>> Narrator: Live form Berlin, Germany, it's the Cube, covering NetApp insight 2017, brought to you by NetApp. Welcome back to the Cube's live coverage of NetApp insight 2017, we're here in Berlin, Germany, I'm your host, Rebecca Night along with my cohost Peter Burris. We have two guests on the program now, we have Rory McBride, who is the technical account manager at Aero and Bryan Mclosky, who is the vice president world wide for hyper converge infrastructure at NetApp. Bryan, Rory, thanks so much for coming on the show. >> Thanks. >> Let me start with you, Bryan, talk a little bit, tell our viewers a little bit about the value, that HCI delivers to customers, especially in terms of simplifying the data. >> In a nutshell, what NetApp HCI does is it takes what wold normally be hours and hours to implement a solution and 100s of inputs, generally, over 400 inputs and it simplifies it down to under 30 inputs in an installation, that will be done within 45 minutes. Traditionally HCI solutions have similar implementation characteristics, but you lose some of the enterprise flexibility and scale, that customers of NetApp have come to expect over the years. What we've done is we've provided that simplicity, while allowing customers to have the enterprise capabilities and flexibility, that they've grown accustomed to. >> Is this something, that you are talking with customers, in terms of the simplicity, what were you hearing from customers? >> Most customers these days are challenged of, everybody has to find a way to do more with less or to do minimally a lot more with the same. If you think of NetApp, we've always been wonderful about giving customers a great production experience. When you buy a typical NetApp product, you're gonna own it for three, four or five years and it will continue. NetApp has always been great for that three, four and five year time frame and what we've done with HCI is we really simplified the beginning part of that curve of how do you get it from the time it lands on your dock to implement it and usable by our users in a short manner, that's what HCI has brought to the NetApp portfolio, that's incremental to what was there before. >> One of the advantages to third parties, that work closely with NetApp is, that by having a simpler approach of doing things, you can do more of them, but on the other hand, you want to ensure, that you're also focused on the value add. In the field, when you're sitting down with a customer and working with them to ensure, that they get the value, that they want from these products, how do you affect that balance? As the product becomes simpler to the customer now being able to focus more on other things, other than configuration of limitation. >> We've been able to get to doing something with your data is the key. You needed a little bar of entry, which a lot of the software and hardware providers are trying to do today. I think HCI just has to pull all of that together, which is great. We're hearing from third party vendors, that it's great, that from day one, they've been integrated into the overall portfolio message and I think customers are just gonna be pretty excited with what they can do from zero with this hardware. >> When you think about ultimately how they're gonna spend their time, what are they going to be doing instead of now all this all configuration work? What is Aero gonna be doing now, that you're not doing that value added configuration work? >> Hopefully, we'll be helping to realize the full potential of what they bought, rather than spending a lot of time trying to make the hardware work, they're concentrating more on delivering a service or an application back to the business, it's gonna generate some revenue. In Aero we're talking a lot to people about IOT and it's gonna be the next wave of information, that people are gonna have to deal with and having a stable product, that can support and provide value, you have information back to business, it's gonna be key. >> Bryan, HCI, as you noted, dramatically reduces the time to get to value, not only now, but it also sustains that level of simplicity over the life of the utilization of the product. How does it fit into the rest of the NetApp product set, the rest of the NetApp portfolio? What does it make better, what makes it better in addition to just the HCI product? >> NetApp has a really robust portfolio of offerings, that we, at a high level categorize into our next generation offerings, which are Solid Fire, Flexpod Solid Fire, storage grid and hyper converge and then the traditional NetApp on tap based offerings. What the glue between the whole portfolio is the data fabric and HCI is very tightly integrated into the data fabric, one of the innovations we are delivering is snap mirror integration of the RHCI platform into the traditional on tap family of products. You can seamlessly move data from our hyper converge system to a traditional on tap base system and it also gives you seamless mobility to either your own private cloud or to public cloud platforms. As a company with a wide portfolio, it gives us the ability to be consultative with our partners and our customers. What we want is and we feel customers are best served on NetApp and we want them to use NetApp, and if an on tap base system is a better solution for them than hyper converge, then that's absolutely what we will recommend for them. Into your earlier question about the partners, one of the interesting things with HCI is it's the first time as NetAPP were delivering an integrated system with compute and with a hyperviser, it comes preconfigured with the emware and it's a wonderful opportunity for our partners to add incremental value through the sale cycle to what they've brought to NetApp in the past. Because as NetApp, we're really storage experts, where our partners have a much wider and deeper understanding of the whole ecosystem than we do. It's been interesting for us to have discussions with partners, cuz we're learning a lot, because we're now involved in layers and we're deeply involved at higher levels of the stack, than we have been. >> I'm really interested in that, because you say, that you have this consultative relationship with these customers, how are you able to learn from them, their best practices and then do you transfer what you've learned to other partners and other customers? >> From the customer and we try and disseminate the learning as much as we can, but we're a huge organization with many account teams, but it all starts with what the customers wants to accomplish, minimally they need a solution, that's gonna plug in and do what they expect it to do today. What's the more important part is where what their vision is for where they wanna be three years down the road, five years down the road, 10 years down the road. It's that vision piece, that tends to drive more towards one part of the portfolio, than the other. >> Take us through how this works. You walk into an account, presumably Aero ECS has a customer. The Aero ECS customer says, "Well, we have an issue, that's going to require some specialized capabilities and how we use our data". You can look at a lot of different options, but you immediately think NetApp, what is it, that leads you to NetApp HCI versus on tap, versus Solid Fire, is there immediate characteristic, that you say, "That's HCI"? >> I would say, that the driving factor was the fact, that they wanted something that's simple and easy to manage, they want to get a mango data base up and running or they've got some other application, that really depends on their business. The underlying hardware needs to function. Bryan was saying, that it's got element OS sitting underneath it, which is in its 10th iteration and you've got VM version six, which is the most adopted virtualization platform out there. These are two best breed partnerships coming together and people are happy with that, and can move, and manage it from a single pane of glass moving forward from day one right the way through when they need to transition to a new platform, which is seamless for them. That's great from any application point, because you don't wanna worry about the health of things, you wanna be able to give an application back to the business. We talked about education, this event is gauged towards bringing customers together with NetApp and understanding the messaging around HCI, which is great. >> What are the things, that you keep hearing form customers, does this need for data simplicity, this need for huge time saving products and services? What do you think, if you can think three to five years down the road, what will the next generation of concerns be and how are you, I'm gonna use the word, that we're hearing a lot, future proof, what you're doing now to serve those customers needs of the future? >> Three to five years down the road. I can't predict three to five years out very reliably. >> But you can predict, that they're gonna have more data, they're going to merge it in new and unseen ways and they need to do it more cheaply. >> The future proofing really comes in from the data fabric. With the integration into the data fabric, you could have information, that started on a NetApp system, that was announced eight years ago, seamlessly moves into a solid fire or flash array, which seamlessly moves to a hyperconverge system, which seamlessly moves to your private cloud, which eventually moves off to a public cloud and you can bring it back into any tiers and wherever you want that data in six, seven, eight years, the data fabric will extend to it. Within each individual product, there are investment protection technologies within each one, but it's the data fabric, that should make customers feel comfortable, that no matter where they're gonna end up, taking their first step with NetApp is a step in the right direction. >> The value added ecosystem, that NetApp and others use and Aero ECS has a big play around that, has historically been tied back into hardware assets, how does it feel to be moving more into worrying about your customers data assets? >> I think it's an exciting time to be bringing those things together. At the end of the day, it's what the customer wants, they want a solution, that integrates seamlessly from whether that be the rack right the way up to the application, they want something, that they can get on their phone, they want something they can get on their tablet, they want the same experience regardless whether they're in an airplane or right next to the data center. The demand on data is huge and will only get bigger over the next five years. I was looking at a recent cover of forest magazine, it was from a number of years ago about Nokia and how can anybody ever catch them and where are they now? I think you need to be able to spot the changes and adapt quickly and to steal one of the comments from the key note yesterday, is moving from a survivor to a thriver with your data, it's gonna be key to those companies. >> In talking about the demands on data growing, it's also true, that the demands on data professionals are growing too. How is that changing the way you recruit and retain top talent? >> For us, as NetApp, if you were to look at what we wanted in the CV five years ago, we wanted people, that understood storage, we wanted people, that knew about volumes, that knew about data layouts, that knew how to maximize performance by physical placement of data and now what we're looking for is people, that really understand the whole stack and that can talk to customers about their application needs their business problems, can talk to developers. Because what we've done is we've taken those people, that were good in all those other things I mentioned, when you ask them what did you love about this product, none of them ever came back and said I love the first week I spent installing it. We've taken that away and we've let them do more interesting work. A challenge for us is, us is a collective society, is to make sure we bring people forward from an education perspective skills enablement, so they're capable of rising to that next level of demand, but we're taking a lot of the busy work out. >> Making sure, that they have the skills to be able to take what they're seeing in the data and then take action. >> We want our customers to look at NetApp as data expert, that can work with them on their business problem, not a storage expert, that can explain how an array works. >> Bryan, Rory, thank you so much for coming on the show, it's been a great conversation. >> Thank you. >> Thank you very much. >> You are watching the Cube, we will have more from NetApp insight, I'm Rebecca Night for Peter Burris in just a little bit.

>>Hey everyone. Welcome to Las Vegas. The Cube is here, live at the Venetian Expo Center for AWS Reinvent 2022. Amazing attendance. This is day one of our coverage. Lisa Martin here with Day Ante. David is great to see so many people back. We're gonna be talk, we've been having great conversations already. We have a wall to wall coverage for the next three and a half days. When we talk to companies, customers, every company has to be a data company. And one of the things I think we learned in the pandemic is that access to real time data and real time analytics, no longer a nice to have that is a differentiator and a competitive all >>About data. I mean, you know, I love the topic and it's, it's got so many dimensions and such texture, can't get enough of data. >>I know we have a great guest joining us. One of our alumni is back, Evan Kaplan, the CEO of Influx Data. Evan, thank you so much for joining us. Welcome back to the Cube. >>Thanks for having me. It's great to be here. So here >>We are, day one. I was telling you before we went live, we're nice and fresh hosts. Talk to us about what's new at Influxed since the last time we saw you at Reinvent. >>That's great. So first of all, we should acknowledge what's going on here. This is pretty exciting. Yeah, that does really feel like, I know there was a show last year, but this feels like the first post Covid shows a lot of energy, a lot of attention despite a difficult economy. In terms of, you know, you guys were commenting in the lead into Big data. I think, you know, if we were to talk about Big Data five, six years ago, what would we be talking about? We'd been talking about Hadoop, we were talking about Cloudera, we were talking about Hortonworks, we were talking about Big Data Lakes, data stores. I think what's happened is, is this this interesting dynamic of, let's call it if you will, the, the secularization of data in which it breaks into different fields, different, almost a taxonomy. You've got this set of search data, you've got this observability data, you've got graph data, you've got document data and what you're seeing in the market and now you have time series data. >>And what you're seeing in the market is this incredible capability by developers as well and mostly open source dynamic driving this, this incredible capability of developers to assemble data platforms that aren't unicellular, that aren't just built on Hado or Oracle or Postgres or MySQL, but in fact represent different data types. So for us, what we care about his time series, we care about anything that happens in time, where time can be the primary measurement, which if you think about it, is a huge proportion of real data. Cuz when you think about what drives ai, you think about what happened, what happened, what happened, what happened, what's going to happen. That's the functional thing. But what happened is always defined by a period, a measurement, a time. And so what's new for us is we've developed this new open source engine called IOx. And so it's basically a refresh of the whole database, a kilo database that uses Apache Arrow, par K and data fusion and turns it into a super powerful real time analytics platform. It was already pretty real time before, but it's increasingly now and it adds SQL capability and infinite cardinality. And so it handles bigger data sets, but importantly, not just bigger but faster, faster data. So that's primarily what we're talking about to show. >>So how does that affect where you can play in the marketplace? Is it, I mean, how does it affect your total available market? Your great question. Your, your customer opportunities. >>I think it's, it's really an interesting market in that you've got all of these different approaches to database. Whether you take data warehouses from Snowflake or, or arguably data bricks also. And you take these individual database companies like Mongo Influx, Neo Forge, elastic, and people like that. I think the commonality you see across the volume is, is many of 'em, if not all of them, are based on some sort of open source dynamic. So I think that is an in an untractable trend that will continue for on. But in terms of the broader, the broader database market, our total expand, total available tam, lots of these things are coming together in interesting ways. And so the, the, the wave that will ride that we wanna ride, because it's all big data and it's all increasingly fast data and it's all machine learning and AI is really around that measurement issue. That instrumentation the idea that if you're gonna build any sophisticated system, it starts with instrumentation and the journey is defined by instrumentation. So we view ourselves as that instrumentation tooling for understanding complex systems. And how, >>I have to follow quick follow up. Why did you say arguably data bricks? I mean open source ethos? >>Well, I was saying arguably data bricks cuz Spark, I mean it's a great company and it's based on Spark, but there's quite a gap between Spark and what Data Bricks is today. And in some ways data bricks from the outside looking in looks a lot like Snowflake to me looks a lot like a really sophisticated data warehouse with a lot of post-processing capabilities >>And, and with an open source less >>Than a >>Core database. Yeah. Right, right, right. Yeah, I totally agree. Okay, thank you for that >>Part that that was not arguably like they're, they're not a good company or >>No, no. They got great momentum and I'm just curious. Absolutely. You know, so, >>So talk a little bit about IOx and, and what it is enabling you guys to achieve from a competitive advantage perspective. The key differentiators give us that scoop. >>So if you think about, so our old storage engine was called tsm, also open sourced, right? And IOx is open sourced and the old storage engine was really built around this time series measurements, particularly metrics, lots of metrics and handling those at scale and making it super easy for developers to use. But, but our old data engine only supported either a custom graphical UI that you'd build yourself on top of it or a dashboarding tool like Grafana or Chronograph or things like that. With IOCs. Two or three interventions were important. One is we now support, we'll support things like Tableau, Microsoft, bi, and so you're taking that same data that was available for instrumentation and now you're using it for business intelligence also. So that became super important and it kind of answers your question about the expanded market expands the market. The second thing is, when you're dealing with time series data, you're dealing with this concept of cardinality, which is, and I don't know if you're familiar with it, but the idea that that it's a multiplication of measurements in a table. And so the more measurements you want over the more series you have, you have this really expanding exponential set that can choke a database off. And the way we've designed IIS to handle what we call infinite cardinality, where you don't even have to think about that design point of view. And then lastly, it's just query performance is dramatically better. And so it's pretty exciting. >>So the unlimited cardinality, basically you could identify relationships between data and different databases. Is that right? Between >>The same database but different measurements, different tables, yeah. Yeah. Right. Yeah, yeah. So you can handle, so you could say, I wanna look at the way, the way the noise levels are performed in this room according to 400 different locations on 25 different days, over seven months of the year. And that each one is a measurement. Each one adds to cardinality. And you can say, I wanna search on Tuesdays in December, what the noise level is at 2:21 PM and you get a very quick response. That kind of instrumentation is critical to smarter systems. How are >>You able to process that data at at, in a performance level that doesn't bring the database to its knees? What's the secret sauce behind that? >>It's AUM database. It's built on Parque and Apache Arrow. But it's, but to say it's nice to say without a much longer conversation, it's an architecture that's really built for pulling that kind of data. If you know the data is time series and you're looking for a time measurement, you already have the ability to optimize pretty dramatically. >>So it's, it's that purpose built aspect of it. It's the >>Purpose built aspect. You couldn't take Postgres and do the same >>Thing. Right? Because a lot of vendors say, oh yeah, we have time series now. Yeah. Right. So yeah. Yeah. Right. >>And they >>Do. Yeah. But >>It's not, it's not, the founding of the company came because Paul Dicks was working on Wall Street building time series databases on H base, on MyQ, on other platforms and realize every time we do it, we have to rewrite the code. We build a bunch of application logic to handle all these. We're talking about, we have customers that are adding hundreds of millions to billions of points a second. So you're talking about an ingest level. You know, you think about all those data points, you're talking about ingest level that just doesn't, you know, it just databases aren't designed for that. Right? And so it's not just us, our competitors also build good time series databases. And so the category is really emergent. Yeah, >>Sure. Talk about a favorite customer story they think really articulates the value of what Influx is doing, especially with IOx. >>Yeah, sure. And I love this, I love this story because you know, Tesla may not be in favor because of the latest Elon Musker aids, but, but, but so we've had about a four year relationship with Tesla where they built their power wall technology around recording that, seeing your device, seeing the stuff, seeing the charging on your car. It's all captured in influx databases that are reporting from power walls and mega power packs all over the world. And they report to a central place at, at, at Tesla's headquarters and it reports out to your phone and so you can see it. And what's really cool about this to me is I've got two Tesla cars and I've got a Tesla solar roof tiles. So I watch this date all the time. So it's a great customer story. And actually if you go on our website, you can see I did an hour interview with the engineer that designed the system cuz the system is super impressive and I just think it's really cool. Plus it's, you know, it's all the good green stuff that we really appreciate supporting sustainability, right? Yeah. >>Right, right. Talk about from a, what's in it for me as a customer, what you guys have done, the change to IOCs, what, what are some of the key features of it and the key values in it for customers like Tesla, like other industry customers as well? >>Well, so it's relatively new. It just arrived in our cloud product. So Tesla's not using it today. We have a first set of customers starting to use it. We, the, it's in open source. So it's a very popular project in the open source world. But the key issues are, are really the stuff that we've kind of covered here, which is that a broad SQL environment. So accessing all those SQL developers, the same people who code against Snowflake's data warehouse or data bricks or Postgres, can now can code that data against influx, open up the BI market. It's the cardinality, it's the performance. It's really an architecture. It's the next gen. We've been doing this for six years, it's the next generation of everything. We've seen how you make time series be super performing. And that's only relevant because more and more things are becoming real time as we develop smarter and smarter systems. The journey is pretty clear. You instrument the system, you, you let it run, you watch for anomalies, you correct those anomalies, you re instrument the system. You do that 4 billion times, you have a self-driving car, you do that 55 times, you have a better podcast that is, that is handling its audio better, right? So everything is on that journey of getting smarter and smarter. So >>You guys, you guys the big committers to IOCs, right? Yes. And how, talk about how you support the, develop the surrounding developer community, how you get that flywheel effect going >>First. I mean it's actually actually a really kind of, let's call it, it's more art than science. Yeah. First of all, you you, you come up with an architecture that really resonates for developers. And Paul Ds our founder, really is a developer's developer. And so he started talking about this in the community about an architecture that uses Apache Arrow Parque, which is, you know, the standard now becoming for file formats that uses Apache Arrow for directing queries and things like that and uses data fusion and said what this thing needs is a Columbia database that sits behind all of this stuff and integrates it. And he started talking about it two years ago and then he started publishing in IOCs that commits in the, in GitHub commits. And slowly, but over time in Hacker News and other, and other people go, oh yeah, this is fundamentally right. >>It addresses the problems that people have with things like click cows or plain databases or Coast and they go, okay, this is the right architecture at the right time. Not different than original influx, not different than what Elastic hit on, not different than what Confluent with Kafka hit on and their time is you build an audience of people who are committed to understanding this kind of stuff and they become committers and they become the core. Yeah. And you build out from it. And so super. And so we chose to have an MIT open source license. Yeah. It's not some secondary license competitors can use it and, and competitors can use it against us. Yeah. >>One of the things I know that Influx data talks about is the time to awesome, which I love that, but what does that mean? What is the time to Awesome. Yeah. For developer, >>It comes from that original story where, where Paul would have to write six months of application logic and stuff to build a time series based applications. And so Paul's notion was, and this was based on the original Mongo, which was very successful because it was very easy to use relative to most databases. So Paul developed this commitment, this idea that I quickly joined on, which was, hey, it should be relatively quickly for a developer to build something of import to solve a problem, it should be able to happen very quickly. So it's got a schemaless background so you don't have to know the schema beforehand. It does some things that make it really easy to feel powerful as a developer quickly. And if you think about that journey, if you feel powerful with a tool quickly, then you'll go deeper and deeper and deeper and pretty soon you're taking that tool with you wherever you go, it becomes the tool of choice as you go to that next job or you go to that next application. And so that's a fundamental way we think about it. To be honest with you, we haven't always delivered perfectly on that. It's generally in our dna. So we do pretty well, but I always feel like we can do better. >>So if you were to put a bumper sticker on one of your Teslas about influx data, what would it >>Say? By the way, I'm not rich. It just happened to be that we have two Teslas and we have for a while, we just committed to that. The, the, so ask the question again. Sorry. >>Bumper sticker on influx data. What would it say? How, how would I >>Understand it be time to Awesome. It would be that that phrase his time to Awesome. Right. >>Love that. >>Yeah, I'd love it. >>Excellent time to. Awesome. Evan, thank you so much for joining David, the >>Program. It's really fun. Great thing >>On Evan. Great to, you're on. Haven't Well, great to have you back talking about what you guys are doing and helping organizations like Tesla and others really transform their businesses, which is all about business transformation these days. We appreciate your insights. >>That's great. Thank >>You for our guest and Dave Ante. I'm Lisa Martin, you're watching The Cube, the leader in emerging and enterprise tech coverage. We'll be right back with our next guest.

>>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 want to 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.

>>When Oracle acquired my SQL via the Sun acquisition, nobody really thought the company would put much effort into the platform preferring to focus all the wood behind its leading Oracle database, Arrow pun intended. But two years ago, Oracle surprised many folks by announcing my SQL Heatwave a new database as a service with a massively parallel hybrid Columbia in Mary Mary architecture that brings together transactional and analytic data in a single platform. Welcome to our latest database, power panel on the cube. My name is Dave Ante, and today we're gonna discuss Oracle's MySQL Heat Wave with a who's who of cloud database industry analysts. Holgar Mueller is with Constellation Research. Mark Stammer is the Dragon Slayer and Wikibon contributor. And Ron Westfall is with Fu Chim Research. Gentlemen, welcome back to the Cube. Always a pleasure to have you on. Thanks for having us. Great to be here. >>So we've had a number of of deep dive interviews on the Cube with Nip and Aggarwal. You guys know him? He's a senior vice president of MySQL, Heatwave Development at Oracle. I think you just saw him at Oracle Cloud World and he's come on to describe this is gonna, I'll call it a shock and awe feature additions to to heatwave. You know, the company's clearly putting r and d into the platform and I think at at cloud world we saw like the fifth major release since 2020 when they first announced MySQL heat wave. So just listing a few, they, they got, they taken, brought in analytics machine learning, they got autopilot for machine learning, which is automation onto the basic o l TP functionality of the database. And it's been interesting to watch Oracle's converge database strategy. We've contrasted that amongst ourselves. Love to get your thoughts on Amazon's get the right tool for the right job approach. >>Are they gonna have to change that? You know, Amazon's got the specialized databases, it's just, you know, the both companies are doing well. It just shows there are a lot of ways to, to skin a cat cuz you see some traction in the market in, in both approaches. So today we're gonna focus on the latest heat wave announcements and we're gonna talk about multi-cloud with a native MySQL heat wave implementation, which is available on aws MySQL heat wave for Azure via the Oracle Microsoft interconnect. This kind of cool hybrid action that they got going. Sometimes we call it super cloud. And then we're gonna dive into my SQL Heatwave Lake house, which allows users to process and query data across MyQ databases as heatwave databases, as well as object stores. So, and then we've got, heatwave has been announced on AWS and, and, and Azure, they're available now and Lake House I believe is in beta and I think it's coming out the second half of next year. So again, all of our guests are fresh off of Oracle Cloud world in Las Vegas. So they got the latest scoop. Guys, I'm done talking. Let's get into it. Mark, maybe you could start us off, what's your opinion of my SQL Heatwaves competitive position? When you think about what AWS is doing, you know, Google is, you know, we heard Google Cloud next recently, we heard about all their data innovations. You got, obviously Azure's got a big portfolio, snowflakes doing well in the market. What's your take? >>Well, first let's look at it from the point of view that AWS is the market leader in cloud and cloud services. They own somewhere between 30 to 50% depending on who you read of the market. And then you have Azure as number two and after that it falls off. There's gcp, Google Cloud platform, which is further way down the list and then Oracle and IBM and Alibaba. So when you look at AWS and you and Azure saying, hey, these are the market leaders in the cloud, then you start looking at it and saying, if I am going to provide a service that competes with the service they have, if I can make it available in their cloud, it means that I can be more competitive. And if I'm compelling and compelling means at least twice the performance or functionality or both at half the price, I should be able to gain market share. >>And that's what Oracle's done. They've taken a superior product in my SQL heat wave, which is faster, lower cost does more for a lot less at the end of the day and they make it available to the users of those clouds. You avoid this little thing called egress fees, you avoid the issue of having to migrate from one cloud to another and suddenly you have a very compelling offer. So I look at what Oracle's doing with MyQ and it feels like, I'm gonna use a word term, a flanking maneuver to their competition. They're offering a better service on their platforms. >>All right, so thank you for that. Holger, we've seen this sort of cadence, I sort of referenced it up front a little bit and they sat on MySQL for a decade, then all of a sudden we see this rush of announcements. Why did it take so long? And and more importantly is Oracle, are they developing the right features that cloud database customers are looking for in your view? >>Yeah, great question, but first of all, in your interview you said it's the edit analytics, right? Analytics is kind of like a marketing buzzword. Reports can be analytics, right? The interesting thing, which they did, the first thing they, they, they crossed the chasm between OTP and all up, right? In the same database, right? So major engineering feed very much what customers want and it's all about creating Bellevue for customers, which, which I think is the part why they go into the multi-cloud and why they add these capabilities. And they certainly with the AI capabilities, it's kind of like getting it into an autonomous field, self-driving field now with the lake cost capabilities and meeting customers where they are, like Mark has talked about the e risk costs in the cloud. So that that's a significant advantage, creating value for customers and that's what at the end of the day matters. >>And I believe strongly that long term it's gonna be ones who create better value for customers who will get more of their money From that perspective, why then take them so long? I think it's a great question. I think largely he mentioned the gentleman Nial, it's largely to who leads a product. I used to build products too, so maybe I'm a little fooling myself here, but that made the difference in my view, right? So since he's been charged, he's been building things faster than the rest of the competition, than my SQL space, which in hindsight we thought was a hot and smoking innovation phase. It kind of like was a little self complacent when it comes to the traditional borders of where, where people think, where things are separated between OTP and ola or as an example of adjacent support, right? Structured documents, whereas unstructured documents or databases and all of that has been collapsed and brought together for building a more powerful database for customers. >>So I mean it's certainly, you know, when, when Oracle talks about the competitors, you know, the competitors are in the, I always say they're, if the Oracle talks about you and knows you're doing well, so they talk a lot about aws, talk a little bit about Snowflake, you know, sort of Google, they have partnerships with Azure, but, but in, so I'm presuming that the response in MySQL heatwave was really in, in response to what they were seeing from those big competitors. But then you had Maria DB coming out, you know, the day that that Oracle acquired Sun and, and launching and going after the MySQL base. So it's, I'm, I'm interested and we'll talk about this later and what you guys think AWS and Google and Azure and Snowflake and how they're gonna respond. But, but before I do that, Ron, I want to ask you, you, you, you can get, you know, pretty technical and you've probably seen the benchmarks. >>I know you have Oracle makes a big deal out of it, publishes its benchmarks, makes some transparent on on GI GitHub. Larry Ellison talked about this in his keynote at Cloud World. What are the benchmarks show in general? I mean, when you, when you're new to the market, you gotta have a story like Mark was saying, you gotta be two x you know, the performance at half the cost or you better be or you're not gonna get any market share. So, and, and you know, oftentimes companies don't publish market benchmarks when they're leading. They do it when they, they need to gain share. So what do you make of the benchmarks? Have their, any results that were surprising to you? Have, you know, they been challenged by the competitors. Is it just a bunch of kind of desperate bench marketing to make some noise in the market or you know, are they real? What's your view? >>Well, from my perspective, I think they have the validity. And to your point, I believe that when it comes to competitor responses, that has not really happened. Nobody has like pulled down the information that's on GitHub and said, Oh, here are our price performance results. And they counter oracles. In fact, I think part of the reason why that hasn't happened is that there's the risk if Oracle's coming out and saying, Hey, we can deliver 17 times better query performance using our capabilities versus say, Snowflake when it comes to, you know, the Lakehouse platform and Snowflake turns around and says it's actually only 15 times better during performance, that's not exactly an effective maneuver. And so I think this is really to oracle's credit and I think it's refreshing because these differentiators are significant. We're not talking, you know, like 1.2% differences. We're talking 17 fold differences, we're talking six fold differences depending on, you know, where the spotlight is being shined and so forth. >>And so I think this is actually something that is actually too good to believe initially at first blush. If I'm a cloud database decision maker, I really have to prioritize this. I really would know, pay a lot more attention to this. And that's why I posed the question to Oracle and others like, okay, if these differentiators are so significant, why isn't the needle moving a bit more? And it's for, you know, some of the usual reasons. One is really deep discounting coming from, you know, the other players that's really kind of, you know, marketing 1 0 1, this is something you need to do when there's a real competitive threat to keep, you know, a customer in your own customer base. Plus there is the usual fear and uncertainty about moving from one platform to another. But I think, you know, the traction, the momentum is, is shifting an Oracle's favor. I think we saw that in the Q1 efforts, for example, where Oracle cloud grew 44% and that it generated, you know, 4.8 billion and revenue if I recall correctly. And so, so all these are demonstrating that's Oracle is making, I think many of the right moves, publishing these figures for anybody to look at from their own perspective is something that is, I think, good for the market and I think it's just gonna continue to pay dividends for Oracle down the horizon as you know, competition intens plots. So if I were in, >>Dave, can I, Dave, can I interject something and, and what Ron just said there? Yeah, please go ahead. A couple things here, one discounting, which is a common practice when you have a real threat, as Ron pointed out, isn't going to help much in this situation simply because you can't discount to the point where you improve your performance and the performance is a huge differentiator. You may be able to get your price down, but the problem that most of them have is they don't have an integrated product service. They don't have an integrated O L T P O L A P M L N data lake. Even if you cut out two of them, they don't have any of them integrated. They have multiple services that are required separate integration and that can't be overcome with discounting. And the, they, you have to pay for each one of these. And oh, by the way, as you grow, the discounts go away. So that's a, it's a minor important detail. >>So, so that's a TCO question mark, right? And I know you look at this a lot, if I had that kind of price performance advantage, I would be pounding tco, especially if I need two separate databases to do the job. That one can do, that's gonna be, the TCO numbers are gonna be off the chart or maybe down the chart, which you want. Have you looked at this and how does it compare with, you know, the big cloud guys, for example, >>I've looked at it in depth, in fact, I'm working on another TCO on this arena, but you can find it on Wiki bod in which I compared TCO for MySEQ Heat wave versus Aurora plus Redshift plus ML plus Blue. I've compared it against gcps services, Azure services, Snowflake with other services. And there's just no comparison. The, the TCO differences are huge. More importantly, thefor, the, the TCO per performance is huge. We're talking in some cases multiple orders of magnitude, but at least an order of magnitude difference. So discounting isn't gonna help you much at the end of the day, it's only going to lower your cost a little, but it doesn't improve the automation, it doesn't improve the performance, it doesn't improve the time to insight, it doesn't improve all those things that you want out of a database or multiple databases because you >>Can't discount yourself to a higher value proposition. >>So what about, I wonder ho if you could chime in on the developer angle. You, you followed that, that market. How do these innovations from heatwave, I think you used the term developer velocity. I've heard you used that before. Yeah, I mean, look, Oracle owns Java, okay, so it, it's, you know, most popular, you know, programming language in the world, blah, blah blah. But it does it have the, the minds and hearts of, of developers and does, where does heatwave fit into that equation? >>I think heatwave is gaining quickly mindshare on the developer side, right? It's not the traditional no sequel database which grew up, there's a traditional mistrust of oracles to developers to what was happening to open source when gets acquired. Like in the case of Oracle versus Java and where my sql, right? And, but we know it's not a good competitive strategy to, to bank on Oracle screwing up because it hasn't worked not on Java known my sequel, right? And for developers, it's, once you get to know a technology product and you can do more, it becomes kind of like a Swiss army knife and you can build more use case, you can build more powerful applications. That's super, super important because you don't have to get certified in multiple databases. You, you are fast at getting things done, you achieve fire, develop velocity, and the managers are happy because they don't have to license more things, send you to more trainings, have more risk of something not being delivered, right? >>So it's really the, we see the suite where this best of breed play happening here, which in general was happening before already with Oracle's flagship database. Whereas those Amazon as an example, right? And now the interesting thing is every step away Oracle was always a one database company that can be only one and they're now generally talking about heat web and that two database company with different market spaces, but same value proposition of integrating more things very, very quickly to have a universal database that I call, they call the converge database for all the needs of an enterprise to run certain application use cases. And that's what's attractive to developers. >>It's, it's ironic isn't it? I mean I, you know, the rumor was the TK Thomas Curian left Oracle cuz he wanted to put Oracle database on other clouds and other places. And maybe that was the rift. Maybe there was, I'm sure there was other things, but, but Oracle clearly is now trying to expand its Tam Ron with, with heatwave into aws, into Azure. How do you think Oracle's gonna do, you were at a cloud world, what was the sentiment from customers and the independent analyst? Is this just Oracle trying to screw with the competition, create a little diversion? Or is this, you know, serious business for Oracle? What do you think? >>No, I think it has lakes. I think it's definitely, again, attriting to Oracle's overall ability to differentiate not only my SQL heat wave, but its overall portfolio. And I think the fact that they do have the alliance with the Azure in place, that this is definitely demonstrating their commitment to meeting the multi-cloud needs of its customers as well as what we pointed to in terms of the fact that they're now offering, you know, MySQL capabilities within AWS natively and that it can now perform AWS's own offering. And I think this is all demonstrating that Oracle is, you know, not letting up, they're not resting on its laurels. That's clearly we are living in a multi-cloud world, so why not just make it more easy for customers to be able to use cloud databases according to their own specific, specific needs. And I think, you know, to holder's point, I think that definitely lines with being able to bring on more application developers to leverage these capabilities. >>I think one important announcement that's related to all this was the JSON relational duality capabilities where now it's a lot easier for application developers to use a language that they're very familiar with a JS O and not have to worry about going into relational databases to store their J S O N application coding. So this is, I think an example of the innovation that's enhancing the overall Oracle portfolio and certainly all the work with machine learning is definitely paying dividends as well. And as a result, I see Oracle continue to make these inroads that we pointed to. But I agree with Mark, you know, the short term discounting is just a stall tag. This is not denying the fact that Oracle is being able to not only deliver price performance differentiators that are dramatic, but also meeting a wide range of needs for customers out there that aren't just limited device performance consideration. >>Being able to support multi-cloud according to customer needs. Being able to reach out to the application developer community and address a very specific challenge that has plagued them for many years now. So bring it all together. Yeah, I see this as just enabling Oracles who ring true with customers. That the customers that were there were basically all of them, even though not all of them are going to be saying the same things, they're all basically saying positive feedback. And likewise, I think the analyst community is seeing this. It's always refreshing to be able to talk to customers directly and at Oracle cloud there was a litany of them and so this is just a difference maker as well as being able to talk to strategic partners. The nvidia, I think partnerships also testament to Oracle's ongoing ability to, you know, make the ecosystem more user friendly for the customers out there. >>Yeah, it's interesting when you get these all in one tools, you know, the Swiss Army knife, you expect that it's not able to be best of breed. That's the kind of surprising thing that I'm hearing about, about heatwave. I want to, I want to talk about Lake House because when I think of Lake House, I think data bricks, and to my knowledge data bricks hasn't been in the sites of Oracle yet. Maybe they're next, but, but Oracle claims that MySQL, heatwave, Lakehouse is a breakthrough in terms of capacity and performance. Mark, what are your thoughts on that? Can you double click on, on Lakehouse Oracle's claims for things like query performance and data loading? What does it mean for the market? Is Oracle really leading in, in the lake house competitive landscape? What are your thoughts? >>Well, but name in the game is what are the problems you're solving for the customer? More importantly, are those problems urgent or important? If they're urgent, customers wanna solve 'em. Now if they're important, they might get around to them. So you look at what they're doing with Lake House or previous to that machine learning or previous to that automation or previous to that O L A with O ltp and they're merging all this capability together. If you look at Snowflake or data bricks, they're tacking one problem. You look at MyQ heat wave, they're tacking multiple problems. So when you say, yeah, their queries are much better against the lake house in combination with other analytics in combination with O ltp and the fact that there are no ETLs. So you're getting all this done in real time. So it's, it's doing the query cross, cross everything in real time. >>You're solving multiple user and developer problems, you're increasing their ability to get insight faster, you're having shorter response times. So yeah, they really are solving urgent problems for customers. And by putting it where the customer lives, this is the brilliance of actually being multicloud. And I know I'm backing up here a second, but by making it work in AWS and Azure where people already live, where they already have applications, what they're saying is, we're bringing it to you. You don't have to come to us to get these, these benefits, this value overall, I think it's a brilliant strategy. I give Nip and Argo wallet a huge, huge kudos for what he's doing there. So yes, what they're doing with the lake house is going to put notice on data bricks and Snowflake and everyone else for that matter. Well >>Those are guys that whole ago you, you and I have talked about this. Those are, those are the guys that are doing sort of the best of breed. You know, they're really focused and they, you know, tend to do well at least out of the gate. Now you got Oracle's converged philosophy, obviously with Oracle database. We've seen that now it's kicking in gear with, with heatwave, you know, this whole thing of sweets versus best of breed. I mean the long term, you know, customers tend to migrate towards suite, but the new shiny toy tends to get the growth. How do you think this is gonna play out in cloud database? >>Well, it's the forever never ending story, right? And in software right suite, whereas best of breed and so far in the long run suites have always won, right? So, and sometimes they struggle again because the inherent problem of sweets is you build something larger, it has more complexity and that means your cycles to get everything working together to integrate the test that roll it out, certify whatever it is, takes you longer, right? And that's not the case. It's a fascinating part of what the effort around my SQL heat wave is that the team is out executing the previous best of breed data, bringing us something together. Now if they can maintain that pace, that's something to to, to be seen. But it, the strategy, like what Mark was saying, bring the software to the data is of course interesting and unique and totally an Oracle issue in the past, right? >>Yeah. But it had to be in your database on oci. And but at, that's an interesting part. The interesting thing on the Lake health side is, right, there's three key benefits of a lakehouse. The first one is better reporting analytics, bring more rich information together, like make the, the, the case for silicon angle, right? We want to see engagements for this video, we want to know what's happening. That's a mixed transactional video media use case, right? Typical Lakehouse use case. The next one is to build more rich applications, transactional applications which have video and these elements in there, which are the engaging one. And the third one, and that's where I'm a little critical and concerned, is it's really the base platform for artificial intelligence, right? To run deep learning to run things automatically because they have all the data in one place can create in one way. >>And that's where Oracle, I know that Ron talked about Invidia for a moment, but that's where Oracle doesn't have the strongest best story. Nonetheless, the two other main use cases of the lake house are very strong, very well only concern is four 50 terabyte sounds long. It's an arbitrary limitation. Yeah, sounds as big. So for the start, and it's the first word, they can make that bigger. You don't want your lake house to be limited and the terabyte sizes or any even petabyte size because you want to have the certainty. I can put everything in there that I think it might be relevant without knowing what questions to ask and query those questions. >>Yeah. And you know, in the early days of no schema on right, it just became a mess. But now technology has evolved to allow us to actually get more value out of that data. Data lake. Data swamp is, you know, not much more, more, more, more logical. But, and I want to get in, in a moment, I want to come back to how you think the competitors are gonna respond. Are they gonna have to sort of do a more of a converged approach? AWS in particular? But before I do, Ron, I want to ask you a question about autopilot because I heard Larry Ellison's keynote and he was talking about how, you know, most security issues are human errors with autonomy and autonomous database and things like autopilot. We take care of that. It's like autonomous vehicles, they're gonna be safer. And I went, well maybe, maybe someday. So Oracle really tries to emphasize this, that every time you see an announcement from Oracle, they talk about new, you know, autonomous capabilities. It, how legit is it? Do people care? What about, you know, what's new for heatwave Lakehouse? How much of a differentiator, Ron, do you really think autopilot is in this cloud database space? >>Yeah, I think it will definitely enhance the overall proposition. I don't think people are gonna buy, you know, lake house exclusively cause of autopilot capabilities, but when they look at the overall picture, I think it will be an added capability bonus to Oracle's benefit. And yeah, I think it's kind of one of these age old questions, how much do you automate and what is the bounce to strike? And I think we all understand with the automatic car, autonomous car analogy that there are limitations to being able to use that. However, I think it's a tool that basically every organization out there needs to at least have or at least evaluate because it goes to the point of it helps with ease of use, it helps make automation more balanced in terms of, you know, being able to test, all right, let's automate this process and see if it works well, then we can go on and switch on on autopilot for other processes. >>And then, you know, that allows, for example, the specialists to spend more time on business use cases versus, you know, manual maintenance of, of the cloud database and so forth. So I think that actually is a, a legitimate value proposition. I think it's just gonna be a case by case basis. Some organizations are gonna be more aggressive with putting automation throughout their processes throughout their organization. Others are gonna be more cautious. But it's gonna be, again, something that will help the overall Oracle proposition. And something that I think will be used with caution by many organizations, but other organizations are gonna like, hey, great, this is something that is really answering a real problem. And that is just easing the use of these databases, but also being able to better handle the automation capabilities and benefits that come with it without having, you know, a major screwup happened and the process of transitioning to more automated capabilities. >>Now, I didn't attend cloud world, it's just too many red eyes, you know, recently, so I passed. But one of the things I like to do at those events is talk to customers, you know, in the spirit of the truth, you know, they, you know, you'd have the hallway, you know, track and to talk to customers and they say, Hey, you know, here's the good, the bad and the ugly. So did you guys, did you talk to any customers my SQL Heatwave customers at, at cloud world? And and what did you learn? I don't know, Mark, did you, did you have any luck and, and having some, some private conversations? >>Yeah, I had quite a few private conversations. The one thing before I get to that, I want disagree with one point Ron made, I do believe there are customers out there buying the heat wave service, the MySEQ heat wave server service because of autopilot. Because autopilot is really revolutionary in many ways in the sense for the MySEQ developer in that it, it auto provisions, it auto parallel loads, IT auto data places it auto shape predictions. It can tell you what machine learning models are going to tell you, gonna give you your best results. And, and candidly, I've yet to meet a DBA who didn't wanna give up pedantic tasks that are pain in the kahoo, which they'd rather not do and if it's long as it was done right for them. So yes, I do think people are buying it because of autopilot and that's based on some of the conversations I had with customers at Oracle Cloud World. >>In fact, it was like, yeah, that's great, yeah, we get fantastic performance, but this really makes my life easier and I've yet to meet a DBA who didn't want to make their life easier. And it does. So yeah, I've talked to a few of them. They were excited. I asked them if they ran into any bugs, were there any difficulties in moving to it? And the answer was no. In both cases, it's interesting to note, my sequel is the most popular database on the planet. Well, some will argue that it's neck and neck with SQL Server, but if you add in Mariah DB and ProCon db, which are forks of MySQL, then yeah, by far and away it's the most popular. And as a result of that, everybody for the most part has typically a my sequel database somewhere in their organization. So this is a brilliant situation for anybody going after MyQ, but especially for heat wave. And the customers I talk to love it. I didn't find anybody complaining about it. And >>What about the migration? We talked about TCO earlier. Did your t does your TCO analysis include the migration cost or do you kind of conveniently leave that out or what? >>Well, when you look at migration costs, there are different kinds of migration costs. By the way, the worst job in the data center is the data migration manager. Forget it, no other job is as bad as that one. You get no attaboys for doing it. Right? And then when you screw up, oh boy. So in real terms, anything that can limit data migration is a good thing. And when you look at Data Lake, that limits data migration. So if you're already a MySEQ user, this is a pure MySQL as far as you're concerned. It's just a, a simple transition from one to the other. You may wanna make sure nothing broke and every you, all your tables are correct and your schema's, okay, but it's all the same. So it's a simple migration. So it's pretty much a non-event, right? When you migrate data from an O LTP to an O L A P, that's an ETL and that's gonna take time. >>But you don't have to do that with my SQL heat wave. So that's gone when you start talking about machine learning, again, you may have an etl, you may not, depending on the circumstances, but again, with my SQL heat wave, you don't, and you don't have duplicate storage, you don't have to copy it from one storage container to another to be able to be used in a different database, which by the way, ultimately adds much more cost than just the other service. So yeah, I looked at the migration and again, the users I talked to said it was a non-event. It was literally moving from one physical machine to another. If they had a new version of MySEQ running on something else and just wanted to migrate it over or just hook it up or just connect it to the data, it worked just fine. >>Okay, so every day it sounds like you guys feel, and we've certainly heard this, my colleague David Foyer, the semi-retired David Foyer was always very high on heatwave. So I think you knows got some real legitimacy here coming from a standing start, but I wanna talk about the competition, how they're likely to respond. I mean, if your AWS and you got heatwave is now in your cloud, so there's some good aspects of that. The database guys might not like that, but the infrastructure guys probably love it. Hey, more ways to sell, you know, EC two and graviton, but you're gonna, the database guys in AWS are gonna respond. They're gonna say, Hey, we got Redshift, we got aqua. What's your thoughts on, on not only how that's gonna resonate with customers, but I'm interested in what you guys think will a, I never say never about aws, you know, and are they gonna try to build, in your view a converged Oola and o LTP database? You know, Snowflake is taking an ecosystem approach. They've added in transactional capabilities to the portfolio so they're not standing still. What do you guys see in the competitive landscape in that regard going forward? Maybe Holger, you could start us off and anybody else who wants to can chime in, >>Happy to, you mentioned Snowflake last, we'll start there. I think Snowflake is imitating that strategy, right? That building out original data warehouse and the clouds tasking project to really proposition to have other data available there because AI is relevant for everybody. Ultimately people keep data in the cloud for ultimately running ai. So you see the same suite kind of like level strategy, it's gonna be a little harder because of the original positioning. How much would people know that you're doing other stuff? And I just, as a former developer manager of developers, I just don't see the speed at the moment happening at Snowflake to become really competitive to Oracle. On the flip side, putting my Oracle hat on for a moment back to you, Mark and Iran, right? What could Oracle still add? Because the, the big big things, right? The traditional chasms in the database world, they have built everything, right? >>So I, I really scratched my hat and gave Nipon a hard time at Cloud world say like, what could you be building? Destiny was very conservative. Let's get the Lakehouse thing done, it's gonna spring next year, right? And the AWS is really hard because AWS value proposition is these small innovation teams, right? That they build two pizza teams, which can be fit by two pizzas, not large teams, right? And you need suites to large teams to build these suites with lots of functionalities to make sure they work together. They're consistent, they have the same UX on the administration side, they can consume the same way, they have the same API registry, can't even stop going where the synergy comes to play over suite. So, so it's gonna be really, really hard for them to change that. But AWS super pragmatic. They're always by themselves that they'll listen to customers if they learn from customers suite as a proposition. I would not be surprised if AWS trying to bring things closer together, being morely together. >>Yeah. Well how about, can we talk about multicloud if, if, again, Oracle is very on on Oracle as you said before, but let's look forward, you know, half a year or a year. What do you think about Oracle's moves in, in multicloud in terms of what kind of penetration they're gonna have in the marketplace? You saw a lot of presentations at at cloud world, you know, we've looked pretty closely at the, the Microsoft Azure deal. I think that's really interesting. I've, I've called it a little bit of early days of a super cloud. What impact do you think this is gonna have on, on the marketplace? But, but both. And think about it within Oracle's customer base, I have no doubt they'll do great there. But what about beyond its existing install base? What do you guys think? >>Ryan, do you wanna jump on that? Go ahead. Go ahead Ryan. No, no, no, >>That's an excellent point. I think it aligns with what we've been talking about in terms of Lakehouse. I think Lake House will enable Oracle to pull more customers, more bicycle customers onto the Oracle platforms. And I think we're seeing all the signs pointing toward Oracle being able to make more inroads into the overall market. And that includes garnishing customers from the leaders in, in other words, because they are, you know, coming in as a innovator, a an alternative to, you know, the AWS proposition, the Google cloud proposition that they have less to lose and there's a result they can really drive the multi-cloud messaging to resonate with not only their existing customers, but also to be able to, to that question, Dave's posing actually garnish customers onto their platform. And, and that includes naturally my sequel but also OCI and so forth. So that's how I'm seeing this playing out. I think, you know, again, Oracle's reporting is indicating that, and I think what we saw, Oracle Cloud world is definitely validating the idea that Oracle can make more waves in the overall market in this regard. >>You know, I, I've floated this idea of Super cloud, it's kind of tongue in cheek, but, but there, I think there is some merit to it in terms of building on top of hyperscale infrastructure and abstracting some of the, that complexity. And one of the things that I'm most interested in is industry clouds and an Oracle acquisition of Cerner. I was struck by Larry Ellison's keynote, it was like, I don't know, an hour and a half and an hour and 15 minutes was focused on healthcare transformation. Well, >>So vertical, >>Right? And so, yeah, so you got Oracle's, you know, got some industry chops and you, and then you think about what they're building with, with not only oci, but then you got, you know, MyQ, you can now run in dedicated regions. You got ADB on on Exadata cloud to customer, you can put that OnPrem in in your data center and you look at what the other hyperscalers are, are doing. I I say other hyperscalers, I've always said Oracle's not really a hyperscaler, but they got a cloud so they're in the game. But you can't get, you know, big query OnPrem, you look at outposts, it's very limited in terms of, you know, the database support and again, that that will will evolve. But now you got Oracle's got, they announced Alloy, we can white label their cloud. So I'm interested in what you guys think about these moves, especially the industry cloud. We see, you know, Walmart is doing sort of their own cloud. You got Goldman Sachs doing a cloud. Do you, you guys, what do you think about that and what role does Oracle play? Any thoughts? >>Yeah, let me lemme jump on that for a moment. Now, especially with the MyQ, by making that available in multiple clouds, what they're doing is this follows the philosophy they've had the past with doing cloud, a customer taking the application and the data and putting it where the customer lives. If it's on premise, it's on premise. If it's in the cloud, it's in the cloud. By making the mice equal heat wave, essentially a plug compatible with any other mice equal as far as your, your database is concern and then giving you that integration with O L A P and ML and Data Lake and everything else, then what you've got is a compelling offering. You're making it easier for the customer to use. So I look the difference between MyQ and the Oracle database, MyQ is going to capture market more market share for them. >>You're not gonna find a lot of new users for the Oracle debate database. Yeah, there are always gonna be new users, don't get me wrong, but it's not gonna be a huge growth. Whereas my SQL heatwave is probably gonna be a major growth engine for Oracle going forward. Not just in their own cloud, but in AWS and in Azure and on premise over time that eventually it'll get there. It's not there now, but it will, they're doing the right thing on that basis. They're taking the services and when you talk about multicloud and making them available where the customer wants them, not forcing them to go where you want them, if that makes sense. And as far as where they're going in the future, I think they're gonna take a page outta what they've done with the Oracle database. They'll add things like JSON and XML and time series and spatial over time they'll make it a, a complete converged database like they did with the Oracle database. The difference being Oracle database will scale bigger and will have more transactions and be somewhat faster. And my SQL will be, for anyone who's not on the Oracle database, they're, they're not stupid, that's for sure. >>They've done Jason already. Right. But I give you that they could add graph and time series, right. Since eat with, Right, Right. Yeah, that's something absolutely right. That's, that's >>A sort of a logical move, right? >>Right. But that's, that's some kid ourselves, right? I mean has worked in Oracle's favor, right? 10 x 20 x, the amount of r and d, which is in the MyQ space, has been poured at trying to snatch workloads away from Oracle by starting with IBM 30 years ago, 20 years ago, Microsoft and, and, and, and didn't work, right? Database applications are extremely sticky when they run, you don't want to touch SIM and grow them, right? So that doesn't mean that heat phase is not an attractive offering, but it will be net new things, right? And what works in my SQL heat wave heat phases favor a little bit is it's not the massive enterprise applications which have like we the nails like, like you might be only running 30% or Oracle, but the connections and the interfaces into that is, is like 70, 80% of your enterprise. >>You take it out and it's like the spaghetti ball where you say, ah, no I really don't, don't want to do all that. Right? You don't, don't have that massive part with the equals heat phase sequel kind of like database which are more smaller tactical in comparison, but still I, I don't see them taking so much share. They will be growing because of a attractive value proposition quickly on the, the multi-cloud, right? I think it's not really multi-cloud. If you give people the chance to run your offering on different clouds, right? You can run it there. The multi-cloud advantages when the Uber offering comes out, which allows you to do things across those installations, right? I can migrate data, I can create data across something like Google has done with B query Omni, I can run predictive models or even make iron models in different place and distribute them, right? And Oracle is paving the road for that, but being available on these clouds. But the multi-cloud capability of database which knows I'm running on different clouds that is still yet to be built there. >>Yeah. And >>That the problem with >>That, that's the super cloud concept that I flowed and I I've always said kinda snowflake with a single global instance is sort of, you know, headed in that direction and maybe has a league. What's the issue with that mark? >>Yeah, the problem with the, with that version, the multi-cloud is clouds to charge egress fees. As long as they charge egress fees to move data between clouds, it's gonna make it very difficult to do a real multi-cloud implementation. Even Snowflake, which runs multi-cloud, has to pass out on the egress fees of their customer when data moves between clouds. And that's really expensive. I mean there, there is one customer I talked to who is beta testing for them, the MySQL heatwave and aws. The only reason they didn't want to do that until it was running on AWS is the egress fees were so great to move it to OCI that they couldn't afford it. Yeah. Egress fees are the big issue but, >>But Mark the, the point might be you might wanna root query and only get the results set back, right was much more tinier, which been the answer before for low latency between the class A problem, which we sometimes still have but mostly don't have. Right? And I think in general this with fees coming down based on the Oracle general E with fee move and it's very hard to justify those, right? But, but it's, it's not about moving data as a multi-cloud high value use case. It's about doing intelligent things with that data, right? Putting into other places, replicating it, what I'm saying the same thing what you said before, running remote queries on that, analyzing it, running AI on it, running AI models on that. That's the interesting thing. Cross administered in the same way. Taking things out, making sure compliance happens. Making sure when Ron says I don't want to be American anymore, I want to be in the European cloud that is gets migrated, right? So tho those are the interesting value use case which are really, really hard for enterprise to program hand by hand by developers and they would love to have out of the box and that's yet the innovation to come to, we have to come to see. But the first step to get there is that your software runs in multiple clouds and that's what Oracle's doing so well with my SQL >>Guys. Amazing. >>Go ahead. Yeah. >>Yeah. >>For example, >>Amazing amount of data knowledge and, and brain power in this market. Guys, I really want to thank you for coming on to the cube. Ron Holger. Mark, always a pleasure to have you on. Really appreciate your time. >>Well all the last names we're very happy for Romanic last and moderator. Thanks Dave for moderating us. All right, >>We'll see. We'll see you guys around. Safe travels to all and thank you for watching this power panel, The Truth About My SQL Heat Wave on 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.

>> Okay, so we heard today from three experts on time series and data, how the InfluxDB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in realtime. And we learned that key open source components like Apache Arrow and the Rust Programming environment DataFusion parquet are being leveraged to support realtime data analytics at scale. We also learned about the contributions and importance of open source software and how the InfluxDB community is evolving the platform with minimal disruption to support new workloads, new use cases in the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to thecube.net to find those. Don't forget to check out siliconangle.com for all the news related to things enterprise and emerging tech. And you should also check out influxdata.com. There you can learn about the company's products, you'll find developer resources like free courses, you can 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 Vellante. Thank you for watching Evolving InfluxDB into the Smart Data Platform, made possible by InfluxData and brought to you by theCUBE, 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 that 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. Time series databases have had to evolve to efficiently support realtime data in emerging use, use cases in IOT 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 Vellante, 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 going to hear from Brian Gilmore who is the director of IOT and emerging technologies at Influx Data. And we're going to 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 going to hear a lot about things like rust implementation of Apache Arrow, the use of Parquet and tooling such as data fusion, which are 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 going to hear from Anais Dotis-Georgiou who is a developer advocate at Influx Data. And we're going to get into the "why's" of these open source capabilities, and how they contribute to the evolution of the Influx DB platform. And then we're going to close the program with Tim Yocum. He's the director of engineering at Influx Data, and he's going to 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.

(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)

(soft upbeat music) >> Okay, we're kicking things off with Brian Gilmore. He's the director of IoT, an emerging technology at InfluxData. Brian, welcome to the program. Thanks for coming on. >> Thanks, Dave, great to be here. I appreciate the time. >> Hey, explain why InfluxDB, 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, 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 query support, things like that, we have to figure out a way to execute those for them in a way that will scale long term. And then we also want to 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 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 going to see just great improvements in performance, you know, especially those that are working at the top end of the workload scale, you know, the massive data volumes and things like that. >> Yeah, and we're going to 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. 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 a long journey. (chuckles) I guess, you know, phase one was, 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 optimize for like multi-tenant, multi-cloud, be able to host it in a truly like SAS manner where we could use, you know, some type of customer activity or consumption as the pricing vector. And that was sort of the birth of the real first InfluxDB 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 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 daily basis. And having that sort of big pool of very diverse and varied customers to chat with as they're using the product, as they're giving us feedback, et cetera, 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 new engine. >> All right, so you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really want to understand how much of a pivot this is, and what does it take to make that shift from, you know, time series 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. Time series data is always going to be fundamental in 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. 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 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 time to response on the queries, and can we get that to the point where the result 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, you know, milliseconds of time since it hit the ingest queue. And 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, you can do all those sort of magical things with it. 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 real time queries, 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 limited number of customers, strategic customers and strategic availabilities zones to start, but, you know, everybody over time. >> So you're basically going from what happened to, and 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 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 underlying data collection, the architecture, the infrastructure, the devices, and you know, the sort of highly distributed nature of all of this. So, yeah, I mean, 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 of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >> Yeah, I mean, it is operationally, or operational real time is different. And that's one of the things that really triggered us to know that 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 using us as a process historian on the plant floor. And if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're going to do here is we're going to 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 historians and databases. >> Is this available, these innovations to InfluxDB cloud customers, only who can access this capability? >> Yeah, I mean, commercially and today, yes. I think we want to emphasize that 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 doubled down on sort of our commitment to open source and availability. So, like, anybody today can take a look at the libraries on our GitHub and can inspect it and even can try to implement or execute some of it themselves in their own infrastructure. We are 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 how big we go with this right away. Just sort of both limits, you know, the risk of any issues that can come with new software roll outs, 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 going to 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 going to help deliver on this vision. What should we know there? >> Well, I mean, I think, foundationally, we built the new core on Rust. This is a new very sort of popular systems language. 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've loved working with Go, and a lot of our libraries will continue to be sort of implemented in Go, but when it came to this particular new engine, that power performance and stability of Rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parquet for persistence. I think, for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our time series merge trees, this is a big break from that. You know, Arrow on the sort of in mem side and then Parquet in the on disk side. It allows us to present, you know, a unified set of APIs for those really fast real time queries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that Parquet format, which is also cool because there's an entire ecosystem sort of popping up around Parquet in terms of the machine learning community. And getting that all to work, we had to glue it together with Arrow Flight. That's sort of what we're using as our RPC component. It handles the orchestration and the transportation of the columnar data now, we're moving to like a true columnar database model for 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 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's been around for a long time but it's popularity is, you know, really starting to hit that steep part of the S-curve. And we're going to dig into more of that, but give us, is there anything else that we should know about, Brian? 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 you want to participate or if you want to work sort of in terms of early access with the new engine, please reach out to the team. I'm sure, you know, there's a lot of communications going out and it'll be highly featured on our website. 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 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 want to 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 sort of build the next versions of your business. Because, you know, the whole database, the ecosystem as it expands out into this vertically-oriented stack of cloud services, and enterprise databases, and edge databases, you know, it's going to be what we all make it together, not just those of us who are employed by InfluxDB. And then finally, I would just say, please, like, watch and Anais' and 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 sort of technical details of this than theirs, especially when it comes to the value that these investments will bring to our customers and our communities. So, encourage you 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 David, it was awesome. Looking forward to it. >> Yeah, me too. I'm looking forward to see how the community actually applies these new innovations and goes beyond just the historical into the real time. Really hot area. As Brian said, in a moment, I'll be right back with Anais Dotis-Georgiou to dig into the critical aspects of key open source components of the InfluxDB engine, including Rust, Arrow, Parquet, Data Fusion. Keep it right there. You don't want to miss this. (soft upbeat music)

(bright music) >> Welcome back to the CUBE presents the AWS Startup Showcase The Next Big Thing in cloud startups with AI security and life science tracks, 15 hottest growing startups are presented. And we had a great opening keynote with luminaries in the industry. And now our closing keynote is to get a deeper dive on cracking the code in the enterprise, how startups are changing the game and helping companies change. And they're also changing the game of open source. We have a great guest, Katie Drucker, Head of Business Development, Madrona Venture Group. Katie, thank you for coming on the CUBE for this special closing keynote. >> Thank you for having me, I appreciate it. >> So one of the topics we talked about with Soma from Madrona on the opening keynote, as well as Ali from Databricks is how startups are seeing success faster. So that's the theme of the Cloud speed, agility, but the game has changed in the enterprise. And I want to really discuss with you how growth changes and growth strategy specifically. They talk, go to market. We hear things like good sales to enterprise sales, organic, freemium, there's all kinds of different approaches, but at the end of the day, the most successful companies, the ones that might not be known that just come out of nowhere. So the economics are changing and the buyers are thinking differently. So let's explore that topic. So take us through your view 'cause you have a lot of experience. But first talk about your role at Madrona, what you do. >> Absolutely all great points. So my role at Madrona, I think I have personally one of the more enviable jobs and that my job is to... I get the privilege of working with all of these fantastic entrepreneurs in our portfolio and doing whatever we can as a firm to harness resources, knowledge, expertise, connections, to accelerate their growth. So my role in setting up business development is taking a look at all of those tools in the tool chest and partnering with the portfolio to make it so. And in our portfolio, we have a wide range of companies, some rely on enterprise sales, some have other go to markets. Some are direct to consumer, a wide range. >> Talk about the growth strategies that you see evolving because what's clear with the pandemic. And as we come out of it is that there are growth plays happening that don't look a little bit differently, more obvious now because of the Cloud scale, we're seeing companies like Databricks, like Snowflake, like other companies that have been built on the cloud or standalone. What are some of the new growth techniques, or I don't want to say growth hacking, that is a pejorative term, but like just a way for companies to quickly describe their value to an enterprise buyer who's moving away from the old RFP days of vendor selection. The game has changed. So take us through how you see secret key and unlocking that new equation of how to present value to an enterprise and how you see enterprises evaluating startups. >> Yes, absolutely. Well, and that's got a question, that's got a few components nestled in what I think are some bigger trends going on. AWS of course brought us the Cloud first. I think now the Cloud is more and more a utility. And so it's incumbent upon thinking about how an enterprise 'cause using the Cloud is going to go up the value stack and partner with its cloud provider and other service providers. I think also with that agility of operations, you have thinning, if you will, the systems of record and a lot of new entrance into this space that are saying things like, how can we harness AIML and other emerging trends to provide more value directly around work streams that were historically locked into those systems of record? And then I think you also have some price plans that are far more flexible around usage based as opposed to just flat subscription or even these big clunky annual or multi-year RFP type stuff. So all of those trends are really designed in ways that favor the emerging startup. And I think if done well, and in partnership with those underlying cloud providers, there can be some amazing benefits that the enterprise realizes an opportunity for those startups to grow. And I think that's what you're seeing. I think there's also this emergence of a buyer that's different than the CIO or the site the CISO. You have things with low code, no code. You've got other buyers in the organization, other line of business executives that are coming to the table, making software purchase decisions. And then you also have empowered developers that are these citizen builders and developer buyers and personas that really matter. So lots of inroads in places for a startup to reach in the enterprise to make a connection and to bring value. That's a great insight. I want to ask that just if you don't mind follow up on that, you mentioned personas. And what we're seeing is the shift happens. There's new roles that are emerging and new things that are being reconfigured or refactored if you will, whether it's human resources or AI, and you mentioned ML playing a role in automation. These are big parts of the new value proposition. How should companies posture to the customer? Because I don't want to say pivot 'cause that means it's not working but mostly extending our iterating around their positioning because as new things have not yet been realized, it might not be operationalized in a company or maybe new things need to be operationalized, it's a new solution for that. Positioning the value is super important and a lot of companies often struggle with that, but also if they get it right, that's the key. What's your feeling on startups in their positioning? So people will dismiss it like, "Oh, that's marketing." But maybe that's important. What's your thoughts on the great positioning question? >> I've been in this industry a long time. And I think there are some things that are just tried and true, and it is not unique to tech, which is, look, you have to tell a story and you have to reach the customer and you have to speak to the customer's need. And what that means is, AWS is a great example. They're famous for the whole concept of working back from the customer and thinking about what that customer's need is. I think any startup that is looking to partner or work alongside of AWS really has to embody that very, very customer centric way of thinking about things, even though, as we just talked about those personas are changing who that customer really is in the enterprise. And then speaking to that value proposition and meeting that customer and creating a dialogue with them that really helps to understand not only what their pain points are, but how you were offering solves those pain points. And sometimes the customer doesn't realize that that is their pain point and that's part of the education and part of the way in which you engage that dialogue. That doesn't change a lot, just generation to generation. I think the modality of how we have that dialogue, the methods in which we choose to convey that change, but that basic discussion is what makes us human. >> What's your... Great, great, great insight. I want to ask you on the value proposition question again, the question I often get, and it's hard to answer is am I competing on value or am I competing on commodity? And depending on where you're in the stack, there could be different things like, for example, land is getting faster, smaller, cheaper, as an example on Amazon. That's driving down to low cost high value, but it shifts up the stack. You start to see in companies this changing the criteria for how to evaluate. So an enterprise might be struggling. And I often hear enterprises say, "I don't know how to pick who I need. I buy tools, I don't buy many platforms." So they're constantly trying to look for that answer key, if you will, what's your thoughts on the changing requirements of an enterprise? And how to do vendor selection. >> Yeah, so obviously I don't think there's a single magic bullet. I always liked just philosophically to think about, I think it's always easier and frankly more exciting as a buyer to want to buy stuff that's going to help me make more revenue and build and grow as opposed to do things that save me money. And just in a binary way, I like to think which side of the fence are you sitting on as a product offering? And the best ways that you can articulate that, what opportunities are you unlocking for your customer? The problems that you're solving, what kind of growth and what impact is that going to lead to, even if you're one or two removed from that? And again, that's not a new concept. And I think that the companies that have that squarely in mind when they think about their go-to market strategy, when they think about the dialogue they're having, when they think about the problems that they're solving, find a much faster path. And I think that also speaks to why we're seeing so many explosion in the line of business, SAS apps that are out there. Again, that thinning of the systems of record, really thinking about what are the scenarios and work streams that we can have happened that are going to help with that revenue growth and unlocking those opportunities. >> What's the common startup challenge that you see when they're trying to do business development? Usually they build the product first, product led value, you hear that a lot. And then they go, "Okay, we're ready to sell, hire a sales guy." That seems to be shifting away because of the go to markets are changing. What are some of the challenges that startups have? What are some that you're seeing? >> Well, and I think the point that you're making about the changes are really almost a result of the trends that we're talking about. The sales organization itself is becoming... These work streams are becoming instrumented. Data is being collected, insights are being derived off of those things. So you see companies like Clary or Highspot or two examples or tutorial that are in our portfolio that are looking at that action and making the art of sales and marketing far more sophisticated overall, which then leads to the different growth hacking and the different insights that are driven. I think the common mistakes that I see across the board, especially with earlier stage startups, look you got to find product market fit. I think that's always... You start with a thesis or a belief and a passion that you're building something that you think the market needs. And it's a lot of dialogue you have to have to make sure that you do find that. I think once you find that another common problem that I see is leading with an explanation of technology. And again, not focusing on the buyer or the... Sorry, the buyer about solving a problem and focusing on that problem as opposed to focusing on how cool your technology is. Those are basic and really, really simple. And then I think setting a set of expectations, especially as it comes to business development and partnering with companies like AWS. The researching that you need to adequately meet the demand that can be turned on. And then I'm sure you heard about from Databricks, from an organization like AWS, you have to be pragmatic. >> Yeah, Databricks gone from zero a software sales a few years ago to over a billion. Now it looks like a Snowflake which came out of nowhere and they had a great product, but built on Amazon, they became the data cloud on top of Amazon. And now they're growing just whole new business models and new business development techniques. Katie, thank you for sharing your insight here. The CUBE's closing keynote. Thanks for coming on. >> Appreciate it, thank you. >> Okay, Katie Drucker, Head of Business Development at Madrona Venture Group. Premier VC in the Seattle area and beyond they're doing a lot of cloud action. And of course they know AWS very well and investing in the ecosystem. So great, great stuff there. Next up is Peter Wagner partner at Wing.VX. Love this URL first of all 'cause of the VC domain extension. But Peter is a long time venture capitalist. I've been following his career. He goes back to the old networking days, back when the internet was being connected during the OSI days, when the TCP IP open systems interconnect was really happening and created so much. Well, Peter, great to see you on the CUBE here and congratulations with success at Wing VC. >> Yeah, thanks, John. It's great to be here. I really appreciate you having me. >> Reason why I wanted to have you come on. First of all, you had a great track record in investing over many decades. You've seen many waves of innovation, startups. You've seen all the stories. You've seen the movie a few times, as I say. But now more than ever, enterprise wise it's probably the hottest I've ever seen. And you've got a confluence of many things on the stack. You were also an early seed investor in Snowflake, well-regarded as a huge success. So you've got your eye on some of these awesome deals. Got a great partner over there has got a network experience as well. What is the big aha moment here for the industry? Because it's not your classic enterprise startups anymore. They have multiple things going on and some of the winners are not even known. They come out of nowhere and they connect to enterprise and get the lucrative positions and can create a moat and value. Like out of nowhere, it's not the old way of like going to the airport and doing an RFP and going through the stringent requirements, and then you're in, you get to win the lucrative contract and you're in. Not anymore, that seems to have changed. What's your take on this 'cause people are trying to crack the code here and sometimes you don't have to be well-known. >> Yeah, well, thank goodness the game has changed 'cause that old thing was (indistinct) So I for one don't miss it. There was some modernization movement in the enterprise and the modern enterprise is built on data powered by AI infrastructure. That's an agile workplace. All three of those things are really transformational. There's big investments being made by enterprises, a lot of receptivity and openness to technology to enable all those agendas, and that translates to good prospects for startups. So I think as far as my career goes, I've never seen a more positive or fertile ground for startups in terms of penetrating enterprise, it doesn't mean it's easy to do, but you have a receptive audience on the other side and that hasn't necessarily always been the case. >> Yeah, I got to ask you, I know that you're a big sailor and your family and Franks Lubens also has a boat and sailing metaphor is always good to have 'cause you got to have a race that's being run and they have tactics. And this game that we're in now, you see the successes, there's investment thesises, and then there's also actually bets. And I want to get your thoughts on this because a lot of enterprises are trying to figure out how to evaluate startups and starts also can make the wrong bet. They could sail to the wrong continent and be in the wrong spot. So how do you pick the winners and how should enterprises understand how to pick winners too? >> Yeah, well, one of the real important things right now that enterprise is facing startups are learning how to do and so learning how to leverage product led growth dynamics in selling to the enterprise. And so product led growth has certainly always been important consumer facing companies. And then there's a few enterprise facing companies, early ones that cracked the code, as you said. And some of these examples are so old, if you think about, like the ones that people will want to talk about them and talk about Classy and want to talk about Twilio and these were of course are iconic companies that showed the way for others. But even before that, folks like Solar Winds, they'd go to market model, clearly product red, bottom stuff. Back then we didn't even have those words to talk about it. And then some of the examples are so enormous if think about them like the one right in front of your face, like AWS. (laughing) Pretty good PLG, (indistinct) but it targeted builders, it targeted developers and flipped over the way you think about enterprise infrastructure, as a result some how every company, even if they're harnessing relatively conventional sales and marketing motion, and you think about product led growth as a way to kick that motion off. And so it's not really an either word even more We might think OPLJ, that means there's no sales keep one company not true, but here's a way to set the table so that you can very efficiently use your sales and marketing resources, only have the most attractive targets and ones that are really (indistinct) >> I love the product led growth. I got to ask you because in the networking days, I remember the term inevitability was used being nested in a solution that they're just going to Cisco off router and a firewall is one you can unplug and replace with another vendor. Cisco you'd have to go through no switching costs were huge. So when you get it to the Cloud, how do you see the competitiveness? Because we were riffing on this with Ali, from Databricks where the lock-in might be value. The more value provider is the lock-in. Is their nestedness? Is their intimate ability as a competitive advantage for some of these starts? How do you look at that? Because startups, they're using open source. They want to have a land position in an enterprise, but how do they create that sustainable competitive advantage going forward? Because again, this is what you do. You bet on ones that you can see that could establish a model whatever we want to call it, but a competitive advantage and ongoing nested position. >> Sometimes it has to do with data, John, and so you mentioned Snowflake a couple of times here, a big part of Snowflake's strategy is what they now call the data cloud. And one of the reasons you go there is not to just be able to process data, to actually get access to it, exchange with the partners. And then that of course is a great reason for the customers to come to the Snowflake platform. And so the more data it gets more customers, it gets more data, the whole thing start spinning in the right direction. That's a really big example, but all of these startups that are using ML in a fundamental way, applying it in a novel way, the data modes are really important. So getting to the right data sources and training on it, and then putting it to work so that you can see that in this process better and doing this earlier on that scale. That's a big part of success. Another company that I work with is a good example that I call (indistinct) which works in sales technology space, really crushing it in terms of building better sales organizations both at performance level, in terms of the intelligence level, and just overall revenue attainment using ML, and using novel data sources, like the previously lost data or phone calls or Zoom calls as you already know. So I think the data advantages are really big. And smart startups are thinking through it early. >> It's interest-- >> And they're planning by the way, not to ramble on too much, but they're betting that PLG strategy. So their land option is designed not just to be an interesting way to gain usage, but it's also a way to gain access to data that then enables the expand in a component. >> That is a huge call-out point there, I was going to ask another question, but I think that is the key I see. It's a new go to market in a way. product led with that kind of approach gets you a beachhead and you get a little position, you get some data that is a cloud model, it means variable, whatever you want to call it variable value proposition, value proof, or whatever, getting that data and reiterating it. So it brings up the whole philosophical question of okay, product led growth, I love that with product led growth of data, I get that. Remember the old platform versus a tool? That's the way buyers used to think. How has that changed? 'Cause now almost, this conversation throws out the whole platform thing, but isn't like a platform. >> It looks like it's all. (laughs) you can if it is a platform, though to do that you can reveal that later, but you're looking for adoption, so if it's down stock product, you're looking for adoption by like developers or DevOps people or SOEs, and they're trying to solve a problem, and they want rapid gratification. So they don't want to have an architectural boomimg, placed in front of them. And if it's up stock product and application, then it's a user or the business or whatever that is, is adopting the application. And again, they're trying to solve a very specific problem. You need instant and immediate obvious time and value. And now you have a ticket to the dance and build on that and maybe a platform strategy can gradually take shape. But you know who's not in this conversation is the CIO, it's like, "I'm always the last to know." >> That's the CISO though. And they got him there on the firing lines. CISOs are buying tools like it's nobody's business. They need everything. They'll buy anything or you go meet with sand, they'll buy it. >> And you make it sound so easy. (laughing) We do a lot of security investment if only (indistinct) (laughing) >> I'm a little bit over the top, but CISOs are under a lot of pressure. I would talk to the CISO at Capital One and he was saying that he's on Amazon, now he's going to another cloud, not as a hedge, but he doesn't want to focus development teams. So he's making human resource decisions as well. Again, back to what IT used to be back in the old days where you made a vendor decision, you built around it. So again, clouds play that way. I see that happening. But the question is that I think you nailed this whole idea of cross hairs on the target persona, because you got to know who you are and then go to the market. So if you know you're a problem solving and the lower in the stack, do it and get a beachhead. That's a strategy, you can do that. You can't try to be the platform and then solve a problem at the same time. So you got to be careful. Is that what you were getting at? >> Well, I think you just understand what you're trying to achieve in that line of notion. And how those dynamics work and you just can't drag it out. And they could make it too difficult. Another company I work with is a very strategic cloud data platform. It's a (indistinct) on systems. We're not trying to foist that vision though (laughs) or not adopters today. We're solving some thorny problems with them in the short term, rapid time to value operational needs in scale. And then yeah, once they found success with (indistinct) there's would be an opportunity to be increasing the platform, and an obstacle for those customers. But we're not talking about that. >> Well, Peter, I appreciate you taking the time and coming out of a board meeting, I know that you're super busy and I really appreciate you making time for us. I know you've got an impressive partner in (indistinct) who's a former Sequoia, but Redback Networks part of that company over the years, you guys are doing extremely well, even a unique investment thesis. I'd like you to put the plug in for the firm. I think you guys have a good approach. I like what you guys are doing. You're humble, you don't brag a lot, but you make a lot of great investments. So could you take them in to explain what your investment thesis is and then how that relates to how an enterprise is making their investment thesis? >> Yeah, yeah, for sure. Well, the concept that I described earlier that the modern enterprise movement as a workplace built on data powered by AI. That's what we're trying to work with founders to enable. And also we're investing in companies that build the products and services that enable that modern enterprise to exist. And we do it from very early stages, but with a longterm outlook. So we'll be leading series and series, rounds of investment but staying deeply involved, both operationally financially throughout the whole life cycle of the company. And then we've done that a bunch of times, our goal is always the big independent public company and they don't always make it but enough for them to have it all be worthwhile. An interesting special case of this, and by the way, I think it intersects with some of startup showcase here is in the life sciences. And I know you were highlighting a lot of healthcare websites and deals, and that's a vertical where to disrupt tremendous impact of data both new data availability and new ways to put it to use. I know several of my partners are very focused on that. They call it bio-X data. It's a transformation all on its own. >> That's awesome. And I think that the reason why we're focusing on these verticals is if you have a cloud horizontal scale view and vertically specialized with machine learning, every vertical is impacted by data. It's so interesting that I think, first start, I was probably best time to be a cloud startup right now. I really am bullish on it. So I appreciate you taking the time Peter to come in again from your board meeting, popping out. Thanks for-- (indistinct) Go back in and approve those stock options for all the employees. Yeah, thanks for coming on. Appreciate it. >> All right, thank you John, it's a pleasure. >> Okay, Peter Wagner, Premier VC, very humble Wing.VC is a great firm. Really respect them. They do a lot of great investing investments, Snowflake, and we have Dave Vellante back who knows a lot about Snowflake's been covering like a blanket and Sarbjeet Johal. Cloud Influencer friend of the CUBE. Cloud commentator and cloud experience built clouds, runs clouds now invests. So V. Dave, thanks for coming back on. You heard Peter Wagner at Wing VC. These guys have their roots in networking, which networking back in the day was, V. Dave. You remember the internet Cisco days, remember Cisco, Wellfleet routers. I think Peter invested in Arrow Point, remember Arrow Point, that was about in the 495 belt where you were. >> Lynch's company. >> That was Chris Lynch's company. I think, was he a sales guy there? (indistinct) >> That was his first big hit I think. >> All right, well guys, let's wrap this up. We've got a great program here. Sarbjeet, thank you for coming on. >> No worries. Glad to be here todays. >> Hey, Sarbjeet. >> First of all, really appreciate the Twitter activity lately on the commentary, the observability piece on Jeremy Burton's launch, Dave was phenomenal, but Peter was talking about this dynamic and I think ties this cracking the code thing together, which is there's a product led strategy that feels like a platform, but it's also a tool. In other words, it's not mutually exclusive, the old methods thrown out the window. Land in an account, know what problem you're solving. If you're below the stack, nail it, get data and go from there. If you're a process improvement up the stack, you have to much more of a platform longer-term sale, more business oriented, different motions, different mechanics. What do you think about that? What's your reaction? >> Yeah, I was thinking about this when I was listening to some of the startups pitching, if you will, or talking about what they bring to the table in this cloud scale or cloud era, if you will. And there are tools, there are applications and then they're big monolithic platforms, if you will. And then they're part of the ecosystem. So I think the companies need to know where they play. A startup cannot be platform from the get-go I believe. Now many aspire to be, but they have to start with tooling. I believe in, especially in B2B side of things, and then go into the applications, one way is to go into the application area, if you will, like a very precise use cases for certain verticals and stuff like that. And other parties that are going into the platform, which is like horizontal play, if you will, in technology. So I think they have to understand their age, like how old they are, how new they are, how small they are, because when their size matter when you are procuring as a big business, procuring your technology vendors size matters and the economic viability matters and their proximity to other windows matter as well. So I think we'll jump into that in other discussions later, but I think that's key, as you said. >> I would agree with that. I would phrase it in my mind, somewhat differently from Sarbjeet which is you have product led growth, and that's your early phase and you get product market fit, you get product led growth, and then you expand and there are many, many examples of this, and that's when you... As part of your team expansion strategy, you're going to get into the platform discussion. There's so many examples of that. You take a look at Ali Ghodsi today with what's happening at Databricks, Snowflake is another good example. They've started with product led growth. And then now they're like, "Okay, we've got to expand the team." Okta is another example that just acquired zero. That's about building out the platform, versus more of a point product. And there's just many, many examples of that, but you cannot to your point, very hard to start with a platform. Arm did it, but that was like a one in a million chance. >> It's just harder, especially if it's new and it's not operationalized yet. So one of the things Dave that we've observed the Cloud is some of the best known successes where nobody's not known at all, database we've been covering from the beginning 'cause we were close to that movement when they came out of Berkeley. But they still were misunderstood and they just started generating revenue in only last year. So again, only a few years ago, zero software revenue, now they're approaching a billion dollars. So it's not easy to make these vendor selections anymore. And if you're new and you don't have someone to operate it or your there's no department and the departments changing, that's another problem. These are all like enterprisey problems. What's your thoughts on that, Dave? >> Well, I think there's a big discussion right now when you've been talking all day about how should enterprise think about startups and think about most of these startups they're software companies and software is very capital efficient business. At the same time, these companies are raising hundreds of millions, sometimes over a billion dollars before they go to IPO. Why is that? A lot of it's going to promotion. I look at it as... And there's a big discussion going on but well, maybe sales can be more efficient and more direct and so forth. I really think it comes down to the golden rule. Two things really mattered in the early days in the startup it's sales and engineering. And writers should probably say engineering and sales and start with engineering. And then you got to figure out your go to market. Everything else is peripheral to those two and you don't get those two things right, you struggle. And I think that's what some of these successful startups are proving. >> Sarbjeet, what's your take on that point? >> Could you repeat the point again? Sorry, I lost-- >> As cloud scale comes in this whole idea of competing, the roles are changing. So look at IOT, look at the Edge, for instance, you got all kinds of new use cases that no one actually knows is a problem to solve. It's just pure opportunity. So there's no one's operational I could have a product, but it don't know we can buy it yet. It's a problem. >> Yeah, I think the solutions have to be point solutions and the startups need to focus on the practitioners, number one, not the big buyers, not the IT, if you will, but the line of business, even within that sphere, like just focus on the practitioners who are going to use that technology. I talked to, I think it wasn't Fiddler, no, it was CoreLogics. I think that story was great today earlier in how they kind of struggle in the beginning, they were trying to do a big bang approach as a startup, but then they almost stumbled. And then they found their mojo, if you will. They went to Don the market, actually, that's a very classic theory of disruption, like what we study from Harvard School of Business that you go down the market, go to the non-consumers, because if you're trying to compete head to head with big guys. Because most of the big guys have lot of feature and functionality, especially at the platform level. And if you're trying to innovate in that space, you have to go to the practitioners and solve their core problems and then learn and expand kind of thing. So I think you have to focus on practitioners a lot more than the traditional oracle buyers. >> Sarbjeet, we had a great thread last night in Twitter, on observability that you started. And there's a couple of examples there. Chaos searches and relatively small company right now, they just raised them though. And they're part of this star showcase. And they could've said, "Hey, we're going to go after Splunk." But they chose not to. They said, "Okay, let's kind of disrupt the elk stack and simplify that." Another example is a company observed, you've mentioned Jeremy Burton's company, John. They're focused really on SAS companies. They're not going after initially these complicated enterprise deals because they got to get it right or else they'll get churn, and churn is that silent killer of software companies. >> The interesting other company that was on the showcase was Tetra Science. I don't know if you noticed that one in the life science track, and again, Peter Wagner pointed out the life science. That's an under recognized in the press vertical that's exploding. Certainly during the pandemic you saw it, Tetra science is an R&D cloud, Dave, R&D data cloud. So pharmaceuticals, they need to do their research. So the pandemic has brought to life, this now notion of tapping into data resources, not just data lakes, but like real deal. >> Yeah, you and Natalie and I were talking about that this morning and that's one of the opportunities for R&D and you have all these different data sources and yeah, it's not just about the data lake. It's about the ecosystem that you're building around them. And I see, it's really interesting to juxtapose what Databricks is doing and what Snowflake is doing. They've got different strategies, but they play a part there. You can see how ecosystems can build that system. It's not one company is going to solve all these problems. It's going to really have to be connections across these various companies. And that's what the Cloud enables and ecosystems have all this data flowing that can really drive new insights. >> And I want to call your attention to a tweet Sarbjeet you wrote about Splunk's earnings and they're data companies as well. They got Teresa Carlson there now AWS as the president, working with Doug, that should change the game a little bit more. But there was a thread of the neath there. Andy Thry says to replies to Dave you or Sarbjeet, you, if you're on AWS, they're a fine solution. The world doesn't just revolve around AWS, smiley face. Well, a lot of it does actually. So (laughing) nice point, Andy. But he brings up this thing and Ali brought it up too, Hybrid now is a new operating system for what now Edge does. So we got Mobile World Congress happening this month in person. This whole Telco 5G brings up a whole nother piece of the Cloud puzzle. Jeff Barr pointed out in his keynote, Dave. Guys, I want to get your reaction. The Edge now is... I'm calling it the super Edge because it's not just Edge as we know it before. You're going to have these pops, these points of presence that are going to have wavelength as your spectrum or whatever they have. I think that's the solution for Azure. So you're going to have all this new cloud power for low latency applications. Self-driving delivery VR, AR, gaming, Telemetry data from Teslas, you name it, it's happening. This is huge, what's your thoughts? Sarbjeet, we'll start with you. >> Yeah, I think Edge is like bound to happen. And for many reasons, the volume of data is increasing. Our use cases are also expanding if you will, with the democratization of computer analysis. Specialization of computer, actually Dave wrote extensively about how Intel and other chip players are gearing up for that future if you will. Most of the inference in the AI world will happen in the field close to the workloads if you will, that can be mobility, the self-driving car that can be AR, VR. It can be healthcare. It can be gaming, you name it. Those are the few use cases, which are in the forefront and what alarm or use cases will come into the play I believe. I've said this many times, Edge, I think it will be dominated by the hyperscalers, mainly because they're building their Metro data centers now. And with a very low latency in the Metro areas where the population is, we're serving the people still, not the machines yet, or the empty areas where there is no population. So wherever the population is, all these big players are putting their data centers there. And I think they will dominate the Edge. And I know some Edge lovers. (indistinct) >> Edge huggers. >> Edge huggers, yeah. They don't like the hyperscalers story, but I think that's the way were' going. Why would we go backwards? >> I think you're right, first of all, I agree with the hyperscale dying you look at the top three clouds right now. They're all in the Edge, Hardcore it's a huge competitive battleground, Dave. And I think the missing piece, that's going to be uncovered at Mobile Congress. Maybe they'll miss it this year, but it's the developer traction, whoever wins the developer market or wins the loyalty, winning over the market or having adoption. The applications will drive the Edge. >> And I would add the fourth cloud is Alibaba. Alibaba is actually bigger than Google and they're crushing it as well. But I would say this, first of all, it's popular to say, "Oh not everything's going to move into the Cloud, John, Dave, Sarbjeet." But the fact is that AWS they're trend setter. They are crushing it in terms of features. And you'd look at what they're doing in the plumbing with Annapurna. Everybody's following suit. So you can't just ignore that, number one. Second thing is what is the Edge? Well, the edge is... Where's the logical place to process the data? That's what the Edge is. And I think to your point, both Sarbjeet and John, the Edge is going to be won by developers. It's going to be one by programmability and it's going to be low cost and really super efficient. And most of the data is going to stay at the Edge. And so who is in the best position to actually create that? Is it going to be somebody who was taking an x86 box and throw it over the fence and give it a fancy name with the Edge in it and saying, "Here's our Edge box." No, that's not what's going to win the Edge. And so I think first of all it's huge, it's wide open. And I think where's the innovation coming from? I agree with you it's the hyperscalers. >> I think the developers as John said, developers are the kingmakers. They build the solutions. And in that context, I always talk about the skills gravity, a lot of people are educated in certain technologies and they will keep using those technologies. Their proximity to that technology is huge and they don't want to learn something new. So as humans we just tend to go what we know how to use it. So from that front, I usually talk with consumption economics of cloud and Edge. It has to focus on the practitioners. And in this case, practitioners are developers because you're just cooking up those solutions right now. We're not serving that in huge quantity right now, but-- >> Well, let's unpack that Sarbjeet, let's unpack that 'cause I think you're right on the money on that. The consumption of the tech and also the consumption of the application, the end use and end user. And I think the reason why hyperscalers will continue to dominate besides the fact that they have all the resource and they're going to bring that to the Edge, is that the developers are going to be driving the applications at the Edge. So if you're low latency Edge, that's going to open up new applications, not just the obvious ones I did mention, gaming, VR, AR, metaverse and other things that are obvious. There's going to be non-obvious things that are going to be huge that are going to come out from the developers. But the Cloud native aspect of the hyperscalers, to me is where the scales are tipping, let me explain. IT was built to build a supply resource to the businesses who were writing business applications. Mostly driven by IBM in the mainframe in the old days, Dave, and then IT became IT. Telcos have been OT closed, "This is our thing, that's it." Now they have to open up. And the Cloud native technologies is the fastest way to value. And I think that paths, Sarbjeet is going to be defined by this new developer and this new super Edge concept. So I think it's going to be wide open. I don't know what to say. I can't guess, but it's going to be creative. >> Let me ask you a question. You said years ago, data's new development kit, does low code and no code to Sarbjeet's point, change the equation? In other words, putting data in the hands of those OT professionals, those practitioners who have the context. Does low-code and no-code enable, more of those protocols? I know it's a bromide, but the citizen developer, and what impact does that have? And who's in the best position? >> Well, I think that anything that reduces friction to getting stuff out there that can be automated, will increase the value. And then the question is, that's not even a debate. That's just fact that's going to be like rent, massive rise. Then the issue comes down to who has the best asset? The software asset that's eating the world or the tower and the physical infrastructure. So if the physical infrastructure aka the Telcos, can't generate value fast enough, in my opinion, the private equity will come in and take it over, and then refactor that business model to take advantage of the over the top software model. That to me is the big stare down competition between the Telco world and this new cloud native, whichever one yields in valley is going to blink first, if you say. And I think the Cloud native wins this one hands down because the assets are valuable, but only if they enable the new model. If the old model tries to hang on to the old hog, the old model as the Edge hugger, as Sarbjeet says, they'll just going to slowly milk that cow dry. So it's like, it's over. So to me, they have to move. And I think this Mobile World Congress day, we will see, we will be looking for that. >> Yeah, I think that in the Mobile World Congress context, I think Telcos should partner with the hyperscalers very closely like everybody else has. And they have to cave in. (laughs) I usually say that to them, like the people came in IBM tried to fight and they cave in. Other second tier vendors tried to fight the big cloud vendors like top three or four. And then they cave in. okay, we will serve our stuff through your cloud. And that's where all the buyers are congregating. They're going to buy stuff along with the skills gravity, the feature proximity. I've got another term I'll turn a coin. It matters a lot when you're doing one thing and you want to do another thing when you're doing all this transactional stuff and regular stuff, and now you want to do data science, where do you go? You go next to it, wherever you have been. Your skills are in that same bucket. And then also you don't have to write a new contract with a new vendor, you just go there. So in order to serve, this is a lesson for startups as well. You need to prepare yourself for being in the Cloud marketplaces. You cannot go alone independently to fight. >> Cloud marketplace is going to replace procurement, for sure, we know that. And this brings up the point, Dave, we talked about years ago, remember on the CUBE. We said, there's going to be Tier two clouds. I used that word in quotes cause nothing... What does it even mean Tier two. And we were talking about like Amazon, versus Microsoft and Google. We set at the time and Alibaba but they're in China, put that aside for a second, but the big three. They're going to win it all. And they're all going to be successful to a relative terms, but whoever can enable that second tier. And it ended up happening, Snowflake is that example. As is Databricks as is others. So Google and Microsoft as fast as they can replicate the success of AWS by enabling someone to build their business on their cloud in a way that allows the customer to refactor their business will win. They will win most of the lion's share my opinion. So I think that applies to the Edge as well. So whoever can come in and say... Whichever cloud says, "I'm going to enable the next Snowflake, the next enterprise solution." I think takes it. >> Well, I think that it comes back... Every conversation coming back to the data. And if you think about the prevailing way in which we treated data with the exceptions of the two data driven companies in their quotes is as we've shoved all the data into some single repository and tried to come up with a single version of the truth and it's adjudicated by a centralized team, with hyper specialized roles. And then guess what? The line of business, there's no context for the business in that data architecture or data Corpus, if you will. And then the time it takes to go from idea for a data product or data service commoditization is way too long. And that's changing. And the winners are going to be the ones who are able to exploit this notion of leaving data where it is, the point about data gravity or courting a new term. I liked that, I think you said skills gravity. And then enabling the business lines to have access to their own data teams. That's exactly what Ali Ghodsi, he was saying this morning. And really having the ability to create their own data products without having to go bow down to an ivory tower. That is an emerging model. All right, well guys, I really appreciate the wrap up here, Dave and Sarbjeet. I'd love to get your final thoughts. I'll just start by saying that one of the highlights for me was the luminary guests size of 15 great companies, the luminary guests we had from our community on our keynotes today, but Ali Ghodsi said, "Don't listen to what everyone's saying in the press." That was his position. He says, "You got to figure out where the puck's going." He didn't say that, but I'm saying, I'm paraphrasing what he said. And I love how he brought up Sky Cloud. I call it Sky net. That's an interesting philosophy. And then he also brought up that machine learning auto ML has got to be table stakes. So I think to me, that's the highlight walk away. And the second one is this idea that the enterprises have to have a new way to procure and not just the consumption, but some vendor selection. I think it's going to be very interesting as value can be proved with data. So maybe the procurement process becomes, here's a beachhead, here's a little bit of data. Let me see what it can do. >> I would say... Again, I said it was this morning, that the big four have given... Last year they spent a hundred billion dollars more on CapEx. To me, that's a gift. In so many companies, especially focusing on trying to hang onto the legacy business. They're saying, "Well not everything's going to move to the Cloud." Whatever, the narrative should change to, "Hey, thank you for that gift. We're now going to build value on top of the Cloud." Ali Ghodsi laid that out, how Databricks is doing it. And it's clearly what Snowflake's new with the data cloud. It basically a layer that abstracts all that underlying complexity and add value on top. Eventually going out to the Edge. That's a value added model that's enabled by the hyperscalers. And that to me, if I have to evaluate where I'm going to place my bets as a CIO or IT practitioner, I'm going to look at who are the ones that are actually embracing that investment that's been made and adding value on top in a way that can drive my data-driven, my digital business or whatever buzzword you want to throw on. >> Yeah, I think we were talking about the startups in today's sessions. I think for startups, my advice is to be as close as you can be to hyperscalers and anybody who awards them, they will cave in at the end of the day, because that's where the whole span of gravity is. That's what the innovation gravity is, everybody's gravitating towards that. And I would say quite a few times in the last couple of years that the rate of innovation happening in a non-cloud companies, when I talk about non-cloud means are not public companies. I think it's like diminishing, if you will, as compared to in cloud, there's a lot of innovation. The Cloud companies are not paying by power people anymore. They have all sophisticated platforms and leverage those, and also leverage the marketplaces and leverage their buyers. And the key will be how you highlight yourself in that cloud market place if you will. It's like in a grocery store where your product is placed and you have to market around it, and you have to have a good story telling team in place as well after you do the product market fit. I think that's a key. I think just being close to the Cloud providers, that's the way to go for startups. >> Real, real quick. Each of you talk about what it takes to crack the code for the enterprise in the modern era now. Dave, we'll start with you. What's it take? (indistinct) >> You got to have it be solving a problem that is 10X better at one 10th a cost of anybody else, if you're a small company, that rule number one. Number two is you obviously got to get product market fit. You got to then figure out. And I think, and again, you're in your early phases, you have to be almost processed builders, figure out... Your KPIs should all be built around retention. How do I define customer success? How do I keep customers and how do I make them loyal so that I know that my cost of acquisition is going to be at least one-third or lower than my lifetime value of that customer? So you've got to nail that. And then once you nail that, you've got to codify that process in the next phase, which really probably gets into your platform discussion. And that's really where you can start to standardize and scale and figure out your go to market and the relationship between marketing spend and sales productivity. And then when you get that, then you got to move on to figure out your Mot. Your Mot might just be a brand. It might be some secret sauce, but more often than not though, it's going to be the relationship that you build. And I think you've got to think about those phases and in today's world, you got to move really fast. Sarbjeet, real quick. What's the secret to crack the code? >> I think the secret to crack the code is partnership and alliances. As a small company selling to the bigger enterprises, the vendors size will be one of the big objections. Even if they don't say it, it's on the back of their mind, "What if these guys disappear tomorrow what would we do if we pick this technology?" And another thing is like, if you're building on the left side, which is the developer side, not on the right side, which is the operations or production side, if you will, you have to understand the sales cycles are longer on the right side and left side is easier to get to, but that's why we see a lot more startups. And on the left side of your DevOps space, if you will, because it's easier to sell to practitioners and market to them and then show the value correctly. And also understand that on the left side, the developers are very know how hungry, on the right side people are very cost-conscious. So understanding the traits of these different personas, if you will buyers, it will, I think set you apart. And as Dave said, you have to solve a problem, focus on practitioners first, because you're small. You have to solve political problems very well. And then you can expand. >> Well, guys, I really appreciate the time. Dave, we're going to do more of these, Sarbjeet we're going to do more of these. We're going to add more community to it. We're going to add our community rooms next time. We're going to do these quarterly and try to do them as more frequently, we learned a lot and we still got a lot more to learn. There's a lot more contribution out in the community that we're going to tap into. Certainly the CUBE Club as we call it, Dave. We're going to build this actively around Cloud. This is another 20 years. The Edge brings us more life with Cloud, it's really exciting. And again, enterprise is no longer an enterprise, it's just the world now. So great companies here, the next Databricks, the next IPO. The next big thing is in this list, Dave. >> Hey, John, we'll see you in Barcelona. Looking forward to that. Sarbjeet, I know in a second half, we're going to run into each other. So (indistinct) thank you John. >> Trouble has started. Great talking to you guys today and have fun in Barcelona and keep us informed. >> Thanks for coming. I want to thank Natalie Erlich who's in Rome right now. She's probably well past her bedtime, but she kicked it off and emceeing and hosting with Dave and I for this AW startup showcase. This is batch two episode two day. What do we call this? It's like a release so that the next 15 startups are coming. So we'll figure it out. (laughs) Thanks for watching everyone. Thanks. (bright music)

>>Okay. Okay. Now we're gonna go into the Green Lake Power Panel. Talk about the cloud landscape hybrid cloud and how the partner ecosystem and customers are thinking about cloud hybrid cloud as a service and, of course, Green Lake. And with me or CR Houdyshell, president of Advise X. Ron Nemecek, Who's the business Alliance manager at C B. T s. Harry Zaric is president of competition, and Benjamin Clay is VP of sales and alliances at Arrow Electronics. Great to see you guys. Thanks so much for coming on the Cube. >>Thanks for having us >>would be here. >>Okay, here's the deal. So I'm gonna ask you guys each to introduce yourselves and your company's add a little color to my brief intro and then answer the following question. How do you and your customers think about hybrid cloud and think about it in the context of where we are today and where we're going? Not just the snapshot, but where we are today and where we're going. CR, why don't you start, please? >>Sure. Thanks a lot. They appreciate it. And, uh, again cr Howdy Shell President of advising. I've been with the company for 18 years the last four years as president. So had the great great opportunity here to lead a 45 year old company with a very strong brand and great culture. Uh, as it relates to advise X and where we're headed to with hybrid Cloud is it's a journey, so we're excited to be leading that journey for the company as well as HP. We're very excited about where HP is going with Green Lake. We believe it's it's a very strong solution when it comes to hybrid. Cloud have been an HP partner since since 1980. So for 40 years it's our longest standing OM relationship, and we're really excited about where HP is going with Green Lake from a hybrid cloud perspective. Uh, we feel like we've been doing the hybrid cloud solutions in the past few years with everything that we've focused on from a VM Ware perspective. But now, with where HP is going, we think really changing the game and it really comes down to giving customers at cloud experience with the on Prem solution with Green Lake, and we've had great response from our customers and we think we're gonna continue to see how that kind of increased activity and reception. >>Great. Thank you. Cr and yeah, I totally agree. It is. It is a journey. And we've seen it really come a long way in the last decade. Ron, I wonder if you could kick off your little first intro there, please? >>Sure. Dave, thanks for having me today. And it's a pleasure being here with all of you. My name is Ron Nemecek, business Alliance manager at C B. T. S. In my role, I am responsible for RHP Green Lake relationship globally. I've enjoyed a 33 year career in the I T industry. I'm thankful for the opportunity to serve in multiple functional and senior leadership roles that have helped me gather a great deal of education and experience that could be used to aid our customers with their evolving needs for business outcomes. The best position them for sustainable and long term success. I'm honored to be part of the C B. T s and Annex Canada Organization, C B T s stands for consult Bill transform and support. We have a 35 year relationship with HP or a platinum and inner circle partner. We're headquartered in Cincinnati Ohio. We service 3000 customers, generating over a billion dollars in revenue, and we have over 2000 associates across the globe. Our focus is partnering with our customers to deliver innovative solutions and business results through thought leadership. We drive this innovation VR team of the best and brightest technology professionals in the industry that have secured over 2800 technical certifications 260 specifically with HP and in our hybrid cloud business. We have clearly found the technology new market demands for instant responses and experiences evolving economic considerations with detailed financial evaluation and, of course, the global pandemic have challenged each of our customers across all industries to develop an optimal cloud strategy we have. We now play an enhanced strategic role for our customers as there Technology Advisor and their guide to the right mix of cloud experiences that will maximize their organizational success with predictable outcomes. Our conversations have really moved from product roadmaps and speeds and feeds to return on investment, return on capital and financial statements, ratios and metrics. We collaborate regularly with our customers at all levels and all departments to find an effective, comprehensive cloud strategy for their workloads and applications, ensuring proper alignment and costs with financial return. >>Great. Thank you, Ron. Yeah, Today it's all about the business value. Harry, please, >>I Dave. Thanks for the opportunity and greetings from the Great White North, where Canadian based company headquartered in Toronto, with offices across the country. We've been in the tech industry for a very long time. What we would call a solution provider hard for my mother to understand what that means. But our goal is to help our customers realize the business value of their technology investments just to give you an example of what it is we try and do. We just finished a build out of a new networking and point in data center technology for a brand new hospital is now being mobilized for covid high risk patients. So talk about are all being an essential industry, providing essential services across the whole spectrum of technology. Now, in terms of what's happening in the marketplace, our customers are confused. No question about it. They hear about cloud and cloud first, and everyone goes to the cloud. But the reality is there's lots of technology, lots of applications that actually still have to run on premises for a whole bunch of reasons. And what customers want is solid senior serious advice as to how they leverage what they already have in terms of their existing infrastructure but modernized and updated So it looks and feels a lot like a cloud. But they have the security. They have the protection that they need to have for reasons that are dependent on their industry and business to allow them to run on from. And so the Green Lake philosophy is perfect. That allows customers to actually have 1 ft in the cloud, 1 ft in their traditional data center, but modernize it so it actually looks like one enterprise entity. And it's that kind of flexibility that gives us an opportunity collectively, ourselves, our partners, HP to really demonstrate that we understand how to optimize the use of technology across all of the business applications they need to rest >>your hair. It's interesting about what you said is is cloud is it is kind of chaotic. My word not yours, but but there is a lot of confusion out there. I mean, it's what's cloud right? Is it Public Cloud is a private cloud the hybrid cloud. Now, now it's the edge. And of course, the answer is all of the above. Ben, what's your perspective on all this? >>Um, from a cloud perspective. You know, I think as an industry, you know, I think we we've all accepted that public cloud is not necessarily gonna win the day and were, in fact, in a hybrid world, there's certainly been some some commentary impress. Um, you know, that would sort of validate that. Not that necessarily needs any validation. But I think it's the linkages between on Prem, Um, and cloud based services have increased. Its paved the way for customers to more effectively deploy hybrid solutions in the model that they want that they desired. You know, Harry was commenting on that a moment ago. Um, you know, as the trend continues, it becomes much easier for solution providers and service providers to drive there services, initiatives, uh, you know, in particular managed services. So, you know, from from an arrow perspective, as we think about how we can help scale in particular from Greenland perspective, we've got the ability to stand up some some cloud capabilities through our aero secure platform. um that can really help customers adopt Green Lake. Uh, and, uh, benefit to benefit from, um, some alliances, opportunities as well. And I'll talk more about that as we go through >>that. I didn't mean to squeeze you on a narrow. I mean, you got arrows. Been around longer than computers. I mean, if you google the history of arrow, it'll blow your mind. But give us a little, uh, quick commercial. >>Yeah, absolutely. So, um, I've been with arrow for about 20 years. I've got responsibility for alliances, organization, North America for Global value, added distribution, business consulting and channel enablement Company. Uh, you know, we bring scope, scale and and, uh, expertise as it relates to the I t industry. Um, you know, I love the fast paced, the fast paced that comes with the market, that we're all all in, and I love helping customers and suppliers both, you know, be positioned for long term success. And, you know, the subject matter here today is just a great example of that. So I'm happy to be here and or to the discussion. >>All right, We got some good brain power in the room. Let's let's cut right to the chase. Ron, Where's the pain? What are the main problems that C B. T s. I love the what it stands for. Consult Bill Transform and support the What's the main pain point that that customers are asking you to solve when it comes to their cloud strategies. >>Third day of our customers' concerns and associated risk come from the market demands to deliver their products, services and experiences instantaneously. And then the challenges is how do they meet those demands because they have aging infrastructure processes and fiscal constraints. Our customers really need us now more than ever to be excellent listeners so we can collaborate on an effective map for the strategic placement of workloads and applications in that spectrum of cloud experiences, while managing their costs and, of course, mitigating risk to their business. This collaboration with our customer customers often identify significant costs that have to be evaluated, justified or eliminated. We find significant development, migration and egress charges in their current public cloud experience, coupled with significant over provisioning, maintenance, operational and stranded asset costs in their on premise infrastructure environment. When we look at all these costs holistically through our customized workshops and assessments. We can identify the optimal cloud experience for the respective workloads and applications through our partnership with HP and the availability of the HP Green Lake Solutions. Our customers now have a choice to deliver SLA's economics and business outcomes for their workloads and applications that best reside on premise in a private cloud and have that experience. This is a rock solid solution that eliminates, you know, the development costs at the experience and the egress charges that are associated with the public cloud while utilizing HP Green Lake to eliminate over provisioning costs and the maintenance costs on aging infrastructure hardware. Lastly, our customers only have to pay for actual infrastructure usage with no upfront capital expense. And now that achieves true utilization to cost economics. You know, with HP Green Lake solution from C B. T s. >>I love to focus on the business case because it's measurable. That sort of follow the money. That's where it's where the opportunity is. Okay, See, I got a question for you thinking about advise X customers. How are they? Are they leaning into Green Lake? You know, what are they telling you? Is the business impact when they when they experience Green Lake, >>I think it goes back to what Ron was talking about. We have to solve the business challenges first, and so far the reception's been positive. When I say that is, customers are open, everybody wants to. The C suite wants to hear about cloud and hybrid cloud fits, but what we're hearing, what we're seeing from our customers is we're seeing more adoption from customers that it may be their first put in, if you will. But as importantly, we're able to share other customers with our potentially new clients that that say, What's the first thing that happens with regard to Green like Well, number one, it works. It works as advertised and as a as a service. That's a big step. There are a lot of people out there dabbling today, but when you can say we have a proven solution, it's working in in in our environment today. That's key. I think the second thing is is flexibility. You know, when customers are looking for this, this hybrid solution, you've got to be flexible for again. I think Ron said it well, you don't have a big capital outlay but also what customers want to be able to. We're gonna build for growth, but we don't want to pay for it, so we'll pay as we grow. Not as not as we have to use because we used to do It was upfront of the capital expenditure, and I will just pay as we grow and that really facilitates. In another great examples, you'll hear from a customer, uh, this afternoon, but you'll hear where one of the biggest benefits they just acquired a $570 million company, and their integration is going to be very seamless because of their investment in Green Lake. They're looking at the flexibility to add the Green Lake as a big opportunity to integrate for acquisitions and finally is really we see it really brings the cloud experience and as a service to our customers bring. And with HP Green Lake, it brings best to breathe. So it's not just what HP has to offer. When you look at hyper converged, they have Nutanix kohi city, so I really believe it brings best to breathe. So, uh to net it out and close it out with our customers thus far, the customer experience has been exceptional with Green Lake Central has interface. Customers have had a lot of success. We just had our first customer from about a year and a half ago, just re up, and it was a highly competitive situation. But they just said, Look, it's proven it works and it gives us that cloud experience So I had a lot of great success thus far, looking forward to more. >>Thank you. So, Harry, I want to pick up on something, CR said, And get your perspectives. So when you when I talk to the C suite, they do all want to hear about, you know, Cloud, they have a cloud agenda and and what they tell me is it's not just about their I t transformation. They want, they want that. But they also want to transform their business. So I wonder if you could talk Harry about competence, perspective on the potential business impact of Green Lake, and and also, you know, I'm interested in how you guys are thinking about workloads, how to manage work, you know how to cost optimize in i t. But also the business value that comes out of that capability. >>Yes. So, Dave, you know, if you were to talk to CFO and I have the good fortune to talk to lots of CFOs, they want to pay the cost. When they generate the revenue, they don't want to have all the cost up front and then wait for the revenue to come through. A good example of where that's happening right now is related to the pandemic. Employees that used to work at the office have now moved to working from home, and now they have to. They have to connect remotely to run the same application. So use this thing called VD virtual interfacing to allow them to connect to the applications that they need to run in the off. Don't want to get into too much detail. But to be able to support that from an at home environment, they needed to buy a lot more computing capacity to handle this. Now there's an expectation that hopefully six months from now, maybe sooner than that people will start returning to the office. They may not need that capacity so they can turn down on the cost. And so the idea of having the capacity available when you need it, But then turning it off when you don't need it is really a benefit of a variable cost model. Another example that I would use is one in new development if a customer is going to implement and you, let's say, line of business application essay P is very, very popular, you know, it actually, unfortunately takes six months to two years to actually get that application setup installed, validated, test it and then moves through production. You know what used to happen before they would buy all that capacity at front and basically sit there for two years? And then when they finally went to full production, then they were really getting value out of that investment. But they actually lost a couple of years of technology, literally sitting almost idle. And so, from a CFO perspective, his ability to support the development of those applications as he scales it perfect Green Lake is the ideal solution that allows them to do that. >>You know, technology has saved businesses in this pandemic. There's no question about it and what Harry was just talking about with regard to VD. You think about that. There's the dialing up and dialing down piece, which is awesome from an i t perspective and then the business impact. There is the productivity of Of of the end users, and most C suite executives I've talked to said Productivity actually went up during covid with work from home, which is kind of astounding if you think about it. Ben, you know Ben, I We said Arrow has been around for a long, long time, certainly before all of us were born and it's gone through many, many industry transitions during our lifetimes. How does arrow and how do How do your partners think about building cloud experience experiences? And where does Green Lake fit in from your perspective? >>A great question. So from a narrow perspective, when you think about cloud experience and, of course, us taking a view as a distribution partner, we want to be able to provide scale and efficiency to our network of partners. So we do that through our aero screw platform. Um, just just a bit of a you know, a bit of a commercial. I mean, you get single quote single bill auto provision compared multi supplier, if you will Subscription management utilization reporting from the platform itself. So if we pivot that directly to HP, you're going to get a bit of a scoop here, Dave. So we're excited today to have Green Lake live in our platform available for our part of community to consume in particular the swift solutions that HP has announced. So we're very excited to to share that today, Um, maybe a little bit more on Green Lake. I think at this point in time, there it's differentiated, Um, in a sense that if you think about some of the other offerings in the market today and further with, um uh, having the solutions himself available in a row sphere So, you know, I would say, Do we identify the uniqueness, um, and quickly partner with HP to to work with our atmosphere platform? One other sort of unique thing is, you know, when you think about platform itself, you've got to give a consistent experience the different geographies around the world. So, you know, we're available in north of 20 countries. There's thousands of resellers and transacting on the platform on a regular basis, and frankly, hundreds of thousands and customers are leveraging today, so that creates an opportunity for both Arrow HP and our partner community. So we're excited. >>Uh, you know, I just want to open it up and we don't have much time left, but thoughts on on on differentiation. You know, when people ask me Okay, what's really different about H P E and Green Lake? As others you know are doing things that with with as a service to me, it's a I I always say cultural. It starts from the top with Antonio, and it's like the company's all in. But But I wonder from your perspective because you guys are hands on. Are there other differential factors that you would point to let me just open that up to the group? >>Yeah, if I could make a comment. You know, Green Lake is really just the latest invocation of the as a service model. And what does that mean? What that actually means is you have a continuous ongoing relationship with the customer. It's not a cell. And forget not that we ever forget about customers, but there are highlights. Customer buys, it gets installed, and then for two or three years, you may have an occasional engagement with them. But it's not continuous. When you move to a Green Lake model, you're actually helping them manage that you are in the core in the heart of their business. No better place to be if you want to be sticky and you want to be relevant, and you want to be always there for them. >>You know, I wonder if somebody else could add to and and and in your in your remarks from your perspective as a partner because, you know, Hey, a lot of people made a lot of money selling boxes, but those days are pretty much gone. I mean, you have to transform into a services mindset. But other thoughts, >>I think I think Dad did that day. I think Harry's right on right. What he the way he positioned Exactly. You get on the customer. Even another step back for us is we're able to have the business conversation without leading with what you just said. You don't have to leave with a storage solution to leave with a compute. You can really have step back, have a business conversation, and we've done that where you don't even bring up hp Green Lake until you get to the point of the customer says, So you can give me an on prem cloud solution that gives me scalability, flexibility, all the things you're talking about. How does that work then? Then you bring up. It's all through this HP Green link tool. It really gives you the ability to have a business conversation. And you're solving the business problems versus trying to have a technology conversation. And to me, that's clear differentiation for HP. Green length. >>All right, guys. CR Ron. Harry. Ben. Great discussion. Thank you so much for coming on the program. Really appreciate it. >>Thanks for having us, Dave. >>All >>right. Keep it right there for more great content at Green Lake Day. Right back? Yeah.

(bright upbeat music) >> Announcer: We are entering an age of insight where data moves freely between environments to work together powerfully, from wherever it lives. A new era driven by next generation cloud services. It's freedom that accelerates innovation and digital transformation, but it's only for those who dare to propel their business toward a new future that pushes beyond the usual barriers. To a place that unites all information under a fluid yet consistent operating model, across all your applications and data. To a place called HPE GreenLake. HPE GreenLake pushes beyond the obstacles and limitations found in today's infrastructure because application entanglements, data gravity, security, compliance, and cost issues simply aren't solved by current cloud options. Instead, HPE GreenLake is the cloud that comes to you, bringing with it, increased agility, broad visibility, and open governance across your entire enterprise. This is digital transformation unlocked, incompatibility solved, data decentralized, and insights amplified. For those thinkers, makers and doers who want to create on the fly scale up or down with a single click, stand up new ideas without risk, and view it all as a single agile system of systems. HPE GreenLake is here and all are invited. >> The definition of cloud is evolving and now clearly comprises hybrid and on-prem cloud. These trends are top of mind for every CIO and the space is heating up as every major vendor has been talking about as-a-Service models and making moves to better accommodate customer needs. HPE was the first to market with its GreenLake brand, and continues to make new announcements designed to bring the cloud experience to far more customers. Come here from HPE and its partners about the momentum that they're seeing with this trend and what actions you can take to stay ahead of the competition in this fast moving market. (bright soft music) Okay, we're with Keith White, Senior Vice President and General Manager for GreenLake at HPE, and George Hope, who's the Worldwide Head of Partner Sales at Hewlett Packard Enterprise. Welcome gentlemen, good to see you. >> Awesome to be here. >> Yeah. Thanks so much. >> You're welcome, Keith, last we spoke, we talked about how you guys were enabling high performance computing workloads to get green-late right for enterprise markets. And you got some news today, which we're going to get to but you guys, you put out a pretty bold position with GreenLake, basically staking a claim if you will, the edge, cloud as-a-Service all in. How are you thinking about its impacts for your customers so far? >> You know, the impact's been amazing and, you know, in essence, I think the pandemic has really brought forward this real need to accelerate our customer's digital transformation, their modernization efforts, and you know, frankly help them solve what was amounting to a bunch of new business problems. And so, you know, this manifests itself in a set of workloads, set of solutions, and across all industries, across all customer types. And as you mentioned, you know GreenLake is really bringing that value to them. It brings the cloud to the customer in their data center, in their colo, or at the edge. And so frankly, being able to do that with that full cloud experience. All is a pay per use, you know, fully consumption-based scenario, all managed for them so they get that as I mentioned, true cloud experience. It's really sort of landing really well with customers and we continue to see accelerated growth. We're adding new customers, we're adding new technology. And we're adding a whole new set of partner ecosystem folks as well that we'll talk about. >> Well, you know, it's interesting you mentioned that just cause as a quick aside it's, the definition of cloud is evolving and it's because customers, it's the way customers look at it. It's not just vendor marketing. It's what customers want, that experience across cloud, edge, you know, multiclouds, on-prem. So George, what's your take? Anything you'd add to Keith's response? >> I would, you've heard Antonio Neri say it several times and you probably saying it for yourself. The cloud is an experience, it's not a destination. The digital transformation is pushing new business models and that demands more flexible IT. And the first round of digital transformation focused on a cloud first strategy. For our customers we're looking to get more agility. As Keith mentioned, the next phase of transformation will be characterized by bringing the cloud speed and agility to all apps and data, regardless of where they live, According to IDC, by the end of 2021, 80% of the businesses will have some mechanism in place to shift the cloud centric, infrastructure and apps and twice as fast as before the pandemic. So the pandemic has actually accelerated the impact of the digital divide, specifically, in the small and medium companies which are adapting to technology change even faster and emerging stronger as a result. You know, the analysts agree cloud computing and digitalization will be key differentiators for small and medium business in years to come. And speed and automation will be pivotal as well. And by 2022, at least 30% of the lagging SMBs will accelerate digitalization. But the fair focus will be on internal processes and operations. The digital leaders, however, will differentiate by delivering their customers, a dynamic experience. And with our partner ecosystem, we're helping our customers embrace our as-a-Service vision and stand out wherever they are. on their transformation journey. >> Well, thanks for those stats, I always liked the data. I mean, look, if you're not a digital business today I feel like you're out of business only 'cause.... I'm sure there's some exceptions, but you got to get on the digital bandwagon. I think pre-pandemic, a lot of times people really didn't know what it meant. We know now what it means. Okay, Keith, let's get into the news when we do these things. I love that you guys always have something new to share. What do you have? >> No, you got it. And you know, as we said, the world is hybrid and the world is multicloud. And so, customers are expecting these solutions. And so, we're continuing to really drive up the innovation and we're adding additional cloud services to GreenLake. We just recently went to General AVailability of our MLOps, Machine Learning Operations, and our containers for cloud services along with our virtual desktop which has become very big in a pandemic world where a lot more people are working from home. And then we have shipped our SAP HEC, customer edition, which allows SAP customers to run on their premise whether it's the data center or the colo. And then today we're introducing our new Bare Metal capabilities as well as containers on Bare Metal as a Service, for those folks that are running cloud native applications that don't require any sort of hypervisor. So we're really excited about that. And then second, I'd say similar to that HPC as a Service experience we talked about before, where we were bringing HPC down to a broader set of customers. We're expanding the entry point for our private cloud, which is virtual machines, containers, storage, compute type capabilities in workload optimized systems. So again, this is one of the key benefits that HPE brings is it combines all of the best of our hardware, software, third-party software, and our services, and financial services into a package. And we've workload optimized this for small, medium, large and extra-large. So we have a real sort of broader base for our customers to take advantage of and to really get that cloud experience through HPE GreenLake. And, you know, from a partner standpoint we also want to make sure that we continue to make this super easy. So we're adding self-service capabilities we're integrating into our distributors marketplaces through a core set of APIs to make sure that it plugs in for a very smooth customer experience. And this expands our reach to over 100,000 additional value-added resellers. And, you know, we saw just fantastic growth in the channel in Q1, over 118% year over year growth for GreenLake Cloud Services through the channel. And we're continuing to expand, extend and expand our partner ecosystem with additional key partnerships like our colos. The colocation centers are really key. So Equinix, CyrusOne and others that we're working with and I'll let George talk more about. >> Yeah, I wonder if you could pick up on that George. I mean, look, if I'm a partner and and I mean, I see an opportunity here.. Maybe, you know, I made a lot of money in the old days moving iron. But I got to move, I got to pivot my business. You know, COVID's actually, you know, accelerating a lot of those changes, but there's a lot of complexity out there and partners can be critical in helping customers make that journey. What do you see this meaning to partners, George? >> So I completely agree with Keith and through and with our partners we give our customers choice. Right, they don't have to worry about security or cost as they would with public cloud or the hyperscalers. We're driving special initiatives via Cloud28 which we run, which is the world's largest cloud aggregator. And also, in collaboration with our distributors in their marketplaces as Keith mentioned. In addition, customers can leverage our expertise and support of our service provider ecosystem, our SI's, our ISV's, to find the right mix of hybrid IT and decide where each application or workload should be hosted. 'Cause customers are now demanding robust ecosystems, cloud adjacency, and efficient low latency networks. And the modern workload demands, secure, compliant, highly available, and cost optimized environments. And Keith touched on colocation. We're partnering with colocation facilities to provide our customers with the ability to expand bandwidth, reduce latency, and get access to a robust ecosystem of adjacent providers. We touched on Equinix a bit as one of them, but we're partnering with them to enable customers to connect to multiple clouds with private on-demand interconnections from hundreds of data center locations around the globe. We continue to invest in the partner and customer experience, you know, making ourselves easier to do business with. We've now fully integrated partners in GreenLake Central, and could provide their customers end to end support and managing the entire hybrid IT estate. And lastly, we're providing partners with dedicated and exclusive enablement opportunities so customers can rely on both HPE and partner experts. And we have a competent team of specialists that can help them transform and differentiate themselves. >> Yeah, so, I'm hearing a theme of simplicity. You know, I talked earlier about this being customer-driven. To me what the customer wants is they want to come in, they want simple, like you mentioned, self-serve. I don't care if it's on-prem, in the cloud, across clouds, at the edge, abstract, all that complexity away from me. Make it simple to do, not only the technology to work, you figure out where the workload should run and let the metadata decide and that's a bold vision. And then, make it easy to do business. Let me buy as-a-Service if that's the way I want to consume. And partners are all about, you know, reducing friction and driving that. So, anyway guys, final thoughts, maybe Keith, you can close it out here and maybe George can call it timeout. >> Yeah, you summed it up really nice. You know, we're excited to continue to provide what we view as the largest and most flexible hybrid cloud for our customers' apps, data, workloads, and solutions. And really being that leading on-prem solution to meet our customer's needs. At the same time, we're going to continue to innovate and our ears are wide open, and we're listening to our customers on what their needs are, what their requirements are. So we're going to expand the use cases, expand the solution sets that we provide in these workload optimized offerings to a very very broad set of customers as they drive forward with that digital transformation and modernization efforts. >> Right, George, any final thoughts? >> Yeah, I would say, you know, with our partners we work as one team and continue to hone our skills and embrace our competence. We're looking to help them evolve their businesses and thrive, and we're here to help now more than ever. So, you know, please reach out to our team and our partners and we can show you where we've already been successful together. >> That's great, we're seeing the expanding GreenLake portfolio, partners key part of it. We're seeing new tools for them and then this ecosystem evolution and build out and expansion. Guys, thanks so much. >> Yeah, you bet, thank you. >> Thank you, appreciate it. >> You're welcome. (bright soft music) >> Okay, we're here with Jo Peterson the VP of Cloud & Security at Clarify360. Hello, Jo, welcome to theCUBE. >> Hello. >> Great to see you. >> Thanks for having me. >> You're welcome, all right, let's get right into it. How do you think about cloud where we are today in 2021? The definitions evolve, but where do you see it today and where do you see it going? >> Well, that's such an interesting question and is so relevant because the labels are disappearing. So over the last 10 years, we've sort of found ourselves defining whether an environment was public or whether it was private or whether it was hybrid. Here's the deal, cloud is infrastructure and infrastructure is cloud. So at the end of the day cloud in whatever form it's taking is a platform, and ultimately, this enablement tool for the business. Customers are consuming cloud in the best way that works for their businesses. So let's also point out that cloud is not a destination, it's this journey. And clients are finding themselves at different places on that road. And sometimes they need help getting to the next milestone. >> Right, and they're really looking for that consistent experience. Well, what are the big waves and trends that you're seeing around cloud out there in the marketplace? >> So I think that this hybrid reality is happening in most organizations. Their actual IT portfolios include a mix of on-premise and cloud infrastructure, and we're seeing this blurred line happening between the public cloud and the traditional data center. Customers want a bridge that easily connects one environment to the other environment, and they want end-to-end visibility. Customers are becoming more intentional and strategic about their cloud roadmaps. So some of them are intentionally and strategically selecting hybrid environments because they feel that it affords them more control, cost, balance, comfort level around their security. In a way, cloud itself is becoming borderless. The major tech providers are extending their platforms in an infrastructure agnostic manner and that's to work across hybrid environments, whether they be hosted in the data center, whether it includes multiple cloud providers. As cloud matures, workload environments fit is becoming more of a priority. So forward thinking where the organizations are matching workloads to the best environment. And it's sort of application rationalization on this case by case basis and it really makes sense. >> Yeah, it does makes sense. Okay, well, let's talk about HPE GreenLake. They just announced some new solutions. What do you think it means for customers? >> I think that HPE has stepped up. They've listened to not only their customers but their partners. Customers want consumable infrastructure, they've made that really clear. And HPE has expanded the cloud service portfolio for clients. They're offering more choices to not only enterprise customers but they're expanding that offering to attract this mid-market client base. And they provided additional tools for partners to make selling GreenLake easier. This is all helping to drive channel sales. >> Yeah, so better granularity, just so it increases the candidates, better optionality for customers. And this thing is evolving pretty quickly. We're seeing a number of customers that we talked to interested in this model, trying to understand it better and ultimately, I think they're going to really lean in hard. Jo, I wonder if you could maybe think about or share with us which companies are, I got to say, getting it right? And I'm really interested in the partner piece, because if you think about the partner business, it's really, it's changing a lot, right? It's gone from this notion of moving boxes and there was a lot of money to be made over the decades in doing that, but they have to now become value-add suppliers and really around cloud services. And in the early days of cloud, I think the channel was a little bit freaked out, saying, uh-oh, they're going to cut out the middleman. But what's actually happened is those smart agile partners are adding substantial value, they've got deep relationships with customers and they're serving as really trusted advisors and executors of cloud strategies. What do you see happening in the partner community? >> Well, I think it's been a learning curve and everything that you said was spot on. It's a two way street, right? In order for VARs to sell residual services, monthly recurring services, there has to have been some incentive to do that and HPE really got it right. Because they, again listened to that partner community, and they said, you know what? We've got to incentivize these guys to start selling this way. This is a partnership and we expect it to be a partnership. And the tech companies that are getting right are doing that same sort of thing, they're figuring out ways to make it palatable to that VAR, to help them along that journey. They're giving them tools, they're giving them self-serve tools, they're incentivizing them financially to make that shift. That's what's going to matter. >> Well, that's a key point you're making, I mean, the financial incentives, that's new and different. Paying, you know, incentivizing for as-a-Service models versus again, moving hardware and paying for, you know, installing iron. That's a shift in mindset, isn't it? >> It definitely is. And HPE, I think is getting it right because I didn't notice but I learned this, 70% of their annual sales are actually transacted through their channel. And they've seen this 116% increase in HPE GreenLake orders in Q1, from partners. So what they're doing is working. >> Yeah, I think you're right. And you know, the partner channel it becomes super critical. It's funny, Jo, I mean, again, in the early days of cloud, the channel was feeling like they were going to get disrupted. I don't know about you, but I mean, we've both been analysts for awhile and the more things get simple, the more they get complicated, right? I mean the consumerization of IT, the cloud, swipe your credit card, but actually applying that to your business is not easy. And so, I see that as great opportunities for the channel. Give you the last word. >> Absolutely, and what's going to matter is the tech companies that step up and realize we've got this chance, this opportunity to build that bridge and provide visibility, end-to-end visibility for clients. That's what going to matter. >> Yeah, I like how you're talking about that bridge, because that's what everybody wants. They want that bridge from on-prem to the public cloud, across clouds, going to to be moving out to the edge. And that is to your point, a journey that's going to evolve over the better part of this coming decade. Jo, great to see you. Thanks so much for coming on theCUBE today. >> Thanks for having me. (bright soft music) >> Okay, now we're going to into the GreenLake power panel to talk about the cloud landscape, hybrid cloud, and how the partner ecosystem and customers are thinking about cloud, hybrid cloud as a Service and of course, GreenLake. And with me are C.R. Howdyshell, President of Advizex. Ron Nemecek, who's the Business Alliance Manager at CBTS. Harry Zarek is President of Compugen. And Benjamin Klay is VP of Sales and Alliances at Arrow Electronics. Great to see you guys, thanks so much for coming on theCUBE. >> Thanks for having us. >> Good to be here. >> Okay, here's the deal. So I'm going to ask you guys each to introduce yourselves and your companies, add a little color to my brief intro, and then answer the following question. How do you and your customers think about hybrid cloud? And think about it in the context of where we are today and where we're going, not just the snapshot but where we are today and where we're going. C.R., why don't you start please? >> Sure, thanks a lot, Dave, appreciate it. And again, C.R. Howdyshell, President of Advizex. I've been with the company for 18 years, the last four years as president. So had the great opportunity here to lead a 45 year old company with a very strong brand and great culture. As it relates to Advizex and where we're headed to with hybrid cloud is it's a journey. So we're excited to be leading that journey for the company as well as HPE. We're very excited about where HPE is going with GreenLake. We believe it's a very strong solution when it comes to hybrid cloud. Have been an HPE partner since, well since 1980. So for 40 years, it's our longest standing OEM relationship. And we're really excited about where HPE is going with GreenLake. From a hybrid cloud perspective, we feel like we've been doing the hybrid cloud solutions, the past few years with everything that we've focused on from a VMware perspective. But now with where HPE is going, we think, probably changing the game. And it really comes down to giving customers that cloud experience with the on-prem solution with GreenLake. And we've had great response for customers and we think we're going to continue to see that kind of increased activity and reception. >> Great, thank you C.R., and yeah, I totally agree. It is a journey and we've seen it really come a long way in the last decade. Ron, I wonder if you could kickoff your little first intro there please. >> Sure Dave, thanks for having me today and it's a pleasure being here with all of you. My name is Ron Nemecek, I'm a Business Alliance manager at CBTS. In my role, I'm responsible for our HPE GreenLake relationship globally. I've enjoyed a 33 year career in the IT industry. I'm thankful for the opportunity to serve in multiple functional and senior leadership roles that have helped me gather a great deal of education and experience that could be used to aid our customers with their evolving needs, for business outcomes to best position them for sustainable and long-term success. I'm honored to be part of the CBTS and OnX Canada organization. CBTS stands for Consult Build Transform and Support. We have a 35 year relationship with HPE. We're a platinum and inner circle partner. We're headquartered in Cincinnati, Ohio. We service 3000 customers generating over a billion dollars in revenue and we have over 2000 associates across the globe. Our focus is partnering with our customers to deliver innovative solutions and business results through thought leadership. We drive this innovation via our team of the best and brightest technology professionals in the industry that have secured over 2,800 technical certifications, 260 specifically with HPE. And in our hybrid cloud business, we have clearly found that technology, new market demands for instant responses and experiences, evolving economic considerations with detailed financial evaluation, and of course the global pandemic, have challenged each of our customers across all industries to develop an optimal cloud strategy. We now play an enhanced strategic role for our customers as their technology advisor and their guide to the right mix of cloud experiences that will maximize their organizational success with predictable outcomes. Our conversations have really moved from product roadmaps and speeds and feeds to return on investment, return on capital, and financial statements, ratios, and metrics. We collaborate regularly with our customers at all levels and all departments to find an effective comprehensive cloud strategy for their workloads and applications ensuring proper alignment and cost with financial return. >> Great, thank you, Ron. Yeah, today it's all about the business value. Harry, please. >> Hi Dave, thanks for the opportunity and greetings from the Great White North. We're a Canadian-based company headquartered in Toronto with offices across the country. We've been in the tech industry for a very long time. We're what we would call a solution provider. How hard for my mother to understand what that means but what our goal is to help our customers realize the business value of their technology investments. Just to give you an example of what it is we try and do. We just finished a build out of a new networking endpoint and data center technology for a brand new hospital. It's now being mobilized for COVID high-risk patients. So talk about our all being in an essential industry, providing essential services across the whole spectrum of technology. Now, in terms of what's happening in the marketplace, our customers are confused. No question about it. They hear about cloud, I mean, cloud first, and everyone goes to the cloud, but the reality is there's lots of technology, lots of applications that actually still have to run on premises for a whole bunch of reasons. And what customers want is solid senior serious advice as to how they leverage what they already have in terms of their existing infrastructure, but modernize it, update it, so it looks and feels a lot like the cloud. But they have the security, they have the protection that they need to have for reasons that are dependent on their industry and business to allow them to run on-prem. And so, the GreenLake philosophy is perfect. That allows customers to actually have one foot in the cloud, one foot in their traditional data center but modernize it so it actually looks like one enterprise entity. And it's that kind of flexibility that gives us an opportunity collectively, ourselves, our partners, HPE, to really demonstrate that we understand how to optimize the use of technology across all of the business applications they need to run. >> You know Harry, it's interesting about what you said is, the cloud it is kind of chaotic my word, not yours. But there is a lot of confusion out there, I mean, what's cloud, right? Is it public cloud, is it private cloud, the hybrid cloud? Now, it's the edge and of course the answer is all of the above. Ben, what's your perspective on all this? >> From a cloud perspective, you know, I think as an industry, I think we we've all accepted that public cloud is not necessarily going to win the day and we're in fact, in a hybrid world. There's certainly been some commentary and press that was sort of validate that. Not that it necessarily needs any validation but I think is the linkages between on-prem and cloud-based services have increased. It's paved the way for customers more effectively, deploy hybrid solutions in in the model that they want or that they desire. You know, Harry was commenting on that a moment ago. As the trend continues, it becomes much easier for solution providers and service providers to drive their services initiatives, you know, in particular managed services. >> From an Arrow perspective is we think about how we can help scale in particular from a GreenLake perspective. We've got the ability to stand up some cloud capabilities through our ArrowSphere platform that can really help customers adopt GreenLake and to benefit from some alliances opportunities, as well. And I'll talk more about that as we go through. >> And Ben, I didn't mean to squeeze you on Arrow. I mean, Arrow has been around longer than computers. I mean, if you Google the history of Arrow it'll blow your mind, but give us a little quick commercial. >> Yeah, absolutely. So I've been with Arrow for about 20 years. I've got responsibility for Alliance organization in North America, We're a global value added distribution, business consulting and channel enablement company. And we bring scope, scale and and expertise as it relates to the IT industry. I love the fast pace that comes with the market that we're all in. And I love helping customers and suppliers both, be positioned for long-term success. And you know, the subject matter here today is just a great example of that. So I'm happy to be here and look forward to the discussion. >> All right, we got some good brain power in the room. Let's cut right to the chase. Ron, where's the pain? What are the main problems that CBTS I love what it stands for, Consult Build Transform and Support. What's the main pain point that customers are asking you to solve when it comes to their cloud strategies? >> Sure, Dave. Our customers' concerns and associated risks come from the market demands to deliver their products, services, and experiences instantaneously. And then the challenge is how do they meet those demands because they have aging infrastructure, processes, and fiscal constraints. Our customers really need us now more than ever to be excellent listeners so we can collaborate on an effective map with the strategic placement of workloads and applications in that spectrum of cloud experiences while managing their costs, and of course, mitigating risks to their business. This collaboration with our customers, often identify significant costs that have to be evaluated, justified or eliminated. We find significant development, migration, and egress charges in their current public cloud experience, coupled with significant over provisioning, maintenance, operational, and stranded asset costs in their on-premise infrastructure environment. When we look at all these costs holistically, through our customized workshops and assessments, we can identify the optimal cloud experience for the respective workloads and applications. Through our partnership with HPE and the availability of the HPE GreenLake solutions, our customers now have a choice to deliver SLA's, economics, and business outcomes for their workloads and applications that best reside on-premise in a private cloud and have that experience. This is a rock solid solution that eliminates, the development costs that they experience and the egress charges that are associated with the public cloud while utilizing HPE GreenLake to eliminate over provisioning costs and the maintenance costs on aging infrastructure hardware. Lastly, our customers only have to pay for actual infrastructure usage with no upfront capital expense. And now, that achieves true utilization to cost economics, you know, with HPE GreenLake solutions from CBTS. >> I love focus on the business case, 'cause it's measurable and it's sort of follow the money. That's where the opportunity is. Okay, C.R., so question for you. Thinking about Advizex customers, how are they, are they leaning into GreenLake? What are they telling you is the business impact when they experience GreenLake? >> Well, I think it goes back to what Ron was talking about. We had to solve the business challenges first and so far, the reception's been positive. When I say that is customers are open. Everybody wants to, the C-suite wants to hear about cloud and hybrid cloud fits. But what we hear and what we're seeing from our customers is we're seeing more adoption from customers that it may be their first foot in, if you will, but as important, we're able to share other customers with our potentially new clients that say, what's the first thing that happens with regard to GreenLake? Well, number one, it works. It works as advertised and as-a-Service, that's a big step. There are a lot of people out there dabbling today but when you can say we have a proven solution it's working in our environment today, that's key. I think the second thing is,, is flexibility. You know, when customers are looking for this hybrid solution, you got to be flexible for, again, I think Ron said (indistinct). You don't have a big capital outlay but also what customers want to be able to do is we want to build for growth but we don't want to pay for it. So we'll pay as we grow not as we have to use, as we used to do, it was upfront, the capital expenditure. Now we'll just pay as we grow, and that really facilitates in another great example as you'll hear from a customer, this afternoon. But you'll hear where one of the biggest benefits they just acquired a $570 million company and their integration is going to be very seamless because of their investment in GreenLake. They're looking at the flexibility to add to GreenLake as a big opportunity to integrate for acquisitions. And finally is really, we see, it really brings the cloud experience and as-a-Service to our customers. And with HPE GreenLake, it brings the best of breed. So it's not just what HPE has to offer. When you look at Hyperconverged, they have Nutanix, they have Cohesity. So, I really believe it brings best of breeds. So, to net it out and close it out with our customers, thus far, the customer experience has been exceptional. I mean, with GreenLake Central, as interface, customers have had a lot of success. We just had our first customer from about a year and a half ago just reopened, it was a highly competitive situation, but they just said, look, it's proven, it works, and it gives us that cloud experience so. Had a lot of great success thus far and looking forward to more. >> Thank you, so Harry, I want to pick up on something C.R. said and get your perspectives. So when I talk to the C-suite, they do all want to hear about, you know, cloud, they have a cloud agenda. And what they tell me is it's not just about their IT transformation. They want that but they also want to transform their business. So I wonder if you could talk, Harry, about Compugen's perspective on the potential business impact of GreenLake. And also, I'm interested in how you guys are thinking about workloads, how to manage work, you know, how to cost optimize in IT, but also, the business value that comes out of that capability. >> Yeah, so Dave, you know if you were to talk to CFO and I have the good fortune to talk to lots of CFOs, they want to pay the costs when they generate the revenue. They don't want to have all the costs upfront and then wait for the revenue to come through. A good example of where that's happening right now is you know, related to the pandemic, employees that used to work at the office have now moved to working from home. And now, they have to connect remotely to run the same application. So use this thing called VDI, virtual interfacing to allow them to connect to the applications that they need to run in the office. I don't want to get into too much detail but to be able to support that from an an at-home environment, they needed to buy a lot more computing capacity to handle this. Now, there's an expectation that hopefully six months from now, maybe sooner than that, people will start returning to the office. They may not need that capacity so they can turn down on the costs. And so, the idea of having the capacity available when you need it, but then turning it off when you don't need it, is really a benefit of the variable cost model. Another example that I would use is one in new development. If a customer is going to implement a new, let's say, line of business application. SAP is very very popular. You know, it actually, unfortunately, takes six months to two years to actually get that application set up, installed, validated, tested, then moves through production. You know, what used to happen before? They would buy all that capacity upfront, and it would basically sit there for two years, and then when they finally went to full production, then they were really value out of that investment. But they actually lost a couple of years of technology, literally sitting almost sidle. And so, from a CFO perspective, his ability to support the development of those applications as he scales it, perfect. GreenLake is the ideal solution that allows him to do that. >> You know, technology has saved businesses in this pandemic. There's no question about it. When Harry was just talking about with regard to VDI, you think about that, there's the dialing up and dialing down piece which is awesome from an IT perspective. And then the business impact there is the productivity of the end users. And most C-suite executives I've talked to said productivity actually went up during COVID with work from home, which is kind of astounding if you think about it. Ben, we said Arrow's been around for a long, long time. Certainly, before all of us were born and it's gone through many many industry transitions during our lifetimes. How does Arrow and how do your partners think about building cloud experiences and where does GreenLake fit in from your perspective? >> Great question. So from an Arrow perspective, when you think about cloud experience in of course us taking a view as a distribution partner, we want to be able to provide scale and efficiency to our network of partners. So we do that through our ArrowSphere platform. Just a bit of, you know, a bit of a commercial. I mean, you get single quote, single bill, auto provision, multi supplier, if you will, subscription management, utilization reporting from the platform itself. So if we pivot that directly to HPE, you're going to get a bit of a scoop here, Dave. And we're excited today to have GreenLake live in our platform available for our partner community to consume. In particular, the Swift solutions that HPE has announced so we're very excited to share that today. Maybe a little bit more on GreenLake. I think at this point in time, that it's differentiated in a sense that, if you think about some of the other offerings in the market today and further with having the the solutions themselves available in ArrowSphere. So, I would say, that we identify the uniqueness and quickly partner with HPE to work with our ArrowSphere platform. One other sort of unique thing is, when you think about platform itself, you've got to give a consistent experience. The different geographies around the world so, you know, we're available in North of 20 countries, there's thousands of resellers and transacting on the platform on a regular basis. And frankly, hundreds of thousands end customers. that are leveraging today. So that creates an opportunity for both Arrow, HPE and our partner community. So we're excited. >> You know, I just want to open it up. We don't have much time left, but thoughts on differentiation. Some people ask me, okay, what's really different about HPE and GreenLake? These others, you know, are doing things with as-a-Service. To me, I always say cultural, it starts from the top with Antonio, and it's like the company's all in. But I wonder from your perspectives, 'cause you guys are hands on. Are there other differentiable factors that you would point to? Let me just open that up to the group. >> Yeah, if I could make a comment. GreenLake is really just the latest invocation of the as-a-Service model. And what does that mean? What that actually means is you have a continuous ongoing relationship with the customer. It's not a sell and forget. Not that we ever forget about customers but there are highlights. Customer buys, it gets installed, and then for two or three years you may have an occasional engagement with them but it's not continuous. When you move to our GreenLake model, you're actually helping them manage that. You are in the core, in the heart of their business. No better place to be if you want to be sticky and you want to be relevant and you want to be always there for them. >> You know, I wonder if somebody else could add to it in your remarks. From your perspective as a partner, 'cause you know, hey, a lot of people made a lot of money selling boxes, but those days are pretty much gone. I mean, you have to transform into a services mindset, but other thoughts? >> I think to add to that Dave. I think Harry's right on. The way he positioned it it's exactly where he did own the customer. I think even another step back for us is, we're able to have the business conversation without leading with what you just said. You don't have to leave with a storage solution, you don't have to lead with compute. You know, you can really have step back, have a business conversation. And we've done that where you don't even bring up HPE GreenLake until you get to the point where the customer says, so you can give me an on-prem cloud solution that gives me scalability, flexibility, all the things you're talking about. How does that work? Then you bring up, it's all through this HPE GreenLake tool. And it really gives you the ability to have a business conversation. And you're solving the business problems versus trying to have a technology conversation. And to me, that's clear differentiation for HPE GreenLake. >> All right guys, C.R., Ron, Harry, Ben. Great discussion, thank you so much for coming on the program. Really appreciate it. >> Thanks for having us, Dave. >> Appreciate it Dave. >> All right, keep it right there for more great content at GreenLake Day, be right back. (bright soft music) (upbeat music) (upbeat electronic music)

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital experience Brought to you by Dell Technologies. Hello, everyone. And welcome back to the cubes Coverage of Del Tech World 2020. With me is Jeff Boudreau, the president general manager of Infrastructure Solutions group Deltek. Jeff, always good to see you, my friend. How you doing? >>Good. Good to see you. >>I wish we were hanging out a Sox game or a pat's game, but, uh, I guess this will dio But, you know, it was about a year ago when you took over leadership of I s G. I actually had way had that sort of brief conversation. You were in the room with Jeff Clark. I thought it was a great, great choice. How you doing? How you feeling Any sort of key moments the past 12 months that you you feel like sharing? >>Sure. So I first I want to say, I do remember that about a year ago. So thank you for reminding me. Yeah, it's, uh it's been a very interesting year, right? It's been it's been one year. It was in September was one year since I took over I s G. But I'm feeling great. So thank you for asking. I hope you're doing the same. And I'm really optimistic about where we are and where we're heading. Aziz, you know, it's been an extremely challenging year in a very unpredictable year, as we've all experienced. And I'd say for the, you know, the first part of the year, especially starting in March on I've been really focused on the health and safety of our, you know, the families, our customers and our team members of the team on a lot of it's been shifting, you know, in regards to helping our customers around, you know, work from home or education and learn from home. And, you know, during all this time, though, I'll tell you, as a team, we've accomplished a lot. There's a handful of things that I'm very proud of, you know, first and foremost, that states around the customer experience we have delivered on our best quality in our product. NPS scores in our entire history. So something I'm extremely proud of during this time around our innovation and innovation engine, we part of the entire portfolio which you're well aware of. We had nine launches in nine weeks back in that May in June. Timeframe. So something I'm really proud of the team on, uh, on. Then last, I'd say it's around the team and right, we shifted about 90% of our workforce from the office tow home, you know, from an engineering team. That could be, you know, 85% of my team is engineers and writing code. And so, you know, people were concerned about that. But we didn't skip a beat, so, you know, pretty impressed by the team and what they've done there. So, you know, the strategy remains unchanged. Uh, you know, we're focused on our customers integrating across the entire portfolio and the businesses like VM ware and really focused on getting share. So despite all the uncertainty in the market, I'm pretty pleased with the team and everything that's been going on. So uh, yeah, it's it's been it's been an interesting year, but it's really great. I'm really optimistic about what we have in front of us. >>Yeah, I mean, there's not much you could do a control about the macro condition on it, you know it. Z dealt to us and we have to deal with it. I mean, in your space. It's the sort of countervailing things here one is. Look, you're not selling laptops and endpoint security. That's not your business right in the data center. Eso. But the flip side of that is you mentioned your portfolio refresh. You know, things like Power Store. You got product cycles now kicking in. So that could be, you know, a buffer. What are you seeing with Power Store and what's the uptake look like? They're >>sure. Well, specifically, let me take a step back and the regards the portfolio. So first, you know, the portfolio itself is a direct reflection in the feedback from all our partners and our customers over the last couple of years on Day two, ramp up that innovation. I spent a lot of time in the last few years simplifying under the power brands, which you're well aware of, right? So we had a lot of for a legacy EMC and Legacy dollars. Really? How do we simplify under a set of brands really over delivering innovation on a fewer set of products that really accelerating in exceeding customer needs? And we did that across the board. So from power edge servers, you know, power Max, the high end storage, the Powerball, all that we didn't hear one. And just most recently. And, you know, it's part of the big launches. We had power scale. We have power flex for software to find. And, of course, the new flagship offer for the mid range, which is power store. Um, Specifically, the policy of the momentum has been building since our launch back in May. And the feedback from our partners and our customers has been fantastic. And we've had a lot of big wins against, you know, a lot of a lot of our core competitors. A couple examples one is Arrow Electronics SAA, Fortune 500 Global Elektronik supplier. They leverage power Store to provide, you know, basically both, you know, enterprise computing and storage needs for their for their broader bases around the world on there, really taking advantage of the 41 data reduction, really helping them simplify their capacity planning and really improve operational efficiencies specifically without impacting performance. So it's it's one. We're given the data reductions, but there's no impact on performance, which is a huge value proffer for arrow another big customers tickets and write a global law firm on their reporting to us that over 90 they've had a 90% reduction in their rack space, and they've had over five times two performance over a core competitors storage systems azi. They've deployed power store around the world, really, and it's really been helping them. Thio easily migrate workloads across, so the feedback from the customers and partners has been extremely positive. Um, there really citing benefits around the architecture, the flexibility architecture around the micro services, the containers they're loving, the D M or integration. They're loving the height of the predictable data reduction capabilities in line with in line performance or no performance penalties with data efficiencies, the workload support, I'd say the other big things around the anytime upgrades is another big thing that customers we're really talking about so very excited and optimistic in regards as we continue to re empower store the second half of the year into next year really is the full full year for power store. >>So can I ask you about that? That in line data reduction with no performance hit is that new ipe? I mean, you're not doing some kind of batch data reduction, right? >>No, it's It's new, I p. It's all patented. We've actually done a lot of work in regards to our technologies. There's some of the things we talk about GPS and deep use and smart Knicks and things like that. We've used some offload engines to help with that. So between the software and the hardware, we've had leverage new I. P. So we can actually provide that predictable data reduction. But right with the performance customers need, So we're not gonna have a trade off in regards. You get more efficiencies and less performance or more performance and less efficiency. >>That's interesting. Yeah, when I talked to the chip guys, they talk about this sort of the storage offloads and other offloads we're seeing. These alternative processors really start to hit the market videos. The obvious one. But you're seeing others. Aziz. Well, you're really it sounds like you're taking advantage of that. >>Yeah, it's a huge benefit. I mean, we should, you know, with our partners, if it's Intel's and in videos and folks like that broad comes, it's really leveraging the great innovation that they do, plus our innovation. So if you know the sum of the parts, can you know equal Mauritz a benefit to our customers in the other day? That's what it's all about. >>So it sounds like Cove. It hasn't changed your strategy. I was talking toe Dennis Hoffman and he was saying, Look, you know, fundamentally, we're executing on the same strategy. You know, tactically, there's things that we do differently. But what's your summarize your strategy coming in tow 2021. You know, we're still early in this decade. What are you seeing is the trends that you're trying to take advantage of? What do you excited about? Maybe some things that keep you up at night? >>Yeah, so I'd say, you know, I'll stay with what Dennis said. You know, it's our strategy is not changing its a company. You probably got that from Michael and from job, obviously, Dennis just recently. But for me, it's a two pronged approach. One's all about winning the consolidation in the core infrastructure markets that we could just paid in today. So I think Service Storage Network, we're already clear leader across all those segments that we serve in our you know, we'll continue to innovate within our existing product categories. And you saw that with the nine launches in the nine weeks in my point on that one is we're gonna always make sure that we have best debris offers. If it's a three tier, two tier or converge or hyper converged offer, we wanna make sure that we serve that and have the best innovation possible. In addition to that, though, the secondary piece of the strategy really is around. How do we differentiate value across or innovating across I S G? You know, Dell Technologies and even the broader ecosystems and some of the examples I'll give you right now that we're doing is if you think about innovating across icy, that's all about providing improved customer experience, a set of solutions and offers that really helped simplify customer operations, right? And really give them better T CEOs or better. S L. A. An example of something like that's cloud like it's a SAS based off of that we have. That really helps provide great insights and telemetry to our customers. That helps them simplify their I T operations, and it's a major step forward towards, you know, autonomous infrastructure which is really what they're asking for. Customers of a very happy with the work we've done around Day one, you know, faster, time to value. But now it's like Day two and beyond. How do you really helped me Kinda accelerate the operations and really take that away from a three other big pieces innovating across all technologies. And you know, we do this with VM Ware now live today, and that's just writing. So things like VX rail is an example where we work together and where the clear leader in H C I. Things like Delta Cloud Uh, when we built in V M V C F A, B, M or cloud foundation in Tan Xue delivering an industry leading hybrid cloud platform just recently a VM world. I'm sure you heard about it, but Project Monterey was just announced, and that's an effort we're doing with VM Ware and some other partners. They're really about the next generation of infrastructure. Um, you know, I guess taking it up a notch out of the infrastructure and I've g phase, you know, some of the areas that we're gonna be looking at the end to end solutions to help our customers around six key areas. I'm sure John Rose talking about the past, but things like cloud Edge five g A i m l data management security. So those will be the big things. You'll see us lean into a Z strategies consistent. Some big themes that you'll see us lean into going into next year. >>Yeah, I mean, it is consistent, right? You guys have always tried to ride the waves, vector your portfolio into those waves and add value. I'm particularly impressed with your focus on customer experience, and I think that's a huge deal. You know, in the past, a lot of companies yours included your predecessor. You see, Hey, throwing so many products at me, I can't I don't understand the portfolio. So I mean, focusing on that I think is huge right now because people want that experience, you know, to be mawr cloudlike. And that's that's what you got to deliver. What about any news from from Dell Tech world? Any any announcements that you you wanna highlight that we could talk about? >>Sure. And actually, just touching back on the point you had no about the simplification that is a major 10 of my in regards the organization. So there's three key components that I drive once around customer focus, and that's keeping customers first and foremost. And everything we do to is around axillary that innovation. Engine three is really bringing everything together as one team. So we provide a better outcome to our customers. You know, in that simplification after that you talk about is court toe what we're driving. So I want to do less things, I guess better in the notion of how we do that. What that means to me is, as I make decisions that want to move away from other technologies and really leverage our best of breed type shared type, that's technology. I p people I p I can, you know, e can exceed customer needs in those markets that were serving. So it's actually allows me to x Sorry, my innovation engine, because I shift more and more resource is onto the newer stock now for Del Tech world. Yes, We got some cool stuff coming. You probably heard about a few of them. Uh, we're gonna be announcing a project project Apex. Hopefully you've been briefed on that already. This isn't new news or I'll be in trouble. But that's really around. Our strategy about delivering, simple, consistent as a service experiences for our customers bringing together are dealt technology as a service offering and our cloud strategy together. Onda also our technology offerings in our go to market all under a single unified effort, which Ellison do would be leading. Um, you know, on behalf of our executive leadership team s, that's one big area. And there is also another big one that I'll talk about a sui expand our as a service offers. And we think there's a big power to that in regards to our Dell Technologies. Cloud console solving will be launching a new cloud console that will provide uniformed experience across all the resources and give users and ability toe instantly managed every aspect of their cloud journey with just a few clicks. So going back to your broader point, it's all about simplicity. >>Yeah, we definitely all over Apex. That's something I wanted to ask you about this notion of as a service, really requiring it could have a new mindset, certainly from a pricing and how you talk about the customer experience that it's a whole new customer experience. Your you're basically giving them access. Thio What I would consider more of a platform on giving them some greater flexibility. Yeah, there's some constraints in there, but of course, you know the physical only put so much capacity and before him. But the idea of being ableto dial up, dial down within certain commitments is, I think, a powerful one. How does it change the way in which you you think about how you go about developing products just in terms of you know, this AP economy Infrastructure is code. How how you converse about those products internally and externally. How would you see that shaking >>out Dave? That's an awesome question. And it's actually for its front center. For everything we do, obviously, customers one choice and flexibility what they do. And to your point as we evolved warm or as a service, no specific product and product brands and logos on probably the way of the future. It's the services. It's the experience that you provide in regards to how we do that. So if you think about me, you know, in in infrastructure making infrastructure as a service, you really want to define what that customer experiences. That s L. A. That they're trying toe realize. And then how do we make sure that we build the right solutions? Products feature functions to enable that a law that goes back to the core engineering stuff that we need to dio right now, a lot of that stuff is about making sure that we have the right things around. If it's around developer community. If it's around AP rich, it's around. SdK is it's all about how do we leverage if it's internal source or external open source, if you will. It's regards to How do we do that? No. A thing that I think we all you know what you're well aware but we ought to keep in mind is that the cloud native applications are really relevant. Toe both the on premises, wealthy off premise. So think about things around portability reusability. You know, those are some great examples of just kind of how we think about this as we go forward. But those modern applications were required modern infrastructure, and regardless of how that infrastructure is abstracted now, just think about things like this. Aggregation or compose ability or Internet based computing. It's just it's a huge trend that we have to make sure we're thinking of. So is we. We just aggregate between the physical layers to the software layers and how we provide that to a service that could be think of a modern container based asset that could be repurposed. Either could be on a purpose built thing. It could be deployed in a converge or hyper converged. Or it could be two points a software feature in a cloud. Now, that's really how we're thinking about that, regards that we go forward. So we're talking about building modern assets or components That could be you right once we used many type model, and we can deploy that wherever you want because of some of the abstraction of desegregation that we're gonna do. >>E could see customers in the in the near term saying, I don't care so much about the product. I want the fast one all right with the cheaper one e. >>It's kind of what you talking about, that I talked about the ways. If you think about that regards, you know, maybe it's on a specific brand or portfolio. You look into and you say, Hey, what's the service level that I'd wanted to your point like Hey, for compute or for storage, it's really gonna end up being the specific S l A. And that's around performance or Leighton see, or cost or resiliency they want. They want that experience in that that you know, And that's why they're gonna be looking for the end of the end state. That's what we have to deliver is an engineering. >>So there's an opportunity here for you guys that I wonder if you could comment on. And that's the storage admin E. M. C essentially created. You know, you get this army of people that you know pretty good of provisioning lungs, although that's not really that's a great career path for folks. But program ability is, and this notion of infrastructure is code as you as you make your systems more programmable. Is there a skill set opportunity to take that army of constituents that you guys helped train and grow and over their careers and bring them along into sort of the next decade? This new era? >>I think the the easy answer is yes, I obviously that's a hard thing to do and you go forward. But I think embracing the change in the evolution of change, I think is a great opportunity. And I think there is e mean if you look step back and you think about data management, right? And you think about all the you know all data is not created equal and you know, and it has a life cycle, if you will. And so if it's on edge to Korda, Cloward, depending think about data vaults and data mobility and all that stuff. There's gonna be a bunch of different personas and people touching data along the way. I think the I T advance and the storage admin. They're just one of those personas that we have to help serve and way talk about How do we make them heroes, if you will, in regards to their broader environment. So if they're providing, if they evolve and really helped provide a modern infrastructure that really enables, you know infrastructure is a code or infrastructure as a service, they become a nightie hero, if you will for the rest of team. So I think there's a huge opportunity for them to evolve as the technology evolves. >>Yeah, you talked about you know, your families, your employees, your team s o. You obviously focused on them. You got your products going hitting all the marks. How are you spending your time these days? >>Thes days right now? Well, we're in. We're in our cycle for fiscal 22 planning. Right? And right now, a lot of that's above the specific markets were serving. It's gonna be about the strategy and making sure that we have people focused on those things. So it really comes back to some of the strategy tents were driving for next year. Now, as I said, our focus big time. Well, I guess for the for this year is one is consolidation of the core markets. Major focus for May 2 is going to be around winning in storage, and I want to be very specific. It's winning midrange storage. And that was one of the big reasons why Power Store came. That's gonna be a big focus on Bennett's really making sure that we're delivering on the as a service stuff that we just talked about in regards to all the technology innovation that's required to really provide the customer experience. And then, lastly, it's making sure that we take advantage of some of these growth factors. So you're going to see a dentist. Probably talked a lot about Telco, but telco on edge and as a service and cloud those things, they're just gonna be key to everything I do. So if you think about from poor infrastructure to some of these emerging opportunities Z, I'm spending all my time. >>Well, it's a It's a big business and a really important one for Fidel. Jeff Boudreau. Thanks so much for coming back in the Cube. Really a pleasure seeing you. I hope we can see each other face to face soon. >>You too. Thank you for having me. >>You're very welcome. And thank you for watching everybody keep it right there. This is Dave Volonte for the Cube. Our continuing coverage of Del Tech World 2020. We'll be right back right after this short break

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

>> Jeff: Hello everybody and thank you for joining us today for the Virtual VERTICA BDC 2020. Today's breakout session has been titled, "VERTICA Database Designer Today and Tomorrow." I'm Jeff Healey, Product VERTICA Marketing, I'll be your host for this breakout session. Joining me today is Yuanzhe Bei, Senior Technical Manager from VERTICA Engineering. But before we begin, (clearing throat) I encourage you to submit questions or comments during the virtual session. You don't have to wait, just type your question or comment in the question box below the slides and click Submit. As always, there will be a Q&A session at the end of the presentation. We'll answer as many questions, as we're able to during that time, any questions we don't address, we'll do our best to answer them offline. Alternatively, visit VERTICA forums at forum.vertica.com to post your questions there after the session. Our engineering team is planning to join the forums, to keep the conversation going. Also, a reminder that you can maximize your screen by clicking the double arrow button at the lower right corner of the slides. And yes, this virtual session is being recorded and will be available to view on demand this week. We will send you a notification as soon as it's ready. Now let's get started. Over to you Yuanzhe. >> Yuanzhe: Thanks Jeff. Hi everyone, my name is Yuanzhe Bei, I'm a Senior Technical Manager at VERTICA Server RND Group. I run the query optimizer, catalog and the disaggregated engine team. Very glad to be here today, to talk about, the "VERTICA Database Designer Today and Tomorrow". This presentation will be organized as the following; I will first refresh some knowledge about, VERTICA fundamentals such as Tables and Projections, which will bring to the question, "What is Database Designer?" and "Why we need this tool?". Then I will take you through a deep dive, into a Database Designer or we call DBD, and see how DBD's internals works, after that I'll show you some exciting DBD improvements, we have planned for 10.0 release and lastly, I will share with you, some DBD future roadmap we planned next. As most of you should already know, VERTICA is built on a columnar architecture. That means, data is stored column wise. Here we can see a very simple example, of table with four columns, and the many of you may also know, table in VERTICA is a virtual concept. It's just a logical representation of data, which means user can write SQL query, to reference the table names and column, just like other relational database management system, but the actual physical storage of data, is called Projection. A Projection can reference a subset, or all of the columns all to its anchor table, and must be sorted by at least one column. Each table need at least one C for projection which reference all the columns to the table. If you load data to a table with no projection, and automated, auto production will be created, which will be arbitrarily assorted by, the first couple of columns in the table. As you can imagine, even though such other production, can be used to answer any query, the performance is not optimized in most cases. A common practice in VERTICA, is to create multiple projections, contain difference step of column, and sorted in different ways on the same table. When query is sent to the server, the optimizer will pick the projection, that can answer the query in the most efficient way. For example, here you can say, let's say you have a query, that select columns B, D, C and sorted by B and D, the third projection will be ideal, because the data is already sorted, so you can save the sorting costs while executing the query. Basically when you choose the design of the projection, you need to consider four things. First and foremost, of course the sort order. The data already sorted in the right way, can benefit quite a lot of the query actually, like Ordered by, Group By, Analytics, Merge, Join, Predicates and so on. The select column group is also important, because the projection must contain, all the columns referenced by your workflow query. Even missing one column in the projection, this projection cannot be used for a particular query. In addition, VERTICA is the distributed database, and allow projection to be segmented, based on the hash of a set of columns, which is beneficial if the segmentation merged, the join keys or group keys. And finally encoding of each per columns is also part of the design, because the data is sorted in different way, may completely change the optimal encoding for each column. This example only show the benefit of the first two, but you can imagine the rest too are also important. But even for that, it doesn't sound that hard, right? Well I hope you change your mind already when you see this, at least I do. These machine generated queries, really beats me. It will probably take an experienced DBA hours, to figure out which projection can be benefit these queries, not even mentioning there could be hundreds of such queries, in the regular work logs in the real world. So what can we do? That's why we need DBD. DBD is a tool integrated in the VERTICA server, that it can help DBA to perform an access, on their work log query, tabled schema and data, and then automatically figure out, the most optimized projection design for their workload. In addition, DBD also a sophisticated tool, that can take customize by a user, by sending a lot of parameters objectives and so on. And lastly, DBD has access to the optimizer, so DB knows what kind of attribute, the projection need to have, in order to have the optimizer to benefit from them. DBD has been there for years, and I'm sure there are plenty of materials available online, to show you how DBD can be used in different scenarios, whether to achieve the query optimize, or load optimize, whether it's the comprehensive design, or the incremental design, whether it's a dumping deployment script, and manual deployment later, or let the DBD do the order deployment for you, and the many other options. I'm not planning to talk about this today, instead, I will take the opportunity today, to open this black box DBD, and show you what exactly hide inside. DBD is a complex tool and I have tried my best to summarize the DBD design process into seven steps; Extract, Permute, Prune, Build, Score, Identify and Encode. What do they mean? Don't worry, I will show you step by step. The first step is Extract. Extract Interesting Columns. In this step, DBD pass the design queries, and figure out the operations that can be benefited, by the potential projection design, and extract the corresponding columns, as interesting columns. So Predicates, Group By, Order By, Joint Condition, and analytics are all interesting Column to the DBD. As you can see this three simple sample queries, DBD can extract the interest in column sets on the right. Some of these column sets are unordered. For example, the green one for Group By a1 and b1, the DBD extracts the interesting column set, and put them in the own orders set, because either data sorted by a1 first or b1 first, can benefit from this Group By operation. Some of the other sets are ordered, and the best example is here, order by clause a2 and b2, and obviously you cannot sort it by b2 and then a2. These interesting columns set will be used as if, to extend to actual projection sort order candidates. The next step is Permute, once DBD extract all the C's, it will enumerate sort order using C, and how does DBD do that? I'm starting with a very simple example. So here you can see DBD can enumerate two sort orders, by extending d1 with the unordered set a1, b1, and the derived at two sort order candidates, d1, a1, b1, and d1, b1, a1. This sort order can benefit queries with predicate on d1, and also benefit queries by Group By a1, b1, when a1, sorry when d1 is constant. So with the same idea, DBD will try to extend other States with each other, and populate more sort order permutations. You can imagine that how many of them, there could be many of them, these candidates, based on how many queries you have in the design and that can be handled of the sort order candidates. That comes to the third step, which is Pruning. This step is to limit the candidates sort order, so that the design won't be running forever. DBD uses very simple capping mechanism. It sorts all the, sort all the candidates, are ranked by length, and only a certain number of the sort order, with longest length, will be moved forward to the next step. And now we have all the sort orders candidate, that we want to try, but whether this sort order candidate, will be actually be benefit from the optimizer, DBD need to ask the optiizer. So this step before that happens, this step has to build those projection candidate, in the catalog. So this step will build, will generates the projection DBL's, surround the sort order, and create this projection in the catalog. These projections won't be loaded with real data, because that takes a lot of time, instead, DBD will copy over the statistic, on existing projections, to this projection candidates, so that the optimizer can use them. The next step is Score. Scoring with optimizer. Now projection candidates are built in the catalog. DBD can send a work log queries to optimizer, to generate a query plan. And then optimizer will return the query plan, DBD will go through the query plan, and investigate whether, there are certain benefits being achieved. The benefits list have been growing over time, when optimizer add more optimizations. Let's say in this case because the projection candidates, can be sorted by the b1 and a1, it is eligible for Group By Pipe benefit. Each benefit has a preset score. The overall benefit score of all design queries, will be aggregated and then recorded, for each projection candidate. We are almost there. Now we have all the total benefit score, for the projection candidates, we derived on the work log queries. Now the job is easy. You can just pick the sort order with the highest score as the winner. Here we have the winner d1, b1 and a1. Sometimes you need to find more winners, because the chosen winner may only benefit a subset, of the work log query you provided to the DBD. So in order to have the rest of the queries, to be also benefit, you need more projections. So in this case, DBD will go to the next iteration, and let's say in this case find to another winner, d1, c1, to benefit the work log queries, that cannot be benefit by d1, b1 and a1. The number of iterations and thus the winner outcome, DBD really depends on the design objective that uses that. It can be load optimized, which means that only one, super projection winner will be selected, or query optimized, where DBD try to create as many projections, to cover most of the work log queries, or somewhat balance an objective in the middle. The last step is to decide encoding, for each projection columns, for the projection winners. Because the data are sorted differently, the encoding benefits, can be very different from the existing projection. So choose the right projection encoding design, will save the disk footprint a significant factor. So it's worth the effort, to find out the best thing encoding. DBD picks the encoding, based on the actual sampling the data, and measure the storage footprint. For example, in this case, the projection winner has three columns, and say each column has a few encoding options. DBD will write the sample data in the way this projection is sorted, and then you can see with different encoding, the disk footprint is different. DBD will then compare the disk footprint of each, of different options for each column, and pick the best encoding options, based on the one that has the smallest storage footprint. Nothing magical here, but it just works pretty well. And basic that how DBD internal works, of course, I think we've heard it quite a lot. For example, I didn't mention how the DBD handles segmentation, but the idea is similar to analyze the sort order. But I hope this section gave you some basic idea, about DBD for today. So now let's talk about tomorrow. And here comes the exciting part. In version 10.0, we significantly improve the DBD in many ways. In this talk I will highlight four issues in old DBD and describe how the 10.0 version new DBD, will address those issues. The first issue is that a DBD API is too complex. In most situations, what user really want is very simple. My queries were slow yesterday, with the new or different projection can help speed it up? However, to answer a simple question like this using DBD, user will be very likely to have the documentation open on the side, because they have to go through it's whole complex flow, from creating a projection, run the design, get outputs and then create a design in the end. And that's not there yet, for each step, there are several functions user need to call in order. So adding these up, user need to write the quite long script with dozens of functions, it's just too complicated, and most of you may find it annoying. They either manually tune the projection to themselves, or simply live with the performance and come back, when it gets really slow again, and of course in most situations, they never come back to use the DBD. In 10.0 VERTICA support the new simplified API, to run DBD easily. There will be just one function designer_single_run and one argument, the interval that you think, your query was slow. In this case, user complained about it yesterday. So what does this user to need to do, is just specify one day, as argument and run it. The user don't need to provide anything else, because the DBD will look up his query or history, within that time window and automatically populate design, run design and export the projection design, and the clean up, no user intervention needed. No need to have the documentation on the side and carefully write a script, and a debug, just one function call. That's it. Very simple. So that must be pretty impressive, right? So now here comes to another issue. To fully utilize this single round function, users are encouraged to run DBD on the production cluster. However, in fact, VERTICA used to not recommend, to run a design on a production cluster. One of the reasons issue, is that DBD picks massive locks, both table locks and catalog locks, which will badly interfere the running workload, on a production cluster. As of 10.0, we eliminated all the table and ten catalog locks from DBD. Yes, we eliminate 100% of them, simple improvement, clear win. The third issue, which user may not be aware of, is that DBD writes intermediate result. into real VERTICA tables, the real DBD have to do that is, DBD is the background task. So the intermediate results, some user needs to monitor it, the progress of the DBD in concurrent session. For complex design, the intermediate result can be quite massive, and as a result, many lost files will be created, and written to the disk, and we should both stress, the catalog, and that the disk can slow down the design. For ER mode, it's even worse because, the table are shared on communal storage. So writing to the regular table, means that it has to upload the data, to the communal storage, which is even more expensive and disruptive. In 10.0, we significantly restructure the intermediate results buffer, and make this shared in memory data structure. Monitoring queries will go directly look up, in memory data structure, and go through the system table, and return the results. No Intermediate Results files will be written anymore. Another expensive lubidge of local disk for DBD is encoding design, as I mentioned earlier in the deep dive, to determine which encoding works the best for the new projection design, there's no magic way, but the DBD need to actually write down, the sample data to the disk, using the different encoding options, and to find out which ones have the smallest footprint, or pick it as the best choice. These written sample data will be useless after this, and it will be wiped out right away, and you can imagine this is a huge waste of the system resource. In 10.0 we improve this process. So instead of writing, the different encoded data on the disk, and then read the file size, DBD aggregate the data block size on-the-fly. The data block will not be written to the disk, so the overall encoding and design is more efficient and non-disruptive. Of course, this is just about the start. The reason why we put a significant amount of the resource on the improving the DBD in 10.0, is because the VERTICA DBD, as essential component of the out of box performance design campaign. To simply illustrate the timeline, we are now on the second step, where we significantly reduced, the running overhead of the DBD, so that user will no longer fear, to run DBD on their production cluster. Please be noted that as of 10.0, we haven't really started changing, how DBD design algorithm works, so that what we have discussed in the deep dive today, still holds. For the next phase of DBD, we will briefly make the design process smarter, and this will include better enumeration mechanism, so that the pruning is more intelligence rather than brutal, then that will result in better design quality, and also faster design. The longer term is to make DBD to achieve the automation. What entail automation and what I really mean is that, instead of having user to decide when to use DBD, until their query is slow, VERTICA have to know, detect this event, and have have DBD run automatically for users, and suggest the better projections design, if the existing projection is not good enough. Of course, there will be a lot of work that need to be done, before we can actually fully achieve the automation. But we are working on that. At the end of day, what the user really wants, is the fast database, right? And thank you for listening to my presentation. so I hope you find it useful. Now let's get ready for the Q&A.