thank you all for tuning into our supercomputing footage here, live in Dallas. My name is Savannah Peterson. I feel cooler having sat next to these two gentlemen for the last 15 minutes and I hope you did too. We'll see you again soon.

>>The Cube presents UI Path Forward five. Brought to you by UI Path. >>Welcome back to The Cube's, continuous coverage with day two of UI Path forward. Five. My name is Dave Ante. I'm here with my co-host Dave Nicholson. And you are watching The Cube. It's all about the robots, the automations, the transformations and beyond. Audi Gana is here. She's group executive at Vodacom and Niva is back. She's senior director of telecommunications industry for UiPath. Ladies, welcome to the Cube. >>I thank you very much. >>So Vodacom a leading telco in in Africa across the continent. Tell us more about the company. >>The company is a traditionally telecommunications company, but our vision 2025 is first to transition from being a telco to a technology company. So you'll find that a lot of the use cases that we've actually started embarking on, combined the combination of telco and FinTech. And we've got a lot of RPA bot also supporting the FinTech platform, which is quite a major step in our strategy. >>So, you know, it's interesting Mark Andreessen's famous comment, Every company's a software company. I like to think every company's a technology company, technology driven. So what does that actually mean for you? Is it like a split brain between FinTech? Cuz it's pretty clear that FinTech is always a highly, you know, technology oriented and telecom. Are they sort of together driving a, a technology business? How does that >>Work? It's, it's a, it's a converge use of the technology to add value to the customer. So what we wanna do is to get to the point where we have converged services where the Telecommunicate, cuz at the end of the day in the African market, you'll find that there's a lot of markets that are unbanked. So you find that a cell phone is a means of communication and a a mobile platform for the users. So it's a natural progression for our company to actually play in both spaces. And I think one of the things I find quite interesting is the levels of trust that a lot of citizens have in our financial platform. In that even some of the governments are paying social grants using the platform. And so it almost becomes, without the phones a lot of people cannot function type of scenario. >>Nevas is your role a global role? >>Yes, it is a global role. >>Okay. So it's interesting cause you're I think based down under, right? I am. Is that true? Okay. Obviously spent some time in, in, in the African continent. How are you, what are you seeing in terms of the, the trends in, in telecommunications that, and are you noticing there's gotta be differences across different regions? You know, a lot of times you hear, oh no, there's really kind of a global world out and I know it is, but telco seems to be one of the industries that has some uniqueness within the different breaches. What are you seeing? >>Look, we are privileged to work with more than 200 telecoms around the world. But clearly from a technology perspective, there are some regions that have embraced technology sooner than the others, particularly when it comes to automation. Now we do have use cases with all of them that we are, you know, the 200 we are working with. But the extent to which they become strategic partners, Varie is, you know, what I find is in, in the US we are doing a lot in the customer experience space with the telecoms in aj it's more back of house. And with telecoms like Vodacom, it's really strategic. You know, automation is being applied practically in every facet of work. And you know, sometimes that could be because the demand is just so great for connectivity, you know, at times there's a skills gap, but it does vary. But what's reassuring is that there is a journey and you know, at this event what I have seen is telecoms wanting to learn from other telecoms. And I must say Artie has been in huge demand. We did about 22 meetings yesterday with others wanting to know, which again is that strategic trend. >>Artie, my understanding is you've been at this for a while, this automation journey for quite some time and p i pass. Interesting. I mean it's a company that's founded in 2005 and kind of did sort of its own thing for a long time and then realized it had lightning in a bottle Yeah. For a mid last decade. But my understanding is you've seen it all. You've seen the, the legacy platforms. And so tell me about your personal journey with automation and then the companies. >>Okay, so there's the automation pre rpa, which was strangely enough, I come from banking, got a finance degree, did automated ations in one of the bigger banks. And somehow I transitioned. And I mean from a history perspective, the one of the previous platforms, which was the biggest one at the time, that's where I got to learn about rpa. And then there's another vendor that we then use in another company. So this is almost my third vendor that I'm experiencing in the RPA space. Having joined RPA space in 2015, apparently I'm kind of a veteran, >>So, So what are you seeing is what's the difference between, I mean let's call UI path, that was sort of a modern focus on simple to deploy. That's really how they get started when I first found them. How do you compare what UiPath has? And there are others, there are other modern platforms to sort of the legacy platforms. What's the >>Difference? I think it's the diversity and the applicability of the technology across multiple industries is something that still amazes me up until today. Because the kind of customers I've been meeting today, I, I would not, I met a guy who owns an ice cream company and I'm like, where would automation come in here? But he's actually quite a big customer of, of UiPath, you know. So I think that's one thing I appreciate. I think the ease of use, it has actually allowed for a lot of people to be part of the digital transformation. I think in the his, in the past technology has been seen as something that was a bit elite and that you needed to have X amount of skills and level of education. Whereas the RPA industry has almost bridged that gap in actually bringing along as many people in the journey in terms of digital transformation. And the fourth industrial revolution is now starting to become more inclusive >>Horizontal across industries. >>Yeah. >>So Vodacom headquartered in South Africa. Okay. But presence throughout the continent. Yes. I imagine that various geographies have various twists and quirks to them and different needs. But as a general premise, the African continent has led the rest of the world in terms of embracing these little mobile devices for the most mission. Critical from a personal perspective things, right? Yeah. So if you, if you're already trusting all of your finances and even interaction with your government from a financial perspective. Yeah. When you say technology platform technology is moving forward, what's more critical from that? Or how do you, how do you, how do you branch off from that? What are some of the other things that you can share with us that you're looking towards in the future that may, that that may trickle over here eventually? >>So I think what one of the things we started playing around with quite well is actually the convergence of machine learning, AI and rpa. You would find that a lot of research will tell you that this is the future of the automation and for us, we are actually living the future in that we have civil use cases that are actually extracting a lot of business value. Where we've realized that RPA in of itself, and this is obviously oversimplifying the technology is almost the unlimited hands on keyboards that you could ever have, right? And then machine learning and AI almost the becomes the unlimited brains. So when you then combine the tool, you almost have this strong technology that can revolutionize how we operate and service our customers. >>Well how do, how does that translate? Can you translate that into a user experience at this point? So I mean, we're talking about people who they, they have a motor license, they don't have a desktop computer at home. Yeah. This is their portal into the world. >>So you find that if you're speaking pure telco, and I'm obviously over simplifying there some nowhere an engineer, right? But I think at, at a very simplified level, there's a lot of legacy technology that is used in the telco space and you'll find that because of that, there's a lot of lack of integration. And you'll find that the reasons why a lot of customers call corners is because there's poor integration in a lot of instances. And it's, it's, it's, it's ad hoc. So it's not as if the system is failed completely. So what we've now done is to try and see how do we use machine learning to pick up on those anomalies on the network, right? And because each time something breaks, right, it's almost a fixed way to fix it and therefore the machine learning picking up there normally almost the hands over to the RPA bot to fix the problem within the network element. But that means is that from a customer experience perspective, instead of you actually realizing there's a problem, we've fixed it before you even know that there's a problem. And therefore, and as you can imagine, it means that you then call the course into less because now you don't have the reason to complain because we've proactively identified the problem and we proactively use RPA then to fix it. So we almost have the almost like a self-healing element in within the, the, the RPA AI space. >>You know, I think of, we don't talk about the data, we haven't talked about the data much this week. I think in many respects this industry is, is data industry. Our automation is all about what you can do with the data. You said unlimited hands, unlimited brains. Cuz to me you have unlimited data and a lot of times you just can't handle it. Yeah. So what's the data angle on all this? >>So firstly, I know a lot of people will say data is the new oil. No, >>Right? So I would never >>Say that. I always though, I think I always ask people if I give you a bucket of brain crude oil, right? What are you gonna do with it? Right? Right. And similar to data, right? So I want to almost equate data to that crude oil element, but if you don't know how to refine it, process it, get it to be reliable, it's very useless in of its natural sense. So I think one of the things we've realized is that leveraging of the analogy of the, the machine learning in the brain, if you are in the sales space, you forever trying to push new sales, right? And then chances are when a customer leaves you, you are almost in a reactor state. So, and I imagine a world where you could proactively identify a customer with the propensity to leave your company because a lot of customers don't just, they are situations where they'll be walking down the street competitor calls them, they leave, not because they were unhappy, but a lot of customers actually had several engagements with us that were not pleasant, whatever the definition is. >>So we then saw there was almost five types of attributes that resulted in customers leaving us. So what then that said was imagine if you are an account manager, right? And you got told UiPath P two I limited has experienced 1, 2, 3, 4. Right? Actually, please go engage with them because something is happening. It means that as an account manager, you are then equipped to have a meaningful engagement with the customer because you're saying, hi UiPath, I see you've had X amount of job calls and you've had x amount of complaints in our call center. What is happening is it could be, could be your network, maybe the tower where you are, do you, And then the conversation becomes so meaningful. And I think even during covid what we found is lot of customers started using less of our data, not because they were unhappy, but it became an affordability thing, right? >>Because this is a thousands and thousands of, of data elements and pieces around Yeah. About customer transactions. There's no way one human would be able to go through all of the data and make me meaningful decisions out of it. So we then found that some customers were complaining about affordability. So we then built another model that says if an account manager is talking to a customer and they're struggling from an affordability perspective, what's the next best offer you can make to your customer while you're engaging? And then if in, if, if now your UiPath takes up that offer, then you'd find that the bot does the post engagement provisioning on the system. Because now if you then said, I've only, I can only afford 10 lines, but only pay 10 gigs, but not 10 lines and 20 gigs, that is at least better than us losing the customer. >>Yeah. Right? And we offering them almost a downward migration type of situation. Then the bot does that on the system. So you would find that we almost playing in the space of a human, human centered, intelligent automation where machine learning becomes the brain, the person is amplified in how they operate at the customer. And then the RPA bot becomes the hands that executes on that. And as the account manager you focusing on engagement and convincing, which is really what people are great and selling as opposed to going through all of the pro cause VOCA is a lot of products. So as opposed to having a person going through the products and trying to find the best product for you, you know, so we, we are using machine learning to assist the >>Humans. I I mean in every, every interaction is consistent in that case. I know I sometimes have to call three or four times to find a professional that knows enough that can help me. Yes. Such a frustrating thing as a consumer. So you are, are you, you're attacking churn with automation. So we haven't even talked about how you guys are working together, your journey and all that stuff, but, but how are you guys working here? What are you, what are you doing? You know, in addition to what you just described with with ui. iPad? >>So I think my portfolio's quite wide. So I am, my team is in every single vertical in the organization from customer care to the consumer enterprise business units to finance technology, network compliance. And we do all of this in about six countries, right? So one of the things we've actually realized is that if we are looking at customer service, we wanted to understand why do customers call us? And I think I came from a point of ignorance because I'm not from telco, so I actually realized that if we're talking billing and finance revenue assurance, customers call us because we build them arly. But technically speaking it's our systems that there's something that resulted in the customer calling us. So why do we not know about our own systems? Why are we waiting for the customers to call us? And literally those are the questions I was asking cuz I felt like why are we, why are we waiting for the customers to call us? >>So we then then found a way to try and see within the billing systems where do the breakages happen, right? So that we fix them before the customer has to call us again. So then again from a billing perspective, it means that cuz it the billing element can come in two ways where we are giving you a service and not charging you for it. We then have revenue leakage or we, you are consuming something and we are overcharging you. Then you call us and say, Whatcom is stealing my data. Yeah, you're right out there. I promise you nobody wakes up in the morning and wants to take one gig of your data. So it almost becomes a day integrity initiative that results in good customer service but then result in eradication of course. As opposed to us waiting for customers to tell us what the problem is and trying to help them fast. Cuz that's generally always been what I've picked up the energy around customer service. How do we help you fast? I'm saying why must you call us when our systems had fail that? So we almost trying to see how do we use the technology internally to give customers a better experience and then also have the financial benefits that we are now starting to see happening in the, >>What's the scope of, of your like how many automations, how many bots? Can you give us a sense >>Of this? So right now I think we over on with all of the four, five countries that we are in, we over 400 bots. Wow. Okay. So we started in 2004 years ago, this is my fourth year in Voca. We, and we are not using just one product with UiPath. It became a platform because as we became across more kinds of problems, I think what I've appreciated about part is how we've actually created a partnership. Instead of them trying to sell me products for the sake of consuming products, it became a, this is my problem, right? And then somehow they would whip out the product that solves my problem type of thing. So it became a ecosystem of solutions that >>You must love hanging out with Artie. >>I absolutely do and love, you know, I've spent a career in telecommunications myself and you know, the best days were when you could deliver an outstanding customer experience. And as you can see from what Artie has achieved when you were more proactive and predictive, you can serve your customers so much more effectively and that just lift the morale of the team because we all, you know, have this purpose in doing our jobs. But this is automation and AI built into every part of that customer journey. So end to end, you know, the customer's much happier. You know there's a problem before the customer knows you can solve the problem in most cases before they even know. And that's just what we are all in business to do to make things better. >>Great story. Thank you so much for sharing. Appreciate coming back >>In the queue. Thank you very much. Thank >>You. Thank you for watching. Keep it right there. Don't forget, go to silicon angle.com, all the news, go to the cube.net. You'll see me all these videos are available on demand as well as the other events that we do. Dave VTE for Dave Nicholson. Keep it right there. Right back at forward five UI.

>>Hello, and welcome to the cubes presentation of the AWS startup showcase. This is season two, episode four, the ongoing series covering exciting startups from the AWS ecosystem to talk about cyber security. I'm your host, John feer. And today we're excited to join by a Mediatel Walker, CEO of Quin security and sub IER, vice president of product management of sequence security gentlemen, thanks for joining us today on this showcase. >>Thank you, John PRAs. >>So the title of this session is continuous API protection life cycle to discover, detect, and defend security. APIs are part of it. They're hardened, everyone's using them, but they're they're target for malicious behavior. This is the focus of this segment. You guys are in the leading edge of this. What are the biggest challenges for organizations right now in assessing their security risks? Because you're seeing APIs all over the place in the news, just even this week, Twitter had a whistleblower come out from the security group, talking about their security plans, misleading the FTC on the bots and some of the malicious behavior inside the API interface of Twitter. This is really a mainstream Washington post is reporting on it. New York times, all the global outlets are talking about this story. This is the risk. I mean, yeah, this is what you guys do protect against this. >>Yeah, this is absolutely top of mind for a lot of security folks today. So obviously in the media and the type of attack that that is being discussed with this whistleblower coming out is called reputation bombing. This is not new. This has been going on since I would say at least eight to 10 years where the, the bad actors are using bots or automation and ultimately using APIs on these large social media platforms, whether it's Facebook, whether it's Twitter or some other social media platform and messing with the reputation system of those large platforms. And what I mean by that is they will do fake likes, fake commenting, fake retweeting in the case of Twitter. And what that means is that things that are, should not be very popular, all of a sudden become popular. That that way they're able to influence things like elections, shopping habits, personnel. >>We, we work with similar profile companies and we see this all the time. We, we mostly work on some of the secondary platforms like dating and other sort of social media platforms around music sharing and things like video sharing. And we see this all the time. These, these bots are bad. Actors are using bots, but ultimately it's an API problem. It's not just a bot problem. And that's what we've been trying to sort of preach to the world, which is your bot problem is subset of your API security challenges that you deal as an organization. >>You know, IMIA, we talked about this in the past on a previous conversation, but this really is front and center mainstream for the whole world to see around the challenges. All companies face, every CSO, every CIO, every board member organizations out there looking at this security posture that spans not just information technology, but physical and now social engineering. You have all kinds of new payloads of malicious behavior that are being compromised through, through things like APIs. This is not just about CSO, chief information security officer. This is chief security officer issues. What's your reaction >>Very much so I think the, this is a security problem, but it's also a reputation problem. In some cases, it's a data governance problem. We work with several companies which have very restrictive data governance and data regulations or data residency regulations there to conform to those regulations. And they have to look at that. It's not just a CSO problem anymore. In case of the, the news of the day to day, this is a platform problem. This goes all the way to the, that time CTO of Twitter. And now the CEO of Twitter, who was in charge of dealing with these problems. We see as just to give you an example, we, we work, we work with a similar sort of social media platform that allows Oop based login to their platform that is using tokens. You can sort of sign in with Facebook, sign in with Twitter, sign in with Google. These are API keys that are generated and trusted by these social media platforms. When we saw that Facebook leaked about 50 million of these login credentials or API keys, this was about three, four years ago. I wrote a blog about it. We saw a huge spike in those API keys being used to log to other social media platforms. So although one social platform might be taking care of its, you know, API or what problem, if something else gets reached somewhere else, it has a cascading impact on a variety of platforms. >>You know, that's a really interesting dynamic. And if you think about just the token piece that you mentioned, that's kind of under the coverage, that's a technology challenge, but also you get in the business logic. So let's go back and, and unpack that, okay, they discontinue the tokens. Now they're being reused here. In the case of Twitter, I was talking to an executive here in Silicon valley and they said, yeah, it's a cautionary tale, for sure. Although Twitter's a unique situation, but they abstract out the business value and say, Hey, they had an M and a deal on the table. And so if someone wants to unwind that deal, all I gotta say is, Hey, there's a bot problem. And now you have essentially new kinds of risk in the business have nothing to do with some sign the technology, okay. They got a security breach, but here with Twitter, you have an, an, an M and a deal, an acquisition that's being contested because of the, the APIs. So, so if you're in business, you gotta think to yourself, what am I risking with my API? So every organization should be assessing their security risks, tied to their APIs. This is a huge awakening for them. Where should they start? And that's the, that's the core question. Okay. You got my attention risks with the API. What do I do? >>So when I talked to you in my previous interview, the start is basically knowing what to, in most cases, you see these that are hitting the wire much. Every now there is a major in cases you'll find these APIs are targeted, that are not poorly protected. They're absolutely just not protected at all, which means the security team or any sort of team that is responsible for protecting these APIs are just completely unaware of these APIs being there in the first place. And this is where we talk about the shadow it or shadow API problem. Large enterprises have teams that are geo distributed, and this problem is escalated after the pandemic even more because now you have teams that are completely distributed. They do M and a. So they acquire new companies and have no visibility into their API or security practices. And so there are a lot of driving factors why these APIs are just not protected and, and just unknown even more to the security team. So the first step has to be discover your API attack surface, and then prioritize which APIs you wanna target in terms of runtime protection. >>Yeah. I wanna dig into that API kind of attack surface area management, runtime monitoring capability in a second, but so I wanna get you in here too, because we're talking about APIs, we're talking about attacks. What does an API attack look like? >>Yeah, that's a very good question, John, there are really two different forms of attacks of APIs, one type of attack, exploits, APIs that have known vulnerabilities or some form of vulnerabilities. For instance, APIs that may use a weak form of authentication or are really built with no authentication at all, or have some sort of vulnerability that makes them very good targets for an attacker to target. And the second form of attack is a more subtle one. It's called business logic abuse. It's, it's utilizing APIs in completely legitimate manner manners, but exploiting those APIs to exfiltrate information or key sensitive information that was probably not thought through by the developer or the designers or those APIs. And really when we do API protection, we really need to be able to handle both of those scenarios, protect against abuse of APIs, such as broken authentication, or broken object level authorization APIs with that problem, as well as protecting APIs from business logic abuse. And that's really how we, you know, differentiate against other vendors in this >>Market. So just what are the, those key differentiated ways to identify the, in the malicious intents with APIs? Can you, can you just summarize that real quick, the three ways? >>Sure. Yeah, absolutely. There are three key ways that we differentiate against our competition. One is in the, we have built out a, in the ability to actually detect such traffic. We have built out a very sophisticated threat intelligence network built over the entire lifetime of the company where we have very well curated information about malicious infrastructures, malicious operators around the world, including not just it address ranges, but also which infrastructures do they operate on and stuff like that, which actually helps a lot in, in many environments in especially B2C environments, that alone accounts for a lot of efficacy for us in detecting our weed out bad traffic. The second aspect is in analyzing the request that are coming in the API traffic that is coming in and from the request itself, being able to tell if there is credential abuse going on or credential stuffing going on or known patterns that the traffic is exhibiting, that looks like it is clearly trying to attack the attack, the APM. >>And the third one is, is really more sophisticated as they go farther and farther. It gets more sophisticated where sequence actually has a lot of machine learning models built in which actually profile the traffic that is coming in and separate. So the legitimate or learns the legitimate traffic from the anomalous or suspicious traffic. So as the traffic, as the API requests are coming in, it automatically can tell that this traffic does not look like legitimate traffic does not look like the traffic that this API typically gets and automatically uses that to figure out, okay, where is this traffic coming from? And automatically takes action to prevent that attack? >>You know, it's interesting APIs have been part of the goodness of cloud and cloud scale. And it reminds me of the old Andy Grove quote, founder of, in one of the founders of Intel, you know, let chaos, let, let the chaos happen, then reign it in it's APIs. You know, a lot of people have been creating them and you've got a lot of different stakeholders involved in creating them. And so now securing them and now manage them. So a lot of creation now you're starting to secure them and now you gotta manage 'em. This all is now big focus. As you pointed out, what are some of the dynamics that customers who have to deal with on the product side and, and organization, let, let chaos rain, and then rain in the chaos, as, as the saying goes, what, what do companies do? >>Yeah. Typically companies start off with like, like a mayor talked about earlier. Discovery is really the key thing to start with, like figuring out what your API attack surfaces and really getting your arms around that problem. And typically we are finding customers start that off from the security organization, the CSO organization to really go after that problem. And in some cases, in some customers, we even find like dedicated centers of excellence that are created for API security, which go after that problem to be able to get their arms around the whole API attack surface and the API protection problem statement. So that's where usually that problem starts to get addressed. >>I mean, organizations and your customers have to stop the attacks. A lot of different techniques, you know, run time. You mentioned that earlier, the surface area monitoring, what's the choice. What's the, where are, where are, where is everybody? Is everyone in the, in the boiling water, like the frog and boiling water or they do, they know it's happening? Like what did they do? What's their opportunity to get in >>Position? Yeah. So I, I think let's take a step back a little bit, right? What has happened is if you draw the cloud security market, if you will, right. Which is the journey to the cloud, the security of these applications or APIs at a container level, in terms of vulnerabilities and, and other things that market grew with the journey to the cloud, pretty much locked in lockstep. What has happened in the API side is the API space has kind of lacked behind the growth and explosion in the API space. So what that means is APIs are getting published way faster than the security teams are able to sort of control and secure them. APIs are getting published in environments that the security completely unaware of. We talked about in the past about the parameter, the parameter, as we know, it doesn't exist anymore. It used to be the case that you hit a CDN, you terminate your SSL, you stop your layer three and four DDoS. >>And then you go into the application and do the business logic. That parameter is just gone because it's now could be living in multi-cloud environment. It could be living in the on-prem environment, which is PubNet is friendly. And so security teams that are used to protecting apps, using a perimeter defense plus changes, it's gone. You need to figure out where your perimeter is. And therefore we sort of recommend an approach, which is have a uniform view across all your APIs, wherever they could be distributed and have a single point of control across those with a solution like sequence. And there are others also in this space, which is giving you that uniform view, which is first giving you that, you know, outside and looking view of what APIs to protect. And then let's, you sort of take the journey of securing the API life cycle. >>So I would say that every company now hear me out on this indulges me for a second. Every company in the world will be non perimeter based, except for maybe 5% because of maybe unique reason, proprietary lockdown, information, whatever. But for most, most companies, everyone will be in the cloud or some cloud native, non perimeter based security posture. So the question is, how does your platform fit into that trajectory? And specifically, why are you guys in the position in your mind to help customers solve this API problem? Because again, APIs have been the greatest thing about the cloud, right? Yeah. So the goodness is there because of APS. Now you gotta reign it in reign in the chaos. Yeah. What, what about your platform share? What is it, why is it win? Why should customers care about this? >>Absolutely. So if you think about it, you're right, the parameter doesn't exist. People have APIs deployed in multiple environments, multicloud hybrid, you name it sequence is uniquely positioned in a way that we can work with your environment. No matter what that environment is. We're the only player in this space that can protect your APIs purely as a SA solution or purely as an on-prem deployment. And that could be a SaaS platform. It doesn't need to be RackN, but we also support that and we could be a hybrid deployment. We have some deployments which are on your prem and the rest of this solution is in our SA. If you think about it, customers have secured their APIs with sequence with 15 minutes, you know, going live from zero to life and getting that protection instantaneously. We have customers that are processing a billion API calls per day, across variety of different cloud environments in sort of six different brands. And so that scale, that flexibility of where we can plug into your infrastructure or be completely off of your infrastructure is something unique to sequence that we offer that nobody else is offering >>Today. Okay. So I'll be, I'll be a naysayer. Yeah, look, it, we are perfectly coded APIs. We are the best in the business. We're locked down. Our APIs are as tight as a drum. Why do I need you? >>So that goes back to who's answer. Of course, >>Everyone's say that that's, that's great, but that's my argument. >>There are two types of API attacks. One is a tactic problem, which is exploiting a vulnerability in an API, right? So what you're saying is my APIs are secure. It does not have any vulnerability I've taken care of all vulnerabilities. The second type of attack that targets APIs is the business logic. Use this stuff in the news this week, which is the whistleblower problem, which is, if you think APIs that Twitter is publishing for users are perfectly secure. They are taking care of all the vulnerabilities and patching them when they find new ones. But it's the business logic of, you know, REWE liking or commenting that the bots are targeting, which they have no against. Right. And then none of the other social networks too. Yeah. So there are many examples. Uber wrote a program to impersonate users in different geo locations to find lifts, pricing, and driver information and passenger information, completely legitimate use of APIs for illegitimate, illegitimate purpose using bots. So you don't need bots by the way, don't, don't make this about bot versus not. Yeah. You can use APIs sort of for the, the purpose that they're not designed for sort of exploiting their business logic, either using a human interacting, a human farm, interacting with those APIs or a bot form targeting those APIs, I think. But that's the problem when you have, even when you've secured all your problem, all your APIs, you still have to worry about these of challenges. >>I think that's the big one. I think the business logic one, certainly the Twitter highlights that the Uber example is a good one. That is basically almost the, the backlash of having a simplistic API, which people design to. Right. Yeah. You know, as you point out, Twitter is very simple API, hardened, very strong security, but they're using it to maliciously manipulate what's inside. So in a way that perimeter's dead too. Right. So how do you stop that business logic? What's the, what's the solution what's the customer do about that? Because their goal is to create simple, scalable APIs. >>Yeah. I'll, I'll give you a little bit, and then I think Subaru should maybe go into a little bit of the depth of the problem, but what I think that the answer lies in what Subaru spoke earlier, which is our ML. AI is, is good at profiling plus split between the API users, are these legitimate users, humans versus bots. That's the first split we do. The split second split we do is even when these, these are classified users as bots, we will say there are some good bots that are necessary for the business and bad bots. So we are able to split this across three types of users, legitimate humans, good bots and bad bots. And just to give you an example of good bots is there are in the financial work, there are aggregators that are scraping your data and aggregating for end users to consume, right? Your, your, and other type of financial aggregators FinTech companies like MX. These are good bots and you wanna allow them to, you know, use your APIs, whereas you wanna stop the bad bots from using your APIs super, if you wanna add so, >>So good bots versus bad bots, that's the focus. Go ahead. Weigh in, weigh in on your thought on this >>Really breaks down into three key areas that we talk about here, sequence, right? One is you start by discovering all your APIs. How many APIs do I have in my environment that ly immediately highlight and say, Hey, you have, you know, 10,000 APIs. And that usually is an eye opener to many customers where they go, wow. I thought we had a 10th of that number. That usually is an eyeopener for them to, to at least know where they're at. The second thing is to tell them detection information. So discover, detect, and defend detect will tell them, Hey, your APIs are getting traffic from. So and so it addresses so and so infrastructure. So and so countries and so on that usually is another eye opener for them. They then get to see where their API traffic is coming from. Let's say, if you are a, if you're running a pizza delivery service out of California and your traffic is coming from Eastern Europe to go, wait a minute, nobody's trying, I'm not, I'm not, I don't deliver pizzas in Eastern Europe. Why am I getting traffic from that part of the world? So that sort of traffic immediately comes up and it will tell you that it is hitting your unauthenticated API. It is hitting your API. That has, that is vulnerable to a broken object level, that authorization, vulnerable be and so on. >>Yeah, I think, and >>Then comes the different aspect. Yeah. The different aspect is where you can take action and say, I wanna block certain types of traffic, or I wanna rate limit certain types of traffic. If, if you're seeing spikes there or you could maybe insert header so that it passes on to the end application and the application team can use that bit to essentially take a, a conscious response. And so, so the platform is very flexible in allowing them to take an action that suits their needs. >>Yeah. And I think this is the big trend. This is why I like what you guys are doing. One APIs we're built for the goodness of cloud. They're now the plumbing, you know, anytime you see plumbing involved, connection points, you know, that's pretty important. People are building it out and it has made the cloud what it is. Now, you got a security challenge. You gotta add more intelligence, more smarts to it. This is where I think platform versus tools matter. Can you guys just quickly share your thoughts on that? Cuz a lot of your customers and, and future customers have dealt with the sprawls of all these different tools. Right? I got a tool for this. I got a tool for that, but people are gravitating towards platforms, but how many platforms can a customer have? So again, this brings up the point point around how you guys are engaging with customers. Can you share your thoughts on tooling platforms? Your customers are constantly inundated with the same tsunami. Isn't new thing. Why, what, how should they look at this? >>Yeah, I mean, we don't wanna be, we don't wanna add to that alert fatigue problem that affects much of the cybersecurity industry by generating a whole bunch of alerts and so on. So what we do is we actually integrate very well with S IEM systems or so systems and allow customers to integrate the information that we are detecting or mitigating and feed them onto enterprise systems like a Splunk or a Datadog where they may have sophisticated processes built in to monitor, you know, spikes in anomalous traffic or actions that are taken by sequence. And that can be their dashboard where a whole bunch of alerting and reporting actually happens. So we play in the security ecosystem very well by integrating with other products and integrate very tightly with them, right outta the box. >>Okay. Mia, this is a wrap up now for the showcase. Really appreciate you guys sharing your awesome technology and very relevant product for your customers and where we are right now in this we call Supercloud or now multi-cloud or hybrid world of cloud. Share a, a little bit about the company, how people can get involved in your solution, how they can consume it and things they should know about, about sequence security. >>Yeah, we've been on this journey, an exciting journey it's been for, for about eight years. We have very large fortune 100 global 500 customers that use our platform on a daily basis. We have some amazing logos, both in Europe and, and, and in us customers are, this is basically not the shelf product customers not only use it, but depend on sequence. Several retailers. We are sitting in front of them handling, you know, black Friday, cyber, Monday, Christmas shopping, or any sort of holiday seasonality shopping. And we have handled that the journey starts by, by just simply looking at your API attack surface, just to a discover call with sequence, figure out where your APIs are posted work with you to prioritize how to protect them in a sort of a particular order and take the whole life cycle with sequence. This is, this is an exciting phase exciting sort of stage in the company's life. We just raised a very sort of large CDC round of funding in December from Menlo ventures. And we are excited to see, you know, what's next in, in, in the next, you know, 12 to 18 months. It certainly is the, you know, one of the top two or three items on the CSOs, you know, budget list for next year. So we are extremely busy, but we are looking for, for what the next 12 to 18 months are, are in store for us. >>Well, congratulations to all the success. So will you run the roadmap? You know, APIs are the plumbing. If you will, you know, they connection points, you know, you want to kind of keep 'em simple, as they say, keep the pipes dumb and make the intelligence around it. You seem to see more and more intelligence coming around, not just securing it, but does, where does this go in your mind? Where, where do we go beyond once we secure everything and manage it properly, APRs, aren't going away, they're only gonna get better and smarter. Where's the intelligence coming share a little bit. >>Absolutely. Yeah. I mean, there's not a dull moment in the space. As digital transformation happens to most enterprise systems, many applications are getting transformed. We are seeing an absolute explosion in the volume of APIs and the types of APIs as well. So the applications that were predominantly limited to data centers sort of deployments are now splintered across multiple different cloud environments are completely microservices based APIs, deep inside a Kubernetes cluster, for instance, and so on. So very exciting stuff in terms of proliferation of volume of APIs, as well as types of APIs, there's nature of APIs. And we are building very sophisticated machine learning models that can analyze traffic patterns of such APIs and automatically tell legitimate behavior from anomalous or suspicious behavior and so on. So very exciting sort of breadth of capabilities that we are looking at. >>Okay. I mean, yeah. I'll give you the final words since you're the CEO for the CSOs out there, the chief information security officers and the chief security officers, what do you want to tell them? If you could give them a quick shout out? What would you say to them? >>My shout out is just do an assessment with sequence. I think this is a repeating thing here, but really get to know your APIs first, before you decide what and where to protect them. That's the one simple thing I can mention for thes >>Am. Thank you so much for, for joining me today. Really appreciate it. >>Thank you. >>Thank you. Okay. That is the end of this segment of the eight of his startup showcase. Season two, episode four, I'm John for your host and we're here with sequin security. Thanks for watching.

(upbeat music) >> We're here on theCube covering AWS Storage Day 2022. Kevin Miller joins us. He's the vice president and general manager of Amazon S3. Hello, Kevin, good to see you again. >> Hey Dave, it's great to see you as always. >> It seems like just yesterday we were celebrating the 15th anniversary of S3, and of course the launch of the modern public cloud, which started there. You know, when you think back Kevin, over the past year, what are some of the trends that you're seeing and hearing from customers? What do they want to see AWS focus more on? What's the direction that you're setting? >> Yeah, well Dave, really I think there's probably three trends that we're seeing really pop this year. I think one just given the kind of macroeconomic situation right now is cost optimization. That's not a surprise. Everyone's just taking a closer look at what they're using, and where they might be able to pair back. And you know, I think that's a place that obviously S3 has a long history of helping customers save money. Whether it's through our new storage classes, things like our Glacier Instant Retrieval, storage class that we launched to reinvent last year. Or things like our S3 storage lens capability to really dig in and help customers identify where their costs are are being spent. But so certainly every, you know, a lot of customers are focused on that right now, and for obvious reasons. I think the second thing that we're seeing is, just a real focus on simplicity. And it kind of goes hand in hand with cost optimization, because what a lot of customers are looking for is, how do I take the staff that I have, and do more this year. Right, continue to innovate, continue to bring new applications or top line generating revenue applications to the market, but not have to add a lot of extra headcount to do that. And so, what they're looking for is management and simplicity. How do I have all of this IT infrastructure, and not have to have people spending a lot of their time going into kind of routine maintenance and operations. And so that's an area that we're spending a lot of time. We think we have a lot of capability today, but looking at ways that we can continue to simplify, make it easier for customers to manage their infrastructure. Things like our S3 intelligent tiering storage class, which just automatically gives cost savings for data that's not routinely accessed. And so that's a big focus for us this year as well. And then I think the last and probably third thing I would highlight is an emerging theme or it's been a theme, but really continuing to increase in volume, is all around sustainability. And you know, our customers are looking for us to give them the data and the assurances for them, for their own reports and their own understanding of how sustainable is my infrastructure. And so within AWS, of course, you know we're on a path towards operating with 100% renewable energy by 2025. As well as helping the overall Amazon goal of achieving net zero carbon by 2040. So those are some big lofty goals. We've been giving customers greater insights with our carbon footprint tool. And we think that, you know the cloud continues to be just a great place to run and reduce customer's carbon footprint for the similar you know, storage capacity or similar compute capacity. But that's just going to continue to be a trend and a theme that we're looking at ways that we can continue to help customers do more to aggressively drive down their carbon footprint. >> I mean, it makes sense. It's like you're partnering up with the cloud, you know, you did same thing on security, you know, there's that shared responsibility model, same thing now with ESG. And on the macro it's interesting Kevin, this is the first time I can remember where, you know it used to be, if there's a downturn it's cost optimization, you go to simplicity. But at the same time with digital, you know, the rush to digital, people still are thinking about, okay how do I invest in the future? So but let's focus on cost for a moment then we'll come back to sort of the data value. Can you tell us how AWS helps customers save on storage, you know, beyond just the price per terabyte actions that you could take. I mean I love that, you guys should keep doing that. >> Absolutely. >> But what other knobs are you turning? >> Yeah, right and we've had obviously something like 15 cost reductions or price reductions over the years, and we're just going to continue to use that lever where we can, but it's things like the launch of our Glacier Instant Retrieval storage class that we did last year at Reinvent, where that's now you know, 4/10ths of a cent per gigabyte month. For data that customers access pretty infrequently maybe a few times a year, but they can now access that data immediately and just pay a small retrieval fee when they access that data. And so that's an example of a new capability that reduces customer's total cost of ownership, but is not just a straight up price reduction. I mentioned S3 Intelligent-Tiering, that's another case where, you know, when we launch Glacier Instant Retrieval, we integrated that with Intelligent-Tiering as well. So we have the archive instant access tier within Intelligent-Tiering. And so now data that's not accessed for 90 days is just automatically put into AIA and and then results in a reduced storage cost to customers. So again, leaning into this idea that customers are telling us, "Just do, you know what should be done "for my data to help me reduce cost, can you just do it, "and sort of give me the right defaults." And that's what we're trying to do with things like Intelligent-Tiering. We've also, you know, outside of the S3 part of our portfolio, we've been adding similar kinds of capabilities within some of our file services. So things like our, you know elastic file service launched a one zone storage class as well as an intelligent tiering capability to just automatically help customers save money. I think in some cases up to 92% on their their EFS storage costs with this automatic intelligent tiering capability. And then the last thing I would say is that we also are just continuing to help customers in other ways, like I said, our storage lens is a great way for customers to really dig in and figure out. 'Cause you know, often customers will find that they may have, you know, certain data sets that someone's forgotten about or, they're capturing more data than they expected perhaps in a logging application or something that ends up generating a lot more data than they expected. And so storage lens helps them really zoom in very quickly on, you know this is the data, here's how frequently it's being accessed and then they can make decisions about use that data I keep, how long do I keep it? Maybe that's good candidates to move down into one of our very cold storage classes like Glacier Deep Archive, where they they still have the data, but they don't expect to need to actively retrieve it on a regular basis. >> SDL bromide, if you can measure it, you can manage it. So if I can see it, visualize it, that I can take actions. When you think about S3- >> That's right. it's always been great for archival workloads but you made some updates to Glacier that changed the way that we maybe think about archive data. Can you talk about those changes specifically, what it means for how customers should leverage AWS services going forward? >> Yeah, and actually, you know, Glacier's coming up on its 10 year anniversary in August, so we're pretty excited about that. And you know, but there's just been a real increase in the pace of innovation, I think over the last three or four years there. So we launched the Glacier Deep Archive capability in 2019, 2018, I guess it was. And then we launched Glacier Instant Retrieval of course last year. So really what we're seeing is we now have three storage classes that cover are part of the Glacier family. So everything from millisecond retrieval for that data, that needs to be accessed quickly when it is accessed, but isn't being accessed, you know, regularly. So maybe a few times a year. And there's a lot of use cases that we're seeing really quickly emerge for that. Everything from, you know, user generated content like photos and videos, to big broadcaster archives and particularly in media and entertainment segment. Seeing a lot of interest in Glaciers Instant Retrieval because that data is pretty cold on a regular basis. But when they want to access it, they want a huge amount of data, petabytes of data potentially back within seconds, and that's the capability we can provide with Glacier Instant Retrieval. And then on the other end of the spectrum, with Glacier Deep Archive, again we have customers that have huge archives of data that they be looking to have that 3-AZ durability that we provide with Glacier, and make sure that data is protected. But really, you know expect to access it once a year if ever. Now it could be a backup copy of data or secondary or tertiary copy of data, could be data that they just don't have an active use for it. And I think that's one of the things we're starting to see grow a lot, is customers that have shared data sets where they may not need that data right now but they do want to keep it because as they think about, again these like new applications that can drive top line growth, they're finding that they may go back to that data six months or nine months from now and start to really actively use it. So if they want that option value to keep that data so they can use it down the road, Glacier Deep Archive, or Glacier Flexible Retrieval, which is kind of our storage class right in the middle of the road. Those are great options for customers to keep the data, keep it safe and secure, but then have it, you know pretty accessible when they're ready to get it back. >> Got it, thank you for that. So, okay, so customers have choices. I want to get into some of the competitive differentiators. And of course we were talking earlier about cost optimization, which is obviously an important topic given the macro environment you know, but there's more. And so help us understand what's different about AWS in terms of helping customers get value from their data, cost reduction as a component of value, part of the TCO, for sure. But just beyond being a cloud bit bucket, you know just a storage container in the cloud, what are some of the differentiators that you can talk to? >> Yeah, well Dave, I mean, I think that when it comes to value, I think there's tremendous benefits in AWS, well beyond just cost reduction. I think, you know, part of it is S3 now has built, I think, an earned reputation for being resilient, for storing, you know, at massive scale giving customers that confidence that they will be able to scale up. You know, we store more than 200 trillion objects. We regularly peak at over 100 million requests per second. So customers can build on S3 and Glacier with the confidence that we're going to be there to help their applications grow and scale over time. And then I think that in all of the applications both first party and third party, the customers can use, and services that they can use to build modern applications is an incredible benefit. So whether it's all of our serverless offerings, things like Lambda or containers and everything we have to manage that. Or whether it's the deep analytics and machine learning capabilities we have to help really extract, you know value and insight from data in near real time. You know, we're just seeing an incredible number of customers build those kinds of applications where they're processing data and feeding their results right back into their business right away. So I'm just going to briefly mention a couple, like, you know one example is ADP that really helps their customers measure, compare and sort of analyze their workforce. They have a couple petabytes of data, something like 25 billion individual data points and they're just processing that data continuously through their analytics and machine learning applications to then again, give those insights back to their customers. Another good example is AstraZeneca. You know, they are processing petabytes and petabytes of genomic sequencing data. And they have a goal to analyze 2 million genomes over the next four years. And so they're just really scaling up on AWS, both from a pure storage point of view, but more importantly, from all of the compute and analytics capability on top that is really critical to achieving that goal. And then, you know, beyond the first party services we have as I mentioned, it's really our third party, right? The AWS partner network provides customers an incredible range of choice in off the shelf applications that they can quickly provision and make use of the data to drive those business insights. And I think today the APN has something like 100,000 partners over in 150 countries. And we specifically have a storage competency partner where customers can go to get those applications that directly work, you know, on top of their data. And really, like I said, drive some of that insight. So, you know, I think it's that overall benefit of being able to really do a lot more with their data than just have it sit idle. You know, that's where I think we see a lot of customers interested in driving additional value. >> I'm glad you mentioned the ecosystem, and I'm glad you mentioned the storage competency as well. So there are other storage partners that you have, even though you're a head of a big storage division. And then I think there's some other under the cover things too. I've recently wrote, actually have written about this a lot. Things like nitro and rethinking virtualization and how to do, you know offloads. The security that comes, you know fundamentally as part of the platform is, I think architecturally is something that leads the way in the industry for sure. So there's a lot we could unpack, but you've fundamentally changed the storage market over the last 16 years. And again, I've written about this extensively. We used to think about storage in blocks or you got, you know, somebody who's really good in files, there were companies that dominated each space with legacy on-prem storage. You know, when you think about object storage Kevin, it was a niche, right? It was something used for archival, it was known for its simple, get put syntax, great for cheap and deep storage, and S3 changed that. Why do you think that's happened and S3 has evolved, the object has evolved the way it has, and what's the future hold for S3? >> Yeah I mean, you know, Dave, I think that probably the biggest overall trend there is that customers are looking to build cloud native applications. Where as much of that application is managed as they can have. They don't want to have to spend time managing the underlying infrastructure, the compute and storage and everything that goes around it. And so a fully managed service like S3, where there's no provisioning storage capacity, there's, you know we provide the resiliency and the durability that just really resonates with customers. And I think that increasingly, customers are seeing that they want to innovate across the entire range of business. So it's not about a central IT team anymore, it's about engineers that are embedded within lines of business, innovating around what is critical to achieve their business results. So, you know, if they're in a manufacturing segment, how can we pull data from sensors and other instrumentation off of our equipment and then make better decisions about when we need to do predictive maintenance, how quickly we can run our manufacturing line, looking for inefficiencies. And so we've developed around our managed offerings like S3, we've just developed, you know, customers who are investing and executing on plans and you know transformations. That really give them, you know put digital technology directly into the line of business that they're looking for. And I think that trend is just going to continue. People sometimes ask me, well "I mean, 16 years, you know, isn't S3 done?" And I would say, "By no stretcher are we done." We have plenty of feedback from customers on ways that we can continue to simplify, reduce the kinds of things they need to do, when they're looking for example and rolling out new security policies and parameters across their entire organization. So raising the bar there, finding, you know, raising the bar on how they can efficiently manage their storage and reduce costs. So I think we have plenty of innovation ahead of us to continue to help customers provide that fully managed capability. >> Yeah I often say Kevin, the next 10 years ain't going to be like the last in cloud. So I really thank you for coming on theCube and sharing your insights, really appreciate it. >> Absolutely Dave, thanks for having me. >> You're welcome. Okay keep it right there for more coverage of AWS Storage Day 2022 in theCube. (calm bright music)

>>Okay, welcome back everyone. This is the Cube's coverage here in Las Vegas. Back at events re Mars, Amazon re Mars. I'm your host, John fur with the cube. Mars stands for machine learning, automation, robotics, and space. It's great event brings together a lot of the industrial space machine learning and all the new changes in scaling up from going on the moon to, you know, doing great machine learning. And we've got two great guests here with kinda called lunar outpost, Justin Sears, CEO, Lauren, man. He's the co-founder and chief strategy officer lunar outpost. They're right next to us, watching their booth. Love the name, gentlemen. Welcome to the cube. >>Yeah. Thanks for having us, John. >>All right. So lunar outpost, I get the clues here. Tell us what you guys do. Start with that. >>Absolutely. So lunar outpost, we're a company based outta Colorado that has two missions headed to the moon over the course of the next 24 months. We're currently operating on Mars, which forest will tell you a little bit more about here in a second. And we're really pushing out towards expanding the infrastructure on the lunar surface. And then we're gonna utilize that to provide sustainable access to other planetary bodies. >>All right, far as teeing it up for you. Go, how cool is this? We don't, we wanna use every minute. What's the lunar surface look like? What's the infrastructure roads. You gonna pave it down. You what's going on. Well, >>Where we're going. No one has ever been. So, um, our first mission is going to Shackleton connecting Ridge on the south pole, the moon, and that's ripe to add infrastructure such as landing pads and other things. But our first Rover will be primarily driving across the surface, uh, exploring, uh, what the material looks like, prospecting for resources and testing new technologies. >>And you have a lot of technology involved. You're getting data in, you're just doing surveillance. What's the tech involved there. >>Yeah. So the primary technology that we're demonstrating is a 4g network for NOK. Um, we're providing them mobility services, which is basically like the old Verizon commercial. Can you hear me now? Uh, where the Rover drives farther and farther away from the Lander to test their signal strength, and then we're gonna have some other payloads ride sharing along with us for the ride >>Reminds me the old days of wifi. We used to call it war drive and you go around and try to find someone's wifi hotspot <laugh> inside the thing, but no, this is kind of cool. It brings up the whole thing. Now on lunar outpost, how big is the company? What's how what's to some of the stats heres some of the stats. >>Absolutely. So lunar outpost, 58 people, uh, growing quite quickly on track to double. So any of you watching, you want a job, please apply <laugh>. But with lunar outpost, uh, very similar to how launch companies provide people access to different parts of space. Lunar outpost provides people access to different spots on planetary bodies, whether it's the moon, Mars or beyond. So that's really where we're starting. >>So it's kinda like a managed service for all kinds of space utilities. If you kind of think about it, you're gonna provide services. Yeah, >>Absolutely. Yeah. It, it's definitely starting there and, and we're pushing towards building that infrastructure and that long term vision of utilizing space resources. But I can talk about that a little bit more here in a sec. >>Let's get into that. Let's talk about Mars first. You guys said what's going on with >>Mars. Absolutely. >>Yeah. So right now, uh, lunar outpost is part of the science team for, uh, Moxi, which is an instrument on the perseverance Rover. Yeah. Moxi is the first demonstration of space resource utilization on another planet. And what space resource utilization is basically taking resources on another planet, turning them into something useful. What Moxi does is it takes the CO2 from the atmosphere of Mars and atmosphere of Mars is mostly CO2 and it uses a process called solid oxide electrolysis to basically strip oxygen off of that CO2 to produce oh two and carbon monoxide. >>So it's what you need to self sustain on the surface. >>Exactly. It's not just sustaining, um, the astronauts, but also for producing oxygen for propellant. So it'll actually produce, um, it's a, it's a technology that'll produce a propellant for return rockets, um, to come back for Mars. So >>This is the real wildcard and all this, this, this exploration is how fast can the discoveries invent the new science to provide the life and the habitat on the surface. And that seems to be the real focus in the, in the conversations I heard on the keynote as well, get the infrastructure up so you can kinda land and, and we'll pull back and forth. Um, where are we on progress? You guys have the peg from one zero to 10, 10 being we're going, my grandmother's going, everyone's going to zero. Nothing's moving. >>We're making pretty rapid >>Progress. A three six, >>You know, I'll, I'll put it on an eight, John an >>Eight, I'll put it on >>Eight. This is why the mission force was just talking about that's launching within the next 12 months. This is no longer 10 years out. This is no longer 20 years away, 12 months. And then we have mission two shortly after, and that's just the beginning. We have over a dozen Landers that are headed to line surface this decade alone and heavy lift Landers and launchers, uh, start going to the moon and coming back by 2025. >>So, and you guys are from Colorado. You mentioned before you came on camera, right with the swap offices. So you got some space in Colorado, then the rovers to move around. You get, you get weird looks when people drive by and see the space gear. >>Oh yeah, definitely. So we have, um, you know, we have our facility in golden and our Nevada Colorado, and we'll take the vehicles out for strolls and you'll see construction workers, building stuff, and looking over and saying, what's >>Good place to work too. So you're, you're hiring great. You're doubling on the business model side. I can see a lot of demand. It's cheaper to launch stuff now in space. Is there becoming any rules of engagement relative to space? I don't wanna say verified, but like, you know, yet somehow get to the point where, I mean, I could launch a satellite, I could launch something for a couple hundred grand that might interfere with something legitimate. Do you see that on the radar because you guys are having ease of use so smaller, faster, cheaper to get out there. Now you gotta refine the infrastructure, get the services going. Is there threats from just random launches? >>It's a, it's a really interesting question. I mean, current state of the art people who have put rovers on other planetary bodies, you're talking like $3 billion, uh, for the March perseverance Rover. So historically there hasn't been that threat, but when you start talking about lowering the cost and the access to some of these different locations, I do think we'll get to the point where there might be folks that interfere with large scale operations. And that's something that's not very well defined in international law and something you won't really probably get any of the major space powers to agree to. So it's gonna be up to commercial companies to operate responsibly so we can make that space sustainable. And if there is a bad actor, I think it they'll weed themselves out over time. >>Yeah. It's gonna be of self govern, I think in the short term. Good point. Yeah. What about the technology? Where are we in the technology? What are some of the big, uh, challenges that we're overcoming now and what's that next 20 M stare in terms of the next milestone? Yeah, a tech perspective. >>Yeah. So the big technology technological hurdle that has been identified by many is the ability to survive the LUN night. Um, it gets exceptionally cold, uh, when the sun on the moon and that happens every 14 days for another, for, you know, for 14 days. So these long, cold lunar nights, uh, can destroy circuit boards and batteries and different components. So lunar outpost has invested in developing thermal technologies to overcome this, um, both in our offices, in the United States, but we also have opened a new office in, uh, Luxembourg in Europe. That's focusing specifically on thermal technologies to survive the lunar night, not just for rovers, but all sorts of space assets. >>Yeah. Huge. That's a hardware, you know, five, nine kind of like meantime between failure conversation, right. >><laugh> and it's, it gets fun, right? Because you talk five nines and it's such like, uh, you know, ingrained part of the aerospace community. But what we're pitching is we can send a dozen rovers for the cost of one of these historical rovers. So even if 25% of 'em fail, you still have eight rovers for the cost of one of the old rovers. And that's just the, economy's a scale. >>I saw James Hamilton here walking around. He's one of the legendary Amazonians who built out the data center. You might come by the cube. That's just like what they did with servers. Hey, if one breaks throw it away. Yeah. Why buy the big mainframe? Yeah. That's the new model. All right. So now about, uh, space space, that's a not space space, but like room to move around when you start getting some of these habitats going, um, how does space factor into the size of the location? Um, cuz you got the, to live there, solve some of the thermal problems. How do I live on space? I gotta have, you know, how many people gonna be there? What's your forecast? You think from a mission standpoint where there'll be dozens of people or is it still gonna be small teams? >>Yeah. >>Uh, what's that look like? >>I mean you >>Can guess it's okay. >>I mean, my vision's thousands of people. Yep. Uh, living and working in space because it's gonna be, especially the moon I think is a destination that's gonna grow, uh, for tourism. There's an insane drive from people to go visit a new destination. And the moon is one of the most unique experiences you could imagine. Yep. Um, in the near term for Artis, we're gonna start by supporting the Artis astronauts, which are gonna be small crews of astronauts. Um, you know, two to six in the near term. >>And to answer your question, uh, you know, in a different way, the habitat that we're actually gonna build, it's gonna take dozens of these robotic systems to build and maintain over time. And when we're actually talking, timelines, force talks, thousands of people living and working in space, I think that's gonna happen within the next 10 to 15 years. The first few folks are gonna be on the moon by 2025. And we're pushing towards having dozens of people living and working in space and by 2030. >>Yeah. I think it's an awesome goal. And I think it's doable question I'll have for you is the role of software in all this. I had a conversation with, uh, space nerd and we were talking and, and I said open sources everywhere now in the software. Yeah. How do you repair in space? Does you know, you don't want to have a firmware be down. So send down backhoe back to the United States. The us, wait a minute, it's the planet. I gotta go back to earth. Yeah. To get apart. So how does break fix work in space? How, how do you guys see that problem? >>So this one's actually quite fun. I mean, currently we don't have astronauts that can pick up a or change a tire. Uh, so you have to make robots that are really reliable, right. That can continuously operate for years at a time. But when you're talking about long-term repairs, there's some really cool ideas and concepts about standardization of some of these parts, you know, just like Lu knots on your car, right? Yeah. If everyone has the same Lu knots on their wheel, great. Now I can go change it out. I can switch off different parts that are available on the line surface. So I think we're moving towards, uh, that in the long >>Term you guys got a great company. Love the mission. Final question for both of you is I noticed that there's a huge community development around Mars, living on Mars, living on the moon. I mean, there's not a chat group that clubhouse app used, used to be around just kind of dying. But now it's when the Twitter spaces Reddit, you name it, there's a fanatical fan base that loves to talk about an engineer and kind of a collective intelligence, not, may not be official engineering, but they just love to talk about it. So there's a huge fan base for space. How does someone get involved if they really want to dive in and then how do you nurture that audience? How does that, is it developing? What's your take on this whole movement? It's it's beyond just being interested. It's it's become, I won't say cult-like but it's been, there's very, a lot of people in young people interested in space. >>Yeah. >>Yeah. There's, there's a whole, lots of places to get involved. There's, you know, societies, right? Like the Mar society there's technical committees, um, there's, you know, even potentially learning about these, you know, taking a space, resources master program and getting into the field and, and joining the company. So, um, we really, uh, thrive on that energy from the community and it really helps press us forward. And we hope to, uh, have a way to take everyone with us on the mission. And so stay tuned, follow our website. We'll be announcing some of that stuff soon. >>Awesome. And just one last, uh, quick pitch for you, John, I'll leave you with one thought. There are two things that space has an infinite amount of the first is power and the second is resources. And if we can find a way to access either of those, we can fundamentally change the way humanity operates. Yeah. So when you're talking about living on Mars long term, we're gonna need to access the resource from Mars. And then long term, once we get the transportation infrastructure in place, we can start bringing those resources back here to earth. So of course there are gonna be those people that sign up for that first mission out to Mars with SpaceX. But, uh, we'd love for folks to join on with us at lunar outpost and be a part of that kind of next leap accessing those resources. >>I love the mission, as always said, once in the cube, everything in star Trek will be invented someday. <laugh>, we're almost there except for the, the, uh, the transporter room. We don't have that done yet, but almost soon be there. All right. Well, thanks for coming. I, I really appreciate Justin for us for sharing. Great story. Final minute. Give a plug for the company. What are you guys looking for? You said hiring. Yep. Anything else you'd like to share? Put a plug in for lunar outpost. >>Absolutely. So we're hiring across the board, aerospace engineering, robotics engineering, sales marketing. Doesn't really matter. Uh, we're doubling as a company currently around 58 people, as we said, and we're looking for the top people that want to make an impact in aerospace. This is truly a unique moment. First time we've ever had continuous reliable operations. First time NASA is pushing really hard on the public private partnerships for commercial companies like ours to go out and create this sustainable presence on the moon. So whether you wanna work with us, our partner with us, we'd be excited to talk to you and, uh, yeah. Please contact us at info. Lunar outpost.com. >>We'll certainly follow up. Thanks for coming. I love the mission we're behind you and everyone else is too. You can see the energy it's gonna happen. It's the cube coverage from re Mars new actions happening in space on the ground, in the, on the moon you name it's happening right here in Vegas. I'm John furrier. Thanks for watching.

>>We're here live in Las Vegas with a cubes coverage of Amazon re Mars. It's a reinvent re Mars reinforced. The big three shows called the res. This is Mars machine learning, automation, robotic and space. It's a program about the future it and the future innovation around industrial cloud scale climate change the moon, a lot of great topics, really connecting all the dots together here in Las Vegas with Amazon re Mars I'm John ER, host of the cube. Our first guest is Howard Hughes program manager, necess Ryan program. Howard is involved with all the action and space and the moon project, which we'll get into Howard. Thanks for coming on the cube. >>Well, Hey, thanks for having me here this morning. Appreciate you guys inviting me here. >>So this show is not obvious to the normal tech observer, the insiders in, in the industry. It's the confluence of a lot of things coming together. It's gonna be obvious very soon because the stuff they're showing here is pretty impressive. It's motivating, it's positive and it's a force for change in good. All of it coming together, space, machine learning, robotics, industrial, you have one of the coolest areas, the space what's going on with your Orion program. You guys got the big moon project statement to >>Explain. Well, let me tell you, I'll start with Orion. Orion is our next human space craft. That's gonna take humans beyond low earth orbit and we're part of the broader Artis campaign. So Artis is our plan, our NASA plan to return the first person of color, first woman, back to the moon. And we're very excited to do that. We have several missions that I could talk to you about starting with in a very few months, Artis one. So Artis one is going to fly on the space launch system, which is gonna be the biggest rocket we call the mega rocket has been built since the Saturn five on top of the SLS is the Ryan spacecraft and that Ryan spacecraft houses four crew members for up to 21 days in deep space. And we'll have an unru test in a few months launch on the S SLS. And Orion's gonna go around the moon for up to 40 days on Aus two, we will have the first test of the humans on board Orion. So four people will fly on Aus two. We will also circle the moon for about 10 to 12 days. And then our third mission will be our landing. >>So the moon is back in play, obviously it's close to the earth. So it's a short flight, relatively speaking the Mars a little bit further out. I'll see everyone as know what's going on in Mars. A lot of people are interested in Mars. Moon's closer. Yes, but there's also new things going on around discovery. Can you share the big story around why the moon what's? Why is the moon so important and why is everyone so excited about it? >>Yeah. You, you know, you know, coming to this conference and talking about sustainability, you know, I mean it is exploration is I think ingrained in our DNA, but it's more than just exploration is about, you know, projecting human presence beyond our earth. And these are the stepping stones. You know, we talk about Amazon talked about day one, and I think about, we are on those very early days where we're building the infrastructure Ryans of transportation infrastructure, and we're gonna build infrastructure on the moon to learn how to live on a surface and how to utilize the assets. And then that's very important because you know, it's very expensive to carry fuel, to carry water and all the necessities that you need to survive as a human being and outer space. If you can generate that on the surface or on the planet you go to, and this is a perfect way to do it because it's very in your backyard, as I told you earlier. So for future mission, when you want to go to Mars, you're nine months out, you really wanna make sure you have the technologies and you're able to utilize those technologies robustly and in a sustainable way. >>Yeah, we were talking before you came on, came camera camping in your backyard is a good practice round. Before you go out into the, to the wilderness, this is kind of what's going on here, but there's also the discovery angle. I mean, I just see so much science going on there. So if you can get to the moon, get a base camp there, get set up, then things could come out of that. What are some of the things that you guys are talking about that you see as possible exploration upside? >>Yeah. Well, several things. One is power generation recently. We just released some contracts that from vision power, so long, sustainable power capability is very, very important. You know, the other technologies that you need utilize is regenerative, you know, air, water, things that are, you need for that, but then there's a science aspect of it, which is, you know, we're going to the south pole where we think there's a lot of water potentially, or, or available water that we can extract and utilize that to generate fuel. So liquid hydrogen liquid oxygen is one of the areas that are very interesting. And of course, lunar minerals are very exciting, very interesting to bring and, and, and be able to mine potentially in the future, depending on what is there. >>Well, a lot of cool stuff happening. What's your take on this show here, obviously NASA's reputation as innovators and deep technologists, you know, big moonshot missions, pun intended here. You got a lot of other explorations. What's this show bring together, share your perspective because I think the story here to me is you got walkout retail, like the Amazon technology, you got Watson dynamics, the dog, everyone loves that's walking on. Then you got supply chain, robotics, machine learning, and space. It all points to one thing, innovation around industrial. I think what, what, what's your, what's your, what's your take? >>You know, I think one of the things is, is, you know, normally we are innovating in a, in our aerospace industry. You know, I think there's so much to learn from innovation across all these areas you described and trying to pull some of that into the spacecraft. You know, when, when you're a human being sitting in spacecraft is more than just flying the spacecraft. You know, you have interaction with displays, you have a lot of technologies that you normally would want to interact with on the ground that you could apply in space to help you and make your tasks easier. And I think those are things that are really important as we look across, you know, the whole entire innovative infrastructure that I see here in this show, how can we extract some that and apply it in the space program? I think there is a very significant leveraging that you could do off of that. >>What are some of the look at what's going on in donors? What are some of the cool people who aren't following the day to day? Anything? >>Well, well, certainly, you know, the Artman's mission Artis campaign is one of the, the, the coolest things I could think of. That's why I came into, you know, I think wrapping around that where we are not only just going to a destination, but we're exploring, and we're trying to establish a very clear, long term presence that will allow us to engage. What I think is the next step, which is science, you know, and science and the, and the things that can, can come out of that in terms of scientific discoveries. And I think the cool, coolest thing would be, Hey, could we take the things that we are in the labs and the innovation relative to power generation, relative to energy development of energy technologies, robotics, to utilize, to help explore the surface. And of course the science that comes out of just naturally, when you go somewhere, you don't know what to expect. And I think that's what the exciting thing. And for NASA, we're putting a program, an infrastructure around that. I think that's really exciting. Of course, the other parts of NASA is science. Yeah. And so the partnering those two pieces together to accomplish a very important mission for everybody on planet earth is, is really important. >>And also it's a curiosity. People are being curious about what's going on now in space, cuz the costs are down and you got universities here and you got the, of robotics and industrial. This is gonna provide a, a new ground for education, younger, younger generation coming up. What would you share to teachers and potential students, people who wanna learn what's different about now than the old generation and what's the same, what what's the same and what's new. What's how does someone get their arms around this, their mind around it? Where can they jump in? This is gonna open up the aperture for, for, for talent. I mean with all the technology, it's not one dimensional. >>Yeah. I think what is still true is core sciences, math, you know, engineering, the hard science, chemistry, biology. I mean, I think those are really also very important, but what we're we're getting today is the amount of collaboration we're able to do against organically. And I think the innovation that's driven by a lot of this collaboration where you have these tools and your ability to engage and then you're able to, to get, I would say the best out of people in lots of different areas. And that's what I think one of the things we're learning at NASA is, you know, we have a broad spectrum of people that come to work for us and we're pulling that. And now we're coming to these kinds of things where we're kind getting even more innovation ideas and partnerships so that we are not just off on our own thinking about the problem we're branching out and allowing a lot of other people to help us solve the problems that >>We need. You know, I've noticed with space force too. I had the same kind of conversations around those with those guys as well. Collaboration and public private partnerships are huge. You've seen a lot more kind of cross pollination of funding, col technology software. I mean, how do you do break, fix and space at software, right? So you gotta have, I mean, it's gotta work. So you got security challenges. Yeah. This is a new frontier. It is the cybersecurity, the usability, the operationalizing for humans, not just, you know, put atypical, you know, scientists and, and, and astronauts who are, you know, in peak shape, we're talking about humans. Yeah. What's the big problem to solve? Is it security? Is it, what, what would you say the big challenges >>Are? Yeah. You know, I think information and access to information and how we interact with information is probably our biggest challenge because we have very limited space in terms of not only mass, but just volume. Yeah. You know, you want to reserve the space for the people and they, they need to, you know, you want maximize your space that you're having in spacecraft. And so I think having access to information, being able to, to utilize information and quickly access systems so you can solve problems cuz you don't know when you're in deep space, you're several months out to Mars, what problems you might encounter and what kind of systems and access to information you need to help you solve the problems. You know, both, both, both from a just unplanned kind of contingencies or even planned contingencies where you wanna make sure you have that information to do it. So information is gonna be very vital as we go out into deep >>Space and the infrastructure's changed. How has the infrastructure changed in terms of support services? I mean see, in the United States, just the growth of a aerospace you mentioned earlier is, is just phenomenal. You've got smaller, faster, cheaper equipment density, it solved the technology. Where's there gonna be the, the big game changing move movement. Where do you see it go? Is it AIST three? It kind of kicks in AIST ones, obviously the first one unmanned one. But where do in your mind, do you see key milestones that are gonna be super important to >>Watch? I think, I think, I think, you know, we've already, you know, pushed the boundaries of what we, we are, you know, in terms of applying our aerospace technologies for AIST one and certainly two, we've got those in, in work already. And so we've got that those vehicles already in work and built yeah. One already at the, at the Kennedy space center ready for launch, but starting with three because you have a lot more interaction, you gotta take the crew down with a Lander, a human landing system. You gotta build rovers. You've gotta build a, a capability which they could explore. So starting with three and then four we're building the gateway gateways orbiting platform around the moon. So for all future missions after Rist three, we're gonna take Aion to the gateway. The crew gets into the orbiting platform. They get on a human landing system and they go down. >>So all that interaction, all that infrastructure and all the support equipment you need, not only in the orbit of the moon, but also down the ground is gonna drive a lot of innovation. You're gonna have to realize, oh, Hey, I needed this. Now I need to figure out how to get something there. You know? And, and how much of the robotics and how much AI you need will be very interesting because you'll need these assistance to help you do your daily routine or lessen your daily routine. So you can focus on the science and you can focus on doing the advancing those technologies that you're gonna >>Need. And you gotta have the infrastructure. It's like a road. Yeah. You know, you wanna go pop down to the moon, you just pop down, it's already built. It's ready for you. Yep. Come back up. So just ease of use from a deployment standpoint is, >>And, and the infrastructure, the things that you're gonna need, you know, what is a have gonna look like? What are you gonna need in a habitat? You know, are, are you gonna be able to have the power that you're gonna have? How many station power stations are you gonna need? Right. So all these things are gonna be really, things are gonna be driven by what you need to do the mission. And that drives, I think a lot of innovation, you know, it's very much like the end goal. What are you trying to solve? And then you go, okay, here's what I need to solve to build things, to solve that >>Problem. There's so many things involved in the mission. I can imagine. Safety's huge. Number one, gotta be up safe. Yep. Space is dangerous game. Yes. Yeah. It's not pleasant there. Not for the faint of heart. As you say, >>It's not for the faint >>Heart. That's correct. What's the big safety concerns obviously besides blowing up and oxygen and water and the basic needs. >>I think, I think, you know, I think you, you said it very well, you know, it is not for the faint of heart. We try to minimize risk. You know, asset is one of the big, you're sitting under 8.8 million pounds of thrust on the launch vehicle. So it is going very fast and you're flying and you, and, and it's it's light cuz we got solid rocket motors too as well. Once they're lit. They're lit. Yeah. So we have a escape system on Orion that allows a crew to be safe. And of course we build in redundancy. That's the other thing I think that will drive innovation. You know, you build redundancy in the system, but you also think about the kind of issues that you would run into potentially from a safety perspective, you know, how you gonna get outta situation if you get hit by a meteor, right? Right. You, you, you are going through the band, Ellen belt, you have radiation. So you know, some of these things that are harsh on your vehicle and on, on the human side of this shop too. And so when you have to do these things, you have to think about what are you gonna protect for and how do you go protect for that? And we have to find innovations for >>That. Yeah. And it's also gonna be a really exciting air for engineering work. And you mentioned the data, data's huge simulations, running scenarios. This is where the AI comes in. And that seems to me where the dots connect from me when you start thinking about how to have, how to run those simulations, to identify what's possible. >>I think that's a great point, you know, because we have all this computing capability and because we can run simulations and because we can collect data, we have terabytes of data, but it's very challenging for humans to analyze at that level. So AI is one of the things we're looking at, which is trying to systematically have a process by which data is called through so that the engineering mind is only looking at the things and focus on things that are problematic. So we repeat tests, every flight, you don't have to look at all the terabytes of data of each test. You have a computer AI do that. And you allow yourself to look at just the pieces that don't look right, have anomalies in the data. Then you're going to do that digging, right. That's where the power of those kinds of technologies can really help us because we have that capability to do a lot of computing. >>And I think that's why this show to me is important because it, it, it shows for the first time, at least from my coverage of the industry where technology's not the bottleneck anymore, it's human mind. And we wanna live in a peaceful world with climate. We wanna have the earth around for a while. So climate change was a huge topic yesterday and how the force for good, what could come outta the moon shots is to, is to help for earth. >>Yeah. >>Yeah. Better understanding there all good. What's your take on the show. If you had to summarize this show, re Mars from the NASA perspective. So you, the essence space, what's the what's going on here? What's the big, big story. >>Yeah. For, for me, I think it's eyeopening in terms of how much innovation is happening across a spectrum of areas. And I look at various things like bossy, scientific robots that the dog that's walking around. I mean to think, you know, people are applying it in different ways and then those applications in a lot of ways are very similar to what we need for exploration going forward. And how do you apply some of these technologies to the space program and how do we leverage that? How do we leverage that innovation and how we take the innovations already happening organically for other reasons and how would those help us solve those problems that we're gonna encounter going forward as we try to live on another planet? >>Well, congratulations on a great assignment. You got a great job. I do super fun. I love being an observer and I love space. Love how at the innovations there. And plus space space is cool. I mean, how many millions of live views do you see? Everyone's stopping work to watch SpaceX land and NASA do their work. It's just, it's bringing back the tech vibe. You know what I'm saying? It's just, it's just, things are going you a good tailwind. Yeah. >>Congratulations. Thank you very much. >>Appreciate it on the, okay. This cube coverage. I'm John fur. You're here for the cube here. Live in Las Vegas back at reinvent reinforce re Mars, the reser coverage here at re Mars. We'll be back with more coverage after this short break.

>>Mhm. Mhm. Yeah. Hello, everybody. Welcome to the Cubes. Coverage of Mongo db World 2022. This is the first Mongo live mongo DB World. Since 2019, the Cube has covered a number of of mongo shows actually going back to when the company was called Engine. Some of you may recall Margo since then has done an i p o p o in 2017, it's It's been a rocket ship company. It's up. It'll probably do 1.2 billion in revenue this year. It's got a billion dollars in cash on the balance sheet. Uh, despite the tech clash, it's still got a 19 or $20 million valuation growing above 50% a year. Uh, company just had a really strong quarter, and and there seems to be hitting on all cylinders. My name is Dave Volonte. And here to kick it off with me as Sanjeev Mohan, who was the principal at Sanremo. So great to see you. You become a wonderful cube contributor, Former Gartner analyst. Really sharp? No, the database space in the data space generally really well, so thanks for coming back on >>you. You know, it's just amazing how exciting. The entire data space is like they used to say. Companies are All companies are software companies. All companies are data >>companies, >>so data has become the the foundation. >>They say software is eating the world. Data is eating software and a little little quips here. But this is a good size show. Four or 5000 people? I don't really know exactly. You know the numbers, but it's exciting. And of course, a lot of financial services were here at the Javits Centre. Um, let's let's lay down the basics for people of Mongo, DB is a is a document database, but they've been advancing. That's a document database as an alternative to R D. B M s. Explain that, but explain also how Mongo has broadened its capabilities and serving a lot more use cases. >>So that's my forte is like databases technology. But before even I talk about that, I have to say I am blown away by this mongo db world because mongo db uh, in beckons to all of us during the pandemic has really come of age, and it's a billion dollar company. Now we are in this brand new Javits Centre That's been built during the pandemic. And and now the company is holding this event the high 1000 people last year. So I think this company has really grown. And why has it drawn is because its offerings have grown to more developers than just a document database document databases. Revolution revolutionised the whole DBM s space where no sequel came up. Because for a change, you don't need a structured schema. You could start bringing data in this document model scheme, uh, like varying schema. But since then, they've added, uh, things like such. So they have you seen such? They added a geospatial. They had a time series last year, and this year they keep adding more and more so like, for example, they are going to add some column store indexes. So from being a purely transactional, they are now starting to address analytical. And they're starting to address more use cases, like, you know, uh, like what? What was announced this morning at keynote was faceted search. So they're expanding the going deeper and deeper into these other data >>structures. Taking Lucy made a search of first class citizens, but I want to ask you some basic questions about document database. So it's no fixed schemes. You put anything in there? Actually, so more data friendly. They're trying to simplify the use of data. Okay, that's that's pretty clear. >>What are the >>trade offs of a document database? >>So it's not like, you know, one technology has solved every problem. Every technology comes with its own tradeoffs. So in a document, you basically get rid of joining tables with primary foreign keys because you can have a flexible schemer and so and wouldn't sing single document. So it's very easy to write and and search. But when you have a lot of repeated elements and you start getting more and more complex, your document size can start expanding quite a bit because you're trying to club everything into a single space. So So that is where the complexity goes >>up. So what does that mean for for practitioner, it means they have to think about what? How they how they are ultimately gonna structure, how they're going to query so they can get the best performances that right. So they're gonna put some time in up front in order to make it pay back at the tail end, but clearly it's it's working. But is that the correct way of thinking about >>100% in, uh, the sequel world? You didn't care about the sequel. Analytical queries You just cared about how your data model was structured and then sequel would would basically such any model. But in the new sequel world, you have to know your patterns before you. You invest into the database so it's changed that equation where you come in knowing what you are signing up. >>So a couple of questions, if I can kind of Colombo questions so to Margo talks about how it's really supporting mission critical applications and at the same time, my understanding is the architecture of mongo specifically, or a document database in general. But specifically, you've got a a primary, uh, database, and you and that is the sort of the master, if you will, right and then you can create secondaries. But so help me square the circle between mission critical and really maybe a more of a focus on, say, consistency versus availability. Do customers have to sort of think about and design in that availability? How do they do that? How a Mongol customers handling that. >>So I have to say, uh, my experience of mongo db was was that the whole company, the whole ethos was developed a friendly. So, to be honest, I don't think Mongo DB was as much focused on high availability, disaster, recovery, even security. To some extent, they were more focused on developer productivity. >>And you've experienced >>simplicity. Make it simple, make the developers productive as fast as you can. What has really, uh, was an inflexion point for Mongo DB was the launch of Atlas because the atlas they were able to introduce all of these management features and hide it abstracted from the end users. So now they've got, you know, like 2014 is when Atlas came out and it was in four regions. But today they're in 100 regions, so they keep expanding, then every hyper scale cloud provider, and they've abstracted that whole managed. >>So Atlas, of course, is the managed database as a service in the cloud. And so it's those clouds, cloud infrastructure and cloud tooling that has allowed them to go after those high available application. My other question is when you talk about adding search, geospatial time series There are a lot of specialised databases that take time series persons. You have time series specialists that go deep into time series can accompany like Mongo with an all in one strategy. Uh, how close can they get to that functionality? Do they have to be? You know, it's kind of a classic Microsoft, you know, maybe not perfect, but good enough. I mean, can they compete with those other areas? Uh, with those other specialists? And what happens to those specialists if the answer is yes. What's your take on that? If that question >>makes sense So David, this is not a mongo db only issue This is this is an issue with, you know, anytime serious database, any graph database Should I put a graph database or should I put a multifunctional database multidimensional database? And and I really think there is no right or wrong answer. It just really comes down to your use case. If you have an extremely let's, uh, complex graph, you know, then maybe you should go with best of breed purpose built database. But more and more, we're starting to see that organisations are looking to simplify their environment by going in for maybe a unified database that has multiple data structures. Yeah, well, >>it's certainly it's interesting when you hear Mongo speak. They don't They don't call out Oracle specifically, but when they talk about legacy r d m r d B m s that don't scale and are complex and are expensive, they're talking about Oracle first. And of course, there are others. Um, And then when they talk about, uh, bespoke databases the horses for courses, databases that they show a picture of that that's like the poster child for Amazon. Of course, they don't call out Amazon. They're a great partner of Amazon's. But those are really the sort of two areas that mangoes going after, Um, now Oracle. Of course, we'll talk about their converged strategy, and they're taking a similar approach. But so help us understand the difference. There is just because they're sort of or close traditional r d B M s, and they have all the drawbacks associated with that. But by the way, there are some benefits as well. So how do you see that all playing >>out? So you know it. Really, uh, it's coming down to the the origins of these databases. Uh, I think they're converging to a point where they are offering similar services. And if you look at some of the benchmark numbers or you talk to users, I from a business point of view, I I don't think there's too much of a difference. Uh, technology writes. The difference is that Mongo DB started in the document space. They were more interested in availability rather than consistency. Oracle started in the relation database with focus on financial services, so asset compliance is what they're based on. And since then they've been adding other pieces, so so they differ from where they started. Oracle has been in the industry for some since 19 seventies, so they have that maturity. But then they have that legacy, >>you know, I love. Recently, Oracle announced the mongo db uh, kpi. So basically saying why? Why leave Oracle when you can just, you know, do the market? So that, to me, is a sign that Mongo DB is doing well because the Oracle calls you out, whether your workday or snowflake or mongo. You know, whoever that's a sign to me that you've got momentum and you're stealing share in that marketplace, and clearly Mongo is they're growing at 50 plus percent per year. So thinking about the early I mentioned 10 gen Early on, I remember that one of the first conferences I went to mongo conferences. It was just It was all developers. A lot of developers here as well. But they have really, since 2014, expanded the capabilities you talk about, Atlas, you talked about all these other you know, types of databases that they've added. If it seems like Mongo is becoming a platform company, uh, what are your thoughts on that in terms of them sort of up levelling the message there now, a billion dollar plus company. What's the next? You know, wave for Mongo. >>So, uh, Oracle announced mongo db a p i s a W s has document d. B has cost most db so they all have a p. I compatible a p. I s not the source code because, you know, mongo DB has its own SPL licence, so they have written their own layer on top. But at the end of the day, you know, if you if you these companies have to keep innovating to catch up with Mongo DB because we can announce a brand new capability, then all these other players have to catch up. So other cloud providers have 80% or so of capabilities, but they'll never have 100% of what Mongo DB has. So people who are diehard Mongo DB fans they prefer to stay on mongo db. They are now able to write more applications like you know, mongo DB bought realm, which is their front end. Uh, like, you know, like, if you're on social media kind of thing, you can build your applications and sink it with Atlas. So So mongo DB is now at a point where they are adding more capabilities that more like developers like, You know, five G is coming. Autonomous cars are coming, so now they can address Iot kind of use cases. So that's why it's becoming such a juggle, not because it's becoming a platform rather than a single document database. >>So atlases, the near the midterm future. Today it's about 60% of revenues, but they have what we call self serve, which is really the traditional on premise stuff. They're connecting those worlds. You're bringing up the point that. Of course, they go across clouds. You also bring up the point that they've got edge plays. We're gonna talk to Verizon later on today. And they're they've got, uh, edge edge activity going on with developers. I I call it Super Cloud. Right, This layer that floats above. Now, of course, a lot of the super Cloud concert says we're gonna hide the underlying complexity. But for developers, they wanna they might want to tap those primitives, so presumably will let them do that. But But that hybrid that what we call Super Cloud that is a new wave of innovation, is it not? And do you? Do you agree with that? And do you see that as a real opportunity from Mongo in terms of penetrating a new tan? >>Yes. So I see this is a new opportunity. In fact, one of the reasons mongo DB has grown so quickly is because they are addressing more markets than they had three pandemic. Um, Also, there are all gradations of users. Some users want full control. They want an eye as kind of, uh, someone passed. And some businesses are like, you know, we don't care. We don't want to deal with the database. So today we heard, uh, mongo db. Several went gear. So now they have surveillance capability, their past. But if you if you're more into communities, they have communities. Operator. So they're addressing the full stack of different types of developers different workloads, different geographical regions. So that that's why the market is expected. >>We're seeing abstraction layers, you know, throughout the started a physical virtual containers surveillance and eventually SuperClubs Sanjeev. Great analysis. Thanks so much for taking your time to come with the cube. Alright, Keep it right there. But right back, right after this short break. This is Dave Volonte from the Javits Centre. Mongo db World 2022. Thank you. >>Mm.

>> Okay. We're now going to go into the customer panel, and we'd like to welcome Angelo Fausti, who's a software engineer at the Vera C. Rubin Observatory, and Caleb Maclachlan who's senior spacecraft operations software engineer at Loft Orbital. Guys, thanks for joining us. You don't want to miss folks this interview. Caleb, let's start with you. You work for an extremely cool company, you're launching satellites into space. Of course doing that is highly complex and not a cheap endeavor. Tell us about Loft Orbital and what you guys do to attack that problem. >> Yeah, absolutely. And thanks for having me here by the way. So Loft Orbital is a company that's a series B startup now, who, and our mission basically is to provide rapid access to space for all kinds of customers. Historically, if you want to fly something in space, do something in space, it's extremely expensive. You need to book a launch, build a bus, hire a team to operate it, have a big software teams, and then eventually worry about, a bunch like, just a lot of very specialized engineering. And what we're trying to do is change that from a super specialized problem that has an extremely high barrier of access, to a infrastructure problem. So that it's almost as simple as deploying a VM in AWS or GCP is getting your programs, your mission deployed on orbit with access to different sensors, cameras, radios, stuff like that. So, that's kind of our mission and just to give a really brief example of the kind of customer that we can serve. There's a really cool company called Totum Labs, who is working on building IoT cons, an IoT constellation for, internet of things, basically being able to get telemetry from all over the world. They're the first company to demonstrate indoor IoT which means you have this little modem inside a container that container that you track from anywhere in the world as it's going across the ocean. So, and it's really little, and they've been able to stay a small startup that's focused on their product, which is the, that super crazy, complicated, cool radio, while we handle the whole space segment for them, which just, you know, before Loft was really impossible. So that's our mission is providing space infrastructure as a service. We are kind of groundbreaking in this area and we're serving a huge variety of customers with all kinds of different missions, and obviously generating a ton of data in space that we've got to handle. >> Yeah. So amazing Caleb, what you guys do. Now, I know you were lured to the skies very early in your career, but how did you kind of land in this business? >> Yeah, so, I guess just a little bit about me. For some people, they don't necessarily know what they want to do like earlier in their life. For me I was five years old and I knew I want to be in the space industry. So, I started in the Air Force, but have stayed in the space industry my whole career and been a part of, this is the fifth space startup that I've been a part of actually. So, I've kind of started out in satellites, spent some time in working in the launch industry on rockets, then, now I'm here back in satellites and honestly, this is the most exciting of the different space startups that I've been a part of. >> Super interesting. Okay. Angelo, let's talk about the Rubin Observatory. Vera C. Rubin, famous woman scientist, galaxy guru. Now you guys, the Observatory, you're up way up high, you get a good look at the Southern sky. And I know COVID slowed you guys down a bit, but no doubt you continued to code away on the software. I know you're getting close, you got to be super excited, give us the update on the Observatory and your role. >> All right. So, yeah. Rubin is a state of the art observatory that is in construction on a remote mountain in Chile. And, with Rubin we'll conduct the large survey of space and time. We're going to observe the sky with eight meter optical telescope and take 1000 pictures every night with 2.2 Gigapixel camera. And we are going to do that for 10 years, which is the duration of the survey. >> Yeah, amazing project. Now, you earned a doctor of philosophy so you probably spent some time thinking about what's out there, and then you went out to earn a PhD in astronomy and astrophysics. So, this is something that you've been working on for the better part of your career, isn't it? >> Yeah, that's right, about 15 years. I studied physics in college. Then I got a PhD in astronomy. And, I worked for about five years in another project, the Dark Energy Survey before joining Rubin in 2015. >> Yeah, impressive. So it seems like both your organizations are looking at space from two different angles. One thing you guys both have in common of course is software, and you both use InfluxDB as part of your data infrastructure. How did you discover InfluxDB, get into it? How do you use the platform? Maybe Caleb you could start. >> Yeah, absolutely. So, the first company that I extensively used InfluxDB in, was a launch startup called Astra. And we were in the process of designing our first generation rocket there, and testing the engines, pumps, everything that goes into a rocket. And, when I joined the company our data story was not very mature. We were collecting a bunch of data in LabVIEW and engineers were taking that over to MATLAB to process it. And at first, there, you know, that's the way that a lot of engineers and scientists are used to working. And at first that was, like people weren't entirely sure that that was, that needed to change. But, it's, something, the nice thing about InfluxDB is that, it's so easy to deploy. So as, our software engineering team was able to get it deployed and, up and running very quickly and then quickly also backport all of the data that we collected this far into Influx. And, what was amazing to see and is kind of the super cool moment with Influx is, when we hooked that up to Grafana, Grafana as the visualization platform we used with Influx, 'cause it works really well with it. There was like this aha moment of our engineers who are used to this post process kind of method for dealing with their data, where they could just almost instantly easily discover data that they hadn't been able to see before, and take the manual processes that they would run after a test and just throw those all in Influx and have live data as tests were coming, and, I saw them implementing like crazy rocket equation type stuff in Influx, and it just was totally game changing for how we tested. >> So Angelo, I was explaining in my open, that you could add a column in a traditional RDBMS and do time series, but with the volume of data that you're talking about in the example that Caleb just gave, you have to have a purpose built time series database. Where did you first learn about InfluxDB? >> Yeah, correct. So, I work with the data management team, and my first project was the record metrics that measured the performance of our software, the software that we used to process the data. So I started implementing that in our relational database. But then I realized that in fact I was dealing with time series data and I should really use a solution built for that. And then I started looking at time series databases and I found InfluxDB, and that was back in 2018. The, another use for InfluxDB that I'm also interested is the visits database. If you think about the observations, we are moving the telescope all the time and pointing to specific directions in the sky and taking pictures every 30 seconds. So that itself is a time series. And every point in that time series, we call a visit. So we want to record the metadata about those visits in InfluxDB. That time series is going to be 10 years long, with about 1000 points every night. It's actually not too much data compared to other problems. It's really just a different time scale. >> The telescope at the Rubin Observatory is like, pun intended, I guess the star of the show. And I believe I read that it's going to be the first of the next gen telescopes to come online. It's got this massive field of view, like three orders of magnitude times the Hubble's widest camera view, which is amazing. Like, that's like 40 moons in an image, amazingly fast as well. What else can you tell us about the telescope? >> This telescope it has to move really fast. And, it also has to carry the primary mirror which is an eight meter piece of glass. It's very heavy. And it has to carry a camera which has about the size of a small car. And this whole structure weighs about 300 tons. For that to work, the telescope needs to be very compact and stiff. And one thing that's amazing about it's design is that, the telescope, this 300 tons structure, it sits on a tiny film of oil, which has the diameter of human hair. And that makes an, almost zero friction interface. In fact, a few people can move this enormous structure with only their hands. As you said, another aspect that makes this telescope unique is the optical design. It's a wide field telescope. So, each image has, in diameter the size of about seven full moons. And, with that, we can map the entire sky in only three days. And of course, during operations everything's controlled by software and it is automatic. There's a very complex piece of software called the Scheduler, which is responsible for moving the telescope, and the camera, which is recording 15 terabytes of data every night. >> And Angelo, all this data lands in InfluxDB, correct? And what are you doing with all that data? >> Yeah, actually not. So we use InfluxDB to record engineering data and metadata about the observations. Like telemetry, events, and commands from the telescope. That's a much smaller data set compared to the images. But it is still challenging because you have some high frequency data that the system needs to keep up, and, we need to store this data and have it around for the lifetime of the project. >> Got it. Thank you. Okay, Caleb, let's bring you back in. Tell us more about the, you got these dishwasher size satellites, kind of using a multi-tenant model, I think it's genius. But tell us about the satellites themselves. >> Yeah, absolutely. So, we have in space some satellites already that as you said, are like dishwasher, mini fridge kind of size. And we're working on a bunch more that are a variety of sizes from shoebox to, I guess, a few times larger than what we have today. And it is, we do shoot to have effectively something like a multi-tenant model where we will buy a bus off the shelf. The bus is what you can kind of think of as the core piece of the satellite, almost like a motherboard or something where it's providing the power, it has the solar panels, it has some radios attached to it. It handles the attitude control, basically steers the spacecraft in orbit, and then we build also in-house, what we call our payload hub which is, has all, any customer payloads attached and our own kind of Edge processing sort of capabilities built into it. And, so we integrate that, we launch it, and those things because they're in lower Earth orbit, they're orbiting the earth every 90 minutes. That's, seven kilometers per second which is several times faster than a speeding bullet. So we have one of the unique challenges of operating spacecraft in lower Earth orbit is that generally you can't talk to them all the time. So, we're managing these things through very brief windows of time, where we get to talk to them through our ground sites, either in Antarctica or in the North pole region. >> Talk more about how you use InfluxDB to make sense of this data through all this tech that you're launching into space. >> We basically, previously we started off when I joined the company, storing all of that as Angelo did in a regular relational database. And we found that it was so slow and the size of our data would balloon over the course of a couple days to the point where we weren't able to even store all of the data that we were getting. So we migrated to InfluxDB to store our time series telemetry from the spacecraft. So, that's things like power level, voltage, currents, counts, whatever metadata we need to monitor about the spacecraft, we now store that in InfluxDB. And that has, now we can actually easily store the entire volume of data for the mission life so far without having to worry about the size bloating to an unmanageable amount, and we can also seamlessly query large chunks of data. Like if I need to see, you know, for example, as an operator, I might want to see how my battery state of charge is evolving over the course of the year, I can have, plot in an Influx that loads that in a fraction of a second for a year's worth of data because it does, intelligent, it can intelligently group the data by assigning time interval. So, it's been extremely powerful for us to access the data. And, as time has gone on, we've gradually migrated more and more of our operating data into Influx. >> Yeah. Let's talk a little bit about, we throw this term around a lot of, you know, data driven, a lot of companies say, "Oh yes, we're data driven." But you guys really are, I mean, you got data at the core. Caleb, what does that mean to you? >> Yeah, so, you know, I think the, and the clearest example of when I saw this be like totally game changing is what I mentioned before at Astra where our engineer's feedback loop went from a lot of kind of slow researching, digging into the data to like an instant, instantaneous almost, seeing the data, making decisions based on it immediately rather than having to wait for some processing. And that's something that I've also seen echoed in my current role. But to give another practical example, as I said, we have a huge amount of data that comes down every orbit and we need to be able to ingest all of that data almost instantaneously and provide it to the operator in near real time, about a second worth of latency is all that's acceptable for us to react to see what is coming down from the spacecraft. And building that pipeline is challenging from a software engineering standpoint. My primary language is Python which isn't necessarily that fast. So what we've done is started, and the goal of being data-driven is publish metrics on individual, how individual pieces of our data processing pipeline are performing into Influx as well. And we do that in production as well as in dev. So we have kind of a production monitoring flow. And what that has done is allow us to make intelligent decisions on our software development roadmap where it makes the most sense for us to focus our development efforts in terms of improving our software efficiency, just because we have that visibility into where the real problems are. And sometimes we've found ourselves before we started doing this, kind of chasing rabbits that weren't necessarily the real root cause of issues that we were seeing. But now that we're being a bit more data driven there, we are being much more effective in where we're spending our resources and our time, which is especially critical to us as we scale from supporting a couple of satellites to supporting many, many satellites at once. >> Yeah, of course is how you reduced those dead ends. Maybe Angelo you could talk about what sort of data-driven means to you and your teams. >> I would say that, having real time visibility to the telemetry data and metrics is crucial for us. We need to make sure that the images that we collect with the telescope have good quality, and, that they are within the specifications to meet our science goals. And so if they are not, we want to know that as soon as possible and then start fixing problems. >> Caleb, what are your sort of event, you know, intervals like? >> So I would say that, as of today on the spacecraft, the event, the level of timing that we deal with probably tops out at about 20 Hertz, 20 measurements per second on things like our gyroscopes. But, the, I think the core point here of the ability to have high precision data is extremely important for these kinds of scientific applications and I'll give an example from when I worked at, on the rockets at Astra. There, our baseline data rate that we would ingest data during a test is 500 Hertz. So 500 samples per second, and in some cases we would actually need to ingest much higher rate data, even up to like 1.5 kilohertz, so extremely, extremely high precision data there where timing really matters a lot. And, you know, I can, one of the really powerful things about Influx is the fact that it can handle this. That's one of the reasons we chose it, because, there's, times when we're looking at the results of a firing where you're zooming in, you know, I talked earlier about how on my current job we often zoom out to look at a year's worth of data. You're zooming in to where your screen is preoccupied by a tiny fraction of a second, and you need to see same thing as Angelo just said, not just the actual telemetry, which is coming in at a high rate, but the events that are coming out of our controllers, so that can be something like, "Hey, I opened this valve at exactly this time," and that goes, we want to have that at, micro, or even nanosecond precision so that we know, okay, we saw a spike in chamber pressure at this exact moment, was that before or after this valve opened? That kind of visibility is critical in these kind of scientific applications, and absolutely game changing to be able to see that in near real time, and with, a really easy way for engineers to be able to visualize this data themselves without having to wait for us software engineers to go build it for them. >> Can the scientists do self-serve or do you have to design and build all the analytics and queries for your scientists? >> Well, I think that's absolutely, from my perspective that's absolutely one of the best things about Influx and what I've seen be game changing is that, generally I'd say anyone can learn to use Influx. And honestly, most of our users might not even know they're using Influx, because, the interface that we expose to them is Grafana, which is a generic graphing, open source graphing library that is very similar to Influx zone Chronograf. >> Sure. >> And what it does is, it provides this almost, it's a very intuitive UI for building your queries. So, you choose a measurement and it shows a dropdown of available measurements. And then you choose the particular fields you want to look at, and again, that's a dropdown. So, it's really easy for our users to discover and there's kind of point and click options for doing math, aggregations. You can even do like perfect kind of predictions all within Grafana, the Grafana user interface, which is really just a wrapper around the APIs and functionality that Influx provides. >> Putting data in the hands of those who have the context, the domain experts is key. Angelo, is it the same situation for you, is it self-serve? >> Yeah, correct. As I mentioned before, we have the astronomers making their own dashboards because they know what exactly what they need to visualize. >> Yeah, I mean, it's all about using the right tool for the job. I think for us, when I joined the company we weren't using InfluxDB and we were dealing with serious issues of the database growing to an incredible size extremely quickly, and being unable to like even querying short periods of data was taking on the order of seconds, which is just not possible for operations. >> Guys, this has been really formative, it's pretty exciting to see how the edge, is mountaintops, lower Earth orbits, I mean space is the ultimate edge, isn't it? I wonder if you could answer two questions to wrap here. You know, what comes next for you guys? And is there something that you're really excited about that you're working on? Caleb maybe you could go first and then Angelo you can bring us home. >> Basically what's next for Loft Orbital is more satellites, a greater push towards infrastructure, and really making, our mission is to make space simple for our customers and for everyone. And we're scaling the company like crazy now, making that happen. It's extremely exciting, an extremely exciting time to be in this company and to be in this industry as a whole. Because there are so many interesting applications out there, so many cool ways of leveraging space that people are taking advantage of, and with companies like SpaceX and the, now rapidly lowering cost of launch it's just a really exciting place to be in. We're launching more satellites, we are scaling up for some constellations, and our ground system has to be improved to match. So, there's a lot of improvements that we're working on to really scale up our control software to be best in class and make it capable of handling such a large workload, so. >> Are you guys hiring? >> We are absolutely hiring, so I would, we have positions all over the company, so, we need software engineers, we need people who do more aerospace specific stuff. So absolutely, I'd encourage anyone to check out the Loft Orbital website, if this is at all interesting. >> All right, Angelo, bring us home. >> Yeah. So what's next for us is really getting this telescope working and collecting data. And when that's happened is going to be just a deluge of data coming out of this camera and handling all that data is going to be really challenging. Yeah, I want to be here for that, I'm looking forward. Like for next year we have like an important milestone, which is our commissioning camera, which is a simplified version of the full camera, it's going to be on sky, and so yeah, most of the system has to be working by then. >> Nice. All right guys, with that we're going to end it. Thank you so much, really fascinating, and thanks to InfluxDB for making this possible, really groundbreaking stuff, enabling value creation at the Edge, in the cloud, and of course, beyond at the space. So, really transformational work that you guys are doing, so congratulations and really appreciate the broader community. I can't wait to see what comes next from having this entire ecosystem. Now, in a moment, I'll be back to wrap up. This is Dave Vellante, and you're watching theCUBE, the leader in high tech enterprise coverage. >> Welcome. Telegraf is a popular open source data collection agent. Telegraf collects data from hundreds of systems like IoT sensors, cloud deployments, and enterprise applications. It's used by everyone from individual developers and hobbyists, to large corporate teams. The Telegraf project has a very welcoming and active Open Source community. Learn how to get involved by visiting the Telegraf GitHub page. Whether you want to contribute code, improve documentation, participate in testing, or just show what you're doing with Telegraf. We'd love to hear what you're building. >> Thanks for watching Moving the World with InfluxDB, made possible by Influx Data. I hope you learned some things and are inspired to look deeper into where time series databases might fit into your environment. If you're dealing with large and or fast data volumes, and you want to scale cost effectively with the highest performance, and you're analyzing metrics and data over time, times series databases just might be a great fit for you. Try InfluxDB out. You can start with a free cloud account by clicking on the link in the resources below. Remember, all these recordings are going to be available on demand of thecube.net and influxdata.com, so check those out. And poke around Influx Data. They are the folks behind InfluxDB, and one of the leaders in the space. We hope you enjoyed the program, this is Dave Vellante for theCUBE, we'll see you soon. (upbeat music)

>>Time series data is any data that's stamped in time in some way that could be every second, every minute, every five minutes, every hour, every nanosecond, whatever it might be. And typically that data comes from sources in the physical world like devices or sensors, temperature, gauges, batteries, any device really, or things in the virtual world could be software, maybe it's software in the cloud or data and containers or microservices or virtual machines. So all of these items, whether in the physical or virtual world, they're generating a lot of time series data. Now time series data has been around for a long time, and there are many examples in our everyday lives. All you gotta do is punch up any stock, ticker and look at its price over time and graphical form. And that's a simple use case that anyone can relate to and you can build timestamps into a traditional relational database. >>You just add a column to capture time and as well, there are examples of log data being dumped into a data store that can be searched and captured and ingested and visualized. Now, the problem with the latter example that I just gave you is that you gotta hunt and Peck and search and extract what you're looking for. And the problem with the former is that traditional general purpose databases they're designed as sort of a Swiss army knife for any workload. And there are a lot of functions that get in the way and make them inefficient for time series analysis, especially at scale. Like when you think about O T and edge scale, where things are happening super fast, ingestion is coming from many different sources and analysis often needs to be done in real time or near real time. And that's where time series databases come in. >>They're purpose built and can much more efficiently support ingesting metrics at scale, and then comparing data points over time, time series databases can write and read at significantly higher speeds and deal with far more data than traditional database methods. And they're more cost effective instead of throwing processing power at the problem. For example, the underlying architecture and algorithms of time series databases can optimize queries and they can reclaim wasted storage space and reuse it. At scale time, series databases are simply a better fit for the job. Welcome to moving the world with influx DB made possible by influx data. My name is Dave Valante and I'll be your host today. Influx data is the company behind InfluxDB. The open source time series database InfluxDB is designed specifically to handle time series data. As I just explained, we have an exciting program for you today, and we're gonna showcase some really interesting use cases. >>First, we'll kick it off in our Palo Alto studios where my colleague, John furrier will interview Evan Kaplan. Who's the CEO of influx data after John and Evan set the table. John's gonna sit down with Brian Gilmore. He's the director of IOT and emerging tech at influx data. And they're gonna dig into where influx data is gaining traction and why adoption is occurring and, and why it's so robust. And they're gonna have tons of examples and double click into the technology. And then we bring it back here to our east coast studios, where I get to talk to two practitioners, doing amazing things in space with satellites and modern telescopes. These use cases will blow your mind. You don't want to miss it. So thanks for being here today. And with that, let's get started. Take it away. Palo Alto. >>Okay. Today we welcome Evan Kaplan, CEO of influx data, the company behind influx DB. Welcome Evan. Thanks for coming on. >>Hey John, thanks for having me >>Great segment here on the influx DB story. What is the story? Take us through the history. Why time series? What's the story >><laugh> so the history history is actually actually pretty interesting. Um, Paul dicks, my partner in this and our founder, um, super passionate about developers and developer experience. And, um, he had worked on wall street building a number of time series kind of platform trading platforms for trading stocks. And from his point of view, it was always what he would call a yak shave, which means you had to do a ton of work just to start doing work, which means you had to write a bunch of extrinsic routines. You had to write a bunch of application handling on existing relational databases in order to come up with something that was optimized for a trading platform or a time series platform. And he sort of, he just developed this real clear point of view is this is not how developers should work. And so in 2013, he went through why Combinator and he built something for, he made his first commit to open source in flu DB at the end of 2013. And, and he basically, you know, from my point of view, he invented modern time series, which is you start with a purpose-built time series platform to do these kind of workloads. And you get all the benefits of having something right outta the box. So a developer can be totally productive right away. >>And how many people in the company what's the history of employees and stuff? >>Yeah, I think we're, I, you know, I always forget the number, but it's something like 230 or 240 people now. Um, the company, I joined the company in 2016 and I love Paul's vision. And I just had a strong conviction about the relationship between time series and IOT. Cuz if you think about it, what sensors do is they speak time, series, pressure, temperature, volume, humidity, light, they're measuring they're instrumenting something over time. And so I thought that would be super relevant over long term and I've not regretted it. >>Oh no. And it's interesting at that time, go back in the history, you know, the role of databases, well, relational database is the one database to rule the world. And then as clouds started coming in, you starting to see more databases, proliferate types of databases and time series in particular is interesting. Cuz real time has become super valuable from an application standpoint, O T which speaks time series means something it's like time matters >>Time. >>Yeah. And sometimes data's not worth it after the time, sometimes it worth it. And then you get the data lake. So you have this whole new evolution. Is this the momentum? What's the momentum, I guess the question is what's the momentum behind >>You mean what's causing us to grow. So >>Yeah, the time series, why is time series >>And the >>Category momentum? What's the bottom line? >>Well, think about it. You think about it from a broad, broad sort of frame, which is where, what everybody's trying to do is build increasingly intelligent systems, whether it's a self-driving car or a robotic system that does what you want to do or a self-healing software system, everybody wants to build increasing intelligent systems. And so in order to build these increasing intelligent systems, you have to instrument the system well, and you have to instrument it over time, better and better. And so you need a tool, a fundamental tool to drive that instrumentation. And that's become clear to everybody that that instrumentation is all based on time. And so what happened, what happened, what happened what's gonna happen? And so you get to these applications like predictive maintenance or smarter systems. And increasingly you want to do that stuff, not just intelligently, but fast in real time. So millisecond response so that when you're driving a self-driving car and the system realizes that you're about to do something, essentially you wanna be able to act in something that looks like real time, all systems want to do that, want to be more intelligent and they want to be more real time. And so we just happen to, you know, we happen to show up at the right time in the evolution of a >>Market. It's interesting near real time. Isn't good enough when you need real time. >><laugh> yeah, it's not, it's not. And it's like, and it's like, everybody wants, even when you don't need it, ironically, you want it. It's like having the feature for, you know, you buy a new television, you want that one feature, even though you're not gonna use it, you decide that your buying criteria real time is a buying criteria >>For, so you, I mean, what you're saying then is near real time is getting closer to real time as possible, as fast as possible. Right. Okay. So talk about the aspect of data, cuz we're hearing a lot of conversations on the cube in particular around how people are implementing and actually getting better. So iterating on data, but you have to know when it happened to get, know how to fix it. So this is a big part of how we're seeing with people saying, Hey, you know, I wanna make my machine learning algorithms better after the fact I wanna learn from the data. Um, how does that, how do you see that evolving? Is that one of the use cases of sensors as people bring data in off the network, getting better with the data knowing when it happened? >>Well, for sure. So, so for sure, what you're saying is, is, is none of this is non-linear, it's all incremental. And so if you take something, you know, just as an easy example, if you take a self-driving car, what you're doing is you're instrumenting that car to understand where it can perform in the real world in real time. And if you do that, if you run the loop, which is I instrumented, I watch what happens, oh, that's wrong? Oh, I have to correct for that. I correct for that in the software. If you do that for a billion times, you get a self-driving car, but every system moves along that evolution. And so you get the dynamic of, you know, of constantly instrumenting watching the system behave and do it. And this and sets up driving car is one thing. But even in the human genome, if you look at some of our customers, you know, people like, you know, people doing solar arrays, people doing power walls, like all of these systems are getting smarter. >>Well, let's get into that. What are the top applications? What are you seeing for your, with in, with influx DB, the time series, what's the sweet spot for the application use case and some customers give some >>Examples. Yeah. So it's, it's pretty easy to understand on one side of the equation that's the physical side is sensors are sensors are getting cheap. Obviously we know that and they're getting the whole physical world is getting instrumented, your home, your car, the factory floor, your wrist, watch your healthcare, you name it. It's getting instrumented in the physical world. We're watching the physical world in real time. And so there are three or four sweet spots for us, but, but they're all on that side. They're all about IOT. So they're think about consumer IOT projects like Google's nest todo, um, particle sensors, um, even delivery engines like rapid who deliver the Instacart of south America, like anywhere there's a physical location do and that's on the consumer side. And then another exciting space is the industrial side factories are changing dramatically over time. Increasingly moving away from proprietary equipment to develop or driven systems that run operational because what, what has to get smarter when you're building, when you're building a factory is systems all have to get smarter. And then, um, lastly, a lot in the renewables sustainability. So a lot, you know, Tesla, lucid, motors, Cola, motors, um, you know, lots to do with electric cars, solar arrays, windmills, arrays, just anything that's gonna get instrumented that where that instrumentation becomes part of what the purpose >>Is. It's interesting. The convergence of physical and digital is happening with the data IOT. You mentioned, you know, you think of IOT, look at the use cases there, it was proprietary OT systems. Now becoming more IP enabled internet protocol and now edge compute, getting smaller, faster, cheaper AI going to the edge. Now you have all kinds of new capabilities that bring that real time and time series opportunity. Are you seeing IOT going to a new level? What was the, what's the IOT where's the IOT dots connecting to because you know, as these two cultures merge yeah. Operations, basically industrial factory car, they gotta get smarter, intelligent edge is a buzzword, but I mean, it has to be more intelligent. Where's the, where's the action in all this. So the >>Action, really, it really at the core, it's at the developer, right? Because you're looking at these things, it's very hard to get an off the shelf system to do the kinds of physical and software interaction. So the actions really happen at the developer. And so what you're seeing is a movement in the world that, that maybe you and I grew up in with it or OT moving increasingly that developer driven capability. And so all of these IOT systems they're bespoke, they don't come out of the box. And so the developer, the architect, the CTO, they define what's my business. What am I trying to do? Am I trying to sequence a human genome and figure out when these genes express theself or am I trying to figure out when the next heart rate monitor's gonna show up on my apple watch, right? What am I trying to do? What's the system I need to build. And so starting with the developers where all of the good stuff happens here, which is different than it used to be, right. Used to be you'd buy an application or a service or a SA thing for, but with this dynamic, with this integration of systems, it's all about bespoke. It's all about building >>Something. So let's get to the developer real quick, real highlight point here is the data. I mean, I could see a developer saying, okay, I need to have an application for the edge IOT edge or car. I mean, we're gonna have, I mean, Tesla's got applications of the car it's right there. I mean, yes, there's the modern application life cycle now. So take us through how this impacts the developer. Does it impact their C I C D pipeline? Is it cloud native? I mean, where does this all, where does this go to? >>Well, so first of all, you're talking about, there was an internal journey that we had to go through as a company, which, which I think is fascinating for anybody who's interested is we went from primarily a monolithic software that was open sourced to building a cloud native platform, which means we had to move from an agile development environment to a C I C D environment. So to a degree that you are moving your service, whether it's, you know, Tesla monitoring your car and updating your power walls, right. Or whether it's a solar company updating the arrays, right. To degree that that service is cloud. Then increasingly remove from an agile development to a C I C D environment, which you're shipping code to production every day. And so it's not just the developers, all the infrastructure to support the developers to run that service and that sort of stuff. I think that's also gonna happen in a big way >>When your customer base that you have now, and as you see, evolving with infl DB, is it that they're gonna be writing more of the application or relying more on others? I mean, obviously there's an open source component here. So when you bring in kind of old way, new way old way was I got a proprietary, a platform running all this O T stuff and I gotta write, here's an application. That's general purpose. Yeah. I have some flexibility, somewhat brittle, maybe not a lot of robustness to it, but it does its job >>A good way to think about this is versus a new way >>Is >>What so yeah, good way to think about this is what, what's the role of the developer slash architect CTO that chain within a large, within an enterprise or a company. And so, um, the way to think about it is I started my career in the aerospace industry <laugh> and so when you look at what Boeing does to assemble a plane, they build very, very few of the parts. Instead, what they do is they assemble, they buy the wings, they buy the engines, they assemble, actually, they don't buy the wings. It's the one thing they buy the, the material for the w they build the wings, cuz there's a lot of tech in the wings and they end up being assemblers smart assemblers of what ends up being a flying airplane, which is pretty big deal even now. And so what, what happens with software people is they have the ability to pull from, you know, the best of the open source world. So they would pull a time series capability from us. Then they would assemble that with, with potentially some ETL logic from somebody else, or they'd assemble it with, um, a Kafka interface to be able to stream the data in. And so they become very good integrators and assemblers, but they become masters of that bespoke application. And I think that's where it goes, cuz you're not writing native code for everything. >>So they're more flexible. They have faster time to market cuz they're assembling way faster and they get to still maintain their core competency. Okay. Their wings in this case, >>They become increasingly not just coders, but designers and developers. They become broadly builders is what we like to think of it. People who start and build stuff by the way, this is not different than the people just up the road Google have been doing for years or the tier one, Amazon building all their own. >>Well, I think one of the things that's interesting is is that this idea of a systems developing a system architecture, I mean systems, uh, uh, systems have consequences when you make changes. So when you have now cloud data center on premise and edge working together, how does that work across the system? You can't have a wing that doesn't work with the other wing kind of thing. >>That's exactly. But that's where the that's where the, you know, that that Boeing or that airplane building analogy comes in for us. We've really been thoughtful about that because IOT it's critical. So our open source edge has the same API as our cloud native stuff that has enterprise on pre edge. So our multiple products have the same API and they have a relationship with each other. They can talk with each other. So the builder builds it once. And so this is where, when you start thinking about the components that people have to use to build these services is that you wanna make sure, at least that base layer, that database layer, that those components talk to each other. >>So I'll have to ask you if I'm the customer. I put my customer hat on. Okay. Hey, I'm dealing with a lot. >>That mean you have a PO for <laugh> >>A big check. I blank check. If you can answer this question only if the tech, if, if you get the question right, I got all this important operation stuff. I got my factory, I got my self-driving cars. This isn't like trivial stuff. This is my business. How should I be thinking about time series? Because now I have to make these architectural decisions, as you mentioned, and it's gonna impact my application development. So huge decision point for your customers. What should I care about the most? So what's in it for me. Why is time series >>Important? Yeah, that's a great question. So chances are, if you've got a business that was, you know, 20 years old or 25 years old, you were already thinking about time series. You probably didn't call it that you built something on a Oracle or you built something on IBM's DB two, right. And you made it work within your system. Right? And so that's what you started building. So it's already out there. There are, you know, there are probably hundreds of millions of time series applications out there today. But as you start to think about this increasing need for real time, and you start to think about increasing intelligence, you think about optimizing those systems over time. I hate the word, but digital transformation. Then you start with time series. It's a foundational base layer for any system that you're gonna build. There's no system I can think of where time series, shouldn't be the foundational base layer. If you just wanna store your data and just leave it there and then maybe look it up every five years. That's fine. That's not time. Series time series is when you're building a smarter, more intelligent, more real time system. And the developers now know that. And so the more they play a role in building these systems, the more obvious it becomes. >>And since I have a PO for you and a big check, yeah. What is, what's the value to me as I, when I implement this, what's the end state, what's it look like when it's up and running? What's the value proposition for me. What's an >>So, so when it's up and running, you're able to handle the queries, the writing of the data, the down sampling of the data, they're transforming it in near real time. So that the other dependencies that a system that gets for adjusting a solar array or trading energy off of a power wall or some sort of human genome, those systems work better. So time series is foundational. It's not like it's, you know, it's not like it's doing every action that's above, but it's foundational to build a really compelling, intelligent system. I think that's what developers and archs are seeing now. >>Bottom line, final word. What's in it for the customer. What's what, what's your, um, what's your statement to the customer? What would you say to someone looking to do something in time series on edge? >>Yeah. So, so it's pretty clear to clear to us that if you're building, if you view yourself as being in the build business of building systems that you want 'em to be increasingly intelligent, self-healing autonomous. You want 'em to operate in real time that you start from time series. But I also wanna say what's in it for us influx what's in it for us is people are doing some amazing stuff. You know, I highlighted some of the energy stuff, some of the human genome, some of the healthcare it's hard not to be proud or feel like, wow. Yeah. Somehow I've been lucky. I've arrived at the right time, in the right place with the right people to be able to deliver on that. That's that's also exciting on our side of the equation. >>Yeah. It's critical infrastructure, critical, critical operations. >>Yeah. >>Yeah. Great stuff, Evan. Thanks for coming on. Appreciate this segment. All right. In a moment, Brian Gilmore director of IOT and emerging technology that influx day will join me. You're watching the cube leader in tech coverage. Thanks for watching >>Time series data from sensors systems and applications is a key source in driving automation and prediction in technologies around the world. But managing the massive amount of timestamp data generated these days is overwhelming, especially at scale. That's why influx data developed influx DB, a time series data platform that collects stores and analyzes data influx DB empowers developers to extract valuable insights and turn them into action by building transformative IOT analytics and cloud native applications, purpose built and optimized to handle the scale and velocity of timestamped data. InfluxDB puts the power in your hands with developer tools that make it easy to get started quickly with less code InfluxDB is more than a database. It's a robust developer platform with integrated tooling. That's written in the languages you love. So you can innovate faster, run in flex DB anywhere you want by choosing the provider and region that best fits your needs across AWS, Microsoft Azure and Google cloud flex DB is fast and automatically scalable. So you can spend time delivering value to customers, not managing clusters, take control of your time series data. So you can focus on the features and functionalities that give your applications a competitive edge. Get started for free with influx DB, visit influx data.com/cloud to learn more. >>Okay. Now we're joined by Brian Gilmore director of IOT and emerging technologies at influx data. Welcome to the show. >>Thank you, John. Great to be here. >>We just spent some time with Evan going through the company and the value proposition, um, with influx DV, what's the momentum, where do you see this coming from? What's the value coming out of this? >>Well, I think it, we're sort of hitting a point where the technology is, is like the adoption of it is becoming mainstream. We're seeing it in all sorts of organizations, everybody from like the most well funded sort of advanced big technology companies to the smaller academics, the startups and the managing of that sort of data that emits from that technology is time series and us being able to give them a, a platform, a tool that's super easy to use, easy to start. And then of course will grow with them is, is been key to us. Sort of, you know, riding along with them is they're successful. >>Evan was mentioning that time series has been on everyone's radar and that's in the OT business for years. Now, you go back since 20 13, 14, even like five years ago that convergence of physical and digital coming together, IP enabled edge. Yeah. Edge has always been kind of hyped up, but why now? Why, why is the edge so hot right now from an adoption standpoint? Is it because it's just evolution, the tech getting better? >>I think it's, it's, it's twofold. I think that, you know, there was, I would think for some people, everybody was so focused on cloud over the last probably 10 years. Mm-hmm <affirmative> that they forgot about the compute that was available at the edge. And I think, you know, those, especially in the OT and on the factory floor who weren't able to take Avan full advantage of cloud through their applications, you know, still needed to be able to leverage that compute at the edge. I think the big thing that we're seeing now, which is interesting is, is that there's like a hybrid nature to all of these applications where there's definitely some data that's generated on the edge. There's definitely done some data that's generated in the cloud. And it's the ability for a developer to sort of like tie those two systems together and work with that data in a very unified uniform way. Um, that's giving them the opportunity to build solutions that, you know, really deliver value to whatever it is they're trying to do, whether it's, you know, the, the out reaches of outer space or whether it's optimizing the factory floor. >>Yeah. I think, I think one of the things you also mentions genome too, dig big data is coming to the real world. And I think I, OT has been kind of like this thing for OT and, and in some use case, but now with the, with the cloud, all companies have an edge strategy now. So yeah, what's the secret sauce because now this is hot, hot product for the whole world and not just industrial, but all businesses. What's the secret sauce. >>Well, I mean, I think part of it is just that the technology is becoming more capable and that's especially on the hardware side, right? I mean, like technology compute is getting smaller and smaller and smaller. And we find that by supporting all the way down to the edge, even to the micro controller layer with our, um, you know, our client libraries and then working hard to make our applications, especially the database as small as possible so that it can be located as close to sort of the point of origin of that data in the edge as possible is, is, is fantastic. Now you can take that. You can run that locally. You can do your local decision making. You can use influx DB as sort of an input to automation control the autonomy that people are trying to drive at the edge. But when you link it up with everything that's in the cloud, that's when you get all of the sort of cloud scale capabilities of parallelized, AI and machine learning and all of that. >>So what's interesting is the open source success has been something that we've talked about a lot in the cube about how people are leveraging that you guys have users in the enterprise users that IOT market mm-hmm <affirmative>, but you got developers now. Yeah. Kind of together brought that up. How do you see that emerging? How do developers engage? What are some of the things you're seeing that developers are really getting into with InfluxDB >>What's? Yeah. Well, I mean, I think there are the developers who are building companies, right? And these are the startups and the folks that we love to work with who are building new, you know, new services, new products, things like that. And, you know, especially on the consumer side of IOT, there's a lot of that, just those developers. But I think we, you gotta pay attention to those enterprise developers as well, right? There are tons of people with the, the title of engineer in, in your regular enterprise organizations. And they're there for systems integration. They're there for, you know, looking at what they would build versus what they would buy. And a lot of them come from, you know, a strong, open source background and they, they know the communities, they know the top platforms in those spaces and, and, you know, they're excited to be able to adopt and use, you know, to optimize inside the business as compared to just building a brand new one. >>You know, it's interesting too, when Evan and I were talking about open source versus closed OT systems, mm-hmm <affirmative> so how do you support the backwards compatibility of older systems while maintaining open dozens of data formats out there? Bunch of standards, protocols, new things are emerging. Everyone wants to have a control plane. Everyone wants to leverage the value of data. How do you guys keep track of it all? What do you guys support? >>Yeah, well, I mean, I think either through direct connection, like we have a product called Telegraph, it's unbelievable. It's open source, it's an edge agent. You can run it as close to the edge as you'd like, it speaks dozens of different protocols in its own, right? A couple of which MQTT B, C U a are very, very, um, applicable to these T use cases. But then we also, because we are sort of not only open source, but open in terms of our ability to collect data, we have a lot of partners who have built really great integrations from their own middleware, into influx DB. These are companies like ke wear and high bite who are really experts in those downstream industrial protocols. I mean, that's a business, not everybody wants to be in. It requires some very specialized, very hard work and a lot of support, um, you know, and so by making those connections and building those ecosystems, we get the best of both worlds. The customers can use the platforms they need up to the point where they would be putting into our database. >>What's some of customer testimonies that they, that share with you. Can you share some anecdotal kind of like, wow, that's the best thing I've ever used. This really changed my business, or this is a great tech that's helped me in these other areas. What are some of the, um, soundbites you hear from customers when they're successful? >>Yeah. I mean, I think it ranges. You've got customers who are, you know, just finally being able to do the monitoring of assets, you know, sort of at the edge in the field, we have a customer who's who's has these tunnel boring machines that go deep into the earth to like drill tunnels for, for, you know, cars and, and, you know, trains and things like that. You know, they are just excited to be able to stick a database onto those tunnel, boring machines, send them into the depths of the earth and know that when they come out, all of that telemetry at a very high frequency has been like safely stored. And then it can just very quickly and instantly connect up to their, you know, centralized database. So like just having that visibility is brand new to them. And that's super important. On the other hand, we have customers who are way far beyond the monitoring use case, where they're actually using the historical records in the time series database to, um, like I think Evan mentioned like forecast things. So for predictive maintenance, being able to pull in the telemetry from the machines, but then also all of that external enrichment data, the metadata, the temperatures, the pressure is who is operating the machine, those types of things, and being able to easily integrate with platforms like Jupyter notebooks or, you know, all of those scientific computing and machine learning libraries to be able to build the models, train the models, and then they can send that information back down to InfluxDB to apply it and detect those anomalies, which >>Are, I think that's gonna be an, an area. I personally think that's a hot area because I think if you look at AI right now, yeah. It's all about training the machine learning albums after the fact. So time series becomes hugely important. Yeah. Cause now you're thinking, okay, the data matters post time. Yeah. First time. And then it gets updated the new time. Yeah. So it's like constant data cleansing data iteration, data programming. We're starting to see this new use case emerge in the data field. >>Yep. Yeah. I mean, I think you agree. Yeah, of course. Yeah. The, the ability to sort of handle those pipelines of data smartly, um, intelligently, and then to be able to do all of the things you need to do with that data in stream, um, before it hits your sort of central repository. And, and we make that really easy for customers like Telegraph, not only does it have sort of the inputs to connect up to all of those protocols and the ability to capture and connect up to the, to the partner data. But also it has a whole bunch of capabilities around being able to process that data, enrich it, reform at it, route it, do whatever you need. So at that point you're basically able to, you're playing your data in exactly the way you would wanna do it. You're routing it to different, you know, destinations and, and it's, it's, it's not something that really has been in the realm of possibility until this point. Yeah. Yeah. >>And when Evan was on it's great. He was a CEO. So he sees the big picture with customers. He was, he kinda put the package together that said, Hey, we got a system. We got customers, people are wanting to leverage our product. What's your PO they're sell. He's selling too as well. So you have that whole CEO perspective, but he brought up this notion that there's multiple personas involved in kind of the influx DB system architect. You got developers and users. Can you talk about that? Reality as customers start to commercialize and operationalize this from a commercial standpoint, you got a relationship to the cloud. Yep. The edge is there. Yep. The edge is getting super important, but cloud brings a lot of scale to the table. So what is the relationship to the cloud? Can you share your thoughts on edge and its relationship to the cloud? >>Yeah. I mean, I think edge, you know, edges, you can think of it really as like the local information, right? So it's, it's generally like compartmentalized to a point of like, you know, a single asset or a single factory align, whatever. Um, but what people do who wanna pro they wanna be able to make the decisions there at the edge locally, um, quickly minus the latency of sort of taking that large volume of data, shipping it to the cloud and doing something with it there. So we allow them to do exactly that. Then what they can do is they can actually downsample that data or they can, you know, detect like the really important metrics or the anomalies. And then they can ship that to a central database in the cloud where they can do all sorts of really interesting things with it. Like you can get that centralized view of all of your global assets. You can start to compare asset to asset, and then you can do those things like we talked about, whereas you can do predictive types of analytics or, you know, larger scale anomaly detections. >>So in this model you have a lot of commercial operations, industrial equipment. Yep. The physical plant, physical business with virtual data cloud all coming together. What's the future for InfluxDB from a tech standpoint. Cause you got open. Yep. There's an ecosystem there. Yep. You have customers who want operational reliability for sure. I mean, so you got organic <laugh> >>Yeah. Yeah. I mean, I think, you know, again, we got iPhones when everybody's waiting for flying cars. Right. So I don't know. We can like absolutely perfectly predict what's coming, but I think there are some givens and I think those givens are gonna be that the world is only gonna become more hybrid. Right. And then, you know, so we are going to have much more widely distributed, you know, situations where you have data being generated in the cloud, you have data gen being generated at the edge and then there's gonna be data generated sort sort of at all points in between like physical locations as well as things that are, that are very virtual. And I think, you know, we are, we're building some technology right now. That's going to allow, um, the concept of a database to be much more fluid and flexible, sort of more aligned with what a file would be like. >>And so being able to move data to the compute for analysis or move the compute to the data for analysis, those are the types of, of solutions that we'll be bringing to the customers sort of over the next little bit. Um, but I also think we have to start thinking about like what happens when the edge is actually off the planet. Right. I mean, we've got customers, you're gonna talk to two of them, uh, in the panel who are actually working with data that comes from like outside the earth, like, you know, either in low earth orbit or you know, all the way sort of on the other side of the universe. Yeah. And, and to be able to process data like that and to do so in a way it's it's we gotta, we gotta build the fundamentals for that right now on the factory floor and in the mines and in the tunnels. Um, so that we'll be ready for that one. >>I think you bring up a good point there because one of the things that's common in the industry right now, people are talking about, this is kind of new thinking is hyper scale's always been built up full stack developers, even the old OT world, Evan was pointing out that they built everything right. And the world's going to more assembly with core competency and IP and also property being the core of their apple. So faster assembly and building, but also integration. You got all this new stuff happening. Yeah. And that's to separate out the data complexity from the app. Yes. So space genome. Yep. Driving cars throws off massive data. >>It >>Does. So is Tesla, uh, is the car the same as the data layer? >>I mean the, yeah, it's, it's certainly a point of origin. I think the thing that we wanna do is we wanna let the developers work on the world, changing problems, the things that they're trying to solve, whether it's, you know, energy or, you know, any of the other health or, you know, other challenges that these teams are, are building against. And we'll worry about that time series data and the underlying data platform so that they don't have to. Right. I mean, I think you talked about it, uh, you know, for them just to be able to adopt the platform quickly, integrate it with their data sources and the other pieces of their applications. It's going to allow them to bring much faster time to market on these products. It's gonna allow them to be more iterative. They're gonna be able to do more sort of testing and things like that. And ultimately it will, it'll accelerate the adoption and the creation of >>Technology. You mentioned earlier in, in our talk about unification of data. Yeah. How about APIs? Cuz developers love APIs in the cloud unifying APIs. How do you view view that? >>Yeah, I mean, we are APIs, that's the product itself. Like everything, people like to think of it as sort of having this nice front end, but the front end is B built on our public APIs. Um, you know, and it, it allows the developer to build all of those hooks for not only data creation, but then data processing, data analytics, and then, you know, sort of data extraction to bring it to other platforms or other applications, microservices, whatever it might be. So, I mean, it is a world of APIs right now and you know, we, we bring a very sort of useful set of them for managing the time series data. These guys are all challenged with. It's >>Interesting. You and I were talking before we came on camera about how, um, data is, feels gonna have this kind of SRE role that DevOps had site reliability engineers, which manages a bunch of servers. There's so much data out there now. Yeah. >>Yeah. It's like reigning data for sure. And I think like that ability to be like one of the best jobs on the planet is gonna be to be able to like, sort of be that data Wrangler to be able to understand like what the data sources are, what the data formats are, how to be able to efficiently move that data from point a to point B and you know, to process it correctly so that the end users of that data aren't doing any of that sort of hard upfront preparation collection storage's >>Work. Yeah. That's data as code. I mean, data engineering is it is becoming a new discipline for sure. And, and the democratization is the benefit. Yeah. To everyone, data science get easier. I mean data science, but they wanna make it easy. Right. <laugh> yeah. They wanna do the analysis, >>Right? Yeah. I mean, I think, you know, it, it's a really good point. I think like we try to give our users as many ways as there could be possible to get data in and get data out. We sort of think about it as meeting them where they are. Right. So like we build, we have the sort of client libraries that allow them to just port to us, you know, directly from the applications and the languages that they're writing, but then they can also pull it out. And at that point nobody's gonna know the users, the end consumers of that data, better than those people who are building those applications. And so they're building these user interfaces, which are making all of that data accessible for, you know, their end users inside their organization. >>Well, Brian, great segment, great insight. Thanks for sharing all, all the complexities and, and IOT that you guys helped take away with the APIs and, and assembly and, and all the system architectures that are changing edge is real cloud is real. Yeah, absolutely. Mainstream enterprises. And you got developer attraction too, so congratulations. >>Yeah. It's >>Great. Well, thank any, any last word you wanna share >>Deal with? No, just, I mean, please, you know, if you're, if you're gonna, if you're gonna check out influx TV, download it, try out the open source contribute if you can. That's a, that's a huge thing. It's part of being the open source community. Um, you know, but definitely just, just use it. I think when once people use it, they try it out. They'll understand very, >>Very quickly. So open source with developers, enterprise and edge coming together all together. You're gonna hear more about that in the next segment, too. Right. Thanks for coming on. Okay. Thanks. When we return, Dave LAN will lead a panel on edge and data influx DB. You're watching the cube, the leader in high tech enterprise coverage. >>Why the startup, we move really fast. We find that in flex DB can move as fast as us. It's just a great group, very collaborative, very interested in manufacturing. And we see a bright future in working with influence. My name is Aaron Seley. I'm the CTO at HBI. Highlight's one of the first companies to focus on manufacturing data and apply the concepts of data ops, treat that as an asset to deliver to the it system, to enable applications like overall equipment effectiveness that can help the factory produce better, smarter, faster time series data. And manufacturing's really important. If you take a piece of equipment, you have the temperature pressure at the moment that you can look at to kind of see the state of what's going on. So without that context and understanding you can't do what manufacturers ultimately want to do, which is predict the future. >>Influx DB represents kind of a new way to storm time series data with some more advanced technology and more importantly, more open technologies. The other thing that influx does really well is once the data's influx, it's very easy to get out, right? They have a modern rest API and other ways to access the data. That would be much more difficult to do integrations with classic historians highlight can serve to model data, aggregate data on the shop floor from a multitude of sources, whether that be P C U a servers, manufacturing execution systems, E R P et cetera, and then push that seamlessly into influx to then be able to run calculations. Manufacturing is changing this industrial 4.0, and what we're seeing is influx being part of that equation. Being used to store data off the unified name space, we recommend InfluxDB all the time to customers that are exploring a new way to share data manufacturing called the unified name space who have open questions around how do I share this new data that's coming through my UNS or my QTT broker? How do I store this and be able to query it over time? And we often point to influx as a solution for that is a great brand. It's a great group of people and it's a great technology. >>Okay. We're now going to go into the customer panel and we'd like to welcome Angelo Fasi. Who's a software engineer at the Vera C Ruben observatory in Caleb McLaughlin whose senior spacecraft operations software engineer at loft orbital guys. Thanks for joining us. You don't wanna miss folks this interview, Caleb, let's start with you. You work for an extremely cool company. You're launching satellites into space. I mean, there, of course doing that is, is highly complex and not a cheap endeavor. Tell us about loft Orbi and what you guys do to attack that problem. >>Yeah, absolutely. And, uh, thanks for having me here by the way. Uh, so loft orbital is a, uh, company. That's a series B startup now, uh, who and our mission basically is to provide, uh, rapid access to space for all kinds of customers. Uh, historically if you want to fly something in space, do something in space, it's extremely expensive. You need to book a launch, build a bus, hire a team to operate it, you know, have a big software teams, uh, and then eventually worry about, you know, a bunch like just a lot of very specialized engineering. And what we're trying to do is change that from a super specialized problem that has an extremely high barrier of access to a infrastructure problem. So that it's almost as simple as, you know, deploying a VM in, uh, AWS or GCP is getting your, uh, programs, your mission deployed on orbit, uh, with access to, you know, different sensors, uh, cameras, radios, stuff like that. >>So that's, that's kind of our mission. And just to give a really brief example of the kind of customer that we can serve. Uh, there's a really cool company called, uh, totem labs who is working on building, uh, IOT cons, an IOT constellation for in of things, basically being able to get telemetry from all over the world. They're the first company to demonstrate indoor T, which means you have this little modem inside a container container that you, that you track from anywhere in the world as it's going across the ocean. Um, so they're, it's really little and they've been able to stay a small startup that's focused on their product, which is the, uh, that super crazy complicated, cool radio while we handle the whole space segment for them, which just, you know, before loft was really impossible. So that's, our mission is, uh, providing space infrastructure as a service. We are kind of groundbreaking in this area and we're serving, you know, a huge variety of customers with all kinds of different missions, um, and obviously generating a ton of data in space, uh, that we've gotta handle. Yeah. >>So amazing Caleb, what you guys do, I, now I know you were lured to the skies very early in your career, but how did you kinda land on this business? >>Yeah, so, you know, I've, I guess just a little bit about me for some people, you know, they don't necessarily know what they wanna do like early in their life. For me, I was five years old and I knew, you know, I want to be in the space industry. So, you know, I started in the air force, but have, uh, stayed in the space industry, my whole career and been a part of, uh, this is the fifth space startup that I've been a part of actually. So, you know, I've, I've, uh, kind of started out in satellites, did spent some time in working in, uh, the launch industry on rockets. Then, uh, now I'm here back in satellites and you know, honestly, this is the most exciting of the difference based startups. That I've been a part of >>Super interesting. Okay. Angelo, let's, let's talk about the Ruben observatory, ver C Ruben, famous woman scientist, you know, galaxy guru. Now you guys the observatory, you're up way up high. You're gonna get a good look at the Southern sky. Now I know COVID slowed you guys down a bit, but no doubt. You continued to code away on the software. I know you're getting close. You gotta be super excited. Give us the update on, on the observatory and your role. >>All right. So yeah, Rubin is a state of the art observatory that, uh, is in construction on a remote mountain in Chile. And, um, with Rubin, we conduct the, uh, large survey of space and time we are going to observe the sky with, uh, eight meter optical telescope and take, uh, a thousand pictures every night with a 3.2 gig up peaks of camera. And we are going to do that for 10 years, which is the duration of the survey. >>Yeah. Amazing project. Now you, you were a doctor of philosophy, so you probably spent some time thinking about what's out there and then you went out to earn a PhD in astronomy, in astrophysics. So this is something that you've been working on for the better part of your career, isn't it? >>Yeah, that's that's right. Uh, about 15 years, um, I studied physics in college, then I, um, got a PhD in astronomy and, uh, I worked for about five years in another project. Um, the dark energy survey before joining rubing in 2015. >>Yeah. Impressive. So it seems like you both, you know, your organizations are looking at space from two different angles. One thing you guys both have in common of course is, is, is software. And you both use InfluxDB as part of your, your data infrastructure. How did you discover influx DB get into it? How do you use the platform? Maybe Caleb, you could start. >>Uh, yeah, absolutely. So the first company that I extensively used, uh, influx DBN was a launch startup called, uh, Astra. And we were in the process of, uh, designing our, you know, our first generation rocket there and testing the engines, pumps, everything that goes into a rocket. Uh, and when I joined the company, our data story was not, uh, very mature. We were collecting a bunch of data in LabVIEW and engineers were taking that over to MATLAB to process it. Um, and at first there, you know, that's the way that a lot of engineers and scientists are used to working. Um, and at first that was, uh, like people weren't entirely sure that that was a, um, that that needed to change, but it's something the nice thing about InfluxDB is that, you know, it's so easy to deploy. So as the, our software engineering team was able to get it deployed and, you know, up and running very quickly and then quickly also backport all of the data that we collected thus far into influx and what, uh, was amazing to see. >>And as kind of the, the super cool moment with influx is, um, when we hooked that up to Grafana Grafana as the visualization platform we used with influx, cuz it works really well with it. Uh, there was like this aha moment of our engineers who are used to this post process kind of method for dealing with their data where they could just almost instantly easily discover data that they hadn't been able to see before and take the manual processes that they would run after a test and just throw those all in influx and have live data as tests were coming. And, you know, I saw them implementing like crazy rocket equation type stuff in influx, and it just was totally game changing for how we tested. >>So Angelo, I was explaining in my open, you know, you could, you could add a column in a traditional RDBMS and do time series, but with the volume of data that you're talking about, and the example of the Caleb just gave you, I mean, you have to have a purpose built time series database, where did you first learn about influx DB? >>Yeah, correct. So I work with the data management team, uh, and my first project was the record metrics that measured the performance of our software, uh, the software that we used to process the data. So I started implementing that in a relational database. Um, but then I realized that in fact, I was dealing with time series data and I should really use a solution built for that. And then I started looking at time series databases and I found influx B. And that was, uh, back in 2018. The another use for influx DB that I'm also interested is the visits database. Um, if you think about the observations we are moving the telescope all the time in pointing to specific directions, uh, in the Skype and taking pictures every 30 seconds. So that itself is a time series. And every point in that time series, uh, we call a visit. So we want to record the metadata about those visits and flex to, uh, that time here is going to be 10 years long, um, with about, uh, 1000 points every night. It's actually not too much data compared to other, other problems. It's, uh, really just a different, uh, time scale. >>The telescope at the Ruben observatory is like pun intended, I guess the star of the show. And I, I believe I read that it's gonna be the first of the next gen telescopes to come online. It's got this massive field of view, like three orders of magnitude times the Hub's widest camera view, which is amazing, right? That's like 40 moons in, in an image amazingly fast as well. What else can you tell us about the telescope? >>Um, this telescope, it has to move really fast and it also has to carry, uh, the primary mirror, which is an eight meter piece of glass. It's very heavy and it has to carry a camera, which has about the size of a small car. And this whole structure weighs about 300 tons for that to work. Uh, the telescope needs to be, uh, very compact and stiff. Uh, and one thing that's amazing about it's design is that the telescope, um, is 300 tons structure. It sits on a tiny film of oil, which has the diameter of, uh, human hair. And that makes an almost zero friction interface. In fact, a few people can move these enormous structure with only their hands. Uh, as you said, uh, another aspect that makes this telescope unique is the optical design. It's a wide field telescope. So each image has, uh, in diameter the size of about seven full moons. And, uh, with that, we can map the entire sky in only, uh, three days. And of course doing operations everything's, uh, controlled by software and it is automatic. Um there's a very complex piece of software, uh, called the scheduler, which is responsible for moving the telescope, um, and the camera, which is, uh, recording 15 terabytes of data every night. >>Hmm. And, and, and Angela, all this data lands in influx DB. Correct. And what are you doing with, with all that data? >>Yeah, actually not. Um, so we are using flex DB to record engineering data and metadata about the observations like telemetry events and commands from the telescope. That's a much smaller data set compared to the images, but it is still challenging because, uh, you, you have some high frequency data, uh, that the system needs to keep up and we need to, to start this data and have it around for the lifetime of the price. Mm, >>Got it. Thank you. Okay, Caleb, let's bring you back in and can tell us more about the, you got these dishwasher size satellites. You're kind of using a multi-tenant model. I think it's genius, but, but tell us about the satellites themselves. >>Yeah, absolutely. So, uh, we have in space, some satellites already that as you said, are like dishwasher, mini fridge kind of size. Um, and we're working on a bunch more that are, you know, a variety of sizes from shoebox to, I guess, a few times larger than what we have today. Uh, and it is, we do shoot to have effectively something like a multi-tenant model where, uh, we will buy a bus off the shelf. The bus is, uh, what you can kind of think of as the core piece of the satellite, almost like a motherboard or something where it's providing the power. It has the solar panels, it has some radios attached to it. Uh, it handles the attitude control, basically steers the spacecraft in orbit. And then we build also in house, what we call our payload hub, which is, has all, any customer payloads attached and our own kind of edge processing sort of capabilities built into it. >>And, uh, so we integrate that. We launch it, uh, and those things, because they're in lower orbit, they're orbiting the earth every 90 minutes. That's, you know, seven kilometers per second, which is several times faster than a speeding bullet. So we've got, we have, uh, one of the unique challenges of operating spacecraft and lower orbit is that generally you can't talk to them all the time. So we're managing these things through very brief windows of time, uh, where we get to talk to them through our ground sites, either in Antarctica or, you know, in the north pole region. >>Talk more about how you use influx DB to make sense of this data through all this tech that you're launching into space. >>We basically previously we started off when I joined the company, storing all of that as Angelo did in a regular relational database. And we found that it was, uh, so slow in the size of our data would balloon over the course of a couple days to the point where we weren't able to even store all of the data that we were getting. Uh, so we migrated to influx DB to store our time series telemetry from the spacecraft. So, you know, that's things like, uh, power level voltage, um, currents counts, whatever, whatever metadata we need to monitor about the spacecraft. We now store that in, uh, in influx DB. Uh, and that has, you know, now we can actually easily store the entire volume of data for the mission life so far without having to worry about, you know, the size bloating to an unmanageable amount. >>And we can also seamlessly query, uh, large chunks of data. Like if I need to see, you know, for example, as an operator, I might wanna see how my, uh, battery state of charge is evolving over the course of the year. I can have a plot and an influx that loads that in a fraction of a second for a year's worth of data, because it does, you know, intelligent, um, I can intelligently group the data by, uh, sliding time interval. Uh, so, you know, it's been extremely powerful for us to access the data and, you know, as time has gone on, we've gradually migrated more and more of our operating data into influx. >>You know, let's, let's talk a little bit, uh, uh, but we throw this term around a lot of, you know, data driven, a lot of companies say, oh, yes, we're data driven, but you guys really are. I mean, you' got data at the core, Caleb, what does that, what does that mean to you? >>Yeah, so, you know, I think the, and the clearest example of when I saw this be like totally game changing is what I mentioned before at Astro where our engineer's feedback loop went from, you know, a lot of kind of slow researching, digging into the data to like an instant instantaneous, almost seeing the data, making decisions based on it immediately, rather than having to wait for some processing. And that's something that I've also seen echoed in my current role. Um, but to give another practical example, uh, as I said, we have a huge amount of data that comes down every orbit, and we need to be able to ingest all of that data almost instantaneously and provide it to the operator. And near real time, you know, about a second worth of latency is all that's acceptable for us to react to, to see what is coming down from the spacecraft and building that pipeline is challenging from a software engineering standpoint. >>Um, our primary language is Python, which isn't necessarily that fast. So what we've done is started, you know, in the, in the goal of being data driven is publish metrics on individual, uh, how individual pieces of our data processing pipeline are performing into influx as well. And we do that in production as well as in dev. Uh, so we have kind of a production monitoring, uh, flow. And what that has done is allow us to make intelligent decisions on our software development roadmap, where it makes the most sense for us to, uh, focus our development efforts in terms of improving our software efficiency. Uh, just because we have that visibility into where the real problems are. Um, it's sometimes we've found ourselves before we started doing this kind of chasing rabbits that weren't necessarily the real root cause of issues that we were seeing. Uh, but now, now that we're being a bit more data driven, there we are being much more effective in where we're spending our resources and our time, which is especially critical to us as we scale to, from supporting a couple satellites, to supporting many, many satellites at >>Once. Yeah. Coach. So you reduced those dead ends, maybe Angela, you could talk about what, what sort of data driven means to, to you and your teams? >>I would say that, um, having, uh, real time visibility, uh, to the telemetry data and, and metrics is, is, is crucial for us. We, we need, we need to make sure that the image that we collect with the telescope, uh, have good quality and, um, that they are within the specifications, uh, to meet our science goals. And so if they are not, uh, we want to know that as soon as possible and then, uh, start fixing problems. >>Caleb, what are your sort of event, you know, intervals like? >>So I would say that, you know, as of today on the spacecraft, the event, the, the level of timing that we deal with probably tops out at about, uh, 20 Hertz, 20 measurements per second on, uh, things like our, uh, gyroscopes, but the, you know, I think the, the core point here of the ability to have high precision data is extremely important for these kinds of scientific applications. And I'll give an example, uh, from when I worked at, on the rocket at Astra there, our baseline data rate that we would ingest data during a test is, uh, 500 Hertz. So 500 samples per second. And in some cases we would actually, uh, need to ingest much higher rate data, even up to like 1.5 kilohertz. So, uh, extremely, extremely high precision, uh, data there where timing really matters a lot. And, uh, you know, I can, one of the really powerful things about influx is the fact that it can handle this. >>That's one of the reasons we chose it, uh, because there's times when we're looking at the results of a firing where you're zooming in, you know, I talked earlier about how on my current job, we often zoom out to look, look at a year's worth of data. You're zooming in to where your screen is preoccupied by a tiny fraction of a second. And you need to see same thing as Angela just said, not just the actual telemetry, which is coming in at a high rate, but the events that are coming out of our controllers. So that can be something like, Hey, I opened this valve at exactly this time and that goes, we wanna have that at, you know, micro or even nanosecond precision so that we know, okay, we saw a spike in chamber pressure at, you know, at this exact moment, was that before or after this valve open, those kind of, uh, that kind of visibility is critical in these kind of scientific, uh, applications and absolutely game changing to be able to see that in, uh, near real time and, uh, with a really easy way for engineers to be able to visualize this data themselves without having to wait for, uh, software engineers to go build it for them. >>Can the scientists do self-serve or are you, do you have to design and build all the analytics and, and queries for your >>Scientists? Well, I think that's, that's absolutely from, from my perspective, that's absolutely one of the best things about influx and what I've seen be game changing is that, uh, generally I'd say anyone can learn to use influx. Um, and honestly, most of our users might not even know they're using influx, um, because what this, the interface that we expose to them is Grafana, which is, um, a generic graphing, uh, open source graphing library that is very similar to influx own chronograph. Sure. And what it does is, uh, let it provides this, uh, almost it's a very intuitive UI for building your queries. So you choose a measurement and it shows a dropdown of available measurements. And then you choose a particular, the particular field you wanna look at. And again, that's a dropdown, so it's really easy for our users to discover. And there's kind of point and click options for doing math aggregations. You can even do like perfect kind of predictions all within Grafana, the Grafana user interface, which is really just a wrapper around the APIs and functionality of the influx provides putting >>Data in the hands of those, you know, who have the context of domain experts is, is key. Angela, is it the same situation for you? Is it self serve? >>Yeah, correct. Uh, as I mentioned before, um, we have the astronomers making their own dashboards because they know what exactly what they, they need to, to visualize. Yeah. I mean, it's all about using the right tool for the job. I think, uh, for us, when I joined the company, we weren't using influx DB and we, we were dealing with serious issues of the database growing to an incredible size extremely quickly, and being unable to like even querying short periods of data was taking on the order of seconds, which is just not possible for operations >>Guys. This has been really formative it's, it's pretty exciting to see how the edge is mountaintops, lower orbits to be space is the ultimate edge. Isn't it. I wonder if you could answer two questions to, to wrap here, you know, what comes next for you guys? Uh, and is there something that you're really excited about that, that you're working on Caleb, maybe you could go first and an Angela, you can bring us home. >>Uh, basically what's next for loft. Orbital is more, more satellites, a greater push towards infrastructure and really making, you know, our mission is to make space simple for our customers and for everyone. And we're scaling the company like crazy now, uh, making that happen, it's extremely exciting and extremely exciting time to be in this company and to be in this industry as a whole, because there are so many interesting applications out there. So many cool ways of leveraging space that, uh, people are taking advantage of. And with, uh, companies like SpaceX and the now rapidly lowering cost, cost of launch, it's just a really exciting place to be. And we're launching more satellites. We are scaling up for some constellations and our ground system has to be improved to match. So there's a lot of, uh, improvements that we're working on to really scale up our control software, to be best in class and, uh, make it capable of handling such a large workload. So >>You guys hiring >><laugh>, we are absolutely hiring. So, uh, I would in we're we need, we have PE positions all over the company. So, uh, we need software engineers. We need people who do more aerospace, specific stuff. So, uh, absolutely. I'd encourage anyone to check out the loft orbital website, if there's, if this is at all interesting. >>All right. Angela, bring us home. >>Yeah. So what's next for us is really, uh, getting this, um, telescope working and collecting data. And when that's happen is going to be just, um, the Lu of data coming out of this camera and handling all, uh, that data is going to be really challenging. Uh, yeah. I wanna wanna be here for that. <laugh> I'm looking forward, uh, like for next year we have like an important milestone, which is our, um, commissioning camera, which is a simplified version of the, of the full camera it's going to be on sky. And so yeah, most of the system has to be working by them. >>Nice. All right, guys, you know, with that, we're gonna end it. Thank you so much, really fascinating, and thanks to influx DB for making this possible, really groundbreaking stuff, enabling value creation at the edge, you know, in the cloud and of course, beyond at the space. So really transformational work that you guys are doing. So congratulations and really appreciate the broader community. I can't wait to see what comes next from having this entire ecosystem. Now, in a moment, I'll be back to wrap up. This is Dave ante, and you're watching the cube, the leader in high tech enterprise coverage. >>Welcome Telegraph is a popular open source data collection. Agent Telegraph collects data from hundreds of systems like IOT sensors, cloud deployments, and enterprise applications. It's used by everyone from individual developers and hobbyists to large corporate teams. The Telegraph project has a very welcoming and active open source community. Learn how to get involved by visiting the Telegraph GitHub page, whether you want to contribute code, improve documentation, participate in testing, or just show what you're doing with Telegraph. We'd love to hear what you're building. >>Thanks for watching. Moving the world with influx DB made possible by influx data. I hope you learn some things and are inspired to look deeper into where time series databases might fit into your environment. If you're dealing with large and or fast data volumes, and you wanna scale cost effectively with the highest performance and you're analyzing metrics and data over time times, series databases just might be a great fit for you. Try InfluxDB out. You can start with a free cloud account by clicking on the link and the resources below. Remember all these recordings are gonna be available on demand of the cube.net and influx data.com. So check those out and poke around influx data. They are the folks behind InfluxDB and one of the leaders in the space, we hope you enjoyed the program. This is Dave Valante for the cube. We'll see you soon.

>> We're back at the Seaport in Boston. Dave Vellante and Paul Gill. You're watching The Cubes coverage of Red Hat Summit, 2022. A little different this year, a smaller venue. Maybe a thousand people. Love the keynotes, compressed. Big virtual audience. So we're happy to be coming to you live, face to face. It's been a while since we've had these, for a lot of folks, this is their first in person event. You know, it's kind of weird getting used to that, but I think in the next few months, it's going to become the new, sort of quasi abnormal. Francis Chow is here. He's the Vice President and GM of In-Vehicle OS and Edge at Red Hat. Francis, welcome. That's the most interesting title we've had all week. So thanks for coming here. >> Thank you, Dave. Thank you, Paul, for having me here. >> So The Edge, I mean The Edge is, we heard about the International Space Station. We heard about ski boots, of course In-Vehicle. What's the Edge to you? >> Well, to me Edge actually could mean many different things, right? The way we look at Edge is, there is the traditional enterprise Edge, where this is the second tier, third tier data centers that this extension from your core, the network and your centralized data center, right to remote locations. And then there are like Telco Edge, right? where we know about the 5G network, right Where you deploy bay stations and which would have a different size of requirements right. Of traditional enterprise edge networks. And then there are Operational Edge where we see the line of business operating on those locations, right? Things like manufacturing for oil rigs, retail store, right? So very wide variety of Edge that are doing OT type of technology, and then last but not least there is the customer on or kind of device edge where we now putting things into things like cars, as you said, like ski booth, and have that interaction with the end consumers. >> Is this why? I mean, there's a lot of excitement at Red. I could tell among the Red hat people about this GM deal here is this why that's so exciting to them? This really encompasses sort of all of those variants of the edge in automotive, in automobile experience. Doesn't it? >> I think why this is exciting to the industry and also to us is that if you look at traditionally how automotive has designed, right the way the architect vehicle today has many subsystems, they are all purpose viewed, very tight cut, coupled with hardware and software. And it's very difficult to reuse, right? So their cause of development is high. The time to develop is long and adding to that there is a lengthy safety certification process which also kind of make it hard. Because every time you make a change in the system you have to re-certify it again. >> Right. >> And typically it takes about six to 12 months to do so. Every time you make a change. So very lengthy passes, which is important because we want to ensure occupants are safe in a vehicle. Now what we bring to the table, which I think is super exciting is we bring this platform approach. Now you can use a consistent platform that is open and you can actually now run multiple doming applications on the same platform which means automakers can reuse components across model years and brands. That will lower the development cost. Now I think one of the key things that we bring to the table is that we introduce a new safety certification approach called Continuous Safety Certification. We actually announced that in our summit last year with the intent, "Hey, we're going to deliver this functional certified Linux platform" Which is the first four Linux. And the way we do it is we work with our partner Excedr to try to define that approach. And at the high level the idea really is to automate that certification process just like how we automate software development. Right, we are adding that monitoring capabilities with functional safety related artifacts in our CI three pipeline. And we are able to aim to cut back that kind of certification time to a fraction of what is needed today. So what we can do, I think with this collaboration with GM, is help them get faster time to market, and then lower development costs. Now, adding to that, if you think about a modern Linux platform, you can update it over the air, right? This is the capability that we are working with GM as well. Now what customers can expect now, right for future vehicle is there will be updates on apps and services, just like your cell phone, right. Which makes your car more capable over time and more relevant for the long term. >> So there's some assumptions you're making at the edge. First of all, you described a spectrum retail store which you know, to me, okay, it's Edge, but you can take an X-86 box or a hyper converged infrastructure throw it in there. And there's some opportunities to do some stuff in real time, but it's kind of an extension natural extension of IT. Whereas in vehicle you got to make some assumptions spotty connectivity to do software download and you can't do truck rolls at the far edge, right? None of that is okay, and so there's some assumptions there and as you say, your role is to compress the time to market, but also deliver a better consumer >> Absolutely. >> Experience, so what can we expect? You started to talk about the future of in vehicle, you know, or EVs, if you will, what should we expect as consumers? You, you're saying over the year software we're seeing that with some of the EV makers, for sure. But what's the future look like? >> I think what consumers can expect is really over a period of time, right? A similar experience, like what you have with your mobile mobile device, right? If you look back 15, 20 years, right? You buy a phone, right? That's the feature that you have with your phone, right? No update, it is what it is right, for the lifetime of the product which is pretty much what you have now, if you buy a vehicle, right. You have those features capabilities and you allow it for the lifetime of the vehicle. >> Sometimes you have to drive in for a maintenance, a service to get a software update. >> We can talk about that too right. But as we make the systems, update-able right you can now expect more frequent and seamless update of both the operating system and the application services that sit on top of that. Right, so I think right in the future consumers can expect more capable vehicles after you purchase it because new developmental software can now be done with an update over the air. >> I assume this relationship with GM is not exclusive. Are you talking with other automakers as well? >> We are talking to auto makers, other auto makers. What we working with GM is really a product that could work for the industry, right? This is actually what we both believe in is the right thing to do right? As we are able to standardize how we approach the infrastructure. I think this is a good thing for the whole industry to help accelerate innovation for the entire industry. >> Well which is sort of natural next question. Are we heading toward an open automotive platform? Like we have an open banking platform in that industry. Do you see the possibility that there could be a single platform that all or most of the auto makers will work on? >> I wouldn't use the word single, but I definitely would use the word open. Right? Our goal is to build this open platform, right. Because we believe in open source, right. We believe in community, right. If we make it open, we have more contributors to come in and help to make the system better in a way faster. And actually like you said, right. Improve the quality, right, better. Right, so that the chance of recall is now lower with, with this approach. >> You're using validated patterns as part of this initiative. Is that right? And what is a validated pattern? How is it different from a reference architecture? Is it just kind of a new name for reference architecture? or what value does it bring to the relation? >> For automotive right, we don't have a validated pattern yet but they can broadly kind of speak about what that is. >> Yeah. >> And how we see that evolve over time. So validated pattern basically is a combination of Red Hat products, multiple Red Hat products and partner products. And we usually build it for specific use case. And then we put those components together run rigorous tests to validate it that's it going to work, so that it becomes more repeatable and deployable for those particular edge use cases. Now we do work with our partners to make it happen, right. Because in the end, right we want to make a solution that is about 80% of the way and allow our partners to kind of add more value and their secret sauce on top and deploy it. Right, and I'll give you kind of one example, right You just have the interview with the Veterans Affairs team, right. One of our patents, right? The Medical Diagnosis Pattern, right. Actually we work with them in the early development stage of that. Right, what it does is to help make assessments on pneumonia with chest X rates, right. So it's a fully automated data pipeline. We get the chest x-ray from an object store use AIML to diagnose whether there's new pneumonia. And then I'll put that in a dashboard automated with the validated pattern. >> So you're not using them today, but can we expect that in the future? It sounds like >> Yes absolutely it's in the works, yes. >> It would be a perfect vertical. >> How do you believe your work with GM? I mean, has implications across Red hat? It seems like there are things you're going to be doing with GM that could affect other parts of your own product portfolio. >> Oh, absolutely. I think this actually is, it's a pivotal moment for Red Hat and the automotive industry. And I think broadly speaking for any safety conscious industry, right. As we create this Proof-point right that we can build a Linux system that is optimized for footprint performance, realtime capabilities, and be able to certify it for safety. Right I think all the adjacent industry, right. You think about transportation, healthcare, right. Industry that have tight safety requirements. It's just opened up the aperture for us to adjust those markets in the future. >> So we talked about a lot about the consumerization of IT over the last decade. Many of us feel as though that what's going on at the Edge, the innovations that are going on at the Edge realtime AI inferencing, you know, streaming data ARM, the innovations that ARM and others are performing certainly in video until we heard today, this notion of, you know, no touch, zero touch provisioning that a lot of these innovations are actually going to find their way into the enterprise. Kind of a follow on fault of what you were just talking about. And there's probably some future disruptions coming. You can almost guarantee that, I mean, 15 years or so we get that kind of disruption. How are you thinking about that? >> Well, I think you company, right. Some of the Edge innovation, right. You're going to kind of bring back to enterprise over time. Right but the one thing that you talk about zero touch provisioning right. Is critical right? You think about edge deployments. You're going to have to deal with a very diverse set of environments on how deployments are happen. Right think about like tail code based stations, right. You have somewhere between 75,000 to 100,000 base stations in the US for each provider right. How do you deploy it? Right, if you let's say you push one update or you want the provision system. So what we bring to the table in the latest open shift release is that, hey we make provisioning zero touch right, meaning you can actually do that without any menu intervention. >> Yeah, so I think the Edge is going to raise the bar for the enterprise, I guess is my premise there. >> Absolutely. >> So Francis, thanks so much for coming on The Cube. It's great to see you and congratulations on the collaboration. It's a exciting area for you guys. >> Thank you again, Dave and Paul. >> Our pleasure, all right keep it right there. After this quick break, we'll be back. Paul Gill and Dave Vellante you're watching The Cubes coverage Red Hat Summit 2022 live from the Boston Seaport. Be right back.