>> Hey, welcome back everybody, Jeff Frick here with theCUBE. It is Data Privacy Day which I just found out has been going on for about 20 years, or 30 years, but we're happy to be at our very first one. We're in downtown San Francisco at the Twitter headquarters, it's a full day event that's actually happening around the world, but we're here in San Francisco and excited to have some of the guests come down that are doing the panels and the discussions and the breakout sessions, and we're excited for our next guest Denelle Dixon, Chief Legal and Business Officer from Mozilla, welcome! >> Thank you, happy to be here. >> So there was a spirited panel to kick off the day, I wonder if you could share some of your thoughts as to some surprises that came out of that conversation? >> So not so many surprises, but we talked a lot about IOT and just the Internet of Things, the web of things, whatever we're going to call it, and the data that's available as a result of that to companies, to governments, to lots of different entities and whether consumers understand that, and the responsibilities that both the consumers and the technology companies have with respect to that data. >> And Mozilla, obviously, was right there at the big change to go to, you know, graphical web interface, which was a sea change really in the internet and how it would interact with people. IoT represents that same kind of thing, and oh, by the way, people are things too, as we like to say on theCUBE, so as you kind of look at the new challenges faced by IoT, what are some of the things that bubble onto your priority list in terms of things that need to really be thought of that really people aren't thinking enough of now? >> I think that one of the most important things about IoT and the idea that this is information that's collected and used by devices and technology companies because of the fact that it can be wearable, it can be things that you have in your house that collect data as you're talking to it. One of the most important things, and just keeping Data Privacy Day in mind, is that we make sure that consumers are aware that this is actually happening, that data is being collected and sent, and how that data is being used. It used to be, back in the day, we could have privacy policies, so we put them up, 15 pages long, and assumed that users understood that. Well, that can't be used with respect to these kinds of devices, so we need to be innovative, we need to be creative, we need to be able to ask questions of these devices and have them tell us what's going on with the data that they collect and how they're doing that. So it's just as incumbent upon the technology companies that create these devices to ensure that users understand that, as it is upon the users to understand that these kinds of actions are happening and these trade offs with respect to it. Really interesting, crazy, exciting in terms of the different technologies that we can use, but really important that we get this right. >> It just strikes me that, I think, so many people just click, yes I accept. Are people really, I mean I'm sure some people are that are paying attention, but it just seems that most people just click and accept, click and accept, click and accept, especially if you've kind of got into that behavior pattern and haven't really thought about the way these applications are evolving, haven't really thought about Facebook on your laptop or on your PC at home, is different from Facebook on your mobile, they haven't really thought about, wow, what are these connected devices now collecting data, that as you said didn't even get the chance to opt in, so how do you educate people to make intelligent choices, and how do we, like, break the EULA up, maybe, so that I can opt in for if I want to share A, B, and C, but not D, E and F, and oh, I forgot, I really need F to make this thing function. It seems like a really complicated kind of disclosure problem. >> It is complicated, and that doesn't mean that we don't have to crack it. So you said the word EULA, that's the End User Legal Agreement, and I don't think we can live in a world of EULAs. I think we live in a world where we put in context notices we have to actually create so that your interface, or whatever small thing that you have, is able to alert you that this data is actually transpiring, so it has to be in context, it has to be creative, it has to be part of product development, it can't be an afterthought. Before it used to be that they would hand this over to the lawyers and say, hey, can you help us figure out how to notify our users. This has to be part of our innovative process today. We're seeing more and more of it. We're seeing technology companies take this seriously and include privacy by design in their product development, make these in context notices part of the way that they think about the product, and not just about the afterthought, and so the more we do this the better it's going to be for all of us, but it's actually, just because it's hard it means that it's a creative, thoughtful amazing process that we all need to engage in. >> So one of the hot topics that we cover a lot is diversity in tech, and women in tech specifically, and not only is it the right thing to do, but there's very clearly defined positive business outcomes when you have a diversity of opinions when you're making decisions. Is there a corollary to what you're describing in terms of being more forthright in your privacy policy that's really not only the right thing to do question, which is fine, but is there a real business benefit that you can see or that you project that's going to be even a better motivator for people to start changing the behavior in the way in which they disclose or interact with people on the privacy issue? >> Yeah, I love the way you introduced that, because from my standpoint one of the things that we don't like to do, that we don't like to be in life is surprised. And so, one of the most important things is, if you think about everything, is a no surprise rule. So if we start thinking about business and our engagement with our users as creating a no surprises opportunity, it actually creates trust, it fosters deeper engagement, it makes it so that we are all going to be happier in terms of that relationship, maybe the users actually give more to the product, maybe the product can actually give more then to the user, so this no surprises rule, and the way that we can operate, creates really nice business cycles and really nice interesting dynamics between consumers and the businesses that they use. >> It's great, the trust word in it, it also plays into kind of the services, in that everything is a service. Because when everything is a service you have to maintain a solid, ongoing relationship, it's not a one time purchase, adios, we're never going to see you again, and so that really plays into this. If it's a trusted service provider that you feel good about, you continue to pay that $9.95 to Spotify or whomever that service provider is, so it's a really different way of looking at the world. >> It is, and it's one of the things that we actually encouraged from the very outset, is this kind of creation of trust. Trust is really easy to lose with respect to your consumer base, and it's the most important thing as you're engaging. We created these initiatives called the lean data practices and then we also have privacy initiatives that we put out there for start ups and other entities that they can utilize and hopefully create for their businesses. Part of it is the no surprises rule, but it's also think about what data you want to collect, so that you actually are collecting what you need, throw away what you don't, anonymize it. Like really create that trusted relationship because you can always grow. If you think, I actually need more data today than I did when I started a year ago, then it's a great way to have that conversation with your consumer base. So it's one of the things, trust starts it all. So from Mozilla's standpoint, we operate that through our products, because we definitely have that in our Firefox browser and the other products that we have on mobile, but one of the things that we care about is creating this awesome opportunity for the web to continue to grow, and so we care about how other companies are approaching this too. >> So you mentioned Firefox, and you guys have a new product coming out today, Firefox Focus, so explain to folks what is Firefox Focus, why should they care, what's different than just kind of traditional Firefox? >> Right, so we've had Focus in iOS before, and today we actually launched it in 27 languages to 27 different areas that you can get it. It's a privacy focused browser, but it can also be performance focused. So that you actually have content you can exclude, some content doesn't get pushed through so that your performance is faster, and you can really focus on what kind of data that you want to share with companies. So try it out, I think that it's an awesome experience, certainly from the standpoint of privacy but also from performance. >> So Denelle, 2017, we just flipped the calendar a few weeks ago, as you look forward in the year you probably went through your annual planning process, what are some of your priorities for 2017, what are you looking forward to that are top of your list for the next 12 months? >> So it's really the top, I run the policy, business and legal teams at Mozilla from a policy standpoint, really focused on encryption, security, privacy, looking at the new administration here in the US as well as what's happening in Europe. I think it's a really important area for us to focus on from a business standpoint. I want to see us really dive into growth with respect to Firefox as our desktop browser. I want to see our mobile space grow, and grow even outside the browser. So I'm really excited about what we can do there. And then from the legal side, I want to continue to push the envelope on this no surprises with respect to doing that in more areas that we can with respect to our products and pushing that idea side too. >> I love that, no surprises, it's like a bumper sticker. (laughs) She's Denelle, I'm Jeff, you're watching theCUBE, see you next time.

>>Okay, we're back. I'm Dave Valante with The Cube and you're watching Evolving Influx DB into the smart data platform made possible by influx data. Anna East Otis 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 realtime analytics. Anna 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 IO X 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 compression efficiency, it's gonna give you faster query speeds, it gonna use store files and object storages. 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 IOCs 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 lift 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 a lot there. Now we talked to Brian about how you're using Rust and and which is not a new programming language and of course we had some drama around Russ during the pandemic with the Mozilla layoffs, but the formation of the Russ 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, 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 Rust was chosen because of his exceptional performance and rebi reliability. So while rust is synt tactically similar to c 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 card for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ, 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 fixed race conditions to protect against buffering overflows and to ensure thread safe ay caching 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 learned about the the new engine and the, 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're really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. 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 IOCs comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of illustrate why calmer 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 neighbor 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 min 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 times stamp and do that for every single row. So you're scanning across a ton more data and that's why row oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, calmer data fit framework. So that's where a lot of the advantages come >>From. Okay. So you've basically described like a traditional database, a row approach, but I've seen like a lot of traditional databases say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native it, is it not as effective as the, is the form 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 to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as its in memory format. So the way that it helps influx DB IOx 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 PDA's data frames as well and all of the machine learning tools associated with pandas. >>Okay. You're also leveraging par K in the platform course. 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 Par K and why is it important? >>Sure. So Par K is the calm oriented durable file format. So it's important because it'll enable bulk import and bulk export. It has compatibility with Python and pandas so it supports a broader ecosystem. Parque 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 these, 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 it 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 IOCs 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 I 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 D DB underscore 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 IOCs, 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 TB 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 you guys super responsive, so really appreciate that. All right, thank you so much and East 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 Yokum. He's the director of engineering for Influx Data and we're gonna talk about how you update a SaaS engine while the plane is flying at 30,000 feet. You don't wanna miss this.

>>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.

(upbeat music) >> Okay, we're back. I'm Dave Vellante with The Cube and you're watching Evolving InfluxDB into the smart data platform made possible by influx data. Anais Dotis-Georgiou is here. She's a developer advocate for influx data and we're going to dig into the rationale and value contribution behind several open source technologies that InfluxDB is leveraging to increase the granularity of time series analysis and bring the world of data into realtime analytics. Anais 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 IOx is being touted as this next gen open source core for InfluxDB. And my understanding is that it leverages in memory, of course for speed. It's a kilometer store, so it gives you compression efficiency it's going to give you faster query speeds, it's going to see you store files and object storages 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 lift tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metric queries we also want to 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 a 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 and you got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really 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++ for example? >> Sure, that's a great question. So Rust was chosen because of his exceptional performance and reliability. So while Rust is syntactically similar to C++ and it has similar performance it also compiles to a native code like C++ But unlike C++ 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++. So Rust like helps meet that requirement of having no limits on cardinality, for example, because it's we're also using the Rust implementation of Apache Arrow and this control over memory and also Rust's packaging system called Crates IO offers everything that you need out of the box to have features like async and await to fix race conditions to protect against buffering overflows and to ensure thread safe async caching 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 cardinality use cases. >> Yeah, and the more I learn about the new engine and the platform IOx et cetera, you see things like the old days not even to even today you do a lot of garbage collection in these systems and there's an inverse, impact relative to performance. So it looks like you're really, the community is modernizing the platform but I want to talk about Apache Arrow for a moment. It's designed to address the constraints that are associated with analyzing large data sets. We know that, but please explain why, what is Arrow and what does it bring to InfluxDB? >> Sure. Yeah. So Arrow is a a framework for defining in memory column data. And so much of the efficiency and performance of IOx comes from taking advantage of column data structures. And I will, if you don't mind, take a moment to kind of illustrate why column 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 store. 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 store. Well, usually our room temperature is regulated so those values don't change very often. So when you have 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 want to define like the min 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 column oriented storage. So if you had a row oriented storage, you'd first have to look at every field like the temperature in the room and the temperature of the store. You'd have to go across every tag value that maybe describes where the room is located or what model the store is. And every timestamp you 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 row oriented doesn't provide the same efficiency as column and Apache Arrow is in memory column data column data fit framework. So that's where a lot of the advantages come from. >> Okay. So you've basically described like a traditional database a row approach, but I've seen like a lot of traditional databases say, okay, now we've got we can handle Column format versus what you're talking about is really kind of native is it not as effective as the former not as effective because it's largely a bolt on? Can you like elucidate on that front? >> Yeah, 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 row oriented storage isn't as efficient as column 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 to the table here? >> Sure. So it's an extensible query execution framework and it uses Arrow as its in memory format. So the way that it helps InfluxDB IOx is that okay it's great if you can write unlimited amount of cardinality into InfluxDB, 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 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 course. 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 Par-K and why is it important? >> Sure. So Par-K is the column oriented durable file format. So it's important because it'll enable bulk import and bulk export. It has compatibility with Python and Pandas so it supports a broader ecosystem. Par-K files also take very little 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 benefits of Par-K. >> Got it. Very popular. So and these, 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 InfluxDB first has contributed a lot of different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing Influx. 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 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 people call it the flywheel. Give us your last thoughts and kind of summarize, 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 InfluxDB IOx and I really encourage if you are interested in learning more about the technologies that Influx is leveraging to produce IOx the challenges associated with it and all of the hard work questions and I just want to 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 InfluxDB underscore IOx channel specifically to learn more about how to join those office hours and those monthly tech talks as well as ask any questions they have about IOx what to expect and what you'd like to learn more about. I as a developer advocate, I want to answer your questions. So if there's a particular technology or stack that you want to dive deeper into and want more explanation about how InfluxDB leverages it to build IOx, 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 you guys super responsive, so really appreciate that. All right, thank you so much Anais 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 Yoakam. He's the director of engineering for Influx Data and we're going to talk about how you update a SaaS engine while the plane is flying at 30,000 feet. You don't want to miss this. (upbeat music)

>>Trump and low park California in the heart of Silicon Valley. It's the cube covering cloud now. Awards 2020 brought to you by Silicon angle media. Now here's Sonya to garden. >>Hi and welcome to the cube. I'm your host Sonia to Gary. And we're on the ground at Facebook headquarters in Menlo park, California covering cloud now's top women entrepreneurs in cloud innovation awards. Joining us today is modest to get day, the cofounder of branch motto. Welcome to the cube. Thank you so much for having me. So you're receiving an award today for being a top female entrepreneur in cloud innovation. How does that feel? >>It feels awesome. I'm humbled to be in such amazing company with some great ladies that have started really great companies, so pretty excited to be here. >>Great. So just give us a brief overview of your background. >>Sure. Uh, my background, well, I probably don't have the regular Silicon Valley background. I was born and raised in communist Romania, uh, in a pretty small town called Barco, uh, in the Rijo Romania called Moldavia. I was very good at math. Um, and my parents, uh, pushed me to explore applying to schools in the United States, which I did. Um, and I applied to 23 colleges and the DOB, uh, getting a full scholarship from Cornell where I studied computer engineering. Um, I dreamt of working for big companies, which I did for a while, uh, until one day when I remember I was doing a master's to Stanford and one professor told me I was, I told him, I was like, I don't think I could ever start a company. And he was like, what if you don't? Like, who do you think? Well, so I was like, Oh, I never thought about it that way. Um, and that's when I think my entrepreneurial dream started. And a few years later I started, um, phone co-founders and started a few different companies that eventually ended up being branch. That's a long answer to your question. >>No, that's perfect. So what inspired you to start branch and how did you navigate getting funding? >>Um, it's a, it's an interesting story. I think we came together, my cofounders and I were in business school, Stanford, we all want to start a company and we did what all business school students do. We just started something that sounded cool but maybe it didn't have such a big market. Um, and uh, then pivoted and ended up building an app. So we worked on an app or the mobile photo printing app called kindred. We worked on the Apple for quite some time. It was, um, over a year we sold over 10,000 photo books. I've seen a lot of images of babies and pets and we reviewed manually every single book and we had a really hard time growing. So if you think about the mobile ecosystem today, and if you compare it to the web on the web, the web is a pretty democratic system. >>You, um, you have the HTTP protocol and you are able to put together a website and make sure that the website gets found through social media to research to all this other platforms. Apps are much harder to discover. Um, the app ecosystem is owned by the platforms. And we had a really hard time applying. I was coming from the web world and all the things I had done to market websites just in the work with the apps. And it was hard. Uh, you know, you could only Mark at the top and how out all the content inside the app. That's a lot more interesting than the app itself. So we, we felt that we were like really, really struggling and we would need it to kind of shut the company down. And then we realized that one of the things that we were trying to build for us to a disability to allow people to share and get to content within the app, which is in our case was photo books was actually something that everyone in the ecosystem needed. >>So we, we asked a lot of people and it seemed like this was a much bigger need. Uh, then, you know, the photo books. And, uh, we had started to already build it to solve our own problem. So we started building a linking and attribution platform, um, to help other app. And mobile companies grow and understand their user journey and help build like interesting connections for the user. So, you know, our mission is to, um, to help people discover content within apps, uh, through links that always work. Uh, and it's been a wonderful, like an F pretty exciting journey ever since. That's really inspiring and, and solving a real world problem, a real world problem. >> So it's interesting when you ask about fundraising. Uh, it was so hard to raise money for the photo book app. And we raised actually from, uh, uh, pay our ventures and they actually, even now I remember, uh, the guy patch man sat us down in a very Silicon Valley fashion at the rosewoods and was a very hot day and there was like Persian tea being served and he gave us money and he said, you know, I just want to do something. >>I am not investing in the idea. I'm investing in you as a team. Uh, and if you pivot away from photo books, you know, uh, which we did and I think we pivoted the way because we ended up finding a much, much bigger problem. And we felt that, you know, we could actually make a, an actual change into the mobile cloud ecosystem. And that's how, that's how it all started. Uh, and it wasn't actually was easier to raise money after we had a really big problem. We had a good team that had been working together for almost two years. We had product market fit. >> So, uh, so yeah. So what are some things that have influenced you in your journey to become an entrepreneur? Um, some things interesting. Um, well I would say the Stanford design school. Um, I think I came from working for Siemens, which is a giant company. >>And I started doing this project and I remember one of the projects was we built, um, an, uh, a toolbar we were supposed to where we're doing a project for, um, Firefox, which, you know, Mozilla was utilize browser, uh, which was in some ways the precursor to Chrome. And we're trying to help it grow. And we didn't know. And one of the ideas was we, we built this toolbar for eBay and eBay hadn't had a toolbar for Firefox. And we, you know, we were some students for two weeks. We build this toolbar bar and then someone bought the car to our toolbar. And I was like, wow. Like how incredible is it that you can just kind of put your thoughts on something and just get something done and make an actual impact someone's life. And I think that's when the spark of the entrepreneurial spark, it was during that time that, um, Michael Dearing course, a professor and one of my D school courses also told me the thing that if I don't do it, who will? >>And I think that's when, that's when it all started. I think the things that have helped me along the way, I mean, my cofounders, I think I've been incredibly lucky to find cofounders that are incredibly eager to be good at what they do and also very different from me. So I think if you think about why many companies implode, it's usually because of the founding team. We've been together for almost seven years now. Uh, and it's been an interesting way to find balance through so many failed companies. So many stages of growth branches over 400 people now. So you know, our roles have shifted over time and it's been like, uh, an interesting journey and I think recently more in the past few years, I think one of the things that has helped me find balance has been having a group of female founder friends. Um, it's really interesting to have a peer group that you can talk about things with and be vulnerable with. >>And I didn't have that in the first few years and I wish I did. My cofounders are amazing, but I think in some ways we are also coworkers. So having an external group has been incredibly helpful in helping me find balance in my life. So I think a lot of women feel that way. They feel that it's really difficult to navigate in this male dominated workspace. So what advice would you give to female entrepreneurs in this space? Yeah, I mean it is really hard and I think confidence is something that I've noticed with myself, my peers, the women that I've invested in. I do investing on the side. Uh, I would say believe that you can do it. Uh, believe that the only, the sky's the limit believe that, um, you can do more than you think you can do. I think sometimes, uh, you know, our, our background and the society around us, um, doesn't necessarily believe that we can do the things that we can do as women. >>So I think believing in ourselves is incredibly important. I think the second part is making sure that we build networks around us. They can tell us that they believe in us. They can push us beyond what we think is possible. And I think those networks can be peers. Like my funeral founder group, we call each other for ministers or, uh, I think investors. Um, I think it can be mentors. And I've had, I've been lucky enough to have amazing women investors, uh, women mentors. Um, and I, it's been a really incredible to see how much they helped me grow. So I think the interesting thing is when I was just getting started, I didn't look for those communities. I didn't look for a guy. I just kinda felt, Oh, I can do it. But I didn't actually realize that being part of a community, being vulnerable, asking questions can actually go help me go so much further. Um, so the advice would be to start early and find a small group of people that you can actually rely on, and that can be your advocates and your champions. So, yeah. Well, thank you so much for those words of wisdom. Thanks for having me. Thank you for being on the cube. I'm your host, Sonia to Gary. Thanks for watching the cube. Stay tuned for more.

(upbeat music) >> Narrator: Live from Nassau, in the Bahamas it's theCUBE. Covering POLYCON18. >> Hello welcome back everyone this is theCUBE's exclusive coverage from the Bahamas, we are here at POLYCON18 Put on by Polymath and Grit Capital This is an amazing event, it's really the cryptocurrency, blockchain, token economics, the decentralized future-internet is happening now. The industry if forming, CUBE is starting its 2018 run. We'll cover all the top events this year, in the cryptos. As you know, we know cloud, big data, we do all those other events, we'll start covering in a big way because the ecosystem is formed, you're seeing people making money. The early whales, the big guys, now you've got institutional investors coming in, a real ecosystem dynamic. This is what industries look like when they're formed. Our next guest is Carlos Domingo, founder of and managing partner at SPiCE VC, and the founder and chairman at Securitize. One of the tell-signs of a maturing ecosystem that's growing very fast is companies that are adding value. You're one of them, Carlos. >> Thank you. >> Welcome to theCUBE. >> Thank you, thank you guys for having me here. >> So, you know Dave Vellante who just had to jump on a plane 'cause the snowstorm in Boston would comment, he would say, 'cause we talk about this all the time, "You know you look "for the big waves, and you see what's happening. "But How do you know when there's a tipping point "in a new industry?" And that when there's stuff being created, value being captured, industry being formed with an ecosystem, and a community, this is absolutely happening. >> Correct. >> You're bringing a very valuable service to market. You guys self-funded this operation, Securitize. You're automating other value chains that were old guard businesses in a new way. >> Correct. >> Take a minute to explain Securitize, why the idea, what you guys have built, what you've got going on, and, What's the disruption of that product? >> Good, so the idea came originally 'cause last year me and my partners, we wanted to tokenize a VC fund. And basically show a security token that contains the economic rights of the fund as a way to provide liquidity to the investors because liquidity on the VC space is one of the biggest problems, right, you invest money and it takes like seven to 10 years and then you can actually get your money back. So we had that idea, at that time Blockchain Capital had done one security token, was the first security token, for a 10 million dollar offering, and we wanted to kind of build on that, so we went out and looked for people that could actually do the issuance of the security token in a regulated way, so the KYC, the AML, the accreditation process per country, not just for the US. And basically ran the ICO in a secure way with secure wallets for different cryptocurrencies, and then also have the smart contract issuing the token, but also smart contract managing what happens with the token on the secondary market, which is very important, right? 'Cause see, in the secondary market the tokens can actually move from a wallet to a wallet, and suddenly you're outside the regulatory framework that you protected at the beginning Right, so we went out and talked to Polymath and many, the few companies that were doing that and no one was actually ready with a platform last year, so, we are all tech entrepreneurs and product people, so we did what we know how to do, we hire a CTO, hire engineers and went and built our own platform for SPiCE VC, for tokenizing the fund. And then when we announced the project around September, October last year, I posted a Medium about the investment process, and the screenshots of the path and how it works, all the features that it has, we also integrated Bancorp as the central exchange to provide liquidity. And then started of getting flooded with people saying, wow, this is very cool yeah, we wanted to do security tokens, think this is the future, and no one actually is ready with the platform and you guys seem to have one, so who has built it? And I told people, we built it, this is our platform. And then we took the decision last year to basically separate the platform from the fund. And the fund becoming the first customer, and we created Securitize. Which is basically an end-to-end issuance platform for security tokens. >> And so this is really filling a void for people who want to either raise money for a startup-like venture, And then also maybe want to raise cryptocurrency in capital for growing a business that they're tokenizing. That's a big trend, so you've got the startup, hey I've got a great idea with a whitepaper, we're going to revolutionize the world, People are interested, some people call it the dumbest idea they've ever seen, which turns into a billion-dollar idea, because that's the way it works. (laughs) So got to raise some cash. And then there's the businesses that are growing saying, you know, I can grow with working capital in a tokenized environment, 'cause the business model shifts for that. >> Correct, I think that what people don't realize is that you know, getting actual liquidity in a market, like doing an IPO is either very difficult, or very expensive, or both things. >> John: Yeah, and the hurdle's very high. >> Yeah, the hurdle is very high, the cost could be like 10 to 12% of the money you raise you know paying the underwriters and paying everyone to get it done, so I think that what tokenizing real assets, like asset-backed tokens or security tokens, this basically allows for two things. One is the network of investors you can actually reach is anyone with an internet connection that within the regulation in their country are allowed to invest. So suddenly you've multiplied by 100 the reach you have of potentially finding investors. And second, is it's cheaper to do it. There's less friction. Third, is managing all of these thousands of investors would not be possible in the traditional financial system, right? Because you have investors from many countries, with different currencies, different bank accounts, different banks, and with the smart contract and tokens you can automate the entire process, >> And from your accent you're obviously not in the US, not an american but you're from? >> I'm from Barcelona. >> Barcelona, so you're really laid back, you're chill about this, but you're hardcore techie, right? >> (laughs) Yes. >> Okay, so let me just go through the process here, so what's interesting to me is, first of all, I love cloud computing and I think what DevOps has done in software with open-source that's clearly, in line with crypto market scene, mission. Automation is a really big deal, when you can automate something down to efficient process, you're doing it, you guys are doing this different, it's well not different it's automated, great, but the investment piece is accredited investors, right? Am I getting it right? >> It depends on the jurisdiction. So, most countries have security laws, so what our platform does, is we'll actually identify through the KYC on the name of the investor, and depending on the jurisdiction where you're from, we will apply a different rule, because in the US it is accredited investors only but in other countries you can take the small portion of retail. Also the meaning of accredited investor is different, how you actually comply with that, the documentation you need to collect or not collect for validating that someone's an accredited investor is not the same in the US and in other jurisdictions. >> Alright so, here's the problem that I see you solving, correct me if I'm wrong, if I'm a company XYZ Corporation, we're growing like crazy and we can tokenize our business, and we say hey, we could raise a token, 'cause we actually have a product and security token is a great vehicle, and so they go their lawyer well you're in the US, you can only use accredited investors, if you want to go outside the US you got to go to the Cayman Islands or somewhere else, set up a new company and do all that stuff, 'cause they have to manage the process, and they got to go find investors, that's hard! >> That's hard. >> Okay, do you solve that problem for them? >> We streamline the problem, so basically, first the fact that you setup a company in Cayman doesn't actually prevent you from, you know, the regulations in each country because the regulators care about where the investor sits, not where the company is. So what we solve the problem, is basically allow them to provide a liquidity event through fundraising and provide liquidity for the investors on the secondary market, so we basically will save them the trouble of having to figure out how to do all these processes country-by-country. >> So it's a liquidity value, too, so it's also getting the process done, streamlined, and then managing some liquidity challenges that the company would have to put cycles into managing it. >> Exactly. >> Okay so here's a question, so this is like a consulting hour for the people watching. I'm a company, XYZ Corporation I want to tokenize my business, now, we've been up and running for a few years and say hey, Securitize is really interesting, these guys are amazing, the same ethos as us, they're cloud guys, they're automating. Let's just go through them. We sign up, we apply to yo. What we do, do we have to set up a new company, is there risk issues, what's your advice on the playbook? >> So the fact, because you're using a security you don't actually have to go through all the jurisdictions, right? You can just do it from wherever you are, because you're issuing a security that assigns some economic interest on you your business, right? Now in terms of us, we're trying to become kind of like a quality security token ICO place, so we create a lot and decide which ones we bring on board or not, first, because we have so many, we have hundreds of leads coming to us all the time. And secondly, because we want to make sure that people who we're securitizing, that those are quality companies that we've vetted, and our lawyers have checked that the company's interesting, that the company is going to do well not only and the fundraising, but later down the road, so, >> What about the legal and regulatory challenges? So again, most people do a new code because they want to protect their corporate shield, there's a corporate shield to protect themselves, you know investors are always are gun-shy or trigger-happy when it comes to suing people. Especially in this economy. How does an entrepreneur or business manager protect against that, do you guys handle some of that, or is it just a buyer beware kind of thing? >> No, so we work with our attorneys, Colten in New York they specialize in securities, and we basically will advise the customer that actually uses our attorneys because they are very experienced in doing this, and in terms of protection, in a security token you're not just getting the token, you're actually signing a subscription agreement which is a legal binding document that explains exactly what the token is going to do, and there's and information memorandum which is basically describing what the business is going to do. So there's a legal framework, off-chain if you want alongside the on-chain token and the smart contract side. >> So all that stuff's happened, so awesome. Alright so we're going to change gears here, Carlos. Talk about, talk about you, why, why do this? What drove you here, are you scratching an itch or are you serial entrepreneur, how did you get here, what's the story? >> So the story is I've been, this is like the third phase of my career. My first 10 years of career, I was at the middle of the dot-com boom, I took company public in Inashik, Japan. And then went through years of corporate companies and then everything crashed so I lived both the up and the down. The second part of my career started in 2006 and then lasted another 10 years, which is during Telefonica, one of the largest telcos in the world, and I lived through all the mobile boom with the iPhone coming out in 2007 and 2008 and all the excitement happening in the industry but to me it was the opposite, I was looking for what is the next thing I do, because all these industries are now not as exciting anymore. So I came across blockchain and crypto, two things. One is I was doing a project in small cities and Dubai, where I live, where we started looking at blockchain and ran some pilots and then one of my colleagues, and friend, Brendan Eich who is the founder of Mozilla and he actually did an ICO for a company called Brave in March last year, when I saw that-- >> Brave browser? >> Yeah, yeah. >> Very familiar, great, great offering. >> He's a great entrepreneur, the guy's invented JavaScript and when I saw he did that, I met him actually a year ago and I met him this week as well in Barcelona at Mobile World Congress and when I say what he did I was like wow this is very revolutionary, right, so this is a completely different way of raising money and it's also a great way for investors because you get liquidity so why not get there and find a project. So, I started with one and then-- >> Serial entrepreneur, great story, lot of experience coming into cryptos, you got some young guns who are inventing, and making some cash, and doing well, also starting funds. You've got developers and business entrepreneurs who are successful and they're becoming investors and then you got the pros coming in, alpha geeks, serial entrepreneurs, pros on the banking side, all think differently, and they see the vision, so I got to ask you, what is your vision of the decentralized internet? You've seen how telcos work and you know their challenge is over the top content, centralized organization, you see what Brave's doing, you've lived the dot-com up and down, what's your vision of decentralized internet, how would you describe how big the wave is, and what's the opportunity? >> So I think that if you think of why people were excited in 1994 1995 over the internet, it was precisely because the internet promised decentralization back then, right? So there were all these protocols that allow you to move voice, move data, move webpages that we're going to disintermediate people. And what happened is that a lot of traditional players got disintermediated but then the weight shifted into players which are now high concentrated and centralized, right, everything on Facebook or Google. So I think that the excitement around crypto's about making a reality, the decentralized internet that didn't happen the first time. And I think that because the protocols have a way to monetize, and there's an economic incentive to be part of the network, this time will be different. >> Cloud computing has also helped a little bit, too. Because with open source and cloud computing you have a great creative environment on technology's side. >> Correct, this is like open-source money if you want to think about like crypto. So I think yes, the fact that the maturity of some adjacent technologies is helping this move faster. >> And open-source has been a proven formula, one, second tier citizen when I was growing up in the open-source community, I remember people were poo-pooing Linux back in the day, and all of the sudden now it's tier one powering the world, and now you have community modeling around how that worked, how would you compare and contrast? And you have other things coming into this, too. You've got cryptography systems you've got gamers and cryptocurrency and you got cloud, how would you tease out the industry and describe the cryptocurrency and the blockchain communities, I mean it's kind of a confluence of a lot of-- >> I think it's a very interesting industry and it has forced myself also to have to learn about adjacent topics, right, because you've got to understand about technology, but you've got to understand about software, cryptography, you've got to understand about finance and economy to understand what a monetary policy is and how you're going to define that into your token. You've got to understand about finance if you do security tokens, you know securities laws, so it is fascinating because of this confluence of different things. >> We were having a joke on one of our broadcasts, I said to my co-host, these startups will soon have a CTO, a CEO, and a Chief Economic Officer, I mean this is kind of token economics! >> Makes all the sense. >> I mean you're going to have to say, hey do we increase the coin rate, do we drop this down? >> A legal counselor. >> I mean it's a big human dynamic there. >> I think this is for me why I am so excited about it. 'cause I was kind of bored of being in an industry for 10 years, you feel that you already know more or less everything, and yet there's new things coming, but are kind of like incremental improvements. This feels like an exponential improvement, something is going to really change things, and as you said it forces you to understand more disciplines than just software technology. >> I mean to use a California example, to end the segment, you know you see the waves coming and the surfers grabbing their boards, and they're on the wave hangin' 10. And that's what's going on, you see the best people attracted to this space because there's problems or opportunities, there's challenges and there's a social impact, mission-driven impact. And I think people are seeing that, and it's attracting new entrants into the space, from banking, all sectors now coming in, they're seeing the ecosystem develop, how would you see that going, because, you do agree that the ecosystem is forming pretty quickly. >> It is forming very, very quickly, surprisingly quickly. And I think that one of the things you mentioned is the fact that, people like me or other people that come from you know long-standing backgrounds in tech are moving into this industry who are also making the industry kind of grow faster, because the industry is a bit immature if you want, in terms of everything technology. This is why there's so many hacks, the usability of the products is still not there, so as more people from a traditional tech industry move here, and start building good products, this will actually change very quickly. >> Great leadership, Carlos, on your end, congratulations. You're seeing an opportunity and you're making a difference. You're putting out a great product service I think people are going to use a lot of, and looking forward to chatting more about it and of course you got to VC fund, and you're doing some investments, you put some skin in the game as well, with your companies, congratulations. This is theCUBE live coverage we'll be back with more, here in the Bahamas, and our friend from Barcelona here. Great entrepreneur, looking forward to chatting more about the decentralized economics, the technology, how the value will be captured, the technology that's going to enable that and the impact to society. It's theCUBE, more live coverage after this short break. (upbeat music)

>> Speaker: Live from Las Vegas. It's the Cube. Covering VMworld 2017. Brought to you by VMware and its ecosystem partners. >> Yeah we're here live the Cube coverage at VMworld 2017. Behind us is the floor of the VMvillage. I'm John Furrier with Dave Vellante. Our next two guest Beth Phalen who's the President and General Manager of Data Protection Division at Dell EMC and Yanbing Li who's the Senior Vice President General Management with Storage and Availability at VMware, vSAN, all the greatness; Welcome back to the Cube. Great to see you guys. >> Yeah, great to see you. >> Got the heavy hitters here, data protection, AWS lot of great relationships synergies happening. >> Yeah. >> Give us the update. >> Yeah well go ahead yeah. >> We've been working together for a long time but recently we've really amped it up to the next level. Great discussions around enabling data protection for vSAN and as announced this week you know with Dell EMC will be first vendor to have data protection for VMware cloud on AWS. So it's a really exciting time to be here and I've been in this business for a long time. This is the best VMworld that I've seen so far and so it's just really great to be here with Yanbing. >> It's been very cohesive, I want to just stay on that for a second. This is the big milestone for VMware. >> It is. >> To have this shipping of the general availability especially with on the heels of the vCloud Air and all that controversy. Andy Jassy's on stage from Amazon web services. >> Yeah. >> Really kind of looking right at the audience and saying we got your back, this is a real deal, and the bridge to the future. I'm paraphrasing, he didn't say those exact words. >> Yeah yeah yeah. >> How do you get that data protection? Because that data protection in the cloud is hard. >> Yeah, well the nice thing is that since we've got all of our data protection running in a cloud environment now we could then use that to build the connections with VMC. So we had Data Domain Virtual Edition running, we have Data Protection Suite running in the cloud. So people can use the same technology they used on prem but now in AWS in conjunction with VMC. >> So you kind have hyper converged infrastructure meets cloud data protection. Yanbing, what is the difference? I mean what's the requirement of hyper converged infrastructure data protection? How does it differ from traditional storage and how is it evolving? >> Ah, great questions you know Beth and I we've known each other for quite a few years. I have to say our relationship hasn't been, you know, this close is and it's getting closer and closer. So coming back to your question in terms of hyper converged infrastructure. We're seeing two fundamental shifts around data protection. One is, the blurring of the boundary between backup and DR and these two really coming together as unified data protection. I think there has been a lot of discussion around this for a long time but this become even more compelling; now we talk about hyper converged infrastructure where you know our customers they so enjoy the benefit of having compute and storage combined together in a common management experience, they're looking for the same for data protection. So we're really seeing customers want to see data protection as a feature of hyper converged, as a capability that's part of that rather than yet another silo they have to manage separately. You know they want policy that manage storage, compute, and backup and DR altogether. So that's why you know that's really drive our partnership so much closer. >> You know it's interesting many of the clients that we've worked with over the years they'll have a backup strategy but they don't really have a DR strategy and they sleep with one eye open at night and they're afraid to go to the board because it's so expensive, it's expensive insurance. So you're seeing that there, sounds like they're blending those 2 together kind of killing 2 birds with one stone. Are there trade offs or things that customers should think about in that regard? How do they sort of go from where they are today which is sort of a backup bolt on to that integrated DR and backup? >> I think one of the key is the technology that we're leveraging now and we leverage something that has like CDP continuous data protection you can use that one to have data path to the secondary storage and you can use that same code to also initiate disaster recovery with near 0 RPO and RTO. So another thing that we announced this week is with our DPS for apps next edition that we now have hypervisor direct back up and what that means is that we're integrated directly with ESX and we are leveraging ProtectPoint through VM's to move data to data domain. That same technology is also leverage within RecoverPoint through VM's and so you can see the engine, the internal engine of the data movements, can be applied both to disaster recovery and to back up with different windows of RTO and RPO. >> I'm glad you said near 0 RPO causes no such thing as 0 RPO but you're seeing, more pressure to get as close to 0 as possible. What's driving that pressure and how are you meeting it? >> Well I think with all of us we know that an industry customers are expecting 24 by, you know 24 by 7 up time right. So they have many many applications that they need to have the confidence that if it does go down for any reason they're going to be able to bring it back up within minutes or hours not days. So that's really the drive for continuous availability. Getting as close to that as possible. >> If I may one more John, the challenge in data protection has always been it's, it's largely been a one size fits all and it's either I'm either under protected or I'm spending and breaking the bank. So are you able to through your technology and process improvements improve the level of granularity for different workloads that require different service levels. >> Two things come to mind, One, we're seeing more and more interesting customers integrating data protection directlywith their applications. Whether it SQL or Oracle and or the VM itself. So that's one thing. So we can custom the data protection to particular application and then on the second piece of that is where the different interfaces that VM offers we're able to do either V80P level integration or more fine grained integration like we do with CheckPoint through VM. So we are getting to the point that we can make different choices either application specific or something that is fine tuned based on the level of mission critical capabilities that application requires. >> I will get you guys perspective just a high level ballistic view for a second. We're seeing convergence of two worlds. The cloud native world that have no walls, have no perimeters they operate in a mindset of there's a security holes everywhere. Then the protections hard. >> They think of a differently. >> Yeah On prem the traditional methods, how are those coming together? Because you have customers that run VMware and do stuff with data protection and then one of them VMware in the cloud. What's different, what do customers need to know that are we on either side of that equation? If I'm on prem and I now want to use VMware in the cloud on AWS. How does data protection fit in that? Is it the same, is there tweaks, how they think about it? >> You want to answer that? >> In terms of on prem or VMware in AWS you know a big value prop is reading at the consistency in the operating model. I'm sure you have heard about this a million times said. >> Yes, talking about it all week. >> All week long. From data protection we're trying to do exactly the same. So for example VMware cloud on AWS, the very first data protection that we certify on that platform is from [Vast 00:07:39] organization is Avamar networker being the first set of solution certified and our customers definitely love the continuity of I already have the experience and licensing associated with my own prem protection solution and they want to carry that forward in today's cloud. >> So same operating module, so from the customers perspective I've been doing it this way >> Exactly. >> With VMware and Dell Data Protection, now it's the same in the cloud. No change in. >> Yeah I mean I think that's really the beauty of it, even with DDVE I mean you can have applications or you can do through different; You know you can have application in the cloud as well as another level of protection of your secondary storage. >> I think some of the changes probably not necessary. So RPD model consistency, Dave we touch upon, hyper convergence is driving a lot of functionality into a single control plate as opposed to these different silos and you know we would like to see that happen in the cloud as well and along that line you know best organization and my organizing are really looking at how we viewed the best next generation integrated technology that truly leverages the strengths of both organizations. >> That's simple and easy to use. >> Simple, easy to use, policy base, you know turn key solutions, so this is, you know what we're doing something pretty innovative by truly bring our engineering together and try to boost our next generation solution. >> Since the synergies that Michael was talking about when we interviewed Michael yesterday he's like look, the synergies are well beyond its expectations. Just it seems to be flowing nicely in the culture. When EMC had the federation there was always kind of like an interesting but now things are flowing differently. It seems to be smoother you guys. >> They are. >> Every action. >> I totally agree with what you said. I mean it feels different and I think as we go forward we have even more opportunities but we're not even a year into it and there was a distinct difference in terms of recognition around the joint opportunity and like you said the smoothness of the conversation I think is >> It's clear, it's clarity. >> It's really helpful. >> Well also you know, the rising tide floats all boats, well VMware stock as gone like this. >> It makes us all happy. >> Its got a nice slope to it. >> I definitely want to hackle Beth on that and the type of collaboration we're seeing between our two organizations, might be you is actually having multiple touch point into Dell and Dell EMC organization whether it's our VxRail and you know the vSAN based collaboration or the data protection angle and we're really seeing that happen across different functions. So we are starting from go to market collaboration you know how we provide the best set of solutions to our customers in joint go to market effort. vSAN is gaining a lot of free print in mission critical workloads and a critical requirement is data protection. So so we're doing a lot of joint solution, joint selling together. And really in the next step is that joint engineering effort leveraging the best of both worlds to build next generation products that's optimized for hyper converged, that's optimized for the cloud. >> For the software defined data centers. >> If I dial back a decade let's say as virtualization generally in VMware specifically saw its ascendancy, data protection totally changed. For a number of reasons, you had less physical resources but backup was still very resource intensive application and so; That's really where Avarmar came before. He walked the floor, back up and data protection is exploding again. It's like the hottest area. So two part question. Why is that and then how does Dell EMC with you know its large portfolio, its big install base, how do you maintain competitiveness with all that new emerging innovation? >> Yeah well I think the first question and I want to hear your answer too but what I would say is because the industry is changing so dramatically it's requiring data protection to change just as dramatically. >> Right. >> Right, so that is a lot of people are seeing opportunity there. Where is maybe, I've had people say, you know, well you don't really have to protect data in the cloud it's all stuff that's magically protected, I've had customers say that to me and I think that we're now beyond that, right and people are realizing, wow you know, just as much of a need or more of a need than it was before. So I think there's plenty of you know companies appreciate opportunity and they see opportunity right now as data protection evolves quickly to address the new IT world that we live in. On anything you would add to the first answer? >> Yeah so I think, several years ago VMworld feels like a storage shelf you know. I think there is still a lot of exciting interesting storage company but there has been quite a bit of consolidation you know. Software defined storage it seems like that market's landscape is becoming clearer and clearer and we're definitely seeing that spreading into secondary storage is now right for a disruption and we're also seeing that is disruption around secondary storage isalso impacting data protection software. It's not just the secondary storage element but you know extent to the entire software stack. I think it's very exciting and also thinking about you know what is going to be the economical benefit of cloud and how do we take best advantage of that and this is why you know our AWS relationship. You know we are rejuvenizing our DR effort. We have successful on prem product like SRM but we're seeing tremendous new opportunity to look at that in the context of cloud to truly leveraging the economy is scale of what cloud has to offer. So lots of driving factors to really revitalize that. >> It's a cloud show and you have no cloud. >> Okay Beth second part of my question is how do you keep pace, it's a pretty tremendous innovations going on, how do you keep pace, what are your thoughts on all that? >> So the really cool thing is because where you know we're Dell Technologies we have not only data protection assets, we also have servers, we also have switches, we have everything we need to build a full integrated stack which we now have without EPA. So within a integrated data protection appliance we have the best of data domain, we have the best of our software, we're leveraging also power at servers and dellium C switches. So we have everything that we need to build that end to end best in class integrated appliance and as customers change how they consume data protection to more like a converged consumption model or hyper converged consumption model we have all the pieces that we need to make that a reality and then to continue to move forward. So when you combine that with our relationship with VMware and the ability that we have to drive innovation jointly I have no doubt that we're going to be really moving ahead into you know modern data protection. >> Final question before we rap. R&D comes up, Micheal also mention and so do Pat, billions of dollars now are in R&D. Free cash was a billion dollars. Three billion for VMware. A lot of observations this week that we kind of looked and read the tea leaves one of them was at least for me was the stack a collision between hardware software stacks as IoT and servers and devices, you have hardware stacks and software stacks. Untested scenario certainly in vSAN; You see a lot of activity around untested new use cases and so it's going to put pressure on engineers. So the question is what's the vision for the R&D for you guys around data protection, because it's not just data protection anymore it's a fundamental linchpin in the equation of cloud >> Yeah. >> Thoughts on engineering road map I mean engineering R&D. >> One thing we're doing actually right now this week is we're restructuring our EMC lab dellium c lab back in Hopkinton to move to more of an open shared pivotal type environment. So you know it's clear that as we go forward doing things like pere programming on test driven development. You know enabling continuous always good known stayed like there is definitely advancements happening in software development that are accelerating innovation and so as we take advantage of that, that's how we keep pace with what's going on around us. Because you're right the number of things to get involved in is endless. >> I just want to point out before we end the segment you guys are very inspirational women in tech. I think you guys are amazing. We talk about the engineer resources. >> Thank you John. Your thoughts on the industry, as there's a lot of controversy in Silicon Valley and around the world around STEM and women in tech. Thoughts that you'd like to share to all the men watching and all the folks and young girls who might inspiration. You know it's passionate for us. >> Yeah, I'll start. So I think, first of all I want to tank the Cube for having such awareness in this topic and you know constantly featuring women in tech on your shows. You guys have been doing a great job raising the visibility women leaders. >> Thank you >> Thanks >> in the industry. Thank you. So certainly this is a topic very dear and near to my heart. This week you know we can still see not only our employee base but our customer base is heavily men dominated. But I think we're seeing unprecedented levels of awareness and attention to this topic in Silicon Valley and across the world. Really I do think we are starting to see much better transparency metric. We're seeing increased accountability in business and business leadership. So I think those and we're seeing a lot of social awareness I think those are going to drive a positive change. So let me give you a concrete example of fuzz for example things we do in VMware, we just gone through bonus allocation and compensation adjustment. I would get a report from it make sure, comparing the percentage of what we have done for the men population and women population and so you get a real time feedback in data and when we see the data is actually quite shocking hopefully we do see, unconsciously you know we may be allocating those >> Unconscious bias if you will. >> Yeah those differently. But because of those real time data and feedback we're good able to you know keep ourself accountable. So just you know this is no longer just talk this is a real data you know in the real HR practices that we are already building into our day to day practice. So I think I'm very optimistic, this will take time but this is you know we're moving in the right direction. >> Historical moment in the world if you think about it. This is super important time. The inspiration and also the young women out there too and also for the men. They need to be aware as well because inclusion includes not just women it's everyone. That seems to be >> Absolutely. >> In fact a trend we had an interview on the Cube and our Simpson who works for Mozilla she's doing some work for Tech Nation, she said they're changing it from diversity inclusion to inclusion and diversity. They're flipping it around where inclusion leads diversity cause they want to lead with the message of inclusion; >> Yeah. >> as a primary message with diversity. So it's not just the diversity message it's inclusion. >> Yeah. >> Love that. >> Yeah the only thing I would add would be the phrase "She can be it if she sees it" I think having people like myself and Yanbing be visible role models it's very impactful, especially for young women to see you know women in tech leadership positions. It's hard to imagine yourself in a role if you don't see anyone similar to in a role. So I think the more that people like us and our peers get out there and really put an effort into being visible. >> Do you see the networks forming more, I mean is there more action flowing happen. Can you compare and contrast just even a few years ago is it on the rise significantly? >> I think it's on the rise. >> Yeah I do get us to be involved in a lot of opportunistic situations, yeah. >> And of course your Twitter handle puts it right out there, @ybhighheels. >> Yeah. >> Right, your not shy about it. >> Yeah, there's nothing shy about it. I realize you know Beth and I, we are both addressed in very feminine way. I do think. >> Your capabilities are off to chart you to great and impressive executives. >> Society is increasingly more inclusive about their notions of female tech leader. It's not just one size fits all and I think it's encouraging us to show who we really are and the authentic self and I think that's very important for young girls to see because I remember when I was a young girl I didn't go into tech expecting I do not get to be who I am >> Yeah and that shouldn't reflect your capability of anyway any kind and that seem to be the greater awareness. The Google memo that went around as all of it so getting us some great videos on Silicon Angle on that topic. Again you guys are great inspiration. We love working with you you guys are great executives. >> Thank you. >> Its great content. >> Your welcome. >> We super passionate about it. We'll be at Grace Hopper for our 4th year we do that. >> Fantastic. >> As we show every year, we're learning more and more and we're going to do a podcast for guys too. >> Nice. >> Different angle. >> Love that. >> A lot of guys want to do what to do. >> Okay that's great. >> Inclusion and diversity of course; I need the help. I'm John Furrier With Dave Vellante Here. Live at Vmworld. More coverage coming after this short break.

(upbeat music) >> Hello and welcome to theCUBE Conversation. I'm John Furrier here in the Palo Alto Studios, with two great guests, Tara Chklovski, who's the founder and CEO of Iridescent, and Anar Simpson, Global Ambassador of Technovation. Thanks for coming in today. Appreciate moving your schedules around to come in. Thanks for coming to our studio. >> You bet, yeah. >> So Sundar Pichai was at your event. That's the big story this past week. There's has been a Google memo from a low level employee who wrote some things that got the whole world sharking around gender biases, role of women in tech. We do a lot of women in tech as you know in theCUBE, hundreds and hundreds of women over the years, friends, and also smart people. This seem a pretty big moment for you guys. You had an event at Google. Sundar canceled his on-hands meeting to address this, under fear of retaliation and safety, but came to your event on Google Campus, surprising to many. It's written up on Recode and The Verge. Pretty notable. So tell us about what happened. >> So, yeah, this was the 2017 Technovation World Pitch Competition and the awards ceremony. And Sundar came and he talked to a lot of the girls who were presenting their ideas to solve problems in their community, and then he had a little bit of a one-on-one conversation to learn a little bit more about the kinds of problems, their interest in technology entrepreneurship, and then he addressed the crowd of 900 plus supporters, and really emphasized that there's a place for women in technology, and more importantly, for him and Google, that there's a place for these girls at Google. >> Great timing for you guys too. And I want to drill more into what happened but I want to just point out this was a scheduled stop for Sundar in terms of it. You guys have a program called Technovation which was a 2017 World Pitch, folks around, you're the Global Ambassador, take a minute to talk about what Technovation is. Why was it on Google's Campuses? What was it all about? What does Global Ambassador mean? Talk about your mission. >> Right, so Technovation's mission is to empower girls to become technology entrepreneurs and it's much more than just learning how to code. It's really about seeing girls and telling girls that if there's a problem in their community, technology can help them have a very powerful voice. We've been running for eight years and Anar is our Global Ambassador who's helped us grow to more than a hundred countries. Technovation's relationship with Google is eight years long. Google has supported Technovation, was the very first technology company to support Technovation way before any other company saw the potential. And since then, since 2010, Google has provided funding, mentors, spaces, not just across the US but globally. And so this year, it was a year long worth of relationship made with code which is their arm focusing on gender equality. They basically provided funding but made this event possible at Google headquarters. >> Anar talk about the Global Ambassador role you have, and kind of comes down to the question for Tara as well, is it beyond entrepreneurship and beyond coding? I mean talk about specifically what you guys are bringing to folks outside the Silicon Valley. >> Oh sure, so my role as the Global Ambassador for Technovation is really getting to girls all over the world and saying to them you need to be engaged in technology. And what we found, as Tara mentioned, we've been doing this now, I've been doing this now for five years, is that we're building a movement. We're bringing in girls, we're bringing in mentors, we're bringing in companies and governments together to make this a reality for girls in tech careers in their own countries. And I want to go back and address Google's relationship with Technovation a little bit more because this is more of an anecdote. I got into Technovation not willingly. Six years ago I had a start-up, it was called Parallel Earth, and I was working hard at it. And I was using the offices at Mozilla because they allow people to do that, you know people like me to work there. And one day somebody sent me a note, it just came on the internal email system, and they said, "You're a woman, you're in tech, "there's an event going on at Andreessen Horowitz "where the luminaries of the Valley are going to be talking." And so the luminaries were Mary Samayo who was at Google at that time, Freada Kapor Klein, Padma Ashriwurier , and I think that there was two other people. And so we went to this event and we sat in a packed room at Andreessen Horowitz. And these women, the luminaries at the Valley at that time, each one of them stood up and told us their story, and afterwards they fed us hors d'oeuvres and offered us wine. And then they said before you go, we have one to ask of you which is could you sign up to be a mentor for Technovation. And I thought to myself, no, I am like over my head in my own company. I don't even have time for myself. And they asked, be a mentor, it's just two hours a week for 12 weeks. And I thought to myself, oh God, man, I drank their wine, I ate their hors d'oeuvres, I listened to them and now how can I say no? And so I signed up. And it was a stretch for me because what happened at that time, the curriculum was still being delivered by a person. And so I've been assigned to the Google Campus in Mountainview. And somebody in engineering at Google had been able to get a room, a very small conference room. And so for 12 weeks I met this team of girls from Mountainview, and there were other mentors like me, and then there was a whole bunch of girls from Sequoia High School. And John, in that 12 weeks, I was a changed woman. Those five girls, they blossomed under me. When I met them, I said to them, "I'm here, I am a type A, this is a competition." >> "I signed up for the Andreessen Horowitz--" >> Exactly, exactly. "Listen, I got my own star, "but we're going to win, this is a competition." So they just rolled their eyes at me, like, who the heck she is, we don't even want to be here. >> John: They draw the short straw on this one. >> Exactly. But those 12 weeks changed my life. >> John: In what way, what way did it change your life? >> I have a degree in Computer Science. I have a Master's in Communication. I went to Stanford for innovation and entrepreneurship. So I've been in the field for a very long time. And what I saw in terms of the curriculum, what I saw in terms of the mentorship, what I learned about design thinking and being able to create an app, I never had that. When people like me, we go in to a university, and doing computer, we never had that kind of stuff. And I thought, oh my God, if I'd had that, I would be, like, soaring the skies right now. And to have girls who really came to this table with nothing, and you see them becoming graphic designers because they had a little bit of access to Microsoft Paint, someone who has the ability to do PowerPoint, one girl, in my team of five, almost never showed up, she was late, she never came, and then two sessions before the Pitch, she showed up and she realized, have we've gone so far without her. So here's what she did, she took that little graphic that that woman who'd done it in Paint, and she got her mom and they went to some t-shirt shop, and they got that graphic printed. And the next time she came, there were five t-shirts that said the name of our team which was Intoxication Station, and one for me. And then it turns out she's a really good speaker. Who knew? So she almost never came, brought these shirts, was the speaker for the group, and we won the local competition and then the next one, then we placed second in the finals. >> She came in, contributed with a t-shirt, and graced you the back end, won the trust of the group, ended up being the speaker and winning the award. >> Yes, they grew, they literally, you know if you take a time lapse and you see a flower blossom, that's exactly what happened. >> Tara talk about your credentials 'coz you have a Ph.D. >> So I have a, yeah, Bachelor's in Physics, and Master's in Aerospace, and I was in the Ph.D. program in Aerospace but I dropped out because I wanted to start Iridescent. >> That's good. Dropping out of Ph.D. has a good track record. A lot of folks who dropped out of Stanford includes some of the big names we now know. What's some examples during your life when you had those kind of changed moments? >> I think, Iridescent, we are now in our 12th year. Every couple of months it's a changed moment because it's a test of grit. And just believing in yourself because I mean, I started with just an idea and grew it to be an organization that's all over the world. And it doesn't come with just full-hearted focus. A lot of courage is what I've seen. I have also seen how much you are passionate about an idea really swings how the other person is thinking. And so the idea only matters so much, I think, of course, I mean, the track record and everything has to be there, but I think a lot of it depends on your own passion for it, and I've come to realize that passion is maybe proportional to the complexity and the impact of the problem you're trying to solve. So if you're only trying to solve a small problem, you lose interest in two years, right, and maybe that's why, I'm always curious, why do so many start-ups fail after two or three years? It's because maybe you came in not thinking that you're going to change the world, maybe you came in because you wanted to make quick money, or et cetera, whatever. And so I think for me this is my life's work. And if you want to bring more and to represent the communities into innovation. And so it's not something that's going to be solved easily. >> Start-up success and then people working on teams, really is about inclusion and letting things bloom and being ready for anything. That's the greatest feat. Let's get back to the Sundar event that you guys were having. Now this is a good conversation to have because one of the things that came out of the aha that became that memo, really was a conversation publicly. And now it's been polarizing. There's just some kind of a hate, hate kind of mindset with it most of the time. Plenty of stuff in the internet to go read there, but what actually are some good conversations in the industry? What was the conversation like during the event? Because this was in full conversation mode while you guys were having your 2017 World Pitch competition of which he presided over and had a speech to the entrepreneurs. What was it like? What are some of the conversations that were taking place? >> I think the most powerful piece of the whole evening was really the girls walking in and seeing the incredible diversity that we have in this world, right. So we had girls, and mentors, and supporters, from over 30 countries and just them coming and waving the flags, and different faces, and different cultures, all trying to make the world a better place. I mean, it's rare that you see that, using technology. And I think it's very fitting that Silicon Valley is the center of this. But I think there was not one dry eye in the group because you realized the conversation is so much bigger than one company, one country. It is something that affects us as all human beings, and you believing in human potential. So I think seeing these young girls, some of them 10 years old, there was this, I think, maybe the crowd's favorite was these 10-year-old girls from Cambodia who want to improve sort of the lives of these people working in cottage industries, right. And they created an app, like, say, Etsy or something, but focused on Cambodian products, and the courage of these little girls, I think everybody walks away feeling okay there's hope even in the midst of all of this discussion. >> It creates a lightning rod in some ways that hopefully will move on to the substantive conversations. How do you guys feel about what happened as you take this mission forward? You guys are doing some amazing work. And we'll do a segment on that in a minute, but given the landscape now, how do you view this? How are you talking with friends and colleagues and family members around it? Because I certainly had conversations with my friends certainly in the east coast, like, "No, no, that's not the way Silicon Valley is." Google actually is a very cool company. It's not what you think it is. They're very open. They support a lot of great initiatives. And they're candid. And then I go on and explain. It's like a university. So me and Larry have this little ecosystem that they've kind of built the university culture if you will. But it's open and there's things that happened that get misrepresented. That was my take for the folks who don't know Silicon Valley. But what's your take? What do you think about what's happened? >> So this is really, really good that you brought up the university campus, environment of it. So I have two girls, they're both millennials, and they're both in a tech world. And we had this discussion. And here is the perfect answer, right. So one of my daughters, Kat, she said that when she read that, she thought it was basically a gathering of his thoughts. And it was a gathering of his thoughts because he was probably asked to adhere to I&D stuff that's going on, in every company right now, right. And so he was like a little bit of a, wait a second, he wants to sort of, respond to his being asked to go to I&D stuff. And then Katya said, "But you know mom, "it was just a gathering of his thoughts. "And if this is an essay, and it was a poorly written one, "and if I was grading it, I would give him a C minus." Then my older daughter said-- >> John: Oh, she'll give him an F on that one. >> Right. >> John: C minus, she's generous. >> No, because he did. He tried to make it very professional and very academic. And she said but it was a first draft. He didn't proceed to toughen it up, solidify it, find more evidence, have it critic. It was just a gathering of his thoughts and he hasn't gone through the process. Both these girls graduated from Berkeley and so I think they would know what a C paper look like versus an A paper. And then my older daughter said, "And the other thing is, "it's not like "I&D efforts "are actually bad, "but what we're trying to do is "we're trying to condense the time "in which we're trying to get women "at equal peering in the tech world." Now women have never been at equal peering in many professions. There were not enough doctors, lawyers, accountants, you name it, right? Main street, Wall Street has never had equality. And now we're looking at technology and the reason everything just flares up in technology is because we live in today's world, where news and information is available all the time. So there's two things going on. Information is readily available. People can come in to the conversation very quickly. And whenever anything happens in Silicon Valley, the effect is massive because all eyes are on Silicon Valley all the time. So it's a bit of a distorted view. But we have gone through this. It took a long time for women to become astronauts. It took a long time for women to become neurosurgeons. It took a long time for women to become lawyers and dentists. It will take a little bit of time for women to become top technologists. But we're hoping that it'll shorten and things happen quickly in the Valley and we're trying to get that quicker. And so we're seeing a little bit of friction. This is responses from millennials. So for me it was like-- >> John: Interesting perspective. >> Yes, great perspective. And when Sundar said these things at the World Pitch, I was sitting in the second row and every time he said something I would clap really loud. And Todd said, "Why are you being so good?" And I said, "I need to hear that. "I need to her him say that because--" >> John: What did he say that moved you? >> Oh, he just said you have a place in technology. And I said yes. We needed to hear you say that right away, all the time, and especially to these girls, these two 18-year-old girls, and all of the ones that come from a hundred countries that weren't at Google but were listening to the live pitch. And I needed to hear it. I'm a veteran but I needed to hear it because-- >> It's interesting too the narrative that the millennials and certainly the younger kids hear is an echo of what comes down. And, interesting, my son who is 15, at dinner last night said, "Dad, I'm a white male. "What does that mean?" >> Poor guy. >> Then I'm like, oh my God, he's a kid. So, again, things are shifting, they're out of context. Tara your thoughts on how this all evolves and the positive things that folks can do. What's your perspective? >> Yeah, I mean, I think, I had a lot of discussion with my husband yesterday on this because he's a white male, right? And, but also we have two daughters, right. And so there's this whole he for she campaign, right. And that I think like our conversation earlier, the discussion has to be very inclusive and you cannot polarize. And I think I have to be careful because, I mean, my passion is what drives the work because the work is hard, but I have to also remind that, okay, there's a whole another segment of the population that cares, right, and, so I think it's just constantly remembering these kinds of things. I think in terms of what the industry can do, I think the normal thing is that people are doing which is really well, investing lower in the pipeline, investing in young girls, and all of that kind of stuff, and also sort of the inclusion and diversity stuff in the workforce. But I think there are some other segments, other industries that we can learn from, and I think one very unique place is actually the aviation industry. But the experimental aircraft, so we're just aviation enthusiasts, right. And so they have this gathering, yearly annual gathering, and 600,000 people come from all over the world, the thing that makes it unique and there's almost equal representation, there are two things that make it very unique. First is the family affair. And I think the tech industry has done a very good job, sort of convening these developer conferences but they are closed and most of them are 100% male, right? I think there could be something there where the, again much more than a company, that the industry has to do. And to make it maybe not commercial but do it as a fun family gathering and not in Silicon Valley. And then I think the second would be to actually lean on the veterans of the industry to share their passion with the young ones. And I think one of the problems of technology is that it's moved so fast that it has become very abstract. And nothing is very hands on. If you open up something, you will not understand anything. And so what the aviation industry had done really well is to showcase the core fundamental principles of how these things work using the old airplanes, old engines, combustion engines. But you can see how things work, right, and so-- >> John: It's like kindergarten. >> Exactly, exactly, start that way and then you can go into the more complex. But I think there's a role for the veterans of the tech world to play here. And I think it's not just sort of gender but it's also maybe age and making it much more about the family, rather than just the developer in the family. >> Tara and Anar, you guys are inspiration. Thanks for taking the time. And I've had the, my age, luxury of spending nine years at Hewlett Packard company before, maybe these early 90s when Bill Hewlett and Dave Packard were around. And one of the things that really influenced me, and I think this is something that I see a positive light coming in this industry, to your point, about so much changes, is that we seem to be going back to a crowd that wants to see respect for the individuals, citizenship. These were company values at Hewlett Packard when I was there that I always remembered was unique. Hey, you can have differences but if you have respect for the individual, and you have the citizenship mindset, that seems to have been lost in tech, and with this whole movement you're seeing, win at all cost, being an asshole, what you going to do to be a CEO, or flip it fast, or programs. So it became a very selfish environment. It seems to be shifting that way with this conversation. Your thoughts? >> So I have to say doing a start-up is not easy. Getting successful in this word is not easy. Shaking the status quo is not easy. So I have to say that the same people and we're not going to name names, but the same people who are very arrogant and have little respect for the laws and rules, they have given us products that are changing people's lives. There is no question about it. With that, they're a provider. With that, they're sort of "I don't care, I'm just going to go over you "if you don't comply with me." A lot of ride sharing, wouldn't even have happened. And to me when you provide employment, when you provide alternative services, when you provide something that takes away the way things were, I see that as a plus, okay. I think what we're seeing is that's needed to a certain extent, and then you realized, okay, now we have to get back to growing it and working it. And if you keep going in that mode, you probably won't succeed. >> So being tough and determined and having grit is what you need to breakthrough those walls as a start-up. You don't need to be necessarily a jerk. But your point is if you're creating value. >> If you're creating value, and that sometimes you actually have to be a jerk because there are a very few brave, non-jerk people who have gone against big unions and big monopolies, right. I would not be able to go against the taxi commission. You need somebody who's a complete a-hole to do that. And he did that and it made a difference. He doesn't have to continue to do that and that's-- >> There was a meme going around the internet, "If you want to make friends, sell ice cream." >> Exactly. >> So you can't always win friends when you're pioneering. >> Right, right. There is a balance and maybe we've fostered the fact that you need to be that attitude for everything and that's not true. The pendulum shifted a bit too much. But I think that we shouldn't scorn them because really they have made a difference. Let everybody get back to-- >> It's a tough world out there to survive. And you have to have that kind of sharp elbows to make things happen. But it's the value your providing, it's how you do it. >> Exactly. >> Well thanks so much guys for coming up. Appreciate to spend the time to talk about your awesome event at 2017 World Pitch as part of Technovation where Sundar represented Google in your great program with young girls go over some tech books. Thanks for sharing. This is CUBE conversation here at Palo Alto. I'm John Furrier. Thanks for watching. (upbeat music)

>> Narrator: Live from Las Vegas it's The Cube covering Interconnect 2017. Brought to you by IBM. >> Okay welcome back everyone. We are live at the Mandalay Bay for IBM Interconnect 2017, The Cube's exclusive coverage. I'm John Frower, Dave Vellante, my co-host. Our next guest is Jim Casey and Michael Gilfix. Michael's the VP of process transformation and Jim is offering manager at IBM. Guys, welcome back to The Cube. >> Both: Thank you. >> So you guys had a big announcement on Monday, the digital assistant, so I've been craving a digital assistant since the little Microsoft little, you know, icon would pop up. >> Michael: You're talking about Clip, aren't you? >> The clip man. >> Don't talk about that. >> We don't like that. >> To me that was once called the digital assistant. It was a help button, but this is now, digital assistant is real automation, and you guys got a whole other take on this. It's totally cloud, cloud first. What's the digital assistant product that you announced? Take us through that. >> So here was our vision. What we found was in the modern, digital workplace, everyone is struggling to just keep up pace. Too many sources of information, and the information is buried everywhere. It's buried in emails, in spreadsheets, in documents. Many corporations have undertaken a BI project. In fact, there's an explosion of all these different dashboards that has all kinds of business data that they could go and see, so no one has the time to read all these things. Meanwhile, everyone in the modern world is trying to do 50 things at once and it's hard to figure out what is the best time to progress something and make progress? Our vision, so what we thought is wouldn't it be great if I could program this assistant, programmable by everyday business users, to watch for the things that matter to me and figure out when I should take action or take automated action on my behalf to save me time. >> So it's an interface, so it's software interface, cloud-based SAS, and the back end, does the user have to, what's the persona of the user that's using your product? >> Well, we want them to be used by non-developers, non-technical users, and so we thought really carefully about how you can teach your assistant these notions of skills, really point to tasks that can really make your life easier on a daily basis and they can pick anything that they like working with, that they can connect to, get the information from, and effectively assemble into these point-to tasks. >> Host: And the data sources are whatever I want them to be, explain how that works? >> Yeah, it can connect to common SAS applications. Those could be things like productivity suites, like G-Suite, they can be things like CRM systems, like Sales Force, campaign management systems like Marketo, and that's just in the beta that we just launched. And of course in the future, they'll be able to connect into their on-premise systems as well. >> So is it to replace the dashboards and all the wrangling that goes on? Most business users will have either a department that does all the data science or data prep for them, wrangling data sets, and then they get reports or spreadsheets or some BI dashboard. >> Yeah, we wanted the assistant to push the work to the user instead of the user having to go and spend time watching all these dashboards that really, they just didn't have time to do. And so the assistant takes all the heavy lifting of watching the data for you, figures out when action is needed, and then taps you on the shoulder. >> So Ginny Ramete was talking about that your customers want to own the data. So that's a great purpose, we buy into that mission, but a lot of the data is spread all over the place, so one of the problems that we're seeing in the big data world, now IOT complicates even further, is that data's everywhere, scattered, and the tools might have stacks and data wrangling within tools so you have complexity out there just on the scaffolding of how the data's managed. Is that part of the problem that you guys help solve? Because that seems to be a pain point. >> Yeah, and I think the amount of time that people spend just searching and aggregating and gathering information so they can figure out what to do, it's staggering. And when you think about the, it takes about two the three hours often for people to gather all the information that they need in order to make a real significant decision, every day, daily, you know operations. You're spending time in your email, you're building spreadsheets. Think of all the time you spend building a spreadsheet, wrangling data, you know. It's a productivity killer, and so a lot of the use cases that we look for, we'll ask our clients show me the ugliest spreadsheet that you use on a day-to-day basis for business operations. That's usually a starting point, or show me how many dashboards are you looking at and what are the decision you make off that? That's the stuff that we want to collapse into what the assistant can provide. >> So I got a use case for you, I'm a walking, I'm like everybody, right, so I've got my email, I've got five or six spreadsheets, Google Docs that I'm in every day all day, maybe there's a base camp, maybe there's a slack. I'm in Sales Force, all right, and then I got my social. >> Tool overdose. >> You just described the typical modern environment. All fragmented tools. >> And I'm in there and I'm like which browser is it, oh is it in Firefox, I'll put my Safari stuff I'll put over here, and I'll put my email in Mozilla, okay. It is just awful, it's a bloody nightmare, I get lost. I got to back up, hit the escape key, and go, okay, where am I, how do I find it again? >> Jim: It's connecting the dots. >> Okay, explain now how you can help me. >> So think of the things that you're looking for in all those different data sources. We're seeing the trend now. It's not about how can I just connect with things, it's how can I connect the dots? It's the actual business data inside of there, and how do I put that in a context that's relevant to you, what you're trying to do? You know, and a great example, we're working with one client who, they're moving, and a lot of people are doing this, they're moving from a point in time sale to being as a service, and in that kind of scenario, relationships with your clients really matter. And preventing customer churn is really important. So they have people who are responsible for making sure that people are not going to churn. That's a lot of dots to connect, right? So with the Digital Business Assistant, what we do is we look for those patterns that are really common that predict churn, but those things are scattered across your sales systems, your marketing systems, the website traffic, social media even, and we're able to combine all those things into a really consumable component called a skill. And then that individual person that's responsible for this set of customers can tailor it to their needs. So it's kind of like how you would buy a suit. When you go in and buy a suit, you don't get just the fabric laid out on a table and they cut it, right? You, most people don't anyway. (they laugh) >> I buy what's on the rack. I say "I want that one." >> Yeah, you walk in and you say that. >> I want what that is. >> 42 long, right? And they make a couple adjustments and then it's yours. >> All right, I'll take that suit up there, what's on the mannequin. >> They make a few adjustments and it's yours. Software should be the same way. You should be able to configure software in a few clicks. >> That's the whole thing, I mean, I joke about the mannequin but that's really kind of what hangs the perfect use case so that would be an automated example of an assistant model for you guys. Sometimes you just want everything to hang together for you, and sometimes you might want to go in and go look at the data. >> Yeah, and we see this across a lot of different industries, so things like customer service and sales and marketing, but we also see it in, let's say I'm a field technician, right? And I got to go out to an oil field. How do I know all the different patterns of information that might predict whether or not I need to, what I need to do when I'm out there. >> So you monitor my patterns, my behavior, and then ultimately train the model, or? >> Well you program it. You tell it what to watch for for you. So to give you an example of the kind of use case, to pick a specific use case, and we shared this again in sort of our unveiling on Monday. We shared the idea of a sales rep who is pursuing a given opportunity, and thinking about all the factors that went into their success and, you know, that sales rep has several different things they need to use to really maximize their chance of closing that deal. So one is they need to be responsive do their customer, and you know, like many different corporations out there who sell many different products and services, while you're busy working on the new opportunity, you've got to service the old. So when some issue comes up, you have to be responsive to it. Well, it's really hard while you're busy working on all these opportunities, to make sure that the issue's being resolved, that you're being responsive to your customer. Meanwhile, everybody in the corporation is coming up with new opportunities, new marketing brochures, new values in the product. And so is your rep knowledgeable about the latest and greatest products? So we imagine that you could teach your assistant how to watch some of this stuff for you and really help you to close your opportunity. And a very pointed example of the kinds of things that it should watch for you, I should be able to say something like hey, if I can have an active opportunity and then my customer goes and opens a service support ticket and that service support ticket hasn't been resolved in a week and meanwhile, I got a bunch of email coming from that client, of tone angry, notice the cognitive part there, about this particular product, and meanwhile I'm on the road and I'm not checking my email. Well, I have a catastrophe waiting to happen. So I can program my assistant to watch for these kinds of things. >> Does it do push notifications? >> Exactly, so you can then have it push to you, look, here's all the information about the active service thing, here's how long it was sitting there waiting for resolution, this is what's happened since, and you can immediately take action. >> So you're orchestrating basically signals that the user connects, like a Google alert on search is a trivial example, right? Someone types, a result comes on Google, you get an email. Here, you're kind of doing that-- >> But it's proactive. You tell your assistant to proactively watch it for you, and that's a unique technology that we developed in-house. Because it's watching all these events happening in the enterprise and figuring out when that thing becomes actionable. >> And the user would know where to look, because like Dave's spreadsheet might say "hey, cash balance" or you know, sales trend, this rep and then something happens, and he can get that pushed to him from three different disparate side-load apps, that's pretty much what it is. >> That's right. >> Okay, so give us the status on the beta right now. It's a beta, so it's sign-up required. Okay, and the requirements to implement it, if you get through the beta, is just log in to a portal? It's a SAS model and then do the connectors? >> So the first thing you do, you go to IBM.com/assistant. You can sign up to. >> That, by the way, might be the easiest URL I think we ever came up with. I'm pretty sure that one's going to be memorable. >> Yeah, so you just go to that site, you sign up, you give us a little bit of information, your email, how to contact you and we'll put you on the waiting list, and what we're going to be doing is opening up more seats as we go through over the next couple weeks, and then we plan in the near term here to make it available as an open beta that you could see, and you'll see that inside of Bloomix as a tile inside of Bloomix. >> And here's the thing, we're doing something really different in the marketplace. This is a very different kind of offering, really targeting, again, non-technical people, this proactive situational awareness that your assistant can do, uses your data, built-in intelligence, intelligence that can customize to the way you work, guide you to the next best action. We have an incredible vision for this. The idea behind the beta is to start getting feedback. We worked very closely with early customers in the initial design and development. We want to open that up and get even more feedback and ideas on this kind of technology. >> So how is this different from Watson's discovery services that they have? I can imagine that you're building on Watson. Is it the cognitive piece within IBM, or is this kind of, I mean how would a customer figure that out, or just more of a-- >> Yeah, so I can give you an example. So we have one of our prototypes that we're actually taking some of the components of Watson discovery service and we package that up as a skill inside of your assistant, and it's a specific implementation, so what it allows you to do in this case is it'll look at your email and it'll look for specific entities, like a customer that matters to you, and if I get three emails of negative sentiment from a customer where I also have an open opportunity in the last week, that's a pattern I want to know about, right? Or we can start to correlate with all sorts of different things, so I think what you're going to see is these skills that we make available with the digital business assistant really up, take consumability of these really, really powerful technologies around cognitive and cloud. We take that to the next level. >> That's the key, how do we make Watson tailorable and put in the hands of every knowledge worker in every company? >> Host: So I presume you guys are dog fooding this personally, is that right? >> We have plans to do that, yes. >> Host: Oh, you haven't started yet? >> Sampling our own champagne. >> But we are, yes. >> He always gets called on that. >> We will be using it, yes. >> We created that champagne. >> We're beer drinkers, that's it, beer. >> We're going back to dog food, we eat beer, we should drink our own beer now. We created that with all our boost men, remember? (laughs) >> So get back to the status of the product. So it's got some Watson capability, but this is for the user to use. I don't have to get IT involved? >> Jim: That's right. >> This is where the user takes a personal productivity approach, and you bring in some Watson-- >> A user may not even know that they're using some of these Watson capabilities. To the end user, what do you want it to do for me? Well, I want it to tell me if, uh, if I think a customer might be upset with me. Well, that might be a combination of a lot of different things, but it just makes it really consumable and easy for people. >> So where do you guys sit within IBM? Because now there's like, because this is a really cool user tool, so is this part of Watson? >> Jim: We think so. >> Is it part of the Watson team? >> Well, honestly our organization doesn't really matter, I mean, we're working with teams across IBM as a whole. It's a great opportunity to take this technology and really reach a whole set of new use cases, I think, across the company, and we want to integrate Watson technology to, like we were saying, really make it easy for the end-user to go and access it. >> Any plans around developer outreach? >> Well, we will, I think, later this year, one of the things we envisioned really early on is that people are going to want to have pre-built skill sets, and that's a great opportunity to build an incredibly powerful ecosystem and we've been in discussion with a lot of our partners about how to do that. >> Well you guys are API based, so this is a beautiful thing, right? >> Well we're going to start to open up some SDKs to our partners, to others, and that's going to allow them to extend the assistant and really create even more powerful industry content. >> You know, the business model of reducing the steps it takes to do something and saving people time, making it easy to use is a magical formula of success. >> And not even just less steps, it's less time reading things, less time sifting through information so you can spend time on stuff that matters. >> Just email by itself, I mean, Dave, your example was the best, because I know, we live that. But we have a multitude of tools and sometimes it just organically goes, because the one guy like, you know, this tool set, or now I got-- >> So do you want to do the deal now or? >> Right, that's what I'm saying, they should be signing up. >> So do we get paid? (they laugh) >> We're already both signed up. We have a testimonial. >> If you can't get it, how can we get it? >> We'll kick the tires on it, and uh, but the thing that gets my excitement is potential for API integration. Because if I know I can the automation to a whole other level and the use cases start to patternize in the enterprise, then it can get interesting. All right guys, thanks so much. What's going on here with the show, what else is happening for you guys? Share some stories for the folks that aren't here, that are watching on IBM Go right now. What's the vibe at the show this week? >> Well, it's been a great vibe. We've had a chance to share some incredible success stories, so in addition to the unveiling of this particular product, on Monday we had a chance for one of our marquee clients to share their story, and I'll tell you a little bit about what they did. It was at the National Health Service of the UK. Part of their blood and transplant, and we were fortunate enough to have Aaron Powell, who's the chief digital officer there, share their story of using process technology to improve the speed at which they get organs in the hands of recipients, and they did it on the cloud. And the results they obtained were unbelievable. So the before and after, they had staff at 2am, writing lists of high-risk patients and how to map their donors and he kidded us not, that when someone's priority changes, they would wipe the board and reset things. And these are people's lives that are at stake in the matching process. >> And they're tired, human error is huge. >> Human error, absolutely, and by the way, when you look at the end-to-end process, there was something like 90 steps if I remember, 96 steps I think end-to-end. All of which were very manual and error-prone, and error-prone means risk. And they were able to improve organ allocation by 3x, so 3x faster, they automated something like 58% of the steps, reducing propensity for manual error, and what he shared in his story is, they successfully a few months ago did the first heart transplant on the cloud. >> Host: Wow, that's amazing. >> So it's an amazing, amazing story. >> That's a great story, yeah. Did he say that in the session? >> He did, actually, he said that. >> That's actually a good thing to chase down for a great blog post, that would be phenomenal. It would have been covered yet on the news? >> So we're going to post actually the video of it online so people can also see him live presenting his story, it was unbelievable. >> Make sure you send me the link. The other thing that they could apply there is two-block chain, I mean some of the block chain stuff coming out is going to be really interesting. >> Absolutely, and we're working very closely with that team to really leverage this kind of process technology, take people's business operations and connect that in to this feature network that's going to power businesses. >> CRM is the human supply chain, I mean, but now extend it out to the internet of things. I mean, it's interesting how this could play out. Guys, thanks so much for coming on The Cube. Thanks for sharing the insight, congratulations on the launch. I just signed up for the beta while we were talking. >> Dave: Me too, so let us cut the line. >> Done. >> We need it. Perfect use case, we need help. It's The Cube, of course, no help here, great guests here on The Cube. I'm John Frower, Dave Vellante, more great coverage, stay with us. Day three of Interconnect 2017, we'll be right back. (techno music)