>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>>Hey everyone. Welcome to the cubes presentation of the AWS startup showcase. This is season two, episode four of our ongoing series. That's featuring exciting startups within the AWS ecosystem. This theme, cybersecurity protect and detect against threats. I'm your host. Lisa Martin. I've got two guests here with me. Please. Welcome back to the program. Sam Kam, a COO and co-founder of security scorecard and bar Roth. Charri team lead solutions marketing at confluent guys. It's great to have you on the program talking about cybersecurity. >>Thanks for having us, Lisa, >>Sam, let's go ahead and kick off with you. You've been on the queue before, but give the audience just a little bit of context about security scorecard or SSC as they're gonna hear it referred to. >>Yeah. AB absolutely. Thank you for that. Well, the easiest way to, to put it is when people wanna know about their credit risk, they consult one of the major credit scoring companies. And when companies wanna know about their cybersecurity risk, they turn to security scorecard to get that holistic view of, of, of the security posture. And the way it works is SSC is continuously 24 7 collecting signals from across the entire internet. I entire IPV four space and they're doing it to identify vulnerable and misconfigured digital assets. And we were just looking back over like a three year period. We looked from 2019 to 2022. We, we, we assessed through our techniques over a million and a half organizations and found that over half of them had at least one open critical vulnerability exposed to the internet. What was even more shocking was 20% of those organizations had amassed over a thousand vulnerabilities each. >>So SSC we're in the business of really building solutions for customers. We mine the data from dozens of digital sources and help discover the risks and the flaws that are inherent to their business. And that becomes increasingly important as companies grow and find new sources of risk and new threat vectors that emerge on the internet for themselves and for their vendor and business partner ecosystem. The last thing I'll mention is the platform that we provide. It relies on data collection and processing to be done in an extremely accurate and real time way. That's a key for that's allowed us to scale. And in order to comp, in order for us to accomplish this security scorecard engineering teams, they used a really novel combination of confluent cloud and confluent platform to build a really, really robust data for streaming pipelines and the data streaming pipelines enabled by confluent allow us at security scorecard to collect the data from a lot of various sources for risk analysis. Then they get feer further analyzed and provided to customers as a easy to understand summary of analytics. >>Rob, let's bring you into the conversation, talk about confluent, give the audience that overview and then talk about what you're doing together with SSC. >>Yeah, and I wanted to say Sam did a great job of setting up the context about what confluent is. So, so appreciate that, but a really simple way to think about it. Lisa is confident as a data streaming platform that is pioneering a fundamentally new category of data infrastructure that is at the core of what SSE does. Like Sam said, the key is really collect data accurately at scale and in real time. And that's where our cloud native offering really empowers organizations like SSE to build great customer experiences for their customers. And the other thing we do is we also help organizations build a sophisticated real time backend operations. And so at a high level, that's the best way to think about comfort. >>Got it. But I'll talk about data streaming, how it's being used in cyber security and what the data streaming pipelines enable enabled by confluent allow SSE to do for its customers. >>Yeah, I think Sam can definitely share his thoughts on this, but one of the things I know we are all sort of experiencing is the, is the rise of cyber threats, whether it's online from a business B2B perspective or as consumers just be our data and, and the data that they're generating and the companies that have access to it. So as the, the need to protect the data really grows companies and organizations really need to effectively detect, respond and protect their environments. And the best way to do this is through three ways, scale, speed, and cost. And so going back to the points I brought up earlier with conference, you can really gain real time data ingestion and enable those analytics that Sam talked about previously while optimizing for cost scale. So those are so doing all of this at the same time, as you can imagine, is, is not easy and that's where we Excel. >>And so the entire premise of data streaming is built on the concepts. That data is not static, but constantly moving across your organization. And that's why we call it data streams. And so at its core, we we've sort of built or leveraged that open source foundation of APA sheet Kafka, but we have rearchitected it for the cloud with a totally new cloud native experience. And ultimately for customers like SSE, we have taken a away the need to manage a lot of those operational tasks when it comes to Apache Kafka. The other thing we've done is we've added a ton of proprietary IP, including security features like role based access control. I mean, some prognosis talking about, and that really allows you to securely connect to any data no matter where it resides at scale at speed. And it, >>Can you talk about bar sticking with you, but some of the improvements, and maybe this is a actually question for Sam, some of the improvements that have been achieved on the SSC side as a result of the confluent partnership, things are much faster and you're able to do much more understand, >>Can I, can Sam take it away? I can maybe kick us off and then breath feel, feel free to chime in Lisa. The, the, the, the problem that we're talking about has been for us, it was a longstanding challenge. We're about a nine year old company. We're a high growth startup and data collection has always been in, in our DNA. It's at it's at the core of what we do and getting, getting the insights, the, and analytics that we synthesize from that data into customer's hands as quickly as possible is the, is the name of the game because they're trying to make decisions and we're empowering them to make those decisions faster. We always had challenges in, in the arena because we, well partners like confluent didn't didn't exist when we started scorecard when, when we we're a customer. But we, we, we think of it as a partnership when we found confluent technology and you can hear it from Barth's description. >>Like we, we shared a common vision and they understood some of the pain points that we were experiencing on a very like visceral and intimate level. And for us, that was really exciting, right? Just to have partners that are there saying, we understand your problem. This is exactly the problem that we're solving. We're, we're here to help what the technology has done for us since then is it's not only allowed us to process the data faster and get the analytics to the customer, but it's also allowed us to create more value for customers, which, which I'll talk about in a bit, including new products and new modules that we didn't have the capabilities to deliver before. >>And we'll talk about those new products in a second exciting stuff coming out there from SSC, bro. Talk about the partnership from, from confluence perspective, how has it enabled confluence to actually probably enhance its technology as a result of seeing and learning what SSC is able to do with the technology? >>Yeah, first of all, I, I completely agree with Sam it's, it's more of a partnership because like Sam said, we sort of shared the same vision and that is to really make sure that organizations have access to the data. Like I said earlier, no matter where it resides so that you can scan and identify the, the potential security security threads. I think from, from our perspective, what's really helped us from the perspective of partnering with SSE is just looking at the data volumes that they're working with. So I know a stat that we talked about recently was around scanning billions of records, thousands of ports on a daily basis. And so that's where, like I, like I mentioned earlier, our technology really excels because you can really ingest and amplify the volumes of data that you're processing so that you can scan and, and detect those threats in real time. >>Because I mean, especially the amount of volume, the data volume that's increasing on a year by basis, that aspect in order to be able to respond quickly, that is paramount. And so what's really helped us is just seeing what SSE is doing in terms of scanning the, the web ports or the data systems that are at are at potential risk. Being able to support their use cases, whether it's data sharing between their different teams internally are being able to empower customers, to be able to detect and scan their data systems. And so the learning for us is really seeing how those millions and billions of records get processed. >>Got it sounds like a really synergistic partnership that you guys have had there for the last year or so, Sam, let's go back over to you. You mentioned some new products. I see SSC just released a tax surface intelligence product. That's detecting thousands of vulnerabilities per minute. Talk to us about that, the importance of that, and another release that you're making. >>There are some really exciting products that we have released recently and are releasing at security scorecard. When we think about, when we think about ratings and risk, we think about it not just for our companies or our third parties, but we think about it in a, in a broader sense of an, of an ecosystem, because it's important to have data on third parties, but we also want to have the data on their third parties as well. No, nobody's operating in a vacuum. Everybody's operating in this hyper connected ecosystem and the risk can live not just in the third parties, but they might be storing processing data in a myriad of other technological solutions, which we want to understand, but it's really hard to get that visibility because today the way it's done is companies ask their third parties. Hey, send me a list of your third parties, where my data is stored. >>It's very manual, it's very labor intensive, and it's a trust based exercise that makes it really difficult to validate. What we've done is we've developed a technology called a V D automatic vendor detection. And what a V D does is it goes out and for any company, your own company or another business partner that you work with, it will go detect all of the third party connections that we see that have a live network connection or data connection to an organization. So that's like an awareness and discovery tool because now we can see and pull the veil back and see what the bigger ecosystem and connectivity looks like. Thus allowing the customers to go hold accountable, not just the third parties, but their fourth parties, fifth parties really end parties. And they, and they can only do that by using scorecard. The attack surface intelligence tool is really exciting for us because well, be before security scorecard people thought what we were doing was fairly, I impossible. >>It was really hard to get instant visibility on any company and any business partner. And at the same time, it was of critical importance to have that instant visibility into the risk because companies are trying to make faster decisions and they need the risk data to steer those decisions. So when I think about, when I think about that problem in, in managing sort of this evolving landscape, what it requires is it requires insightful and actionable, real time security data. And that relies on a couple things, talent and tech on the talent side, it starts with people. We have an amazing R and D team. We invest heavily. It's the heartbeat of what we do. That team really excels in areas of data collection analysis and scaling large data sets. And then we know on the tech side, well, we figured out some breakthrough techniques and it also requires partners like confluent to help with the real time streaming. >>What we realized was those capabilities are very desired in the market. And we created a new product from it called the tech surface intelligence. A tech surface intelligence focuses less on the rating. There's, there's a persona on users that really value the rating. It's easy to understand. It's a bridge language between technical and non-technical stakeholders. That's on one end of the spectrum on the other end of the spectrum. There's customers and users, very technical customers and users that may not have as much interest in a layman's rating, but really want a deep dive into the strong threat Intel data and capabilities and insights that we're producing. So we produced ASI, which stands for attack surface intelligence that allows customers to look at the surface area of attack all of the digital assets for any organization and see all of the threats, vulnerabilities, bad actors, including sometimes discoveries of zero day vulnerabilities that are, that are out in the wild and being exploited by bad guys. So we have a really strong pulse on what's happening on the internet, good and bad. And we created that product to help service a market that was interested in, in going deep into the data. >>So it's >>So critical. Go >>Ahead to jump in there real quick, because I think the points that Sam brought up, we had a great, great discussion recently while we were building on the case study that I think brings this to life, going back to the AVD product that Sam talked about and, and Sam can probably do a better job of walking through the story, but the way I understand it, one of security scorecards customers approached them and told them that they had an issue to resolve and what they ended up. So this customer was using an AVD product at the time. And so they said that, Hey, the car SSE, they said, Hey, your product shows that we used, you were using HubSpot, but we stopped using that age server. And so I think when SSE investigated, they did find a very recent HubSpot ping being used by the marketing team in this instance. And as someone who comes from that marketing background, I can raise my hand and said, I've been there, done that. So, so yeah, I mean, Sam can probably share his thoughts on this, but that's, I think the great story that sort of brings this all to life in terms of how actually customers go about using SSCs products. >>And Sam, go ahead on that. It sounds like, and one of the things I'm hearing that is a benefit is reduction in shadow. It, I'm sure that happens so frequently with your customers about Mar like a great example that you gave of, of the, the it folks saying we don't use HubSpot, have it in years marketing initiates an instance. Talk about that as some of the benefits in it for customers reducing shadow it, there's gotta be many more benefits from a security perspective. >>Yeah, the, there's a, there's a big challenge today because the market moved to the cloud and that makes it really easy for anybody in an organization to go sign, sign up, put in a credit card, or get a free trial to, to any product. And that product can very easily connect into the corporate system and access the data. And because of the nature of how cloud products work and how easy they are to sign up a byproduct of that is they sort of circumvent a traditional risk assessment process that, that organizations go through and organizations invest a, a lot of money, right? So there's a lot of time and money and energy that are invested in having good procurement risk management life cycles, and making sure that contracts are buttoned up. So on one side you have companies investing loads of energy. And then on the other side, any employee can circumvent that process by just going and with a few clicks, signing up and purchasing a product. >>And that's, and, and, and then that causes a, a disparity and Delta between what the technology and security team's understanding is of the landscape and, and what reality is. And we're trying to close that gap, right? We wanna close and reduce any windows of time or opportunity where a hacker can go discover some misconfigured cloud asset that somebody signed up for and maybe forgot to turn off. I mean, it's a lot of it is just human error and it, and it happens the example that Barra gave, and this is why understanding the third parties are so important. A customer contacted us and said, Hey, you're a V D detection product has an error. It's showing we're using a product. I think it was HubSpot, but we stopped using that. Right. And we don't understand why you're still showing it. It has to be a false positive. >>So we investigated and found that there was a very recent live HubSpot connection, ping being made. Sure enough. When we went back to the customer said, we're very confident the data's accurate. They looked into it. They found that the marketing team had started experimenting with another instance of HubSpot on the side. They were putting in real customer data in that instance. And it, it, you know, it triggered a security assessment. So we, we see all sorts of permutations of it, large multinational companies spin up a satellite office and a contractor setting up the network equipment. They misconfigure it. And inadvertently leave an administrator portal to the Cisco router exposed on the public internet. And they forget to turn off the administrative default credentials. So if a hacker stumbles on that, they can ha they have direct access to the network. We're trying to catch those things and surface them to the client before the hackers find it. >>So we're giving 'em this, this hacker's eye view. And without the continuous data analysis, without the stream processing, the customer wouldn't have known about those risks. But if you can automatically know about the risks as they happen, what that does is that prevents a million shoulder taps because the customer doesn't have to go tap on the marketing team's shoulder and go tap on employees and manually interview them. They have the data already, and that can be for their company. That can be for any company they're doing business with where they're storing and processing data. That's a huge time savings and a huge risk reduction, >>Huge risk reduction. Like you're taking blinders off that they didn't even know were there. And I can imagine Sam tune in the last couple of years, as SAS skyrocketed the use of collaboration tools, just to keep the lights on for organizations to be able to communicate. There's probably a lot of opportunity in your customer base and perspective customer base to engage with you and get that really full 360 degree view of their entire organization. Third parties, fourth parties, et cetera. >>Absolutely. Absolutely. CU customers are more engaged than they've ever been because that challenge of the market moving to the cloud, it hasn't stopped. We've been talking about it for a long time, but there's still a lot of big organizations that are starting to dip their toe in the pool and starting to cut over from what was traditionally an in-house data center in the basement of the headquarters. They're, they're moving over to the cloud. And then on, on top of that cloud providers like Azure, AWS, especially make it so easy for any company to go sign up, get access, build a product, and launch that product to the market. We see more and more organizations sitting on AWS, launching products and software. The, the barrier to entry is very, very low. And the value in those products is very, very high. So that's drawing the attention of organizations to go sign up and engage. >>The challenge then becomes, we don't know who has control over this data, right? We don't have know who has control and visibility of our data. We're, we're bringing that to surface and for vendors themselves like, especially companies that sit in AWS, what we see them doing. And I think Lisa, this is what you're alluding to. When companies engage in their own scorecard, there's a bit of a social aspect to it. When they look good in our platform, other companies are following them, right? So now all of the sudden they can make one motion to go look good, make their scorecard buttoned up. And everybody who's looking at them now sees that they're doing the right things. We actually have a lot of vendors who are customers, they're winning more competitive bakeoffs and deals because they're proving to their clients faster that they can trust them to store the data. >>So it's a bit of, you know, we're in a, two-sided kind of market. You have folks that are assessing other folks. That's fun to look at others and see how they're doing and hold them accountable. But if you're on the receiving end, that can be stressful. So what we've done is we've taken the, that situation and we've turned it into a really positive and productive environment where companies, whether they're looking at someone else or they're looking at themselves to prove to their clients, to prove to the board, it turns into a very productive experience for them >>One. Oh >>Yeah. That validation. Go ahead, bro. >>Really. I was gonna ask Sam his thoughts on one particular aspect. So in terms of the industry, Sam, that you're seeing sort of really moving to the cloud and like this need for secure data, making sure that the data can be trusted. Are there specific like verticals that are doing that better than the others? Or do you see that across the board? >>I think some industries have it easier and some industries have it harder, definitely in industries that are, I think, health, healthcare, financial services, a absolutely. We see heavier activity there on, on both sides, right? They they're, they're certainly becoming more and more proactive in their investments, but the attacks are not stopping against those, especially healthcare because the data is so valuable and historically healthcare was under, was an underinvested space, right. Hospitals. And we're always strapped for it folks. Now, now they're starting to wake up and pay very close attention and make heavier investments. >>That's pretty interesting. >>Tremendous opportunity there guys. I'm sorry. We are out of time, but this is such an interesting conversation. You see, we keep going, wanna ask you both where can, can prospective interested customers go to learn more on the SSC side, on the confluence side, through the AWS marketplace? >>I let some go first. >>Sure. Oh, thank thank, thank you. Thank you for on the security scorecard side. Well look, security scorecard is with the help of Colu is, has made it possible to instantly rate the security posture of any company in the world. We have 12 million organizations rated today and, and that, and that's going up every day. We invite any company in the world to try security scorecard for free and experience how, how easy it is to get your rating and see the security rating of, of any company and any, any company can claim their score. There's no, there's no charge. They can go to security, scorecard.com and we have a special, actually a special URL security scorecard.com/free-account/aws marketplace. And even better if someone's already on AWS, you know, you can view our security posture with the AWS marketplace, vendor insights, plugin to quickly and securely procure your products. >>Awesome. Guys, this has been fantastic information. I'm sorry, bro. Did you wanna add one more thing? Yeah. >>I just wanted to give quick call out leads. So anyone who wants to learn more about data streaming can go to www confluent IO. There's also an upcoming event, which has a separate URL. That's coming up in October where you can learn all about data streaming and that URL is current event.io. So those are the two URLs I just wanted to quickly call out. >>Awesome guys. Thanks again so much for partnering with the cube on season two, episode four of our AWS startup showcase. We appreciate your insights and your time. And for those of you watching, thank you so much. Keep it right here for more action on the, for my guests. I am Lisa Martin. We'll see you next time.

>>Okay, welcome back everyone. Cube's coverage here. Live on the floor at AWS summit, 2022, an in person event in San Francisco. Of course, AWS summit, 2022 in New York city is coming up this summer. The cube will be there as well. Make sure you check us out then too, but we day two of coverage had a great guest here. I Han VP of developer relations, Mongo DB, formally of AWS. We've been known each other for a long time doing, uh, developer relations at Mongo DB. Welcome to the queue. Good to see >>You. Thank to be here. Thanks for inviting me, John. It's great >>To, so Mongo DB is, um, first of all, stocks' doing really well right now. Businesswise is good, but I still think it's undervalue. A lot of people think is, is a lot more going huge success with Atlas. So congratulations to the team over there. Um, what's the update? What's the relationship withs, you know, guys have been great partners for years. What's the new thing. Yeah. >>So MongoDB Atlas obviously runs on several different major cloud providers, but AWS is the largest partner that we work with in the public cloud. So the majority of our Atlas workloads for our customers are running on the AWS platform. And just earlier this year, we announced a new strategic collaboration agreement with AWS. That's gonna further strengthen and deepen that partnership that we have with them. >>What's the main product value right now on the scale on, on Atlas, what's the drive in the revenue momentum. >>So, I mean, you know, there's a huge trend in the industry towards cloud managed databases, right? You look back 10, 15 years ago when we first met, most customers were only and operating their own data infrastructure, either running it in their own data centers, or maybe if they were really early using the primitives that cloud providers like AWS offered to run their databases in the cloud when Amazon launched RDS back in 2009, I think it was, we started to see this trend towards cloud managed databases. We followed that with our own Atlas offering back in 2016. And as Andy jazzy from AWS would say very often it's offloading that UND differentiated, heavy lifting, allowing developers to focus on building applications. They don't have to win and operate the data infrastructure. We do it for them, and that has proven incredibly popular amongst our customers. You know, Atlas route right now is growing at 50, sorry, 85% car year on year growth. >>You know, um, I've been following MongoDB for a long, long time. I mean, going back to the lamp stack days, you know, and you think about what Mongo has done as a product because of the developer traction, you know, Mongo can't do this, just keeps getting better every year. And, and the, I think the stickiness with developers is a real big part of that. Can you your view there cuz you're in VE relations. I mean, developers all love Mongo. They're teaching in school. People are picking up a side hustles, they're coding on it, using it all everywhere. I mean it's well known. >>There's a few different reasons for that. I think the main one is the, the document orientated model that we use, the document data models that are used by Mongo DB, just a net way for developers to work with data. And then, uh, we've invested in creating 16 first party drivers that allow developers using various different programming languages, whether that's JavaScript or Python or rust to integrate MongoDB, natively and idiomatic with their software. So it's very, very easy for a developer to pick up MongoDB, grab one of these drivers from their package manager of their choice and then build applications that natively manipulate data inside MongoDB, whether that's MongoDB Atlas or our enterprise edition on their own premises. They get a very consistent and very easy to, I easy to use developer experience with our, with our platform. >>Talk about the go to market with AWS. You guys also have a tightly coupled relationships. There's been announcements there recently. Uh, what's changing most right now that people should pay attention to. Well, >>The first thing is there's a huge amount of technical integration between MongoDB and AWS services. And that's the basis for many of our customers choosing to run Mon Mongo DB on AWS. We're active in 23 AWS regions around the world. And there's many other integration points as well, like cryptographic protection of Mongo MongoDB, stored data using Amazon cryptographic services, for example, or building serverless applications with AWS Lambda and MongoDB servers. So there's a ton of technical integration. Yeah, but what we started to work on now is go to market integration with AWS as well. So you can buy Mongo DB Atlas through AWS's marketplace. You can use the payer, you go offering to pay for it with your AWS bill. And then we're collaborating with AWS on migrations and other joint go to market activities as well. That >>Means get incentives, the sales people at AWS. >>Of course our moreover I mean, it's just really easy for customers, really easy for developers to consume. Yeah, they don't need to contract with MongoDB. They can use their existing AWS contracting, their existing discounting relationships and pre purchasing arrangements with AWS to consume Atlas. >>It's the classic meet the customers where they >>Are exactly right. Meet the developer where they are and meet the customers where they are now with this new model as well. >>Yeah. I love marketplace. I think it's been great. You know, even with its kind of catalog and vibe, I think it's gonna get better and better, uh, over there teams doing good work. Um, and it's easy to consume. That's key. >>Yeah. Super easy. Reduce that friction and make it real easy for developers to adopt this. Right. >>Talk about some of the top customers that you guys share with AWS. What are some of the customers you guys have together and what the benefits of the >>Relationship joint references that we talk about? A lot, one of them is Shutterfly. So in the photographic products area, they built a eCommerce offering with MongoDB and AWS. The second is seven 11 with seven 11. We're doing a lot in the mobile space. So edge applications, we've got a feature in MongoDB Atlas that allows you to synchronize data with databases on mobile devices. Those can be phones point of sale devices or handheld devices that might be used in the parcel industry, for example. So seven 11 using us in that way. And then lastly with Pitney Bowes, we've got a big digital transformation project with Pitney Bowes where they've reimagined their, uh, postage and packaging services, delivering those to their customers, using MongoDB as a data store as well. >>I wanna get in some of the trends, you've got a great per you know, you know, Mongo from Amazon side and now you're there. Um, Mongo's, as you pointed out has, has been around for a long time. What are some of the stats? I mean, how many customers, how many countries? Well, it's pretty massive >>Mind. We've got almost quarter of a billion downloads today, 240 million MongoDB downloads since we launched the first product <laugh>, we've got 33,000 active customers that are using MongoDB Atlas today and we're running well over a million free tier clusters on MongoDB Atlas across all of the different providers where we operate the service as well. So these numbers are, you know, mind blowing in terms of scale. Uh, but of course at the core of that is operational excellence. Customers love Mongo DBS because they don't have to operate it themselves. They don't have to deal with fairly conditions. They don't have to deal with scaling. They don't have to deal with deployment. We all, we do all of those things as part of the service offering and customers get an endpoint that they can use with their applications to store and retrieve data reliably. And with consistently high perform, >>You know, it's, you know, in the media, something has to be dead. Someone's the death of the iPhone, the death of this, nothing that really dies. Mongo DB has always been kind of like talked about, well, it doesn't scale on the high end. Of course, Oracle was saying that, I mean, all the, all the big database vendors were kind of throwing darts at, at Mongo, uh, DB, uh, but it kept scaling. Atlas is a whole nother. Could you just unpack that a little bit more? Why is it so important? Because scale is just, I mean, it's, it's horizontal, but it's also performant. >>Exactly. Right. So with, uh, Mongo DB's document access model that I've described already, you break some of the limitations that exist inside traditional relational databases. So, you know, they don't scale well, if you've got high concurrent and see of data access, and they're typically difficult and expensive to scale because you need to share data. Once you grow beyond individual cluster nodes, and you'll know that all relational databases suffer from these same kinds of issues with non relational systems, no SQL systems like MongoDB, you have to think a little bit more about design at the beginning. So designing database to cater for the different access patterns that you have, but in return for that upfront preparation, that design work, you get near limitless, scalability and performance will scale nearly linearly with that scalability as well. So very much more high performance, very much more simplicity for the developer as their database gets larger and their cluster gets larger to support it. >>Yeah. You know, Amazon web service has always had an a and D jazz. We talk to us all the time, every interview I've done with Swami and Matt wood or whoever on the team and executive levels always said the same thing. There's not one database to rule the world, right? Obvious you're talking about Oracle, but even within AWS customers, they're mixing and matching databases based on use cases. So in distributed environment, they're all working together. So, um, you guys fit nicely into that. So how does that, >>I think strategy slightly counterbalances that so, you know, they would say use the specific tool for the specific task that you have in hand. Yeah. What we try to focus on is creating the simple and most effective developer experience that we can, and then supporting different facets to the product in order to allow developers to different use cases. A really good example with something like MongoDB Atlas search. So we integrated Apache Luine into MongoDB Atlas. Customers can very simply apply Apache Luine search indexes to the data that they've got in MongoDB. And then they can interact with that search data using the same drivers as an API. Yeah, yeah. That they use for regular queries. So if you want to run search on your application data, you don't need a separate open search or elastic search cluster, just turn on MongoDB Atlas search and use that, that search facet. So it's interest and we have other capabilities that it's >>Vertically integrating inside within Mongo, >>Correct? Yes. That's better. Yeah. With the guy, all of creating a really simple and effective developer experience, boosting developer productivity and helping developers get more done in less time. >>You mentioned serverless earlier, what's the serverless angle with AWS when Mongo, >>Is there one? Yeah. So we have MongoDB serverless currently in preview, uh, has the same kind of characteristics that you would, or the characteristics that you would expect from a serverless data base. So consumption based model, you provision an endpoint and that will scale elastically in accordance with your usage and you get billed by consumption units so much like the serverless paradigm that we've seen delivered by AWS, the same kind of model for Mongo, DB, Atlas serverless. >>What, what attracted you to Mongo DBS? So you knew them before, or you moved over there. Um, what's going on there? What's the culture like right now? Oh, >>The culture's great. I mean, it's a much smaller company than AWS where I was before, you know, it's a very large organization. And one of the things that I really like about MongoDB is, as I've said earlier, we can serve the different use cases that a developer might have with a single product, with different aspects, to it, different facets to it. Uh, and it's a really great conversation to have with a, with a developer, with a developer customer, to be able to offer one thing that helps them solve five or six different problems that have traditionally been quite hard for them to wrestle with quite difficult for them to, to deal with. And then we've got this focus on developer experience through these driver packages that we have as well. So it's really great to have as a developer relations pro have that kind of tooling in my kit bag that can help developers become more effective. >>Talk about tooling, cuz you know, I always have, uh, kind of moments where I waffle between more. I love platforms, tools are being over overused, too many tools tool with the tool, you know, the expressions, but we're seeing from developers, the ones that don't want to go into the hood, we serverless plays beautifully. Yep. They want tools. They do. And, and the, the new engineering developers that are coming outta college and universities, they love tools. >>Yeah. And we actually have quite a few of those built into Mongo, DB Atlas. So inside Mongo, DB Atlas, we've got things like an index optimizer, which will suggest the best way that you might index your data for better perform months inside MongoDB, running on Atlas, we've got a data Explorer, which is much like another product that we've got called MongoDB compass that allows you to see and manipulate the data that you have stored within your database natively within the Atlas interface. Uh, and then we also have, uh, whole slew of different metrics, monitoring capabilities built into the platform as well. So these are aspects of Atlas that developers can take advantage of. And then over on the client side, visual studio code plugins. Yeah. So you can manipulate and operate with data directly inside visual studio code, which is obviously the most common and popular IDE out there today, as well as integration with things like infrastructure is code tools. So we support cloud formation for provisioning. We have CDK constructs inside. Yeah. The CDK construct library. We also have a lot of customers using Terraform to provision MongoDB across both AWS and other providers. So having that developer tooling of course is super important. Yeah. Aspect of the developer experience, trying to >>Build out deploying observability is a big one. How does that fit in? Cuz you knew need to talk and not only measure everything here, but talk to other systems. >>Yeah. So we recently announced a provider for Prometheus and Grafana. So we can emit metrics into those providers. Obviously CNCF projects, very common and popular inside customers that are running on Kubernetes. We've got a Kubernetes operator for MongoDB Atlas as well. Good. So you can provision MongoDB Atlas from within Kubernetes as well as having our own native metrics directly within Atlas as well. >>Ian you're crushing it. You got all the, the data, the fingertips. Are you gonna be at Cuban this year? Uh, >>I will be, but some of our team members will definitely be there. >>Yeah, we'll be at, uh, EU. The cube will be there. Great. Thanks for coming on. Appreciate the insight final world. I'll give you the last word. Tell the audience what's going on. What's at Mongo DB. What should they pay attention to? If they've used Mongo and are aware of it? What's the update. What's >>The so you should come to MongoDB world actually in New York at the beginning of June, June 7th, the ninth in the Javit center in New York. Gonna have our own show there. And of course we'd love to see you there. >>Okay. Cube comes here day two of eight, us summit, 2020, this Cub I'm John for your host. Stay with us more. Our coverage as day two winds down. Great coverage.

(bright upbeat music) >> Hey everyone. Welcome to this "CUBE Conversation" featuring Dremio. I'm your host, Lisa Martin. And I'm excited today to be joined by Mark Lyons the VP of product management at Dremio. Mark thanks for joining us today. >> Hey Lisa, thank you for having me. Looking forward to the top. >> Yeah. Talk to me about what's going on at Dremio. I had the chance to talk to your chief product officer Tomer Shiran in a couple months ago but talk to us about what's going on. >> Yeah, I remember that at re:Invent it's been an exciting few months since re:Invent here at Dremio and just in the new year we raised our Series E since then we ran into our subsurface event which we had over seven, 8,000 registrants and attendees. And then we announced our Dremio cloud product generally available including Dremio Sonar, which is SQL query engine and Dremio Arctic in public preview which is a better store for the lakehouse. >> Great. And we're going to dig into both of those. I saw that over 400 million raised in that Series E raising the valuation of Dremio to 2 billion. So a lot of growth and momentum going on at the company I'm sure. If we think about businesses in any industry they've made large investments in data warehouses, proprietary data warehouses. Talk to me about historically what they've been able to achieve, but then what some those bottlenecks are that they're running into. >> Yeah, for sure. My background is actually in the data warehouse space. I spent over the last eight, maybe close to 10 years and we've seen this shift go on from the traditional enterprise data warehouse to the data lake to the the last couple years is really been the time of the cloud data warehouse. And there's been a large amount of adoption of cloud data warehouses, but fundamentally they still come with a lot of the same challenges that have always existed with the data warehouse, which is first of all you have to load your data into it. So that data's coming from lots of different sources. In many cases, it's landing in a files in the data lake like a repository like S3 first. And then there's a loading process, right? An ETL process. And those pipelines have to be maintained and stay operational. And typically as the data warehouse life cycle of processing moves on the scope of the data that consumers get to access gets smaller and smaller. The control of that data gets tighter and change process gets heavier, and it goes from quick changes of adding a column or adding a field to a file to days if not weeks for businesses to modify their data pipelines and test new scenarios offer new features in the application or answer new questions that the business is interested you know, from an analytics standpoint. So typically we see the same thing even with these cloud data warehouses, the scope of the data shrinks, the time to get answers gets longer. And when new engines come along the same story we see, and this is going on right now in the data warehouse space there's new data that are coming and they say, well we're a thousand faster times faster than the last data warehouse. And then it's like, okay, great. But what's the process? The process is to migrate all your data to the new data warehouse, right? And that comes with all the same baggage. Again, it's a proprietary format that you load your data into. So I think people are ready for a change from that. >> People are not only ready for a change, but as every company has to become a data company these days and access to real time data is no longer a nice to have. It's absolutely essential. The ability to scale the ability to harness the value from as much data as possible and to do so fast is real really table stakes for any organization. How is Dremio helping customers in that situation to operationalize their data? >> Yeah, so that's why I was so intrigued and loved about Dremio when I joined three, four, five months back. Coming from the warehouse space, when I first saw the product I was just like, oh my gosh, this is so much easier for folks. They can access a larger scope of their data faster, which to your point, like is table stakes for all organizations these days they need to be able to analyze data sooner. Sooner is the better. Data has a halflife, right? Like it decays. The value of data decays over time. So typically the most valuable data is the newest data. And that all depends on what we're the industries we're talking about the types of data and the use cases, but it's always basically true that newer data is more valuable and they need to be able to analyze as much of it as possible. The story can't be, no, we have to wait weeks or months to get a new data source or the story can't be you know, that data that includes seasonality. You know, we weren't able to keep in the same location because it's too expensive to keep it in the warehouse or whatever. So for Dremio and our customers our story is simple, is leverage the data where it is so access data in all sorts of sources, whether it's a post press database or an S3 bucket, and don't move the data don't copy the data, analyze it in place. And don't limit the scope of the data you're trying to analyze. If you have new use cases you have additional data sets that you want to add to those use cases, just bring them in, into S3 and you are off to the races and you can easily analyze more data and give more power to the end user. So if there's a field that they want to calculate the simple change convert this miles field, the kilometers well, the end users should be empowered to just make a calculation on the data like that. That should not require an entire cycle through a data engineering team and a backlog and a ticket and pushing that to production and so forth which in many cases it does at many organizations. It's a lot of effort to make new calculations on the data or derive new fields, add a new column and so forth. So Dremio makes the data engineers life easier and more productive. It also makes the data consumers life much easier and happier, and they can just do their job without worrying about and waiting. >> Not only can they do their job but from a business, a high level perspective the business is probably has the opportunity to be far more competitive because it's got a bigger scope of data, as you mentioned, access to it more widely faster and those are only good things in terms of- >> More use cases, more experiments, right? So what I've seen a lot is like there's no shortage of ideas of what people can do with the data. And projects that might be able to be undertaken but no one knows exactly how valuable that will be. How whether that's something that should be funded or should not be funded. So like more use cases, more experiments try more things. Like if it's cheap to try these data problems and see if it's valuable to the business then that's better for the business. Ultimately the business will be more competitive. We'll be able to try more new products we'll be able to have better operational kind of efficiencies, lower risk all those things. >> Right. What about data governance? Talk to me about how the Lakehouse enables that across all these disparate data volumes. >> I think this is where things get really interesting with the Lakehouse concept relative to where we used to be with a data lake, which was a parking ground for just lots of files. And that came with a lot of challenges when you just had a lot of files out there in a data lake, whether that was HDFS, right. I do data lake back in the day or now a cloud storage object, storage data lake. So historically I feel like governance, access authentication, auditing all were extremely challenging with the data lake but now in the modern kind of lake in the modern lakehouse world, all those challenges have been solved. You have great everything from the front of the house with all and access policies and data masking everything that you would expect through commits and tables and transactions and inserts and updates and deletes, and auditing of that data able to see, well who made the changes to the data, which engine, which user when were they made and seeing the whole history of a table and not just one, not just a mess of files in a file store. So it's really come a long way. I feel like where the renaissance stage of the 2.0 data lakes or lakehouses as people call them. But basically what you're seeing is a lot of functionality from the traditional warehouse, all available in the lake. And warehouses had a lot of governance built in. And whether that is encryption and column access policies and row access policies. So only the right user saw the right data or some data masking. So that like the social security was masked out but the analyst knew it was a social security number. That was all there. Now that's all available on the lakehouse and you don't need to copy data into a data warehouse just to meet those type of requirements. Huge one is also deletes, right? Like I feel like deletes were one of the Achilles heels of the original data lake when there was no governance. And people were just copying data sets around modifying data sets for whatever their analytics use case was. If someone said, "Hey, go delete the right. To be forgotten GDPR." Now you've got Californias CCPA and others all coming online. If you said, go delete this per you know, this records or set of records from there from a lake original lake. I think that was impossible, probably for many people to do it with confidence, like to say that like I fully deleted this. Now with the Apache like iceberg cable format that is stores in the lakehouse architecture, you actually have delete functionality, right? Which is a key component that warehouses are traditionally brought to the table. >> That's a huge component from a compliance perspective. You mentioned GDPR, CCPA, which is going to be CPRA in less than a year, but there's so many other regulations data privacy regulations that are coming up that the ability to delete that is going to be table stakes for organizations, something that you guys launched. And we just have a couple minutes left, but you launched I love the name, the forever free data Lakehouse platform. That sounds great. Forever Free. Talk to me about what that really means is consisting of two products the Sonar and Arctic that you mentioned, but talk to me about this Forever Free data Lakehouse. >> Yeah. I feel like this is an amazing step forward in this, in the industry. And because of the Dremio cloud architecture, where the execution and data lives in the customer's cloud account we're able to basically say, hey, the Dremio software the Dremio service side of this platform is Forever Free for users. Now there is a paid tier but there's a standard tier that is truly forever free. Now that that still comes with infrastructure bills from like your cloud provider, right? So if you use AWS, you still have an S3 bill like for your data sets because we're not moving them. They're staying in your Amazon account in your S3 bucket. You still do still have to pay for right. The infrastructure, the EC2 and the compute to do the data analytics but the actual softwares is free forever. And there's no one else in our space offering that at in our space, everything's a free trial. So here's your $500 of credit. Come try my product. And what we're saying is with this kind of our unique architectural approach and this is what I think is preferred by customers too. You know, we take care of all the query planning all the engine management, all the administrative the platform, the upgrades fully available zero downtime platform. So they get all the benefits of SaaS as well as the benefits of maintaining control over their data. And because that data staying in their account and the execution of the analytics is staying in their account. We don't incur that infrastructure bill. So we can have a free forever tier a forever free tier of our platform. And we've had tremendous adoption. I think we announced this beginning of March first week of March. So it's not even the end of March. Hundreds and hundreds of signups and many customers actively are users actively on the platform now live querying their data >> Just kind of summarizes the momentum that Dremio we seeing. Mark, thank you so much. We're out of time, but thanks for talking to me- >> Thank you. >> About what's new at Dremio. What you guys are doing. Next time, we'll have to unpack this even more. I'm sure there's loads more we could talk about but we appreciate that. >> Yeah, this was great. Thank you, Lisa. Thank you. >> My pleasure for Mark Lyons. I'm Lisa Martin. Keep it right here on theCUBE your leader in high tech hybrid event coverage. (upbeat music)

>>Welcome everybody to the cubes live coverage of AWS reinvent 2021 we're in person for a real event to live stages, to remain studios, probably the industry's most important hybrid event of the year. Over 100 guests on the cube at AWS reinvent 2021. All right, so let's get right into it. This is 10 years of re-invent. We've been here for nine 20 plus thousand people. Everybody is coming from every industry, learning from each other, learning from AWS, learning from the things that you're observing as well. Of course, last year during the lockdown reinvent was purely virtual. Obviously the pandemic change the world for everyone. We went from thinking about the future to really just trying to make it through the next few weeks. Everybody was scrambling. What do we do? How do we pivot? How do we survive? Mode Wiley gloves, all a very bad thing, but it actually help accelerate customer journey to cloud. We saw a ton of digital innovation, >>Really looking to data and analytics and machine learning to find new opportunities, >>Some returned to the office, but now they have the tools to be able to stay at home and be more flexible. Pandemic has proven a lot of people that digital works here, but it's suppose, but it doesn't work. Every company has to be a data company. It's actually a mustache. And so is location data. They go >>Typically that data with some point we'll come back into the cloud into S3, but it will be used to do heavy duty training. And then those models get pushed back up to the us >>On the ground of all the grown under the drone. It instances where they either can't move or won't move, but their resources into the cloud. There's no conductivity latency comes up a lot in edge conversations. The value is the immediacy. Understanding that value specific to the moment it's being captured. You know, the edge, these are resorts. That's more DME they've done the full cloud in AWS regions. Cloud is something that's changing the world for a lot of customers. Obviously there's cost reasons. There's MNA reasons, remove complexity from users and make it really easy for new workloads to be added to the system. You can leverage all their AIML tools, IOT edge container, block chain, where the most complete portfolio of any cloud provider when it comes to storage and data virtually infinite resources. Now at your disposal. The last thing we wanted to be is a server huggers. We're ready to accelerate these transformations. We actually had an enterprise migrate one of the largest production applications in just four days. How do we secure our data? When we start having infrastructure in a variety of locations >>And decide who's allowed to see it, what they're allowed to see. And if you want to change your mind, you can do that instantly. And all access can be locked. >>Governance should be an enabler, not an inhibitor. You then have the modern apps. The modern apps to me are inherently iterative and inherently scalable and a amenable to change. It's not just the applications, analytics, it's AI, it's machine learning. Human interpretation is pretty talent, I would say. So that's where he comes in to augment the intelligence that we have in terms of extracting information. Now it's true infrastructure as code. If I'm spinning up servers, that means that's a low level primitive. That should be automated. Right? When we look at how we are to move up the stack and other areas, we just continue to listen to our customers. What is your business strategy and your business vision, and how do we design the technology solutions? Working back from that, using tools like insights in order to give customers advice. If you have found good, you have an idea. >>You know, the thing that he wants to disrupt, and we're letting people do that in different ways, giving control back to the end user, whether that's a consumer or an enterprise, they want to say I like it. And then I get the market is deciding I'm going to go with my software runs the best that's right. I get distribution that create value and runs fast. Our vision is eventually to have a community of about 10 to 20 million of the most extraordinary doers in the world. But really the goal is how can customers get value as quickly as possible for as low cost as possible. Point it out black and white. This is your cost. Now this is what your cost is going to be. And everybody's happy. There's a huge ecosystem of data tools out there. That's right, that you guys have as partners that want to snap together. >>So the trend is things are starting to snap together. We talk about the power of three, which is you bring a JSI partner, you bring an ISV partner, you bring AWS, you create that power of three and you take it to all customers. And you can start for a few pennies scale up, you know, into the petabytes and beyond. They have a better don't think that you wish you were 20. Again, with all this code out there, the next 15 years about opportunity transforming faster decision making your work now is to really drive the data cloud, get adoption of build that Supercloud we call it container first, cloud native first, and then make sure that everybody's kind of on that same ship running in the same direction. Five to 15% only of workloads have moved to the cloud. We're at the Dawn of the era of cloud. >>There's plenty of room for a lot of players. They can't hire enough developers. So this is the other thing that's happened during the pandemic. The war for talent is on talent is truly evenly split around the world. But what is not as opportunity in order to enable talent, do you need to educate them? Our skill builder platform provides 500 free training courses in 16 languages press 200 countries. We attract, we retain, we grow. The opportunity for innovation is limitless, and it's all about we're wrapping up four days of coverage. Two sets you go to the events and expect the signal from the noise. Stay tuned because there's always more on the cube. The global leader in live tech coverage. We'll see you next time.

(upbeat music) >> Hey everyone. Welcome to theCUBE's coverage of Women In Tech, International Women's Day 2022. I'm your host, Lisa Martin. I have two guests from AWS here with me. Carolina Pina joins us, the head of Enterprise Enablement for LATAM and Laura Alvarez Modernel is here as well, Public Sector Programs Manager at AWS. Ladies, it's great to have you on theCUBE. >> Nice to meet you. >> Thank you for having us. >> Carolina, let's start with you. Talk to me a little bit about your role, what it is that you're doing there. >> So my role in AWS is to actually create mechanisms of massive training to try to close the talent gap that we have in the region. And when I mentioned talent gap, I'm talking about obviously digital and cloud-computing skills. So that's, that's, in a nutshell what my role entails. >> Lisa: Got it. How long have you been in that role? Just curious. >> So I've been at AWS a little bit over, over two years. I was actually in the public sector team when I joined, leading the education vertical for Latin American Canada. And I recently joined the commercial sector now leading these massive training efforts for the region for LATAM. >> And Laura, you're in public sector. Talk to me a little bit about your role. >> Yes, I'm in public sector. I'm also based in Buenos Aires, Argentina. So yeah, I'm from Latin America, and I lead educational and community impact programs in the Southern cone of Latin America. I also lead diversity, equity and inclusion efforts and I'm part of the Women at Amazon global board. That's our affinity group to make sure we make efforts towards building a more equal world. And on a personal note I'm really passionate about the topic of gender equality because I truly think it affects us all as women and as Latins. So that's something that I'm always interested in collaborating with. >> Lisa: Excellent. Carolina back to you. If we think about from an enablement perspective how is AWS partnering with its customers and its partners to train and employ women particularly in technology? >> Oh, sure. Lisa, so it's not a surprise. We, like I mentioned, you know we have a big cloud skills, talent gap in the region. In fact, you know, 69% of companies have reported talent shortages and difficulty hiring. So, and this represents a 15 year high. So, many of these companies are actually, you know, our own commercial customers. So they approach us saying, you know, asking for for support training and developing their talent. So like I mentioned, in my role I create massive training efforts and initiatives. So we always take into consideration women, minorities, underrepresented community, and not just for the current talent, meaning like the people that are currently employed, but also to ensure that we are proactively implementing initiatives to develop a talent of younger you know, a younger generation and a talent. So we can, you know, to inspire them and, and ensure that they, that we're seeing them represented in companies like AWS, you know and our customers, and in our partners. And obviously we, when we sit down with customers to craft these massive trainings you know, leveraging their ecosystems and communities, we actually try to use all our AWS training and certification portfolio which includes, you know, in live in class with live in structures, in classroom trainings. We also have our AWS Skill Builder platform which is the platform that allows us to, you know to reach a broader audience because it has, you know over 500 free and on-demand classes. And we also have a lot of different other programs that touches in different audiences. You know, we have AWS re/Start for underrepresented, and underemployed minorities. We also have AWS Academy, which is the program that we have for higher education institutions. And we have AWS, you know, Educate which also touches, you know, cloud beginners. So in every single of these programs, we ensure that we are encompassing and really speaking to women and developing training and developing women. >> Lisa: That's a great focus there. Laura, talk to me about upskilling. I know AWS is very much about promoting from within. What are some of the things that it's doing to help women in Latin America develop those tech skills and upskill from where, maybe where they are now? >> Well, Lisa, I think that is super interesting because there's definitely a skills gap problem, right? We have all heard about. And what's funny is also that we have this huge opportunity in Latin America to train people and to help further develop the countries. And we have the companies that need the talent. So why is there still a gap, right? And I think that's because there's no magic solution to solving this problem. No, like epic Hollywood movie scene that it's going to show how we close the gap. And it takes stepping out of our comfort zone. And as Carolina mentioned, collaborating. So, we at AWS have a commitment to help 29 million people globally to grow their technical skills with free cloud-computing skills training by 2025. I know that sounds a lot through educational programs but we do have as Carolina mentioned, a Skill Builder you can go into the website for free, enter, choose your path, get trained. We have Academy that we implement with universities. Re/Start that is a program that's already available in Argentina, Brazil, Chile, Colombia, Mexico, Peru, and Costa Rica. So there are a lot of opportunities, but you also mentioned something else that I would like to dive a bit deeper that is Latin American women. And yesterday we had the opportunity to record a panel about intersectionality with three amazing Latin women. And what we have to learn from that is that these are two minorities that intersect, right. We're talking about females that are minority. Latinas are minority. And in tech, that is also something that is even bigger minority. So there are more difficulties there and we need to make sure that we are meeting that talent that is there that is in Latin America, that exists. We know for sure we have unicorns in Latin America that are even AWS customers like Mercado Libre, and we have to meet them with the opportunities. And that's why we created a program that came from identifying how this problem evolves in Latin America, that there is a lack of confidence in women also that they don't feel prepared or equipped. There is a cultural component why we don't choose tech careers. And we partner with universities, more than 12 universities in Latin America with the International American Development Bank as well to create tech skills that's a free five weeks program in order to get students and get female in Latin America, into the tech world. And we also have them with mentorship. So I think that is an opportunity to truly collaborate because we as AWS are not going to solve these by ourselves, right? We need everyone pitching in on that. >> Lisa: Right. It's absolutely a team effort. You mentioned something important in terms of helping women, and especially minorities get out of their comfort zone. Carolina, I'm curious when you're talking with women and getting them into the program and sharing with them all of the enablement programs that you have, how do you help them be confident to get out of that comfort zone? That's a hard thing to do. >> Yeah, no, for sure. For sure, Lisa, well, I, you know, a lot of times actually I use myself as an example because, you know, I studied engineering and industrial systems engineering many years ago. And you know, a lot of my career has been in in higher education and innovation and startups. And as I mentioned in the intro I've been at AWS for a little bit over two years. So I, my career has not been in cloud and I recently joined the cloud. So I actually had to go through our own trainings and get our own certifications. So I, that's, you know a lot of times I actually, I use my own example, so people understand that you don't have to come from tech, you don't have to come, you can actually be a non-tech person and, and also see the the benefits of the cloud. And you don't have to only, you know, learn cloud if you're in the IT department or in an IT team. So sometimes, I also emphasize that the cloud and the future is absolutely the cloud. In fact, the world economic foreign, you know teaches us that cloud-computing is that the technology that's going to be mostly adopted by 2025. So that's why we need to ensure that every single person, women and others are really knowledgeable in the cloud. So that's why, you know, technical and untechnical. But I, you know, I use myself as an example for them to say, you know, you can actually do it. And obviously also I collaborate with Laura and a lot of the women at Amazon Latin America Group to also you know, ensure that we're doing webinars and panels. So we show them ourselves as role model like, Laura is an incredible role model for our community. And so it's also to to show examples of what the possibilities are. And that's what we do. >> Lisa: I love that you're sharing >> And can I make a note there also? >> Please, yes. >> To add to that. I think it also requires the companies and the, and the private sector to get out of their comfort zone, right? Because we are not going to find solutions doing what we are already doing. We truly need to go and get near these persons with a new message. Their interest is there in these programs we have reached more than 3,000 women already in Latin America with tech skills. So it's not that women are not interested. It's like, how do we reach them with a message that resounds with them, right? Like how we can explain the power of technology to transform the world and to actually improve their communities. I think there's something there also that we need to think further of. >> It's so important. You know, we say often when we're talking about women in tech, that she needs to see what she can be or if she can't see it, she can't be it. So having those role models and those mentors and sponsors is absolutely critical for women to get, I call it getting comfortably uncomfortable out of that comfort zone and recognizing there's so many opportunities. Carolina, to your point, you know, these days every company is a tech company, a data company whether you're talking about a car dealer, a grocery market. So your point about, you know, and obviously the future being cloud there's so much opportunity that that opens up, for everybody really, but that's an important thing for people to recognize how they can be a part of that get out of their comfort zone and try something that they maybe hadn't considered before. >> Yes. And, actually, Lisa I would love to share an example. So we have a group, O Boticário, which is one of our customers one of the, the lead retails in Brazil. And they've been a customer of AWS since 2013 when they realized that, you know the urgency and the importance of embracing state of the art technology, to your point, like, you know this is a retail company that understands that needs to be, you know embrace digital transformation, especially because, you know they get very busy during mother's days and other holidays during the year. So they realized that they, instead of outsourcing their IT requirements to technology experts they decided to actually start developing and bringing the talent, you know within itself, within, you know, technology in-house. So they decided to start training within. And that's when we, obviously we partnered with them to also create a very comprehensive training and certification plan that started with, you know a lot of the infrastructure and security teams but then it was actually then implemented in the rest of the company. So going back to the point like everybody really needs to know. And what we also love about O Boticário is they they really care about the diversion and inclusion aspect of this equation. And we actually collaborated with them as well through this program called Desenvolve with the Brazilian government. And Desenvolve means developing Portuguese and they this program really ensures that we are also closing that gender and that race gap and ensuring that they're actually, you know, developing talent in cloud for Brazil. So we, you know, obviously have been very successful with them and we will continue to do even more things with them particular for this topic. >> Lisa: I've always known how customer focused AWS is every time we get to go to re:Invent or some of the events but it's so nice to hear these the educational programs that you're doing with customers to help them improve DEI to help them enable their own women in their organizations to learn skills. I didn't realize that. I think that's fantastic very much a symbiotic part of AWS. If we think about the theme for this year's International Women's Day, Breaking The Bias I want to get both of your opinions and Laura we'll start with you, what that means to you, and where do you think we are in Latin America with breaking the bias? >> Well, I think breaking the bias is the first step to truly being who we are every day and being able to bring that to our work as well. I think we are in a learning curve of that. The companies are changing culturally, as Carolina mentioned we have customers that are aware of the importance of having women. And as we say at AWS not only because there is a good business reason because there is, because there are studies that show that we can increase the country's CPD, but also because it's important and it's the right thing to do. So in terms of breaking the bias I think we are learning and we have a long way to go. I talked a bit earlier about intersectionality and that is something that is also important to highlight, right? Because we are talking about females but we are also talking about another minorities. We're talking about underrepresented communities, Indigenous People, Latins. So when these overlap, we face even bigger challenges to get where we want to get, right? And to get to decision making places because technology is transforming the ways we take decisions, we live, and we need someone like us taking those decisions. So I think it's important at first to be aware and to see that you can get there and eventually to start the conversation going and to build the conversation, not to just leave it but to make sure we hear people and their input and what they're going through. >> Lisa: Yes. We definitely need to hear them. Carolina, what's your take on breaking the bias and where do you from your experience, where do you think we are with it? >> Yeah, no, I'm as passionate as Laura on this topic. And that's why we, you know we're collaborating in the Women at Amazon Latin America Chapter, because we're both very, I think breaking the bias starts with us and ourselves. And we are very proactive within AWS and externally. And I feel it's also, I mean, Lisa, what we've been doing is not only, obviously gathering you know, the troops and really making sure that, that we have very aggressive goals internally, but also bringing you know, bringing our male counterparts, and other, you know, other members of the other communities, because the change, we're not going to make it alone. Like the change where it is not women only talking to women is going to make the change. We actually need to make sure the male and other groups are represented. And the dialogue that they're that we're very conscious about that. And I feel like we're seeing more and more that the topic is becoming more of a priority not only within AWS and Amazon but we also see it because now that I meet with when I meet with customers around the region they really want to see how we can collaborate in these diversion and inclusion initiatives. So I think we are breaking the bias because now this topic is more top of mind. And then we are being more proactively addressing it and and training people and educating people. And I feel we're really in a pivoted point where the change that we've really been wanting to we will see in the next you know, few years which is very exciting. >> Lisa: Excellent, and we'll see that with the help of women like you guys. Thank you so much for joining me today, talking about what you're doing, how you're helping organizations across AWS's ecosystem, customers, partners, and helping, of course, folks from within you, right. It's a holistic effort, but we are on our way to breaking that bias and again, I thank you both for your insights. >> Thank you. >> Thank you, Lisa, for the opportunity. >> My pleasure. For Carolina Pina and Laura Alvarez Modernel, I'm Lisa Martin. You're watching theCUBE's coverage of Women in Tech, International Women's Day 2022. (upbeat music)

(upbeat music) >> Oracle continues to pursue a multi-mode converged database strategy. The premise of this all in one approach is to make life easier for practitioners and developers. And the most recent example is the Oracle database API for MongoDB, which was announced today. Now, Oracle, they're not the first to come out with a MongoDB compatible API, but Oracle hopes to use its autonomous database as a differentiator and further build a moat around OCI, Oracle Cloud Infrastructure. And with us to talk about Oracle's MongoDB compatible API is Gerald Venzl, who's a distinguished Product Manager at Oracle. Gerald was a guest along with Maria Colgan on the CUBE a while back, and we talked about Oracle's converge database and the kind of Swiss army knife strategy, I called it, of databases. This is dramatically different. It's an approach that we see at the opposite end of the the spectrum, for instance, from AWS, who, for example, goes after the world of developers with a different database for every use case. So, kind of picking up from there, Gerald, I wonder if you could talk about how this new MongoDB API adds to your converged model and the whole strategy there. Where does it fit? >> Yeah, thank you very much, Dave and, by the way, thanks for having me on the CUBE again. A pleasure to be here. So, essentially the MongoDB API to build the compatibility that we used with this API is a continuation of the converge database story, as you said before. Which is essentially bringing the many features of the many single purpose databases that people often like and use, together into one technology so that everybody can benefit from it. So as such, this is just a continuation that we have from so many other APIs or standards that we support. Since a long time, we already, of course to SQL because we are relational database from the get go. Also other standard like GraphQL, Sparkle, et cetera that we have. And the MongoDB API, is now essentially just the next step forward to give the developers this API that they've gotten to love and use. >> I wonder if you could talk about from the developer angle, what do they get out of it? Obviously you're appealing to the Mongo developers out there, but you've got this Mongo compatible API you're pouting the autonomous database on OCI. Why aren't they just going to use MongoDB Atlas on whatever cloud, Azure or AWS or Google Cloud platform? >> That's a very good question. We believe that the majority of developers want to just worry about their application, writing the application, and not so much about the database backend that they're using. And especially in cloud with cloud services, the reason why developers choose these services is so that they don't have to manage them. Now, autonomous database brings many topnotch advanced capabilities to database cloud services. We firmly believe that autonomous database is essentially the next generation of cloud services with all the self-driving features built in, and MongoDB developers writing applications against the MongoDB API, should not have to hold out on these capabilities either. It's like no developer likes to tune the database. No developer likes to take a downtime when they have to rescale their database to accommodate a bigger workload. And this is really where we see the benefit here, so for the developer, ideally nothing will change. You have MongoDB compatible API so they can keep on using their tools. They can build the applications the way that they do, but the benefit from the best cloud database service out there not having to worry about any of these package things anymore, that even MongoDB Atlas has a lot of shortcomings still today, as we find. >> Of cos, this is always a moving target The technology business, that's why we love it. So everybody's moving fast and investing and shaking and jiving. But, I want to ask you about, well, by the way, that's so you're hiding the underlying complexity, That's really the big takeaway there. So that's you huge for developers. But take, I was talking before about, the Amazon's approach, right tool for the right job. You got document DB, you got Microsoft with Cosmos, they compete with Mongo and they've been doing so for some time. How does Oracle's API for Mongo different from those offerings and how you going to attract their users to your JSON offering. >> So, you know, for first of all we have to kind of separate slightly document DB and AWS and Cosmos DB in Azure, they have slightly different approaches there. Document DB essentially is, a document store owned by and built by AWS, nothing different to Mongo DB, it's a head to head comparison. It's like use my document store versus the other document store. So you don't get any of the benefits of a converge database. If you ever want to do a different data model, run analytics over, etc. You still have to use the many other services that AWS provides you to. You cannot all do it into one database. Now Cosmos DB it's more in interesting because they claim to be a multi-model database. And I say claim because what we understand as multi-model database is different to what they understand as multimodel database. And also one of the reasons why we start differentiating with converge database. So what we mean is you should be able to regardless what data format you want to store in the database leverage all the functionality of the database over that data format, with no trade offs. Cosmos DB when you look at it, it essentially gives you mode of operation. When you connect as the application or the user, you have to decide at connection time, how you want, how this database should be treated. Should it be a document store? Should it be a graph store? Should it be a relational store? Once you make that choice, you are locked into that. As long as you establish that connection. So it's like, if you say, I want a document store, all you get is a document store. There's no way for you to crossly analyze with the relational data sitting in the same service. There's no for you to break these boundaries. If you ever want to add some graph data and graph analytics, you essentially have to disconnect and now treat it as a graph store. So you get multiple data models in it, but really you still get, one trick pony the moment you connect to it that you have to choose to. And that is where we see a huge differentiation again with our converge database, because we essentially say, look, one database cloud service on Oracle cloud, where it allows you to do anything, if you wish to do so. You can start as a document store if you wish to do so. If you want to write some SQL queries on top, you can do so. If you want to add some graph data, you can do so. But there's no way for you to have to rewrite your application, use different libraries and frameworks now to connect et cetera, et cetera. >> Got it. Thank you for that. Do you have any data when you talk to customers? Like I'm interested in the diversity of deployments, like for instance, how many customers are using more than one data model? Do for instance, do JSON users need support for other data types or are they happy to stay kind of in their own little sandbox? Do you have any data on that? >> So what we see from the majority of our customers, there is no such thing as one data model fits everything. So, and it's like, there again we have to differentiate the developer that builds a certain microservice, that makes happy to stay in the JSON world or relational world, or the company that's trying to derive value from the data. So it's like the relational model has not gone away since 40 years of it existence. It's still kicking strong. It's still really good at what it does. The JSON data model is really good in what it does. The graph model is really good at what it does. But all these models have been built for different purposes. Try to do graph analytics on relational or JSON data. It's like, it's really tricky, but that's why you use a graph model to begin with. Try to shield yourself from the organization of the data, how it's structured, that's really easy in the relational world, not so much when you get into a document store world. And so what we see about our customers is like as they accumulate more data, is they have many different applications to run their enterprises. The question always comes back, as we have predicted since about six, seven years now, where they say, hey, we have all this different data and different data formats. We want to bring it all together, analyze it together, get value out of the data together. We have seen a whole trend of big data emerge and disappear to answer the question and didn't quite do the trick. And we are basically now back to where we were in the early 2000's when XML databases have faded away, because everybody just allowed you to store XML in the database. >> Got it. So let's make this real for people. So maybe you could give us some examples. You got this new API from Mongo, you have your multi model database. How, take a, paint a picture of how customers are going to benefit in real world use cases. How does it kind of change the customer's world before and after if you will? >> Yeah, absolutely. So, you know the API essentially we are going to use it to accept before, you know, make the lives of the developers easier, but also of course to assist our customers with migrations from Mongo DB over to Oracle Autonomous Database. One customer that we have, for example, that would've benefited of the API several a couple of years ago, two, three years ago, it's one of the largest logistics company on the planet. They track every package that is being sent in JSON documents. So every track package is entries resembled in a JSON document, and they very early on came in with the next question of like, hey, we track all these packages and document in JSON documents. It will be really nice to know actually which packages are stuck, or anywhere where we have to intervene. It's like, can we do this? Can we analyze just how many packages get stuck, didn't get delivered on, the end of a day or whatever. And they found this struggle with this question a lot, they found this was really tricky to do back then, in that case in MongoDB. So they actually approached Oracle, they came over, they migrated over and they rewrote their applications to accommodate that. And there are happy JSON users in Oracle database, but if we were having this API already for them then they wouldn't have had to rewrite their applications or would we often see like worry about the rewriting the application later on. Usually migration use cases, we want to get kind of the migration done, get the data over be running, and then worry about everything else. So this would be one where they would've greatly benefited to shorten this migration time window. If we had already demo the Mongo API back then or this compatibility layer. >> That's a good use case. I mean, it's, one of the most prominent and painful, so anything you could do to help that is key. I remember like the early days of big data, NoSQL, of course was the big thing. There was a lot of confusion. No, people thought was none or not only SQL, which is kind of the more widely accepted interpretation today. But really, it's talking about data that's stored in a non-relational format. So, some people, again they thought that SQL was going to fade away, some people probably still believe that. And, we saw the rise of NoSQL and document databases, but if I understand it correctly, a premise for your Mongo DB API is you really see SQL as a main contributor over Mongo DB's document collections for analytics for example. Can you make, add some color here? Are you seeing, what are you seeing in terms of resurgence of SQL or the momentum in SQL? Has it ever really waned? What's your take? >> Yeah, no, it's a very good point. So I think there as well, we see to some extent history repeating itself from, this all has been tried beforehand with object databases, XML database, et cetera. But if we stay with the NoSQL databases, I think it speaks at length that every NoSQL database that as you write for the sensor you started with NoSQL, and then while actually we always meant, not only SQL, everybody has introduced a SQL like engine or interface. The last two actually join this family is MongoDB. Now they have just recently introduced a SQL compatibility for the aggregation pipelines, something where you can put in a SQL statement and that essentially will then work with aggregation pipeline. So they all acknowledge that SQL is powerful, for us this was always clear. SQL is a declarative language. Some argue it's the only true 4GL language out there. You don't have to code how to get the data, but you just ask the question and the rest is done for you. And, we think that as we, basically, has SQL ever diminished as you said before, if you look out there? SQL has always been a demand. Look at the various developer surveys, etc. The various top skills that are asked for SQL has never gone away. Everybody loves and likes and you wants to use SQL. And so, yeah, we don't think this has ever been, going away. It has maybe just been, put in the shadow by some hypes. But again, we had the same discussion in the 2000's with XML databases, with the same discussions in the 90's with object databases. And we have just frankly, all forgotten about it. >> I love when you guys come on and and let me do my thing and I can pretty much ask any question I want, because, I got to say, when Oracle starts talking about another company I know that company's doing well. So I like, I see Mongo in the marketplace and I love that you guys are calling it out and making some moves there. So here's the thing, you guys have a large install base and that can be an advantage, but it can also be a weight in your shoulder. These specialized cloud databases they don't have that legacy. So they can just kind of move freely about, less friction. Now, all the cloud database services they're going to have more and more automation. I mean, I think that's pretty clear and inevitable. And most if not all of the database vendors they're going to provide support for these kind of converged data models. However they choose to do that. They might do it through the ecosystem, like what Snowflake's trying to do, or bring it in the house themselves, like a watch maker that brings an in-house movement, if you will. But it's like death and taxes, you can't avoid it. It's got to happen. That's what customers want. So with all that being said, how do you see the capabilities that you have today with automation and converge capabilities, How do you see that, that playing out? What's, do you think it gives you enough of an advantage? And obviously it's an advantage, but is it enough of an advantage over the specialized cloud database vendors, where there's clearly a lot of momentum today? >> I mean, honestly yes, absolutely. I mean, we are with some of these databases 20 years ahead. And I give you concrete examples. It's like Oracle had transaction support asset transactions since forever. NoSQL players all said, oh, we don't need assets transactions, base transactions is fine. Yada, yada, yada. Mongo DB started introducing some transaction support. It comes with some limits, cannot be longer than 60 seconds, cannot touch more than a thousand documents as well, et cetera. They still will have to do some catching up there. I mean, it took us a while to get there, let's be honest. Glad We have been around for a long time. Same thing, now that happened with version five, is like we started some simple version of multi version concurrency control that comes along with asset transactions. The interesting part here is like, we've introduced this also an Oracle five, which was somewhere in the 80's before I even started using Oracle Database. So there's a lot of catching up to do. And then you look at the cloud services as well, there's actually certain, a lot of things that we kind of gotten take, we've kind of, we Oracle people have taken for granted and we kind of keep forgetting. For example, our elastic scale, you want to add one CPU, you add one CPU. Should you take downtime for that? Absolutely not. It's like, this is ridiculous. Why would you, you cannot take it downtime in a 24/7 backend system that runs the world. Take any of our customers. If you look at most of these cloud services or you want to reshape, you want to scale your cloud service, that's fine. It's just the VM under the covers, we just shut everything down, give you a VM with more CPU, and you boot it up again, downtown right there. So it's like, there's a lot of these things where we go like, well, we solved this frankly decades ago, that these cloud vendors will run into. And just to add one more point here, so it's like one thing that we see with all these migrations happening is exactly in that field. It's like people essentially started building on whether it's Mongo DB or other of these NoSQL databases or cloud databases. And eventually as these systems grow, as they ask more difficult questions, their use cases expand, they find shortcomings. Whether it's the scalability, whether it's the security aspects, the functionalities that we have, and this is essentially what drives them back to Oracle. And this is why we see essentially this popularity now of pendulum swimming towards our direction again, where people actually happily come over back and they come over to us, to get their workloads enterprise grade if you like. >> Well, It's true. I mean, I just reported on this recently, the momentum that you guys have in cloud because it is, 'cause you got the best mission critical database. You're all about maps. I got to tell you a quick story. I was at a vertical conference one time, I was on stage with Kurt Monash. I don't know if you know Kurt, but he knows this space really well. He's probably forgot and more about database than I'll ever know. But, and I was kind of busting his chops. He was talking about asset transactions. I'm like, well with NoSQL, who needs asset transactions, just to poke him. And he was like, "Are you out of your mind?" And, and he said, look it's everybody is going to head in this direction. It turned out, it's true. So I got to give him props for that. And so, my last question, if you had a message for, let's say there's a skeptical developer out there that's using Mongo DB and Atlas, what would you say to them? >> I would say go try it for yourself. If you don't believe us, we have an always free cloud tier out there. You just go to oracle.com/cloud/free. You sign up for an always free tier, spin up an autonomous database, go try it for yourself. See what's actually possible today. Don't just follow your trends on Hackernews and use a set study here or there. Go try it for yourself and see what's capable of >> All right, Gerald. Hey, thanks for coming into my firing line today. I really appreciate your time. >> Thank you for having me again. >> Good luck with the announcement. You're very welcome, and thank you for watching this CUBE conversation. This is Dave Vellante, We'll see you next time. (gentle music)

(upbeat music) >> Hi, everyone, welcome back to theCUBE's coverage of AWS re:Invent 2021. We're wrapping up four days of coverage, two sets. Two remote sets, one in Boston, one in Palo Alto. And really, it's a pleasure to introduce Benoit Dageville. He's the Press Co-founder of Snowflake and President of Products. Benoit, thanks for taking some time out and coming to theCUBE. >> Yeah, thank you for having me, Dave. >> You know, it's really a pleasure. We've been watching Snowflake since, maybe not 2012, but mid last decade you hit our radar. We said, "Wow, this company is going to go places." And yeah, we made that call correctly. But it's been a pleasure to sort of follow you. We've talked a little bit remotely. I kind of want to go back to some of the fundamentals. First of all, I wanted mention your earnings last night. If you guys didn't see it, again, triple digit growth, $1.8 billion RPO, cashflow actually looking pretty good. So, pretty amazing. Oh, and 173% NRR, you know, wow. And Mike Scarpelli is kind of bummed that you did so well. And I know why, right? Because it's going to be at some point, and he dials it down for the expectations and Wall Street says, "Oh, he's sandbagging." And then at some point you're actually going to meet expectations and people are going to go, "Oh, they met expectations." But anyway, he's a smart guy, he know what he's doing. (Benoit laughing) I loved it, it was so funny listening to him last night. But anyway, I want to go back to, when I talked to practitioners about data warehousing pre-cloud, they would say sound bites like, it's like a snake swallowing a basketball, they would tell me. And the other thing they said, "We just chased the chips. Every time a new Intel chip comes out, we have to bring in new servers, and we're struggling." The cloud changed all that. Your vision and Terry's vision changed all that. Maybe go back to the fundamentals of what you saw. >> Yeah, we really wanted to address what we call the data challenges. And if you remember at that time, data challenge was first of the volume of data, machine-generated data. So it was way more than just structured data, right? Machine-generated data is weblogs, and it's at petabyte scale. And there was no good solution for that type of data. Big data was not a great solution, Hadoop was really bad. And there was no good solution for that. So we thought we should do something for big data. The other aspect was concurrency, right? Everyone wants to use these data analytic platform in an enterprise, right? And you have more and more workload running against the same data, and the systems that were built were not scaling for these workloads. So you had to silo data, right? That's the only way big enterprise could deal with that, is to create many different silos, Oracle, Teradata, data mass, you would hear data mass. All of it was to afloat, right, this data? And then there was the, what do we call, data sharing. How to get access to data which is not born inside the enterprise, right? So with Terry, we wanted to solve all these challenges and we thought the only way to solve it was the cloud. And the cloud has really two free aspects. One is the elasticity, for all of a sudden, you can run every workload that you want concurrently, in parallel, on different computer resources, and you can run them against the same data. So this is kind of the data lake model, if you want. At the same time, you can, in the cloud, create a service. So you can remove complexity from users and make it really easy for new workloads to be added to the system, because you can manage, you can create a managed service, where all the sudden our customers, they don't need to manage infrastructure, they don't need to patch, they don't need to tune. Everything is done by Snowflake, the service, and they can just load in and run their query. And the third aspect is really collaboration. Is how to connect data sets together. And that's almost a new product for Snowflake, this data sharing. So we really at Snowflake was all about combining big data and data warehouse in one system in the cloud, and have only one single system where you can put all your data and all your workload. >> So you weren't necessarily trying to solve the data warehouse problem, you were trying to solve a data problem. And then it just so happened data warehouse was a logical entry point for you. >> It's really not that. Yes, we wanted to solve the data problem. And for us big data was a really important problem to solve. So from day one, Snowflake was all about machine generated data, petabyte scale, but we wanted to do it right. And for us, right was not compromising on data warehouse principle, which is a CDT of transaction, which is really fast response time, and which is also simplicity. So as I said, we wanted to solve kind of all the problems at the time of volume of data, concurrency, and these sharing aspects. >> This was 2012. You knew at that time that Hadoop wasn't going to be the answer. >> No, I mean, we were really, I mean, everyone knew that. Everyone knew Hadoop was really bad. You know, complex to manage, really slow. It had good aspects, right? This was the only system that could manage petabyte scale data sets. That's the only thing- >> Cheaply. >> Yeah, and cheaply which was good. And we wanted really to do that, plus have all the good attributes of data warehouse system. And at the same time, we wanted to build a system where if you are data warehouse customer, if you are coming from Teradata, you can migrate to Snowflake and you will get to a system which is faster than what you had on-premise, right. That's why it's pretty cool. So we wanted to do big data without compromising on data warehouse. >> So several years ago we looked at the hyperscalers and said, "Wow, last year they spent $100 billion in CapEx." And so, we started to think about this abstraction layer. And then we saw what you guys announced with the data cloud. We call it super clouds. And we see that as exactly what you're building. So that's clearly not just a data warehouse or database, it's technology that really hides the underlying complexity of all those clouds, and it allows you to have federated governance and data sharing, all those things. Can you talk about sort of how you think about that architecture? >> So for me, what I say is that really Snowflake is the worldwide web of data. And we are indeed a super cloud, or we are super-posed to the infrastructure cloud, which is our friends at Amazon, and of course, Azure, I mean, Microsoft and Google. And as any cloud, we have regions, Snowflake regions all over the world, and located on different cloud providers. At the same time, our platform is global in the sense that every region interconnects with all the other regions, this is our snow grid and data mesh, if you want. So that as an organization you can have your presence on several Snowflake region. It doesn't matter which cloud provider, so you can mix AWS with Azure. You can use our cloud like that. And indeed you can, this is a cloud where you can store your data, that's the thing that really matters, and data is structured, but it's machine structure, as I say, machine generated, petabyte scale, but there's also unstructured, right? We have added support for images, text, videos, where you can process this data in our system, and that's the workload spout. And workload, what is very important is that you can run this workload, any number of workloads. So the number of workloads is effectively unlimited with Snowflake because each workload can have its dedicated set of compute resources all operating on the same data set. And the type of workloads is also very important. It's not only about dashboards and data warehouse, it's data engineering, it's data science, it's building application. We have many of our customers who are building full-scale cloud applications on top of Snowflake. >> Yeah so the other thing, if you're not familiar with Snowflake, I don't know, maybe your head has been in the sand for a while, but separating compute and storage, I don't know if you were the first, but you were certainly the first to popularize it. And that allowed you to solve that chasing the chips problem and the swallowing the basketball, right? Because you have virtually infinite resources now at your disposal. >> Yeah, this is really the concurrency challenge that I was mentioning. Everyone wants to access the data. And of course, if everyone runs on the same set of compute resources, you have a bottleneck. So Snowflake was really about this multi-workload. We call it Multi-Cluster Shared Data Architecture. But it's not difficult to run multiple cluster if you don't have consistency of data. So how to do that while maintaining transactional property of data as CDT, right? You cannot modify data from different clusters. And when you commit, every other cluster will immediately see the change, right, as if everyone was running on the same cluster. So that was the challenge that we solve when we started Snowflake. >> Used the term data mesh. What is data mesh to Snowflake? Is it a concept, is it fabric? >> No, it's a very interesting point. As much as we like to centralize data, this becomes a bottleneck, right? When you are a large organization with different independent units, everyone wants to manage their own data and they have domain-specific expertise about that data. So having it centralized in IT is not practical. At the same time, you really want to be able to connect these different data sets together and join different data together, right? So that's the data mesh architecture. Each data set is managed independently by business owners, and then there is a contract which is exposed to others, and you can combine. And Snowflake architectures with data sharing, right. Data sharing that can happen within an organization, or across organization, allows you to connect any data with any other data on our platform. >> Yeah, so when I first heard that term, you guys using the term data mesh, I got very excited because it was kind of the data mesh is, my view, anyway, is going to be the fundamental architecture of this decade and beyond. And the principles, if I understand it correctly, you're applying the principles of Jim Octagon's data mesh within Snowflake. So decentralized data doesn't have to be physically in one place. Logically it's in the data cloud. >> It's logically decentralized, right? It's independently managed, and the reason, right, is the data that you need to use is not produced by your, even if in your company you want to centralize the data and having only one organization, let's say IT managing that, let's say, pretend. Yet you need to connect with other datasets, which is managed by other organizations. So by nature, the data that you use cannot be centralized, right? So now that you have this principle, if you have a platform where you can store all the data, wherever it is, and you can connect these data very seamlessly, then we can use that platform for your enterprise, right? To have different business units independently manage their data sets, connects these together so that as a company you have a 360 view of your customers, for example. But you can expand that outside of your enterprise and connect with data sets, which are from your vertical, for example, financial data set that you don't have in your company, or any public data set. >> And the other key principles, I think, that you've touched on really is the line of business now. Increasingly they're building data products that are creating value, and then also there's a self-service component. Assuming there's the fourth principle, governance. You got to have federated governance. And it seems like you've kind of ticked the boxes, more than tick the boxes, but engineered a solution to solve for those. >> No, it's very true. So Snowflake was really built to be really simple to use. And you're right. Our vision was, it would be more than IT, right? Who is going to use Snowflake is going now to be business unit, because you do not have to manage infrastructure. You do not have to patch. You do not have to do these things that business cannot do. You just have to load your data and run your queries, and run your applications. So now business can directly use Snowflake and create value from that. And yes, you're right, then connect that data with other data sets and to get maximum insights. >> Can you please talk about some of the things you do with AWS here at the event. I'm interested in what you're doing with your machine learning initiatives that you've recently announced, the AI piece. >> Yes, so one key aspects is data is not only about SQL, right? We started with SQL, but we expanded our platform to what we call data programmability, which is really about running program at scale across a large volume of data. And this was made popular with a programming model which was introduced by Pendal, DataFrames. Later taken by Spark, and now we have DataFrames in Snowflake, Where we are different than other systems, is that these DataFrame programs, which are in Python, or Java, or Scala, you program with data. These DataFrames are compiled to our single execution platforms. So we have one single execution platform, which is a data flow execution platform, which can run both SQL very efficiently, as I said, data warehouse speed, and also these very complex programs running Python and Java against this data. And this is a single platform. You don't need to use two different systems. >> Now so, you kind of really attack the traditional analytics base. People said, "Wow, Snowflake's really easy." Now you're injecting AI and machine intelligence. I see Databricks coming at it from the other angle. They started with machine learning, now they're sort of going after the analytics. Does there need to be a semantic layer to connect, 'cause it's the same raw data. Does there need to be a semantic layer to connect those two worlds? >> Yes, and that's what we are doing in our platform. And that's very novel to Snowflake. As I said, you interact with data in different program. You pick your program. You are a SQL programmer, use SQL. You are a Python programmer, use DataFrames with Python. It doesn't really matter. And then the semantic layer is our compiler and our processing engine, is going to translate both your program and my program in Python, your program in SQL, to the same execution platform and to the same programming language that Snowflake internally, we don't expose our programming language, but it's a data flow programming language that our execution platform executes. So at the end, we might execute exactly the same program, potentially. And that's very important because we spent all our IP and all our time, engineering time to optimize this platform, to make it the fastest platform. And we want to use that platform for any type of workloads, whether it's data programs or SQL. >> Now, you and Terry were at Oracle, so you know a lot about bench marketing. As Larry would stand up and say, "We killed the competition." You guys are probably behind it, right. So you know all about that. >> We are very behind it. >> So you know a lot about that. I've had some experience, I'm not a technologist, but I'm an observer and analyst. You have to take benchmarking with a very big grain of salt. So you guys have generally stayed away from that. Databricks came out and they came up with all these benchmarks. So you had to respond, because otherwise it's out there. Now you reran the benchmarks, you took out the materialized views and all the expensive stuff that they included in your cost, your price performance, but then you wrote, I thought, a very cogent blog. Maybe you could talk about sort of why you did that and your general philosophy around bench marketing. >> Yeah, from day one, with Terry we say never again we will participate in this really stupid benchmark war, because it's really not in the interest of customers. And we have been really at the frontline of that war with Terry, both of us, really doing special tricks, right? And optimizing this query to death, this query that no one runs apart from the synthetic benchmark. We optimize them to death to have the best number when we were at Oracle. And we decided that this is really not helping customers in the end. So we said, with Snowflake, we'll not do that. And actually, we are not the only one not to do that. If you look at who has published TPC-DS, you will see no one, none of the big vendors. It's not because they cannot run TPC-DS, Oracle can run it, I know that. And all the other big data warehouse vendor can, but it's something of a little bit of past. And TPC was really important at some point, and is not really relevant now. So we are not going to compete. And that's what we said is basically now our blog. We are not interesting in participating in this war. We want to invest our engineering effort and our IP in solving real world issues and performance issues that we have. And we want to improve our engine for these real world customers. And the nice thing with Snowflake, because it's a service, we see exactly all the queries that our customers are executing. So we know where we are struggling as a system, and that's where we want to invest and we want to improve. And if you look at many announcements that we made, it's all about under-the-cover improving Snowflake and getting the benefit of this improvement to our customer. So that was the message of that blog. And yes, the message was okay. Mr. Databricks, it's nice, and it's perfect that, I mean, everyone makes a decision, right? We made the decision not to participate. Databricks made another decision, which is very fine, and that's fine that they publish their number on their system. Where it is not fine is that they published number using Snowflake and misrepresenting our performance. And that's what we wanted also to correct. >> Yeah, well, thank you for going into that. I know it's, look, leaders don't necessarily have to get involved in that mudslide. (crosstalk) Enough said about that, so that's cool. I want to ask you, I interviewed Frank last spring, right after the lockdown, he was kind enough to come on virtually, and I asked him about on-prem. And he was, you know Frank, he doesn't mix words, He said, "We're not getting into a halfway house. That's not going to happen." And of course, you really can't do what you do on-prem. You can't separate compute, some have tried, but it's not the same. But at the same time that you see like Andreessen comes out with this blog that says a huge portion of your cost of goods sold is going to be the cloud, so you're going to have to repatriate. Help me square that circle. Is it cloud forever? Is it will you never say never? What can you share of that? >> I will never say never, it's not my style. I always say you can always change your mind, and maybe different factors can change your mind. What was true at some point might not be true at a later point. But as of now, I don't see any reason for us to go on-premise. As you mentioned at the beginning, right, Snowflake is growing like crazy. The world is moving to the cloud. I think maybe it goes both ways, but I would say 90% or 99% of the world is moving to the cloud. Maybe 1% is coming back for some very specific reasons. I don't think that the world is going to move back on-premise. So in the end we might miss a small percentage of the workload that will stay on-premise and that's okay. >> And as well, if you dig into some of the financial statements you'll see, read the notes where you've renegotiated, right? We're talking big numbers. Hundreds and hundreds of millions of dollars of cost reduction, actually more, over a 10 year period. Billions of your cloud bills. So the cloud suppliers, they don't want to lose you as a customer, right? You're one of their biggest customer. So it's awesome. Last question is kind of, your work now is to really drive the data cloud, get adoption up, build that supercloud, we call it. Maybe you could talk a little bit about how you see the future. >> The future is really broadened, the scope of Snowflake, and really, I would say the marketplace, and data sharing, and services, which are directly built natively on Snowflake and are shared through our platform, and can operate, it can mix data on provider-side with data on consumer-side, and creating this collaboration within the Snowflake data cloud, I think is really the future. And we are really only scratching the surface of that. And you can see the enthusiasm of Snowflake data cloud and vertical industry We have nuanced the final show data cloud. Industry, complete vertical industry, latching on that concept and collaborating via Snowflake, which was not possible before. And I think you talked about machine learning, for example. Machine learning, collaboration through machine learning, the ones who are building this advanced model might not be the same as the one who are consuming this model, right? It might be this collaboration between expertise and consumer of that expertise. So we are really at the beginning of this interconnected world. And to me the world wide web of data that we are creating is really going to be amazing. And it's all about connecting. >> And I'm glad you mentioned the ecosystem. I didn't give enough attention to that. Because as a cloud provider, which essentially you are, you've got to have a strong ecosystem. That's a hallmark of cloud. And then the other, vertical, that we didn't touch on, is media and entertainment. A lot of direct-to-consumer. I think healthcare is going to be a huge vertical for you guys. All right we got to go, Terry. Thanks so much for coming on "theCUBE." I really appreciate you. >> Thanks, Dave. >> And thank you for watching. This a wrap from AWS re:Invent 2021. "theCUBE," the leader in global tech coverage. We'll see you next time. (upbeat music)

(bright music) >> Okay, welcome back everyone. to theCUBE's coverage of AWS re:Invent 2021, we're in person for a real event. I'm John Furrier, your host. We have two sets here on the floor, also a hybrid event online as well for Amazon, also on theCUBE Zone, go to cubereinvent.com and check out all theCUBE footage there. Maureen Lonergan, VP of Training and Certification AWS CUBE alumni, Maureen, great to see you, thanks for coming on. >> Nice to see you. >> So I remember, years ago, at re:Invent when you came on first time on theCUBE, this was when cloud was just getting going, I don't want to say just getting going, it was going, but it was just like training was going, now you're swimming in needs. You got the big milestone for, what's that? 27 million people, what's that number? >> 29 million training free, yeah. >> 29 million is the target for training, we hear the certifications are up, the pandemic has got everyone geared up for training. Give us the update, what's happening? >> Yeah, so we're doing a lot of interesting things. Obviously, the pandemic changed the world for everyone, but it's been a really good opportunity for us to pivot the business and move things to virtual and digital. And, in 2020, we did make that commitment to train 29 million people for free by 2025. And, you know, we've trained 6 million so far, so we're making great progress on that goal. We've largely done that through a couple of different programs. So we just a month ago launched our Skill Builder platform that provides 500 free training courses in 16 languages, across 200 countries. We also launched the AWS Skill Center in Seattle, which is learner acquisition and bringing in people from the community to learn about cloud. And, we also launched a course on Amazon Books. So, we were really excited about-- >> So you guys, again, this is free training. >> All free training. >> Free training. >> Everything I just mentioned is free. >> What's the most important things, skills are people learning right now? >> I think it's still this, you know, it's the same thing, it's solution architecture, security for sure, DevOps, developer, but we're also seeing a huge interest in business, the business roles, really understanding what cloud is and how it can, you know, help them with their business. >> How about organizations? 'Cause they have skill issues too, I know you guys are going all in on training, which is great, and by the way, congratulations on the mission. I know you're getting close to the numbers. I think there was an announcement, we're getting an update as you guys, have you hit the numbers yet? 29 million? >> The 29 million, yeah. So 6 million we've done so far, yeah. >> So you're on your way. What about organizations? How do they get involved? Because they're trying the same thing. Are you partnering with people? >> Yeah, so we partner with, well, for customers, they're looking for the same thing that we are. We also have a program for underserved and unemployed communities where we go in and do a kind of non-tech to tech training. And we're offering that program in 90 locations this year and really trying to address the early pipeline. >> What are some of the most important things that you're working on for AWS, for training and certification right now? >> The biggest thing that we're doing is just trying to make everything as free and accessible as we can and moving as much as we can to digital, making it where we've really focused this year on experiential learning, so labs and getting engaged with the customer and keeping them because obviously, we release services every day, you know? And it's important that we just work with organizations to have a learning, curious culture. >> Is there any way people can get involved, or you guys have any open programs? What can we do to help on theCUBE? Do you guys have new, cool digital ways to get the word out? What's going on? >> Yes, so, I mean, it depends on what you mean, we always are partnering with collaborating organizations, especially for programs like re/Start, so organizations within communities that are trying to get their community skilled up. So we work with a bunch of different partnerships. And I think, for me, it's really just about, we really think we're very, very focused on building diverse builders. And so, we want to make sure that we're getting the message out that cloud's accessible to anybody. And, by providing free training, we hope that that will attract a new set of learners and start to close the gap on their training pipeline. >> So, have you guys got the Gen Z nailed down yet? 'Cause they're hungry for content, they're on the Discord servers, they're on Twitch. >> Yeah, we actually were training to Twitch this year, because you have to meet the learner where they are, right? And I think, you know, traditional instructor-led training just doesn't work for some people. And so, we have content out on Twitch, we're working on some really cool interactive gaming stuff. And so, we really have pivoted. >> So there's a Discord server called "Ace of Diamonds" that's turning out to be quite the business vibe for the young kids. A lot of young kids from 13 to 17 years old in that kind of learning mode and they want to talk about cloud. Like to them, they're geeking out on NVIDIA GPUs, they want to hear about the graviton, they're nerds. >> Yeah, we actually have a very cool program called "Get IT", and it's very focused on girls in tech and we go into schools and run competitions and do hackathons and they present, and it's a really great way to get, you know, girls interested in tech in a big way. >> Cal Poly hosted a robotics competition, that was pretty interesting, the women's division was phenomenal. There's divisions now, I mean, robotics is like a varsity sport now. >> Yeah, exactly, exactly. >> I mean, this just shows you where the interest level is. Okay, so obviously, there's a young demographic and you've got the re-skilling on the higher end of the demographic of age wise that maybe have come from IT. So you've got the IT folks and/or people that had some business training or whatever, and then you have the young, what's the programs that are working the best that you see to getting those folks, the older folks, in retraining? >> For the younger ones, or? >> John: Older ones, not younger ones, older ones. >> I think what we're trying to do is work with organizations to make training accessible and comfortable. We always say it, you know, we want companies to build an environment where they can experiment and learn. So we're working with large organizations to try and transform them and make them cloud fluent and move people from traditional skills onto cloud skills. And, we're having great success with customers in doing that. But I think providing a really comfortable environment and a place and space for them to learn and building communities within that organization is important. >> What did you learn during the pandemic in your evolution? 'Cause you guys were doing like mid-flight of training, I know you've been rolling, you've been working really hard over the years, I know that for a fact. Pandemic hits, it's now virtual, digital is now a priority. What are some of the new things that have been spawned onto you from digital that are working? >> Yeah, I mean, we learned how to, you know, we're building out labs and we learned to cut content into smaller pieces so people could consume them. I think the biggest thing that we learned is that we just need to, that people were hungry to learn. Everyone was at home and we actually saw a tremendous increase in people taking training, especially digital training. And then, we also pivoted all of our certifications to virtual very rapidly so that people could then validate their skills. I think in light of the pandemic, you know, the great resignation is real, right? And people are assessing where they are. And so, we'd like to acquire people that are interested in that. >> And those jobs that are available with certification are very high paying jobs. >> Yes they are, yeah. >> So you walk through a certification, you're looking at some pretty good salary levels and you could be living anywhere. >> I met a guy last night at an event and he was in finance and he moved from a job making 30,000 to six figures and he did all through self-learning and he came to an event, was super excited about that. >> That's the top story right there, we've got to leave it at that. I know you got to go, I know you've got a hard deadline. Thank you for spending the time to come on theCUBE and sharing this important information around the certification, your goal for free training, it's free. >> Maureen: Free. >> If you want to get a raise, get cloud certification, pro tip. >> Please. >> That's a pro tip right there. Thanks for coming on, Maureen, great to see you. >> Appreciate it. Maureen Lonergan, great work she's doing in Amazon getting free content, you don't have to pay for it, it's free. Just like theCUBE content here, bringing you free insights. I'm John Furrier, worldwide leader in tech coverage at theCUBE, here in person in Las Vegas. Thanks for watching. (bright music)

>>I'm john free with the Q we are here. It's exciting news around the next evolution switching, Sony jean Donny, co founder and chief business officer Pensando and David Hughes chief product and technology officer Aruba HP. Welcome back. We just heard from Antonio neary and john Chambers about the HPV Ruba partnership with Pensando and the new switching platform. Tell me more about the exciting news you're announcing? >>Yeah, I'm really excited today to be introducing the CX 10,000 distributed services switch. It's a brand new class of switch way bringing together the best of Aruba switching technology adding to R C X portfolio combining with Pence Sandoz technology that technology embedded in the platform. The problem we're solving is that in a traditional data center, all of those services like fire walling and low balancing provided by centralized appliances. And while that might be okay for north south traffic traffic that's going in and out of the data center. It's not scalable and it's not cost effective to apply to every service in every port to every flow traversing their data center As we all know with microservices more and more of the traffickers east west over 70% today and growing and so what we're doing here with the C X 10,000 is giving enterprises away to take the smart nick technology that's been proven out by hyper scholars and introduce it into their data centers in a very cost effective and easy to deploy way we're embedding that capability in the top of rack switch so that we can apply Fireable services, low balancing services to every port To every flow, delivering 100 times a scale in terms of a CLS 10 times of performance, in terms of encryption at a third of the cost of those traditional network architectures. So it's a super exciting time, >>love the speed, love the energy there. But I gotta ask what makes this a new category of switch. >>Well if you take a look at the journey we have been on as we have evolved our data centers and the applications have evolved for our customers. Uh and the world is now a bold new world of multi cloud. Uh the architecture is in the data center which are leaves spine architectures have become the new norm. Software defined, networking is pervasively deployed by our customers but as this journey began five or seven or even about 10 years ago uh and has culminated into a much more mature set of building blocks. We have taken the problem from one space of automating networks in the data center to then introducing lots and lots of expensive appliances to bring about security for example, or the state full services, whether it's load balancing or whether it's encryption and visibility and telemetry types of services. Now the customers had to try, you know, trombone all the traffic in and out of these appliances driving up the cost uh and the complexity and when time comes to troubleshoot these environments, it's extremely complex because you're trying to rationalize fabrics coming from one place appliances coming from four or five different vendors, maintaining all the software elements that need to be kept track off. Uh and as more and more customers want to aspire towards zero trust security model. Uh we need to start to embrace a lot of the principles that have been implemented by the hyper scholars and the cloud vendors, which is doing away with the appliances doing away with agent technology on servers, but instead to bring that technology for east west uh into play as well as to ensure that if there are bad actors that are landing inside of the data centers that they do not have the ability to, you know, create attack surfaces with complete lateral movement. Today, that is possible. Uh if you look at 70% of all the attacks that have been happening here in the past few years, it's as a result of having a attack surface which is pretty large in the data centers. And that gets further complicated when you move towards a multi cloud environment where the perimeter of the data center is now moving into the edge. Whether that edges, whether fleet resides for our customers or whether that edge happens to be a co location edge where you're building your own rampant off ramps. So I think the compelling event essentially is driven by the whole notion of distribution of services and having them available from a security and from a services point of view and these are state full services as close to the workload as you possibly can get them. >>So you guys really hit on some key points, their cloud, native microservices East west, north south, um no perimeter edge. These are topics that we would talk about kind of individually over the years, it's happening now all at the same time, this is causing a lot of complexities and then the security challenges you just laid out are everywhere. This brings up a big conversation around solving this. How does this new architecture, this solution solve the complexity and the security challenges in the data center. >>If you look at the use cases that our customers are talking about. The first, the initial use case really is to bring about security and state full security for east west traffic right into the fabric of their data centers. So having the ability to deliver that while eliminating the complex appliances only to do the job which they do very well, which is not South protection of services. Uh that also allows us the ability then to start to deliver visibility and telemetry at the same time that we're delivering state full security firewall and micro segmentation services because what I cannot see, I cannot secure. Uh so those two elements are initial use cases out of the box for our customers as we deliver this platform to them and then as more and more use cases that are becoming evident to us through customer interactions come into play. For example, the co location edge that I would like. David to walk you through a bit more in terms of how we help solve for that use case. >>So for the cooler use case, I think we're moving from a world where people talk about data centers to now talking about centers of data and those centers of data. Yes, they can be in a core private data center, they could be in the cloud but more and more they're going to be distributed around the edge in co location environments. And what we need to be able to do is extend those services that were provided in the data center to be provided in those Kahlo's at the edge And again we want to do that without having to deploy a whole rack of appliances that may be cost more than a computer itself and so with the CX- 10,000 we can have that as a top of rack switch for that polo. And from that switch deploy all of the encryption and firewall ng services that that polo requires. And what's important is that we're doing it with the same policy framework under the same management system across the whole enterprise in the data center as well as in these co location environments and out into the cloud. >>So you guys mentioned visibility and a quick follow up on this question because you mentioned visibility can't see it, you can't protect it. But also there's a lot of workloads that people are trying to automate. These are two factors. Can you guys just double down on that? I want to just get that out there because I think this becomes a big thing. >>I think policy having the ability to have an intent based policy that is a foundational technology building block that we are brought together is a very important element. And then when you map it back to tools that Aruba is extending support for including this platform, become very valuable. So David, why don't you walk us >>through? You know, I think one of the advantages that we bring is that this is an extension of the Aruba C X switching portfolio. So yeah, it's a cloud native microservices, very modern switch architecture and we have a comprehensive management platform, the Aruba fabric controller. And so what we are doing is making sure that everything fits together nicely, that we're delivering a complete solution to our customers. But one important thing to mention here is that we are thinking about how customers can do this step by step. So no, we're not requiring them to rebuild their entire data center, They can do this one rack at a time. We can work with their existing spine and deploy one leaf at a time in a very measured way. And so we think it's a great way for enterprises to be able to consume this modern distributed platform. >>That's a great segment. The next question. I mean I totally see this as you guys are talking about the cloud native trend, driving a cloud operational model to every edge. The data center is just another edge. It's a center of data. Love that. I love that line. So I have to kind of ask the operational side of the question, how would an enterprise customers manage all this take us through the nuts and bolts of deploying and managing of his gum? A customer >>That's a very good question. If you take a look at the customer's deployment models and let's let's take the example of they want to now bring in this technology and build a part or highly secure part with it for east west and to make sure that they're protecting 100% of that east west traffic. I think that leveraging all the building blocks that we have innovated between us and Aruba. We want to make sure that the ecosystem that the customer has built, they want whether they have built it with companies like Splunk and service now or Guardianco, they want integration points will be made available to them. If you take a look at, take a step back and say for these environments as you aspire to go toward zero trade security. The issues of inserting security appliances into network flows and having the ability to map it to the knowledge of applications and their dependencies for policy becomes an important function to tackle. So once you accept that, Okay, I have state full security functions built into this top of rack device available for my applications and all workloads, whether they're container workloads, bare metal workload, virtualized workloads uh and I have complete visibility into those workloads without compromising on connectivity and I can control through enforcement of policy where I need it because now security is part of the fabric, it's not a bolt on. Then comes the job of integration with an ecosystem. So whether you're looking at seem and sold companies where we are delivering in close collaboration with Splunk, A Pensando app for Splunk there's also going to be the availability of an elastic module, A plug in module. Uh then turn attention to what's more automation and devops and civil playbooks for the C X 10-K will be made available day one so that where you do not have the ability to deploy the A. F. C. You can use your existing answerable toolkit and they're making those playbooks available to our customers. Uh They want integration with application discovery mapping companies like Guardianco, allowing them to discover who's talking to whom and push and enforce that policy through the C X 10-K will allow for more automated deployments of those policies and finally, compliance integration with vendors like too thin for continuous security compliance monitoring becomes extremely important as the screen depicts a lot of lot of visualization capabilities with companies like Elk which are in beta today and answerable and Splunk and Elk will all be targeted at first customer shipment. So again, telemetry visibility with the integration of the ecosystem. Uh, it becomes a very powerful combination for the customers as they look to operationalize this for day to day three and they, you know, day one, day two, day three automation. >>That's awesome. David, I'd like to let you weigh in on this whole question of operations because you're hitting all the marks here that are relevant cloud, native microservices, apps, explosion and data volume and velocity, hyper scale operational cloud operations, performance, price point security all in this one solution. This is big. Um, it's not like you mentioned earlier, it's not a rip and replace but you can roll it out how how do you see a customer best operational izing this new, >>You know, I think the answer is a little bit different for each customer but you are very careful at the beginning, we introduced this. It's an evolution of switching. It's not a revolution where we have to replace everything and I think that's really exciting is that it builds on the foundational architecture of leaf and spine. And what we're able to do is let that customer introduced these new capabilities one leaf at a time. So maybe when they're upgrading from 10 gigs to 25 gigs, it's a great time for them to introduce this capability into their data center um and then depending on their application, you know, it may be, as Sony said that they've got one particular application, a crown jewel application and so they want to build out that in one rack and provide, you know, very, very robust East west as well as north south um security around that application, but there's so many different ways that customers can deploy this technology and what's really exciting is now is we're beginning to work with our customers, learning about these new use cases and then feeding that back into our roadmap and we all >>know, as you get down lower in the network layer, security is distributed architecture. So everything is paramount like security, super relevant, great conversation, I gotta ask what's next with this technology. Yeah, >>well, you know the teams, the two engineering teams are working together and this is step one on, on a really exciting new path, I don't know, Sony, what would you say? >>I think there's a lot more to come here. This is just a starting point. We have an incredibly strong partnership and go to market partnership here with Uber team with this platform. It is just the beginning uh and it will lead our customers onto the multi cloud journey. Uh and last but not least, I would like to say that you know, in closing uh that are seldom opportunities where you look at disrupting the way things are happening while fitting into customers existing models. So this is, as I said with everything being software defined, you will continue to see as delivering at great velocity more and more software defined services, whether it's encryption, Lord balancing and other state full services over time. Making this technology easier to deploy by fitting into the existing ecosystem and continuing to provide them with the 100 extra scale, 10 X. The performance as well as the ability to do it at a third of the same, you know, at the third of the cost of what they would need to if they had to build this uh today with disparate devices, >>exciting news in the industry. You guys are the pros you've seen all the waves of innovation over the years. I guess my final final question would be, how would you summarize this point in time right now? This is pretty um exciting all this is all happening At the same time, customers are having opportunity to innovate the pandemic has shown a lot of scale and and the need for stability and security. This is a special moment. How would you guys weigh in on that? >>Yeah, I think about it every decade, there's a change in how data centers a belt. And so this is the change that's happening this decade. Moving to a distributed services, switch. The other big mega trend that I see is this move, as I said from data centers to stand as a data and the opportunity for customers to use this technology as they move out to the edge. Have distributed compute and tell us, what do you think Sony? >>I think I couldn't agree more. I think there are so many various technology transitions occurring now. The cloud being the biggest one. Uh the explosion of data and uh, you know, the customers making decisions of having a distributed model And if indeed two thirds, if not 75% of all data will be processed at the edge over the next few years. This architecture is prime for the enterprise to go leverage their best practices of today while they can gradually move that architecture is for the future, which is a multi cloud future >>centers of data, large scale cloud operations automation. The speed of innovation has never seen this before. Uh It's exciting time. Sunny, thank you for coming on. And David, thanks for chatting about this exciting new announcement. Thank you very much. >>Thank you. Thank you. >>This is the power of and hp. Ruba and Pensando partnership. I'm john forward the cube. Thanks for watching. Mhm

>>And welcome back to the cubes coverage of coop con cloud native con 2021. We're in person physical venom, John free hosted a Q a Dave Nicholson, my CO's and Emma Laney, who is our not so roving reporter unemployed, software engineer, unemployed comedian. Great to have you on the cube. >>Thank you for that list of credentials. >>You're doing great. I saw you're having some fun down there. We've got this new show or testing out called the grill. Here it is. Okay. Um, what's the focus, what's the story behind everything. >>Uh, the focus of the show is trying to have some fun with tech. You know, tech has a lot of self seriousness. Uh, there's a lot that's ripe to make fun of. We're also having fun. We're not trying to grill people in. We're not trying to roast them. Right? We're having people come through. They're sharing funny stories. We're having a contest to find the best man split nation of Kubernetes. Right now, I got to say, a woman is in the lead. Oh, she killed that contest, like called me, sweetie. And everything. It just proves that it's not about the man. You identify as it's about the condensation in your heart when it comes to mansplaining. >>Um, what is the best criteria that you, when you get a candidate for the mansplaining competition, what is the criteria? >>I mean, number one, we're looking for condensation. You get extra points for you, the phrase, well, actually we want a supercilious attitude. Uh, if you are partially into explaining it and then you stop yourself because you think you've used too technical of a term and then step it down, all of those gets you extra points in the mansplaining. >>Can I ask you, what's your biggest observation as you kind of look at this ecosystem? I mean, it's a big event, but it's, COVID postpone even in COVID people are wearing masks, not wearing masks. >>I mean, people are wearing masks for the most part. Uh, you know, I did love this, uh, red light, yellow light green light system. They came up with green, meaning please touch me. I've been inside for too long red meaning I still care about COVID yellow. You know, ask me, we'll figure it >>Out. All right. What's the funniest thing you've heard so far. >>The funniest thing I have to say, I asked someone what their favorite tech joke is. And he said it worked on my computer That really stirred up some memories. >>Oh man, we're in LA though. This is a great area. It's literally with the best comedians you could think of or work their way through the system. But with techno and everything is tech with gadgets and with like Kubernetes, I mean, it's, it's the material writes itself. I mean, >>Surely >>You must be having, >>Oh, I'm definitely having a ton of fun. Uh, I wouldn't say the material writes itself. I would say hire me to write material, but it is quite a fertile. >>Okay. What would you write for, uh, looking at the keynote today? Looking at the vibe here, obviously a lot of people show because they're remote, but visually it's a packed house here, but what's your first comedic view of the, as the fog lifts in this community? >>I have to say the thing that really stuck out to me from the keynote addresses was that people have not yet adjusted to being in person. There were some very, very delayed applause breaks where people realize they were not muted watching on a screen and you'd still go, oh, that's right. We should interact. Like God bless those speakers. It's uh, people have been inside for a long time. >>Um, part-time comedian too. I mean, co-hosting queue. Um, I don't, I, >>I don't find anything funny with technology. And I'm curious when you use the word supercilious, is that a, is that a comedic term? I, I, yes. >>I heard that before. It's the Latin form of super silly. Yeah. Which is my brand of comedy. >>So the mansplaining, I don't know if you need to like, woman's plane, some of this stuff to me, but I'll English >>Major Splain. Okay. Okay. Super silliest. >>It sounds super silly. So is it, is it, is it okay to have a ringer come in and attempt make an attempt at the mansplaining or >>Okay. A hundred >>Percent come in wearing it. >>I'm trying to make this a safe space for women at the conference. I'm the only woman you should be mansplaining to. I'm a martyr falling on the sword of mansplaining for all the great technical women at this conference. You slip that in >>And translate that. >>Of course, John, I don't know how to explain that to them more detailed. Um, what I love about the vibe is that this technical people they're snarky. If you get at their core, I mean, we were at the bar. Everyone was like totally leaning into like comedy and more fun because it's almost like they're bust out, come out of the closet and beat comedian. >>Oh, there is a broiling anger in the soul of every developer and every person who's worked on technology. And the question is going to be, can we get it on camera when they are not drunk, we're doing our >>Best to drink. These developers don't >>Think, oh, they do desperately. >>We saw a few partaking in the bar at the GTA merit and a lot going on. You had the, you know, they had warriors game going on. You have a lot of Dodgers were playing the giants. So pretty active bar scene for this crowd. >>Yeah, no, it was, uh, it was very fun. I personally was disappointed that the warriors are not actually staying in our hotel. You know, if this software thing doesn't work out, NBA wife is a possible second. >>And the Ritz Carlton was right behind us. You could be right there too. All right. So the grill is, uh, an experiment. We're having some fun with it, but the purpose is to just chill a bit. What's the, what would you say the goal of the show is for you? >>I'd say the goal is to get people to come out of their shells a little bit, to have some fun, to poke fun at some of the tendencies that we see in tech that we often don't bring up. You know, like I'm having so much fun with the man's pollination. Uh, I've lived it a bit. And my favorite is, uh, as I asked men to mansplain it to me, the panic in their eyes, that's my ultimate goal is just to make men afraid. >>And the panic is because they don't know if they're mansplaining all the time or actually purposely mansplaining is hard enough, but they do it naturally. Sorry. >>I have three daughters and I can't wait for them to see this stuff. I cannot >>Wait. That's going to be >>Great. Well, we have cooler gen Z. >>Well, we have t-shirts right. Let me see the t-shirts give everyone a quick, if you come on, this is day one of coupons. So if you do come on the show with the grill, I'm the t-shirt ferry. The grill is real. It's like the V the cubes version of the view, but >>Wow, just because I'm a woman, the, uh, the t-shirt is a big incentive. I'm sure a lot of people go to tech conferences don't get any free. T-shirts good. >>I got grilled by a net. Lilium, the cube at cube con con not cube >>Con. It's a medium rare grilling. >>I couldn't resist the view jokes. I know I'm in color. We'll keep our day jobs here in the comedian angle. We got to >>Believe that's true. Yes. When I look at the wavelengths of >>Light on that, I'm super stoked to have you try that. I think it's a great program, Greg. God. So you guys doing a great job, loved the vibe, love the energy, love the creativity, having some fun. See the poster one last time. And the idea is to have some fun, right? It's a tough time. We're all coming back from the pandemic, welcoming back from the pandemic. And this is just a fun way to kind of let the air out and have some fun. So thanks for everyone. Thank you so much for doing that. Thank you. All right. Cute coverage here. Coop gone. Cloud native con I'm John Perry, David Nicholson. Be back with more day, one coverage of three days after the short break.

>>Mhm Hello and welcome to the cubes coverage of AWS amazon web services, Global public Sector partner awards program. I'm john for your host of the cube here we're gonna talk about the best covid solution to great guests. Benham or with healthcare and life sciences lead at palantir Ben welcome to the cube SAm Michaels, Director of automation and compound management and Cats. National Center for advancing translational sciences and Cats. Part of the NIH National sort of health Gentlemen, thank you for coming on and and congratulations on the best covid solution. >>Thank you so much john >>so I gotta, I gotta ask you the best solution is when can I get the vaccine? How fast how long it's gonna last but I really appreciate you guys coming on. I >>hope you're vaccinated. I would say john that's outside of our hands. I would say if you've not got vaccinated, go get vaccinated right now, have someone stab you in the arm, you know, do not wait and and go for it. That's not on us. But you got that >>opportunity that we have that done. I got to get on a plane and all kinds of hoops to jump through. We need a better solution anyway. You guys have a great technical so I wanna I wanna dig in all seriousness aside getting inside. Um you guys have put together a killer solution that really requires a lot of data can let's step back and and talk about first. What was the solution that won the award? You guys have a quick second set the table for what we're talking about. Then we'll start with you. >>So the national covered cohort collaborative is a secure data enclave putting together the HR records from more than 60 different academic medical centers across the country and they're making it available to researchers to, you know, ask many and varied questions to try and understand this disease better. >>See and take us through the challenges here. What was going on? What was the hard problem? I'll see everyone had a situation with Covid where people broke through and cloud as he drove it amazon is part of the awards, but you guys are solving something. What was the problem statement that you guys are going after? What happened? >>I I think the problem statement is essentially that, you know, the nation has the electronic health records, but it's very fragmented, right. You know, it's been is highlighted is there's there's multiple systems around the country, you know, thousands of folks that have E H. R. S. But there is no way from a research perspective to actually have access in any unified location. And so really what we were looking for is how can we essentially provide a centralized location to study electronic health records. But in a Federated sense because we recognize that the data exist in other locations and so we had to figure out for a vast quantity of data, how can we get data from those 60 sites, 60 plus that Ben is referencing from their respective locations and then into one central repository, but also in a common format. Because that's another huge aspect of the technical challenge was there's multiple formats for electronic health records, there's different standards, there's different versions. And how do you actually have all of this data harmonised into something which is usable again for research? >>Just so many things that are jumping in my head right now, I want to unpack one at the time Covid hit the scramble and the imperative for getting answers quickly was huge. So it's a data problem at a massive scale public health impact. Again, we were talking before we came on camera, public health records are dirty, they're not clean. A lot of things are weird. I mean, just just massive amount of weird problems. How did you guys pull together take me through how this gets done? What what happened? Take us through the the steps He just got together and said, let's do this. How does it all happen? >>Yeah, it's a great and so john, I would say so. Part of this started actually several years ago. I explain this when people talk about in three C is that and Cats has actually established what we like to call, We support a program which is called the Clinical translation Science Award program is the largest single grant program in all of NIH. And it constitutes the bulk of the Cats budget. So this is extra metal grants which goes all over the country. And we wanted this group to essentially have a common research environment. So we try to create what we call the secure scientific collaborative platforms. Another example of this is when we call the rare disease clinical research network, which again is a consortium of 20 different sites around the nation. And so really we started working this several years ago that if we want to Build an environment that's collaborative for researchers around the country around the world, the natural place to do that is really with a cloud first strategy and we recognize this as and cats were about 600 people now. But if you look at the size of our actual research community with our grantees were in the thousands. And so from the perspective that we took several years ago was we have to really take a step back. And if we want to have a comprehensive and cohesive package or solution to treat this is really a mid sized business, you know, and so that means we have to treat this as a cloud based enterprise. And so in cats several years ago had really gone on this strategy to bring in different commercial partners, of which one of them is Palin tear. It actually started with our intramural research program and obviously very heavy cloud use with AWS. We use your we use google workspace, essentially use different cloud tools to enable our collaborative researchers. The next step is we also had a project. If we want to have an environment, we have to have access. And this is something that we took early steps on years prior that there is no good building environment if people can't get in the front door. So we invested heavily and create an application which we call our Federated authentication system. We call it unified and cats off. So we call it, you know, for short and and this is the open source in house project that we built it and cats. And we wanted to actually use this for all sorts of implementation, acting as the front door to this collaborative environment being one of them. And then also by by really this this this interest in electronic health records that had existed prior to the Covid pandemic. And so we've done some prior work via mixture of internal investments in grants with collaborative partners to really look at what it would take to harmonize this data at scale. And so like you mentioned, Covid hit it. Hit really hard. Everyone was scrambling for answers. And I think we had a bit of these pieces um, in play. And then that's I think when we turned to ban and the team at volunteer and we said we have these components, we have these pieces what we really need. Something independent that we can stand up quickly to really address some of these problems. One of the biggest one being that data ingestion and the harmonization step. And so I can let Ben really speak to that one. >>Yeah. Ben Library because you're solving a lot of collaboration problems, not just the technical problem but ingestion and harmonization ingestion. Most people can understand is that the data warehousing or in the database know that what that means? Take us through harmonization because not to put a little bit of shade on this, but most people think about, you know, these kinds of research or non profits as a slow moving, you know, standing stuff up sandwich saying it takes time you break it down. By the time you you didn't think things are over. This was agile. So take us through what made it an agile because that's not normal. I mean that's not what you see normally. It's like, hey we'll see you next year. We stand that up. Yeah. At the data center. >>Yeah, I mean so as as Sam described this sort of the question of data on interoperability is a really essential problem for working with this kind of data. And I think, you know, we have data coming from more than 60 different sites and one of the reasons were able to move quickly was because rather than saying oh well you have to provide the data in a certain format, a certain standard. Um and three C. was able to say actually just give us the data how you have it in whatever format is easiest for you and we will take care of that process of actually transforming it into a single standard data model, converting all of the medical vocabularies, doing all of the data quality assessment that's needed to ensure that data is actually ready for research and that was very much a collaborative endeavor. It was run out of a team based at johns Hopkins University, but in collaboration with a broad range of researchers who are all adding their expertise and what we were able to do was to provide the sort of the technical infrastructure for taking the transformation pipelines that are being developed, that the actual logic and the code and developing these very robust kind of centralist templates for that. Um, that could be deployed just like software is deployed, have changed management, have upgrades and downgrades and version control and change logs so that we can roll that out across a large number of sites in a very robust way very quickly. So that's sort of that, that that's one aspect of it. And then there was a bunch of really interesting challenges along the way that again, a very broad collaborative team of researchers worked on and an example of that would be unit harmonization and inference. So really simple things like when a lab result arrives, we talked about data quality, um, you were expected to have a unit right? Like if you're reporting somebody's weight, you probably want to know if it's in kilograms or pounds, but we found that a very significant proportion of the time the unit was actually missing in the HR record. And so unless you can actually get that back, that becomes useless. And so an approach was developed because we had data across 60 or more different sites, you have a large number of lab tests that do have the correct units and you can look at the data distributions and decide how likely is it that this missing unit is actually kilograms or pounds and save a huge portion of these labs. So that's just an example of something that has enabled research to happen that would not otherwise have been able >>just not to dig in and rat hole on that one point. But what time saving do you think that saves? I mean, I can imagine it's on the data cleaning side. That's just a massive time savings just in for Okay. Based on the data sampling, this is kilograms or pounds. >>Exactly. So we're talking there's more than 3.5 billion lab records in this data base now. So if you were trying to do this manually, I mean, it would take, it would take to thousands of years, you know, it just wouldn't be a black, it would >>be a black hole in the dataset, essentially because there's no way it would get done. Ok. Ok. Sam take me through like from a research standpoint, this normalization, harmonization the process. What does that enable for the, for the research and who decides what's the standard format? So, because again, I'm just in my mind thinking how hard this is. And then what was the, what was decided? Was it just on the base records what standards were happening? What's the impact of researchers >>now? It's a great quite well, a couple things I'll say. And Ben has touched on this is the other real core piece of N three C is the community, right? You know, And so I think there's a couple of things you mentioned with this, johN is the way we execute this is, it was very nimble, it was very agile and there's something to be said on that piece from a procurement perspective, the government had many covid authorities that were granted to make very fast decisions to get things procured quickly. And we were able to turn this around with our acquisition shop, which we would otherwise, you know, be dead in the water like you said, wait a year ago through a normal acquisition process, which can take time, but that's only one half the other half. And really, you're touching on this and Ben is touching on this is when he mentions the research as we have this entire courts entire, you know, research community numbering in the thousands from a volunteer perspective. I think it's really fascinating. This is a really a great example to me of this public private partnership between the companies we use, but also the academic participants that are actually make up the community. Um again, who the amount of time they have dedicated on this is just incredible. So, so really, what's also been established with this is core governance. And so, you know, you think from assistance perspective is, you know, the Palin tear this environment, the N three C environment belongs to the government, but the N 33 the entire actually, you know, program, I would say, belongs to the community. We have co governance on this. So who decides really is just a mixture between the folks on End Cats, but not just end cast as folks at End Cats, folks that, you know, and I proper, but also folks and other government agencies, but also the, the academic communities and entire these mixed governance teams that actually set the stage for all of this. And again, you know, who's gonna decide the standard, We decide we're gonna do this in Oman 5.3 point one um is the standard we're going to utilize. And then once the data is there, this is what gets exciting is then they have the different domain teams where they can ask different research questions depending upon what has interest scientifically to them. Um and so really, you know, we viewed this from the government's perspective is how do we build again the secure platform where we can enable the research, but we don't really want to dictate the research. I mean, the one criteria we did put your research has to be covid focused because very clearly in response to covid, so you have to have a Covid focus and then we have data use agreements, data use request. You know, we have entire governance committees that decide is this research in scope, but we don't want to dictate the research types that the domain teams are bringing to the table. >>And I think the National Institutes of Health, you think about just that their mission is to serve the public health. And I think this is a great example of when you enable data to be surfaced and available that you can really allow people to be empowered and not to use the cliche citizen analysts. But in a way this is what the community is doing. You're doing research and allowing people from volunteers to academics to students to just be part of it. That is citizen analysis that you got citizen journalism. You've got citizen and uh, research, you've got a lot of democratization happening here. Is that part of it was a result of >>this? Uh, it's both. It's a great question. I think it's both. And it's it's really by design because again, we want to enable and there's a couple of things that I really, you know, we we clamor with at end cats. I think NIH is going with this direction to is we believe firmly in open science, we believe firmly in open standards and how we can actually enable these standards to promote this open science because it's actually nontrivial. We've had, you know, the citizen scientists actually on the tricky problem from a governance perspective or we have the case where we actually had to have students that wanted access to the environment. Well, we actually had to have someone because, you know, they have to have an institution that they come in with, but we've actually across some of those bridges to actually get students and researchers into this environment very much by design, but also the spirit which was held enabled by the community, which, again, so I think they go they go hand in hand. I planned for >>open science as a huge wave, I'm a big fan, I think that's got a lot of headroom because open source, what that's done to software, the software industry, it's amazing. And I think your Federated idea comes in here and Ben if you guys can just talk through the Federated, because I think that might enable and remove some of the structural blockers that might be out there in terms of, oh, you gotta be affiliate with this or that our friends got to invite you, but then you got privacy access and this Federated ID not an easy thing, it's easy to say. But how do you tie that together? Because you want to enable frictionless ability to come in and contribute same time you want to have some policies around who's in and who's not. >>Yes, totally, I mean so Sam sort of already described the the UNa system which is the authentication system that encounters has developed. And obviously you know from our perspective, you know we integrate with that is using all of the standard kind of authentication protocols and it's very easy to integrate that into the family platform um and make it so that we can authenticate people correctly. But then if you go beyond authentication you also then to actually you need to have the access controls in place to say yes I know who this person is, but now what should they actually be able to see? Um And I think one of the really great things in Free C has done is to be very rigorous about that. They have their governance rules that says you should be using the data for a certain purpose. You must go through a procedure so that the access committee approves that purpose. And then we need to make sure that you're actually doing the work that you said you were going to. And so before you can get your data back out of the system where your results out, you actually have to prove that those results are in line with the original stated purpose and the infrastructure around that and having the access controls and the governance processes, all working together in a seamless way so that it doesn't, as you say, increase the friction on the researcher and they can get access to the data for that appropriate purpose. That was a big component of what we've been building out with them three C. Absolutely. >>And really in line john with what NIH is doing with the research, all service, they call this raz. And I think things that we believe in their standards that were starting to follow and work with them closely. Multifactor authentication because of the point Ben is making and you raised as well, you know, one you need to authenticate, okay. This you are who you say you are. And and we're recognizing that and you're, you know, the author and peace within the authors. E what do you authorized to see? What do you have authorization to? And they go hand in hand and again, non trivial problems. And especially, you know, when we basis typically a lot of what we're using is is we'll do direct integrations with our package. We using commons for Federated access were also even using login dot gov. Um, you know, again because we need to make sure that people had a means, you know, and login dot gov is essentially a runoff right? If they don't have, you know an organization which we have in common or a Federated access to generate a login dot gov account but they still are whole, you know beholden to the multi factor authentication step and then they still have to get the same authorizations because we really do believe access to these environment seamlessly is absolutely critical, you know, who are users are but again not make it restrictive and not make it this this friction filled process. That's very that's very >>different. I mean you think about nontrivial, totally agree with you and if you think about like if you were in a classic enterprise, I thought about an I. T. Problem like bring your own device to work and that's basically what the whole world does these days. So like you're thinking about access, you don't know who's coming in, you don't know where they're coming in from, um when the churn is so high, you don't know, I mean all this is happening, right? So you have to be prepared two Provisions and provide resource to a very lightweight access edge. >>That's right. And that's why it gets back to what we mentioned is we were taking a step back and thinking about this problem, you know, an M three C became the use case was this is an enterprise I. T. Problem. Right. You know, we have users from around the world that want to access this environment and again we try to hit a really difficult mark, which is secure but collaborative, Right? That's that's not easy, you know? But but again, the only place this environment could take place isn't a cloud based environment, right? Let's be real. You know, 10 years ago. Forget it. You know, Again, maybe it would have been difficult, but now it's just incredible how much they advanced that these real virtual research organizations can start to exist and they become the real partnerships. >>Well, I want to Well, that's a great point. I want to highlight and call out because I've done a lot of these interviews with awards programs over the years and certainly in public sector and open source over many, many years. One of the things open source allows us the code re use and also when you start getting in these situations where, okay, you have a crisis covid other things happen, nonprofits go, that's the same thing. They, they lose their funding and all the code disappears. Saying with these covid when it becomes over, you don't want to lose the momentum. So this whole idea of re use this platform is aged deplatforming of and re factoring if you will, these are two concepts with a cloud enables SAM, I'd love to get your thoughts on this because it doesn't go away when Covid's >>over, research still >>continues. So this whole idea of re platform NG and then re factoring is very much a new concept versus the old days of okay, projects over, move on to the next one. >>No, you're absolutely right. And I think what first drove us is we're taking a step back and and cats, you know, how do we ensure that sustainability? Right, Because my background is actually engineering. So I think about, you know, you want to build things to last and what you just described, johN is that, you know, that, that funding, it peaks, it goes up and then it wanes away and it goes and what you're left with essentially is nothing, you know, it's okay you did this investment in a body of work and it goes away. And really, I think what we're really building are these sustainable platforms that we will actually grow and evolve based upon the research needs over time. And I think that was really a huge investment that both, you know, again and and Cats is made. But NIH is going in a very similar direction. There's a substantial investment, um, you know, made in these, these these these really impressive environments. How do we make sure the sustainable for the long term? You know, again, we just went through this with Covid, but what's gonna come next? You know, one of the research questions that we need to answer, but also open source is an incredibly important piece of this. I think Ben can speak this in a second, all the harmonization work, all that effort, you know, essentially this massive, complex GTL process Is in the N three Seagate hub. So we believe, you know, completely and the open source model a little bit of a flavor on it too though, because, you know, again, back to the sustainability, john, I believe, you know, there's a room for this, this marriage between commercial platforms and open source software and we need both. You know, as we're strong proponents of N cats are both, but especially with sustainability, especially I think Enterprise I. T. You know, you have to have professional grade products that was part of, I would say an experiment we ran out and cast our thought was we can fund academic groups and we can have them do open source projects and you'll get some decent results. But I think the nature of it and the nature of these environments become so complex. The experiment we're taking is we're going to provide commercial grade tools For the academic community and the researchers and let them use them and see how they can be enabled and actually focus on research questions. And I think, you know, N3C, which we've been very successful with that model while still really adhering to the open source spirit and >>principles as an amazing story, congratulated, you know what? That's so awesome because that's the future. And I think you're onto something huge. Great point, Ben, you want to chime in on this whole sustainability because the public private partnership idea is the now the new model innovation formula is about open and collaborative. What's your thoughts? >>Absolutely. And I mean, we uh, volunteer have been huge proponents of reproducibility and openness, um in analyses and in science. And so everything done within the family platform is done in open source languages like python and R. And sequel, um and is exposed via open A. P. I. S and through get repository. So that as SaM says, we've we've pushed all of that E. T. L. Code that was developed within the platform out to the cats get hub. Um and the analysis code itself being written in those various different languages can also sort of easily be pulled out um and made available for other researchers in the future. And I think what we've also seen is that within the data enclave there's been an enormous amount of re use across the different research projects. And so actually having that security in place and making it secure so that people can actually start to share with each other securely as well. And and and be very clear that although I'm sharing this, it's still within the range of the government's requirements has meant that the, the research has really been accelerated because people have been able to build and stand on the shoulders of what earlier projects have done. >>Okay. Ben. Great stuff. 1000 researchers. Open source code and get a job. Where do I sign up? I want to get involved. This is amazing. Like it sounds like a great party. >>We'll send you a link if you do a search on on N three C, you know, do do a search on that and you'll actually will come up with a website hosted by the academic side and I'll show you all the information of how you can actually connect and john you're welcome to come in. Billion by all means >>billions of rows of data being solved. Great tech he's working on again. This is a great example of large scale the modern era of solving problems is here. It's out in the open, Open Science. Sam. Congratulations on your great success. Ben Award winners. You guys doing a great job. Great story. Thanks for sharing here with us in the queue. Appreciate it. >>Thank you, john. >>Thanks for having us. >>Okay. It is. Global public sector partner rewards best Covid solution palantir and and cats. Great solution. Great story. I'm john Kerry with the cube. Thanks for watching. Mm mm. >>Mhm

(upbeat music) >> Welcome to this CUBE Conversation, I'm Lisa Martin. I've got two guests from Fortinet with me next talking about an very interesting topic that's something that always piques my interest, cybersecurity, and some of the things going on with respect to that. Sandra Wheatley joins us the SVP of marketing, threat intelligence and influencer communications at Fortinet. Sandra, it's great to see you again. >> Thank you, Lisa. I'm delighted to be here today. >> Lisa: Good and Rob Rashotte is here as well, vice-president, global training and technical field enablement at Fortinet. Rob welcome to the program. >> Hi, great to meet you Lisa. Nice to be here. >> Likewise. So since I last saw Fortinet we've had such a challenging year as we all know, that's an understatement, but one of the things that happened so quickly was the distribution of the workforce. And there were already preexisting gaps in IT Visibility and teams being siloed, security teams being siloed as well exacerbated distinct cybersecurity skills gap. So Sandra I want to start with you. Talk to us about what's going on with the cybersecurity skills gap and how it's impacting organizations today. >> Thank you, Lisa. While the cybersecurity skills gap continues to be one of the biggest challenges facing security organizations today, as you know, the cybersecurity space is very dynamic. It's constantly changing and we saw this even through COVID with more people working from home or being educated from home. Cyber adversaries are using remote workers as a way into the enterprise network. And so security organizations today are facing a lot of complexity. They deal with billions of alerts that come in every day and a lot of these have to be managed manually and they just don't have the professionals to keep up with that. So it continues to be a big issue facing organizations. We have seen some progress about a year ago. It was estimated that we would need 4 million professionals come into the industry to close the gap. We are now at probably a little bit over 3 million. So there is progress being made but we still have a long way to go. >> Yeah, good progress there. But what I mean, one of the things that we saw so quickly was with the distribution center was suddenly, there were tons of trusted devices that were off the network perimeter where all these keep going, "Use your own device at home until we can get you something provisioned on the network." So huge challenge that was almost like a light switch for people in any industry. Rob, talk to me from your perspective the ongoing cybersecurity skills gap. What are some of the things that you were seeing through your lens? >> Yeah, well, I mean it has certainly changed our focus over the last year with the pandemic and the change in workforce and so on. And I think as a cybersecurity vendor, a lot of the times when we talk about training and the skills gap we often tend to think pretty quickly about engineers and technical training and like this has really opened up our eyes too. We need to really broaden our scope when we're talking about training and closing the skills gap, because it's a lot more than just engineers. So we've had to really focus more on really anyone sitting in front of a computer screen and ensure that programs are available for people that are working from home that need to understand, the fact that security is just as big an issue if you're working from home or working from the office. So it's really broadened our scope in terms of who we're delivering training to and within a number of our programs, actually, that has happened. When we're dealing with we have a lot of academic partners that we deliver training with them. And one thing that's happened there is we we've traditionally dealt with engineering schools within our academic partners but now we're starting to see a lot of business schools coming and talking to us about delivering training within MBA programs and so on. So that business leaders can start understand, the need to be addressing cybersecurity in the boardroom for example, not just within the it department. So it's I guess the one thing I would say is it's really broadened our scope in terms of who the audience is for cybersecurity and the skills gap is a, you know it impacts a lot of different areas in the organization. >> Yeah, you brought up a great point there that elevation of security to the board level is critical. As we saw like big spikes and things like Ransomware last year. Ransomware getting much more sophisticated kind of playing on people's concerns for buzzwords like COVID-19 for example, and I talked to a lot of organizations where security is at the board level but the talent gap is another challenge. Sandra talk to us about what Fortinet is doing from a partnership perspective to help shrink that gap. >> Well, it's interesting because if you were to do a survey of people about where the responsibility lies to train more professionals for the industry, you'll see a split about 40% of people feel like academia should be providing the training and the curriculum to bring more professionals into the industry. And then others feel like it's a mix between corporate private public partnerships. And that's something that Fortinet believes in. We are tackling this issue on multiple fronts. We recently launched our TAA initiative or our Training Advancement Agenda, and a lot of the pro programs that Rob manages are part of that agenda like our free NSE training, our security academies, but we're also working with a lot of global partners, corporate partners like Salesforce, and IBM. We're also working with the World Economic Forum on this initiative because we really believe it's a joint effort to really make a difference. And so, for example, with Salesforce we provide some of our curriculum and training for free on their training platform, the same with IBM. And we'll continue to scale these partnerships because with these partners, we can reach more people and accelerate the impact that we can have overall. >> Absolutely that ability to expand it especially as we saw such a change in the cyber threat landscape last year as you said, Sandra you've made great progress needing, you know, a deficit of 4 million folks down to 3 million, but also looking at the opportunity to try to find more folks leveraging partners and to rubs point elevating the conversation or expanding that scope. This isn't just a problem for IT and security folks. This is a challenge across the organization that the board needs to be focused on because we've seen in this rapidly changing last year organizations and enough peril in trying to pivot their businesses. And then you add on some of the cyber threats. Rob can you talk a little bit more about the TAA initiative? I know that about your Network Security Expert program NSE program, you guys also do FortiVet program. Tell us a little bit about some of those programs and maybe some of the things that you've done to broaden the scope during the last year. >> Yeah, it certainly can. I mean, there's a number of programs that make up the agenda and you know we've widened the scope in terms of the audiences that we're looking at. But also as Sandra mentioned, trying to expand our reach as ordinary, obviously we have a reach into our partners and our ecosystem, but the ecosystem of the IBM's and the world economic forums and so on go far beyond our reach. But one of the things that we were able to do as a company almost exactly a year ago, we made the conscious decision that the training curriculum that we've built, we wanted to make it available to as many people as we possibly could. So we we've made approximately 400 hours worth of cybersecurity training available to anyone that wants to sign up and take the training in self-paced format, where they want to take it, when they want to take it. So that was a big commitment on our part and that training continues to be free today and we'll keep it free until we start to see the skills gap closed but that that has resulted I guess it was about a month or two ago when we were tracking numbers that we've exceeded over a million registrations for that training, which really was validation to us that the demand for this training is massive. So that's helped us expand our reach but that training as well we're making it available for free, but we have all sorts of different types of partners who are taking that training and making it three free through their learning portals as well. So it's really expanded the reach in that way. You know, another area that we've really focused on is partnering with nonprofits who are representing underrepresented groups. So you mentioned the veterans program that's been a program we've had for quite a while now, but we've looked at that program and thought, well, you know, we can definitely replicate our efforts there and look at other groups as well and start to see how we can partner with different NGOs to really address the diversity and inclusion, within the cybersecurity industry. 'Cause, you know, I think one thing that's interesting here is because of the skill shortage, a lot of hiring managers have had to start to look at recruiting through non traditional streams. And that that can be, you know, looking at if we have policies that say, we must hire people with four year degrees. Well, maybe we want to take a look at that and see well is that really necessary for all the jobs that we're looking at? Maybe we could look at shorter programs even high school students but then also looking at underrepresented groups it is a great way for us to take a look at this skills gap in cybersecurity and align it with our diversity and inclusion initiatives, internally within our organizations and see how we can bring that to bear on problem and really start to have the same time, create a much more diverse workforce within cybersecurity while we're trying to close that skills gap. >> I love that what a great opportunity to expand upon that. I wanted to ask you just really quickly, Rob she said 400 hours of free cyber training available over a million registrations so far. You're right, that definitely shows the demand. I'm curious when we think of backgrounds we think are these, you know need to be IT folks. Is that curriculum broad enough so that somebody with a marketing degree or somebody that doesn't have a degree could kind of get in on level one and start learning their way up the security stack? >> Yeah, it is a very broad scope. When we look at the catalog, it is multiple levels. And in fact our network security expert program it's an eight level program. And the first couple of levels of that program are applicable to anyone that needs an awareness of cybersecurity and the issues. So, yeah, it's perfect. And `in fact the level one of that program is something that we've integrated into a new service offering which is our Cybersecurity Awareness Program that companies can implement internally to provide that base level of cybersecurity awareness to all of their employees. And then as you go up to level two, three, four and five, and so on, it gets more and more technical right up to the NSE level or we're talking about, you know, architects engineers are developing very large critical cyber security infrastructures. >> Lisa, you bring up a very important point that I'd like to make a comment on. There's this misconception that you need a degree in Computer Science or some other technical degree to be in cyber security. And that's absolutely not the case. In fact, half the people in cybersecurity don't have a degree in any Computer Science program, et cetera, but you know there's a lot of skillsets and backgrounds that really map well to cybersecurity. And it's a very broad industry. There was new roles coming all of the time. So I would encourage people to not let that be a barrier to getting into this industry. And in fact our Veteran's program has been extremely successful because people coming out of the defense forces have a lot of the skills that match very well to cyber security like attention to detail, situational awareness, the ability to work under pressure. So it's definitely a misconception that the industry needs to correct. >> I couldn't agree more, especially as the daughter of a Vietnam Combat Veteran and I love what you guys are doing with veterans but you're right. There's so many other skills that people have that are so transportable and transferable that, and it's such an exciting industry. I mean, we all have a million devices scattered around. I think with those new Apple tags that if I put one on my dog's collar, my dog's going to be a connected device. There's so many opportunities to learn but there's also more exposure. The more people that have different backgrounds I think just that with that thought diversity alone, organizations in any industry can benefit. Sandra talk to us about how partners are taking some of these programs and rolling them into their own to help kind of open that door wider as you say, to make sure that barrier isn't there and also get more folks aware of what they can learn. >> Yeah, the encouraging thing is I just see a lot more creativity around this issue. If you think about it, the lack of diversity in IT has been a challenge for everyone that the issue in cybersecurity is just a manifestation of that. And one of the reasons is that it's particularly cybersecurity. A lot of people don't understand how to get into the industry, or they have a lack of awareness about the different types of roles. And we see this in particular with women and young females as well as underserved minority groups. In fact, the veterans program is one way to bring more of that diversity into the industry. And if you think about it today, women make up about 24%. I think it's single digits for underrepresented groups. So we have a huge opportunity there. And I think somehow working with our partners we're doing a lot of different things. Not only are we providing our curriculum and our training and the technical support, but we're also done a lot of work around mapping roles and the steps you need to take to, to achieve those roles. So we've created that for different roles, and we've shared that with some of our training partners and they provide that information on their training platforms. We also regularly have done a lot of different podcasts and interviews with women and minorities have gone through the industry and been very successful talking about how they did that and how they got there. We're working with lots of nonprofits like Women in Cybersecurity speaking to people out there providing them the support. So it's a multi-phase approach. And I do think that private industry need to be doing things like creating entry level kinds of roles to bring more people in the industry and recruit differently. But the good news is there's a huge amount of awareness around this, and you definitely see companies doing a lot more, as well as our partners. >> Well if I could just touch on something there, well Sandra is talking about the different career roles and so on. The industry can get pretty complicated pretty quickly when we're talking about different roles. And there's a lot of buzzwords. And you know when people are looking at this and say, well, how do I even get into this industry? It sounds very technical complicated. And, you know, there are a number of different career patching tools that you can find out there around cyber security but when there's too many of those that even gets confusing. So the career paths that we've developed, we've done that in conjunction with NICE and there's an initiative called the NICE Framework which stands for National Initiative for Cyber Security Education. And so the pathways that we've developed map to that. So, you know, that's one thing I'd like to encourage other organizations to make sure that we're all following that framework so that as we're providing these career paths to people we're using the same terminology. We're using the same titles and career paths and so on. So it just makes it a little bit more understandable for people to pick a path that they want and then start their journey. >> I also think exposing students earlier in their education about cyber security is really important. In fact, we're just released a book called "Cyber Safe" and it's targeting elementary school children and their parents and making them more aware of cybersecurity, the risks, how they should behave online. It talks about cyber bullying and it also helps has guidance in there for parents. And this is a book that we're making freely available to underserved schools and it can easily be accessed online. We've had great reviews, but it's all part of our TAA efforts to educate and make people more aware about the opportunities on the industry overall. >> I love that, Sandra our SVP of marketing. Is there a URL that you can give our audience where they can find that free resource? >> Yes, you can find that I believe on our NSE training page. You can just go to fortinet.com NSE and or TAA and you will find information about how to get the book. >> Excellent so fortinet.com search TAA or NSE you'll find that information. I'm going to check that out myself 'cause maybe you know, for adult children of parents who also need some cybersecurity help I think I might check that out for myself. >> You can (indistinct) copy Lisa. >> Thank you, excellent. It's been great talking to you guys. This is such an interesting topic. I love the efforts that Fortinet is doing to close those gaps and also what you're doing to bridge that with the diversity and inclusion efforts brought out. That's a great effort, Sandra, Rob thank you for joining me today. >> Thank you, Lisa. >> Thank you, Lisa >> For Sandra Wheatley and Rob Rashotte. I'm Lisa Martin. You're watching this CUBE conversation with Fortinet. (gentle music)

>>from around the globe. It's the >>cube with digital >>coverage of IBM >>Think 2020 >>one brought to you by IBM. Welcome back to the cubes coverage of IBM Think 2021 john for your host of the cube we're here. Howard Belleville is the head of hybrid cloud platform for IBM been in the industry for many, many decades as a practitioner heading up organizations now at IBM heading up the hybrid cloud. Howard, great to have you on the cube. >>Pleased to be here, john thank you for your time. Can >>you tell us a little about the digital transformation trends that you've seen in the past year as they have clearly shook the industry? Certainly Covid no one would have predicted provisioning VPN access or remote access for all the employees. I'm sure that wasn't on anyone's radar but many more other disruptions and opportunities for accelerating these new what are now obvious benefits. Can you take your time to explain what you're seeing? >>Yeah, sure. So there's been a huge amount of acceleration of digital transformation. So VPN projects, as you mentioned, the people working from home projects that in the past were taking many, many years to work through then got done literally in weeks, um and they're very complex when you get under the skin of them. Um and companies therefore saw confidence in that and start to look at broader digital transformations um and you can kind of think about in terms of the successes and their failures or the lessons learned from them. So when it's done right, what I've observed from companies that have been it right, they've done it from a business process perspective, they looked at their business processes that they want to transform as opposed to just the independent technology, um but the companies that have been around for a while, I've also been understood that legacy is a problem. So God created the Earth in seven of the world in seven days, but that's because he didn't have any legacy to deal with. So as companies have taken the confidence for the smaller projects to work through, they found in these larger ones where they've got legacy environments to work through. Digital transformation is still very important, but it's not as straightforward as they thought it might be. >>You know, one of the things that's coming out of the hybrid cloud um, discussion is a couple of things. One is everyone now agrees that this is the standard and multi cloud soon around the corner. Um, Hybrid clouds and operating model. Um, and it's a new kind of operating system with with the ability to use kubernetes and containers and microservices and other service message to to integrate legacy. This is huge. What's the biggest pain points that you're seeing from an adoption standpoint that our blockers from clients, What's getting in the way of the obvious now path with hybrid cloud? >>Well actually the first and foremost the position that IBM is created by kind of calling out hybrid cloud where companies will be on premise and off premise because the legacy IFC IOS around the world, the huge sigh of relief and having sat in their seats, I often thought I must be the dumbest person in the room because I don't understand this full on public cloud model because I can't see the benefits to my shareholders that that would deliver. I could see it to the pure play cloud service providers but not to to myself. So talking to C I O S I think thank heavens for that were no longer seen as a Luddite when we're explaining that we'll be on premise and off premise and it'll be heterogeneous environments were operating with within the simple way to think about the blockers and actually done a nice job yourself, john in terms of explaining this is cloud, is is simply another resource tool that you use to run your applications or your data sets on and in the past you had nicely curated environment within it was in your own environment, but there are benefits that you can get by using more innocent technologies like cloud, particularly around developed productivity. But in chapter one of cloud with a pure flare cloud providers, it was kind of a carbuncle that you kind of put onto the side of your organization, which then became very difficult. This kind of Frankenstein's monster of piece parts to put together from an IT operations and cyber security perspective. >>Okay, so you talk about this Franken cloud model before, I've heard heard that come from you. What is this about? You just referenced it there. What is the Franken cloud? >>Yeah, that's the simple way to think about it is um, in the old world, when you run all of your applications, your data sets, your developers in your own data sensors, you would create a curated model that would allow you to very strongly from an architectural perspective, lots of different legacy environments. But the actual architecture put around it would be clean and the operational environment will be clean and the actual Cyprus security controls, you put on a third party capability, whether that's a cloud service provider or a software as a service provider. And you had a world of complexity where you have no control over those environments and you're certainly not driving the architectural standards. So you're putting together these peace parts in the same way as dr Frankenstein put together the monster he created and ultimately, that will turn upon you. It will create technical operational issues that will create economic issues and it absolutely will create cybersecurity issues. So the important thing to think about on these digital transformations is the architecture in a hybrid context is one that will work for you with a multi cloud environment, whether that's from a software as a service provider or from the cloud service >>provider. It's interesting you bring up these other turning on you kind of the Franken cloud. I get that. But let's bring that up to the positive a client customer might say, hey, you know, I did a great job of moving into the public cloud. I brought some stuff on hybrid. Oh my God, look at the push some new stuff and then I pushed new new code and then things breaks. They call this day to operations. Or as you guys are referring to a I ops. These are opportunities. So how does the company get their arms around that? Because that's gonna be the next progression. Okay. I'm operating on distributed basis. Alright, great. I got an edge data center, whatever. But now I'm pushing code all the time. I don't want it to break. >>Yeah, I mean most of my comments are based upon the experiences and the mistakes that I've made in my career. So that element that you talked about there that day to operations, not only are we going through an inflection point in terms of the technologies that are used and the architecture is at a technical level, you have to put together the silicon that you think about. You're going to really think about the carbon, the people and the operating model that you have because a lot of the actual manual work you did previously will be done in a in an automated fashion. So an Ai fashion. So any transformation program needs to look at the actual transformation of the skills of the people you have working for you and they shouldn't feel fearful that it's a place where they actually won't have a role. They just won't have a role with the current skill sets they've got. But there are adjacent skill sets that you can have that they can actually be trained into or get an assignment where they get the experience to operate in that fashion. >>I'd love to get the comment on the edge with this system on a chip soc as it's called As more and more capabilities are going to be at the edge. But I want to stay on this quick cloud thing on Franken cloud because you know, one of the things that I see with the positives of cloud is is that okay? Can be more agile. But then I get worried that if I'm going too fast, I might break something. I get fired. I got all this compliance, don't get sued or you know, there's all kinds of regulations now and compliance around distributed clouds globally. So what's your take on that? What specific challenges do these companies face when they're either in regulated industries or don't want to go too fast? I gotta, I gotta watch that day to make sure it's not gonna be misused. >>Yeah. So the, so the philosophy that we have at IBM is different to Chapter one and the pure player cloud providers, which is, we believe if you build the actual compliance controls in from the outset and have them as a standard of consumption for all customers, they can actually accelerate their adoption of cloud so they can actually get to the benefits of cloud productivity innovation far more quickly. And that's been evidenced by Chapter one where all large institutions in multiple year programs spend tens of millions of dollars and are building the compliance controls themselves. You don't do that with IBM, you get that out of the box for the entire industry. We keep that fresh and current and vibrant going forward. So there's non functional requirements and no longer a consideration for you and you can then focus your energy, your developers in terms of the actual points of innovation, on the functional capabilities that you can provide. >>I want to get your reaction to something and you comment if you don't mind. I mean, there's been a big trend of data clouds built on other people's clouds and you've got the needs of special specialty and industries or vertical needs. Do you see the need or you see a path for specialty clouds or vertical clouds? Specifically as these? The A. I. And data can be relative to these verticals but you want at the same time horizontal scalability for data plane or data access. What's your take on specialty clouds? >>That's at the heart of the thesis. And the idea that we have here at IBM, which is there is a need for specialty clouds of particular industries and their their workloads. And really it's kind of people look back in the very near future. That's a that's an evident thing because again in the old world when it was in your own data center, you would have build types for specific types of applications and the processes that are supported and the risk posture of that and then the associated data sets. Um so the capabilities that we built within our global availability zones is for the large enterprises and that's an area that's obviously being heritage and then it's not just the software level, it's the hardware it runs on. So IBM provides the hardware from a mainframe power X 86. So for all those kind of form factors and an operating system level, obviously through Lennox in terms of the capabilities that we have so we can meet all of that stack but build them specifically for the applications and the data sets for the industries that we serve and the capabilities necessary. >>That's great stuff. I want to take your take shift gears to cybersecurity. I mean every time you look at the headline of a breach, solar winds had more implications than anyone could imagine. You do you hire more firefighters to put out the fire? Do you make fire resistant materials? I mean, there's optimization balance. What do you think is the best way to prevent cyber breaches going forward? What's your take on this? I'm sure you have >>a person, the world world of cyber security, it's all of the above and them anymore because you've got to put checks and balances in terms of every capability having kind of come from an environment where my old bank was named after the country it was in and therefore Nation state to take great delight in terms of trying to breach the area. So all of those controls are necessary um as you, as you put them in the other element to think about on digital supply chains is again, if you actually have your supply chain on a cloud that has the compliance controls built in, they benefit and inherit that as well. Whereas if you don't, you got to actually ensure that they are actually attesting to the controls. The cloud that we built here at IBM gives you continuous monitoring to sure that those software as a service providers are actually adhering to the controls you want in real time. That is a massive game changer. In terms of the, the logging information we can provide to customers to ensure that their digital supply chain does not become compromised >>real quick. While I've got you here as cyber standards become around hybrid. Uh, the early responses were specialized on AWS as your google and they pick one to have a backup cloud and build your teams around that your developer teams. Does that shift with hybrid? How does seesaws change with hybrid? >>Yeah. So, the benefit in terms of the entry to IBM has in the cloud space, which is probably in terms of the current variants, two years old is that we're not dealing with legacy. Um so we're kind of learning from the mistakes of these older cloud providers that have got a wealth of legacy and their environments, both at the actual hardware level, but also the code base level, some more so than others in terms of the issues they have with their code base um and therefore with the ai ops and the actual cyber security tools that we put employers were building upon, the bad experiences they've had, but also other intelligence that we get in terms of threat vectors as they come through, john >>howard. In the last question to end the segment, you've led a lot of digital transformation initiatives through your career. What have you found has been the best practice as that applies now as companies are coming out of covid, they wanna have a growth strategy. You want to make sure the foundations in place, that's solid that they can build upon. What's your, what's your lessons to learn, What's your best practice advice. >>So you've got to deal with the difficult problems first that sometimes a fundamental to get to pierce. So controls appears to be a fairly mundane topic. But unless you can deal with the controls, you can't actually get the accelerated pierce. And when you do these transformations, you have to bring your people along with you at the same time as your transfer transforming the technology. So you need the silicon to be allied with the carbon and then you get people are actually change Hungary as opposed to change resistant. >>Howard bravo. Thanks for coming on the cube head of hybrid cloud platforms at IBM. Thanks for joining us today. >>You're welcome. Thank you, john. >>Okay. I'm John Free with the Cube for IBM think 2021 coverage. Thanks for watching. Mhm Yeah.

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. Welcome to the cubes event. Virtual event. Cuban Cloud. I'm John for your host. We're here talking to all the thought leaders getting all the stories around Cloud What's going on this year and next today, Tomorrow and the future. We gotta featured startup here. Jonah Clipper, who is the CEO and founder of Stack Hawks. Developing security software for developers to have them put security baked in from the beginning. Johnny, thanks for coming on and being featured. Start up here is part of our Cuban cloud. Thanks for joining. >>Thanks so much for having me, John. >>So one of our themes this year is obviously Cloud natives gone mainstream. The pandemic has shown that. You know, a lot of things have to be modern. Modern applications, the emerald all they talked about modern applications. Infrastructure is code. Reinvent, um is here. They're talking about the next gen enterprise. Their public cloud. Now you've got hybrid cloud. Now you've got multi cloud. But for developers, you just wanna be building security baked in and they don't care where the infrastructure is. So this is the big trend. Like to get your thoughts on that. But before we jump in, tell us about Stack Hawk What you guys do your founded in 2019. Tell us about your company and what Your mission is >>Awesome. Yeah, our mission is to put application security in the hands of software developers so that they can find and fix upset books before they deployed a production. And we do that through a dynamic application scanning capability. Uh, that's deployable via docker, so engineers can run it locally. They can run it in C I C. D. On every single PR or merge and find bugs in the process of delivering software rather than after it's been production. >>So everyone's talking about shift left, shift left for >>security. What does >>that mean? Uh, these days. And what if some of the hurdles that people are struggling with because all I hear is shift left shift left from, like I mean, what does What does that actually mean? Now, Can you take us through your >>view? Yes, and we use the phrase a lot, and I and I know it can feel a little confusing or overused. Probably. Um, When I think of shift left, I think of that Mobius that we all look at all of the time, Um, and how we deliver and, like, plan, write code, deliver software and then manage it. Monitor it right like that entire Dev ops workflow. And today, when we think about where security lives, it either is a blocker to deploying production. Or most commonly, it lives long after code has been deployed to production. And there's a security team constantly playing catch up, trying to ensure that the development team whose job is to deliver value to their customers quickly, right, deploy as fast as we can, as many great customer facing features, um there, then, looking at it months after software has been deployed and then hurrying and trying to assess where the bugs are. And, um, trying to get that information back to software developers so that they can fix those issues. Shifting left to me means software engineers are finding those bugs as their writing code or in the CIA CD pipeline long before code has been deployed to production. >>And so you guys attack that problem right there so they don't have to ship the code and then come back and fix it again. Or where we forgot what the hell is going on. That point in time some Q 18 gets it. Is that the kind of problem that that's out there? Is that the main pain point? >>Yeah, absolutely. I mean a lot of the way software, specifically software like ours and dynamic applications scanning works is a security team or a pen tester. Maybe, is assessing applications for security vulnerability these, um, veteran prod that's normally where these tools are run and they throw them back over the wall, you know, interrupting sprints and interrupting the developer workflow. So there's a ton of context switching, which is super expensive, and it's very disruptive to the business to not know about those issues before they're in prod. And they're also higher risk issues because they're in fraud s. So you have to be able to see a >>wrong flywheel. Basically, it's like you have a penetration test is okay. I want to do ship this app. Pen test comes back, okay? We gotta fix the bug, interrupts the cycle. They're not coding there in fire drill mode. And then it's a chaotic death spiral at that point, >>right? Or nothing gets done. God, how did >>you What was the vision? How did you get here? What? How did you start? The company's woke up one morning. Seven started a security company. And how did what was the journey? What got you here? >>Sure. Thanks. I've been building software for software engineers since 2010. So the first startup I worked for was very much about making it easy for software engineers to deploy and manage applications super efficiently on any cloud provider. And we did programmatic updates to those applications and could even move them from cloud to cloud. And so that was sort of cutting my teeth and technology and really understanding the developer experience. Then I was a VP of product at a company called Victor Ops. We were purchased by spunk in 2018. But that product was really about empowering software engineers to manage their own code in production. So instead of having a network operations center right who sat in front of screens and was waiting for something to go wrong and would then just end up dialing there, you know, just this middle man trying to dial to find the person who wrote the software so that they can fix it. We made that way more efficient and could just route issues to software engineers. And so that was a very dev ops focused company in terms of, um, improving meantime to know and meantime to resolve by putting up time in the hands of software engineers where it didn't used to live there before it lived in a more traditional operations type of role. But we deploy software way too quickly and way too frequently to production to assume that another human can just sit there and know how to fix it, because the problems aren't repeatable, right? So So I've been living in the space for a long time, and I would go to conferences and people would say, Well, I love for, you know, we have these digital transformation initiatives and I'm in the security team and I don't feel like I'm part of this. I don't know. I don't know how to insert myself in this process. And so I started doing a lot of research about, um, how we can shift this left. And I was actually doing some research about penetration testing at the time, Um, and found just a ton of opportunity, a ton of problems, right that exist with security and how we do it today. So I really think of this company as a Dev Ops first Company, and it just so happens to be that we're taking security, and we're making it, um, just part of the the application testing framework, right? We're testing for security bugs, just like we would test for any other kind of bucks. >>That's an awesome vision of other great great history there. And thanks for sharing that. I think one of the things that I think this ties into that we have been reporting aggressively on is the movement to Dev Stack Up, Dev, Ops Dev SEC Ops. And you know, just doing an interview with the guy who stood up space force and big space conversation and were essentially riffing on the idea that they have to get modern. It's government, but they got to do more commercial. They're using open source. But the key thing was everything. Software defined. And so, as you move into suffer defined, then they say we want security baked in from the beginning and This is the big kind of like sea level conversation. Bake it in from the beginning, but it's not that easy. And this is where I think it's interesting where you start to think, uh, Dev ops for security because security is broken. So this is a huge trend. It sounds easy to say it baked security in whether it's an i o T edge or multi cloud. There's >>a lot >>of work there. What should people understand when they hear that kind of platitude of? I just baked security and it's really easy. It's not. It's not trivial. What's your thoughts on >>that? It isn't trivial. And in my opinion, there aren't a lot of tools on the market that actually make that very easy. You know, there are some you've had sneak on this program and they're doing an excellent job, really speaking to the developer and being part of that modern software delivery workflow. Um, but because a lot of tools were built to run in production, it makes it really difficult to bake them in from the beginning. And so, you know, I think there are several goals here. One is you make the tooling work so that it works for the software engineer and their workflow. And and there's some different values that we have to consider when its foreign engineer versus when it's for a security person, right? Limit the noise, make it as easy as possible. Um, make sure that we only show the most critical things that are worth an engineer. Stopping what they're doing in terms of building business value and going back and fixing that bugs and then create a way to discuss in triage other issues later outside of the development. Workflow. So you really have to have a lot of empathy and understanding for how software is built and how software engineers behave, I think, in order to get this right. So it's not easy. Um, but we're here and other tools air here. Thio support companies in doing that. >>What's the competitive strategy for you guys going forward? Because there's a big sea change. Now I see an inflection point. Obviously, Cove it highlights. It's not the main reason, but Cloud native has proven it's now gone mainstream kubernetes. You're seeing the big movement there. You're seeing scale be a huge issue. Software defined operations are now being discussed. So I think it's It's a simple moment for this kind of solution. How are you guys going to compete? What's what's the winning strategy? How are you guys gonna compete to win? >>Yeah, so there's two pieces to that one is getting the technology right and making sure that it is a product that developers love. And we put a ton of effort into that because when a software engineer says, Hey, I'd love to use the security product, right? CSOs around the world are going to be like, Yes, please. Did a software engineer just ask me, You have the security product. Thank you, Right. We're here to make it so easy for them and get the tech right. And then the other piece, in terms of being competitive, is the business model. There were something like, I don't You would know better than me, but I think the data point I last saw was like 1300 venture backed security companies since 2012 focused on selling to see SOS and Fortune 2000 companies. It is a mess. It's so noisy, nobody can figure out what anybody actually does. What we have done is said no, we're going to take a modern business model approach to security. So you know, it's a SAS platform that makes it super easy for a software engineer or anybody on the team to try and buy the software. So 14 day trial. You don't have to talk to anybody if you don't want Thio Awesome support to make sure that people can get on boarded and with our on boarding flow, we've seen that our customers go from signing up to first successful scan of their platform or whatever app they chose to scan in a knave ridge of about 10 minutes. The fastest is eight, right? So it's about delivering value to our customers really quickly. And there aren't many companies insecurity on the market today. That do that? >>You know, you mentioned pen test earlier. I I hear that word. Nice shit. And, like, pen test penetration test, as it's called, um, Sock reports. I mean, these are things that are kind of like I got to do that again. I know these people are doing things that are gonna be automated, but one of the things that cloud native has proven as be killer app is integrations because when you build a modern app, it has to integrate with someone else. So there you need these kind of pen tests. You gotta have this kind of code review. And as code, um, is part of, say, a purpose built device where it's an I o T. Edge updates have toe happen. So you need mawr automation. You need more scale around both updating software to, ah, purpose built device or for integration. What's your thoughts in reaction to that? Because this is a riel software challenge from a customer standpoint, because there are too many tools out there and every see so that I talk to says, I just want to get rid of half the tools consolidate down around my clouds that I'm working through my environment and b'more developer oriented, not just purchasing stuff. So you have all this going on? What's your reaction to that? You got the you know, the integration and you've got the software updates on purpose built devices. >>Yeah, I mean, we I make a joke a little bit. That security land is like, you know, acronyms. Dio there are so many types of security that you could choose to implement. And they all have a home and different use cases that are certainly valuable toe organizations. Um, what we like to focus on and what we think is interesting and dynamic application scanning is because it's been hard toe automate dynamic application for especially for modern applications. I think a lot of companies have ignored theon pertuan ity Thio really invest in this capability and what's cool about dynamic. And you were mentioning pen testing. Is that because it's actively attacking your app? It when you get a successful test, it's like a It's like a successful negative test. It's that the test executed, which means that bug is present in your code. And so there's a lot less false positives than in other types of scanning or assessment technologies. Not to say there isn't a home for them. There's a lot of we could we could spend a whole hour kind of breaking down all the different types of bugs that the different tools confined. Um, but we think that if you want to get started developer first, you know there's a lot of great technologies. Pick a couple or one right pick stack hawk pick, sneak and just get started and put it in your developer workflow. So integrations are super important. Um, we have integrations with every C I C. D provider, making it easy to scan your code on every merge or release. And then we also have workflow integrations for software engineers associated with where they want to be doing work and how they want to be interrupted or told about an issue. So, you know, we're very early to market, but right out of the gate, we made sure that we had a slack integration so that scans are running. Or as we're finding new things, it's populating in a specific slack channel for those engineers who work on that part of the app and you're a integration right. If we find issues, we can quickly make tickets and route them and make sure that the right people are working on those issues. Eso That's how I think about sort of the integration piece and just getting started. It's like you can't tackle the whole like every accurate, um, at once like pick something that helps you get started and then continue to build out your program, as you have success. >>A lot of these tools can they get in the hands of developers, and then you kind of win their trust by having functionality. Uh, certainly a winning strategy we've seen. You know, Splunk, you mentioned where you worked for Data Dog and very other tools out there just get started easily. If it's good, it will be used. So I love that strategy. Question. I wanna ask you mentioned Dr earlier. Um, they got a real popular environment, but that speaks to the open source area. How do you see the role of open source playing with you guys? Is that gonna be part of your community outreach? Does the feed into the product? Could you share your vision on how stack hawks engaging and playing an open source? >>Yeah, absolutely. Um So when we started this company, my co founders and I, we sat down and said here, What are the problems? Okay, the world doesn't need a better scanner, right? If you walk the floor of, ah, security, uh, conference. It's like our tool finds a million things and someone else is. My tool finds a million and five things. Right, And that's how they're competing on value. It's really about making it easy to use and put in the pipeline. So we decided not to roll. Our own scanner were based on an open source capability called Zap the Set Attack Proxy. Uh, it is the most the world's most downloaded application scanner. And, uh, actually we just hired the founder of Zap to join the Stack Hawk team, and we're really excited to continue to invest in the open source community. There is a ton of opportunity to grow and sort of galvanize that community. And then the work that we do with our customers and the feedback that we get about the bugs we find if there, ah, false positive or this one's commonly risk accepted, we can go back to the community, which were already doing and saying, Hey, ditch this rule, Nobody likes it or we need to improve this test. Um, so it's a really nice relationship that we have, and we are looking forward to continuing to grow that >>great stuff. You guys are hot. Start of love. The software on security angle again def sec. Cox is gonna be It's gonna be really popular. Can you talk about some of the customer success is What's the What's the feedback from customers? Can you share some of the use cases that you guys are participating in where you're winning? You mentioned developers love it and try It can just give us a couple of use cases and examples. >>Yeah. Ah, few things. Um ah, lot of our customers are already selling on the notion. Like before we even went to G A right. They told all of their customers that they scan for security bugs with every single release. So in really critical, uh, industry is like fintech, right. It's really important that their customers trust that they're taking security seriously, which everybody says they dio. But they show it to their customers by saying here, every single deploy I can show you if there were any new security bugs released with that deploy. So that's really awesome. Other things We've heard our, uh, people being able to deploy really quickly thio the Salesforce marketplace, right? Like if they have toe have a scan to prove that that they can sell on Salesforce, they do that really rapidly. Eso all of that's going really well with our customers. >>How would I wanna How would I be a customer if I was interested in, um, using Stack Hawks say we have some software we wanna stand up, and, uh, it's super grade. And so Amazon Microsoft Marketplace Stairs Force They'll have requirements or say I want to do a deal with an integration they don't want. They want to make sure there's no nothing wrong with the code. This seems to be a common use case. How doe I if I was a customer, get involved or just download software? Um, what's the What's the procurement? What's the consumption side of it looked like, >>Yeah, you just go to Stockholm dot com and you create an account. If you'd like to get started that way so you can have a 14 day free trial. We have extremely extensive documentation, so it's really easy to get set up that way. You should have some familiarity. Or grab a software engineer who has familiarity with a couple of things. So one is how to use Docker, right? So Docker is, ah, deployment mechanism for the scanner. We do that so you can run it anywhere that you would like to, and we don't have to do things like pierce firewalls or other protective measures that you've instrumented on your production environment. You just run it, um, wherever you like in your system. So locally, C I c d So docker is an important thing to understand the way we configure our scanner is through a, um, a file. So if you are getting a scan today, either your security team is doing it or you have a pen tester doing it. Um, the whole like getting ready for that engagement takes a lot of time because the people who are running the tests don't know how the software was built. So the way we think about this is, just ask them. So you just fill out a Yamil file with parameters that tell the scanner what to dio tell it how to authenticate and not log out. Um, feed us an A p. I speak if you want, so weaken super efficiently, scan your app and you can be up and running really quickly, and then that's it. You can work with our team at any time if you need help, and then we have a really efficient procurement process >>in my experience some of the pen tests of firms out there, is it? It's like the house keeping seal of approval. You get it once and then you gotta go back again. Software change, new things come in. And it's like, Wait a minute, what's the new pen test? And then you to write a check or engaged to have enough meeting? I mean, this is the problem. I mean, too many meetings. Do you >>guys solve that problem? Do >>you solve that problem? >>We solve a piece of that problem. So I think you know, part of how I talk about our company is this idea that we live in a world where we deploy software every single day. Yet it seems reasonable that once a year or twice a year, we go get a pen test where human runs readily available, open source software on our product and gives us a like, quite literal. Pdf of issues on. It's like this is so intellectually dishonest, like we deploy all of the time. So here's the thing. Pen tests are important and everybody should do them. But that should not be the introduction to these issues that are also easy to automate and find in your system. So the way we think about how we work with pen testers is, um, run, stack hawk or zapped right in an automated fashion on your system, and then give that, give the configuration and give the most recent results to your pen tester and say, Go find the hard stuff. You shouldn't be cutting checks for $30,000 to a pen tester or something that you could easily meet in your flare up. Klein. You could write the checks for finding finding the hard stuff that's much more difficult to automate. >>I totally agree. Final question. Business model Once I get in, is it a service software and services? A monthly fee? How do you guys make money? >>Yep, it is software as a service, it is. A monthly fee were early to market. So I'm not going to pretend that we have perfectly cracked the pricing. Um, but the way that we think about this is this is a team product for software engineers and for, you know, informed constituents, right? You want a product person in the product. You want a security person in the product? Um, and we also want to incent you to scan your APS And the most modern fashion, which is scanning the smallest amount of http that lives in your app, like in a micro services architecture because it makes a lot easier, is easy to isolate the problems where they live and to fix those issues really quickly. So we bundle team and for a UPS and then we scale within, uh, companies as they add more team. So pen users. 10 APS is 3 99 a month. And as you add software engineers and more applications, we scale within your company that way. >>Awesome. So if you're successful, you pay more, but doesn't matter. You already succeeded, and that's the benefit of by As you go Great stuff. Final question. One more thing. Your vision of the future. What are the biggest challenges you see in the next 24 months? Plus beyond, um, that you're trying to attack? That's a preferred future that you see evolving. What's the vision? >>Yeah, you've touched on this a couple of times in this interview with uh being remote, and the way that we need to build software already has been modernizing, and I feel like every company has a digital transformation initiative, but it has toe happen faster. And along with that, we have to figure out how Thio protect and secure these Moderna Gail. The most important thing that we do the hearts and minds of our support engineers and make it really easy for them to use security capabilities and then continue to growth in the organization. And that's not an easy thing tied off. It's easy change, a different way of being security. But I think we have to get their, uh, in order to prepare the security, uh, in these rapidly deployed and developed applications that our customers expect. >>Awesome. Jodi Clippers, CEO and founder of Stack Hawk. Thank you for coming on. I really appreciate it. Thanks for spending the time featured Startup is part of our Cuban cloud. I'm Sean for your host with silicon angle to Cube. Thanks for watching