>>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.

from the cube studios in Palo Alto in Boston bringing you data-driven insights from the cube and ETR this is breaking analysis with Dave vellante over the past decade cloudflare has built a Global Network that has the potential to become the fourth us-based hyperscale class cloud in our view the company is building a durable Revenue model with hooks into many important markets these include the more mature DDOS protection space to other growth sectors such as zero trust a serverless platform for application development and an increasing number of services such as database and object storage and other network services in essence cloudflare could be thought of as a giant distributed supercomputer that can connect multiple clouds and act as a highly efficient scheduling engine at scale its disruptive DNA is increasingly attracting novel startups and established Global firms alike looking for Reliable secure high performance low latency and more cost-effective alternatives to AWS and Legacy infrastructure Solutions hello and welcome to this week's wikibon Cube insights powered by ETR in this breaking analysis we initiate our deeper coverage of cloudflare we'll briefly explain our take on the company and its unique business model we'll then share some peer comparisons with both the financial snapshot and some fresh ETR survey data finally we'll share some examples of how we think cloudflare could be a disruptive force with a super cloud-like offering that in many respects is what multi-cloud should have been cloudflare has been on our peripheral radar Ben Thompson and many others have written about their disruptive business model and recently a breaking analysis follower who will remain anonymous emailed with some excellent insights on cloudflare that prompted us to initiate more detailed coverage let's first take a look at how cloudflare seize the world in terms of its view of a modern stack this is a graphic from cloudflare that shows a simple three-layer Stack comprising Storage and compute the lower level and application layer and the network and their key message is basically that the big four hyperscalers have replaced the on-prem leaders apps have been satisfied and that mess of network that you see and Security in the upper left can now be handled all by cloudflare and the stack can be rented via Opex versus requiring heavy capex investment so okay somewhat of a simplified view is those companies on the the left are you know not standing still and we're going to come back to that but cloudflare has done something quite amazing I mean it's been a while since we've invoked Russ hanneman of Silicon Valley Fame on breaking analysis but remember when he was in a meeting one of his first meetings if not the first with Richard Hendricks it was the whiz kid on the show Silicon Valley and hanneman said something like if you had a blank check and you could build anything in the world what would it be and Richard's answer was basically a new internet and that led to Pied Piper this peer-to-peer Network powered by decentralized devices and and iPhones and this amazing compression algorithm that enabled high-speed data movement and low latency uh up to no low latency access across the network well in a way that's what cloudflare has built its founding premise reimagined how the internet should be built with a consistent set of server infrastructure where each server had lots of cores lots of dram lots of cash fast ssds and plenty of network connectivity and bandwidth and well this picture makes it look like a bunch of dots and points of presence on a map which of course it is there's a software layer that enables cloudflare to efficiently allocate resources across this Global Network the company claims that it's Network utilization is in the 70 percent range and it has used its build out to enter the technology space from the bottoms up offering for example free tiers of services to users with multiple entry points on different services and selling then more services over time to a customer which of course drives up its average contract value and its lifetime value at the same time the company continues to innovate and add new services at a very rapid cloud-like Pace you can think of cloudflare's initial Market entry as like a lightweight Cisco as a service the company's CFO actually he uses that term he calls it that which really must tick off Cisco who of course has a massive portfolio and a dominant Market position now because it owns the network cloudflare is a marginal cost of adding new Services is very small and goes towards zero so it's able to get software like economics at scale despite all this infrastructure that's building out so it doesn't have to constantly face the increasing infrastructure tax snowflake for example doesn't own its own network infrastructure as it grows it relies on AWS or Azure gcp and and while it gives the company obvious advantages it doesn't have to build out its own network it also requires them to constantly pay the tax and negotiate with hyperscalers for better rental rates now as previously mentioned Cloud Fair cloudflare claims that its utilization is very high probably higher than the hyperscalers who can spin up servers that they can charge for underutilized customer capacity cloudflare also has excellent Network traffic data that it can use to its Advantage with its Analytics the company has been rapidly innovating Beyond its original Core Business adding as I said before serverless zero trust offerings it has announced a database it calls its database D1 that's pretty creative and it's announced an object store called R2 that is S3 minus one both from the alphabet and the numeric I.E minus the egress cost saying no egress cost that's their big claim to fame and they've made a lot of marketing noise around about that and of course they've promised in our a D2 database which of course is R2D2 RR they've launched a developer platform cloudflare can be thought of kind of like first of all a modern CDN they've got a simpler security model that's how they compete for example with z-scaler that brings uh they also bring VPN sd-wan and DDOS protection services that are that are part of the network and they're less expensive than AWS that's kind of their sort of go to market and messaging and value proposition and they're positioning themselves as a neutral Network that can connect across multiple clouds now to be clear unlike AWS in particular cloudflare is not well suited to lift and shift your traditional apps like for instance sap Hana you're not going to run that in on cloudflare's platform rather the company started by making websites more secure and faster and it flew under the radar and much in the same way that clay Christensen described the disruption in the steel industry if you've seen that where new entrants picked off the low margin rebar business then moved up the stack we've used that analogy in the semiconductor business with arm and and even China cloudflare is running a similar playbook in the cloud and in the network so in the early part of the last decade as aws's ascendancy was becoming more clear many of us started thinking about how and where firms could compete and add value as AWS is becoming so dominant so for instance take an industry Focus you could do things like data sharing with snowflake eventually you know uh popularized you could build on top of clouds again snowflake is doing that as are others you could build private clouds and of course connect to hybrid clouds but not many had the wherewithal and or the hutzpah to build out a Global Network that could serve as a connecting platform for cloud services cloudflare has traction in the market as it adds new services like zero trust and object store or database its Tam continues to grow here's a quick snapshot of cloudflare's financials relative to Z scalar which is both a competitor and a customer fastly which is a smaller CDN and Akamai a more mature CDN slash Edge platform cloudflare and fastly both reported earnings this past week Cloud Fair Cloud flare surpassed a billion dollar Revenue run rate but they gave tepid guidance and the stock got absolutely crushed today which is Friday but the company's business model is sound it's growing close to 50 annually it has sas-like gross margins in the mid to high 70s and it's it it's got a very strong balance sheet and a 13x revenue run rate multiple in fact it's Financial snapshot is quite close to that of z-scaler which is kind of interesting which zinc sailor of course doesn't own its own network that's a pure play software company fastly is much smaller and growing more slowly than cloudflare hence its lower multiple well Akamai as you can see is a more mature company but it's got a nice business now on its earnings call this week cloudflare announced that its head of sales was stepping down and the company has brought in a new leader to take the firm to five billion dollars in sales I think actually its current sales leader felt like hey you know my work is done here bring on somebody else to take it to the next level the company is promising to be free cash flow positive by the end of the year and is working hard toward its long-term financial model or so working towards sorry it's a long-term financial model with gross margin Targets in the mid 70s it's targeting 20 non-gaap operating margins so so solid you know very solid not like completely off the charts but you know very good and to our knowledge it has not committed to a long-term growth rate but at that sort of operating profit level you would like to see growth be consistently at least in the 20 range so they could at least be a rule of 40 company or perhaps even even five even higher if they're going to continue to command a premium valuation okay let's take a look at the ETR data ETR is very positive on cloudflare and has recently published a report on the company like many companies cloudflare is seeing an across the board slowdown in spending velocity we've reported on this quite extensively using the ETR data to quantify the degree to that Slowdown and on the data set with ETR we see that many customers they're shifting their spend to Flat spend you know plus or minus let's say you know single digits you know two three percent or even zero or in the market we're seeing a shift from paid to free tiers remember cloudflare offers a lot of free services as you're seeing customers maybe turn off the pay for a while and going with the freebie but we're also seeing some larger customers in the data and the fortune 1000 specifically they're actually spending more which was confirmed on cloudflare's earnings call they did say everything across the board was softer but they did also indicate that some of their larger customers are actually growing faster than their smaller customers and their churn is very very low here's a two-dimensional graphic we'd like to share this view a lot it's got Net score or spending momentum on the vertical axis and overlap or pervasiveness in the survey on the horizontal axis and this cut isolates three segments in the etrs taxonomy that cloudflare plays in Cloud security and networking now the table inserted in that upper left there shows the raw data which informs the position of each company in the dots with Net score in the ends listed in that rightmost column the red dotted line indicates a highly elevated Net score and finally we posted the breakdown those colors in the bottom right of cloudflare's Net score the lime green that's new adoptions the forest green is we're spending more six percent or more the gray is flat plus or minus uh five percent and you can see that the majority of customers you can see that's the majority of the customers that gray area the pink is we're spending Less in other words down six percent or worse and the bright red is churn which is minimal one percent very good indicator for for cloudflare what you do to get etr's proprietary Net score and they've done this for many many quarters so we have that time series data you subtract the Reds from the greens and that's Net score cloudflare is at 39 just under that magic red line now note that cloudflare and zscaler are right on top of each other Cisco has a dominant position on the x-axis that cloudflare and others are eyeing AWS is also dominant but note that its Net score is well above the red dotted line it's incredible Palo Alto networks is also very impressive it's got both a strong presence on the horizontal axis and it's got a Net score that's pretty comparable to cloudflare and z-scaler to much smaller companies Akamai is actually well positioned for a reasonably mature company and you can see fastly ATT Juniper and F5 have far less spending momentum on their platforms than does cloudflare but at least they are in positive Net score territory so what's going to be really interesting to see is whether cloudflare can continue to hold this momentum or even accelerate it as we've seen with some other clouds as it scales its Network and keeps adding more and more services cloudflare has a couple of potential strategic vectors that we want to talk about and it'll be going to be interesting to see how that plays out Now One path is to compete more directly as a Cloud Player offering secure access Edge services like firewall as a service and zero Trust Services like data loss prevention email security from its area one acquisition and other zero trust offerings as well as Network Services like routing and network connectivity this is The Sweet Spot of the company load balancing many others and then add in things like Object Store and database Services more Edge services in the future it might be telecom like services such as Network switching for offices so that's one route and cloudflare is clearly on that path more services more cohorts at innovating and and growing the company and bringing in more Revenue increasing acvs and and increasing long-term value and keeping retention high now the other Vector is what we're just going to refer to as super cloud as an enabler of cross-cloud infrastructure this is new value uh relative to the former Vector that we were just talking about now the title of this episode is what multi-cloud should have been meaning cloudflare could be the control plane providing a consistent experience across clouds one that is fast and secure at global scale now to give you Insight on this let's take a look at some of the comments made by Matthew Prince the CEO and co-founder of cloudflare cloudflare put its R2 Object Store into public beta this past May and I believe it's storing around a petabyte of data today I think that's what they said in their call here's what Prince said about that quote we are talking to very large companies about moving more and more of their stored objects to where we can store that with R2 and one of the benefits is not only can we help them save money on the egress fees but it allows them to then use those object stores or objects across any of the different Cloud platforms they're that they're using so by being that neutral third party we can let people adopt a little bit of Amazon a little bit of Microsoft a little bit of Google a little bit of SAS vendors and share that data across all those different places so what's interesting about this in the super cloud context is it suggests that customers could take the best of each Cloud to power their digital businesses I might like AWS for in redshift for my analytic database or I love Google's machine learning Microsoft's collaboration and I'd like a consistent way to connect those resources but of course he's strongly hinting and has made many public statements that aws's egress fees are a blocker to that vision now at a recent investor event Matthew Prince added some color to this concept when he talked about one metric of success being how much R2 capacity was consumed and how much they sold but perhaps a more interesting Benchmark is highlighted by the following statement that he made he said a completely different measure of success for R2 is Andy jassy says I'm sick and tired of these guys meaning cloudflare taking our objects away we're dropping our egress fees to zero I would be so excited because we've then unlocked the ability to be the network that interconnects the cloud together now of course it would be Adam solipski who would be saying that or maybe Andy Jesse you know still watching over AWS and I think it's highly unlikely that that's going to happen anytime soon and that of course but but in theory gets us closer to the super cloud value proposition and to further drive that point home and we're paraphrasing a little bit his comments here he said something the effect of quote customers need one consistent control plane across clouds and we are the neutral Network that can be consistent no matter which Cloud you're using interesting right that Prince sees the world that's similar to if not nearly identical to the concepts that the cube Community has been putting forth around supercloud now this vision is a ways off let's be real Prince even suggested that his initial vision of an application running across multiple clouds you know that's like super cloud Nirvana isn't what customers are doing today that's that's really hard to do and perhaps you know it's never going to happen but there's a little doubt that cloudflare could be and is positioning itself as that cross-cloud control plane it has the network economics and the business model levers to pull it's got an edge up on the competition at the edge pun intended cloudflare is the definition of Edge and it's distributed platform it's decentralized platform is much better suited for Edge workloads than these giant data centers that are you know set up to to try and handle that today the the hyperscalers are building out you know their Edge networks things like outposts you know going out to the edge and other local zones Etc now cloudflare is increasingly competitive to the hyperscalers and those traditional Stacks that it depositioned on an earlier slide that we showed but you know the likes of AWS and Dell and hpe and Cisco and those others they're not sitting in their hands they have a huge huge customer install bases and they are definitely a moving Target they're investing and they're building out their own Super clouds with really robust stacks as well let's face it it's going to take a decade or more for Enterprises to adopt a developer platform or a new database Cloud plus cloudflare's capabilities when compared to incumbent stacks and the hyperscalers is much less robust in these areas and even in storage you know despite all the great conversation that R2 generated and the buzz you take a specialist like Wasabi they're more mature they're more functional and they're way cheaper even than cloudflare so you know it's not a fake a complete that cloudflare is going to win in those markets but we love the disruption and if cloudflare wants to be the fourth us-based hyperscaler or join the the big four as the as the fifth if we put Alibaba in the mix it's got a lot of work to do in the ecosystem by its own admission as much to learn and is part of the value by the way that it sees in its area one acquisition it's email security company that it bought but even in that case much of the emphasis has been on reseller channels compare that to the AWS ecosystem which is not only a channel play but is as much an innovation flywheel filling gaps where companies like snowflake Thrive side by side with aws's data stores as well all the on-prem stacks are building hybrid connections to AWS and other clouds as a means of providing consistent experiences across clouds indeed many of them see what they call cross-cloud services or what we call super cloud hyper cloud or whatever you know Mega Cloud you want to call it we use super cloud they are really eyeing that opportunity so very few companies frankly are not going after that space but we're going to close with this cloudflare is one of those companies that's in a position to wake up each morning and ask who can we disrupt today and very few companies are in a position to disrupt the hyperscalers to the degree that cloudflare is and that my friends is going to be fascinating to watch unfold all right let's call it a wrap I want to thank Alex Meyerson who's on production and manages the podcast as well as Ken schiffman who's our newest addition to the Boston Studio Kristen Martin and Cheryl Knight help us get the word out on social media and in our newsletters and Rob Hof is our editor-in-chief over at silicon angle thank you to all remember all these episodes are available as podcasts wherever you listen all you're going to do is search breaking analysis podcasts I publish each week on wikibon.com and siliconangle.com you can email me at david.velante at siliconangle.com or DM me at divalante if you comment on my LinkedIn posts and please do check out etr.ai they got the best survey data in the Enterprise Tech business this is Dave vellante for the cube insights powered by ETR thank you very much for watching and we'll see you next time on breaking analysis

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

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

(upbeat music) >> Okay, welcome back everyone to theCUBE's coverage here at re:MARS. I'm John Furrier, host of theCUBE. It's the event where it's part of the "re:" series: re:MARS, re:Inforce, re:Invent. MARS stands for machine learning, automation, robotics, and space. And a lot of conversation is all about AI machine learning. This one's about AI and business transformation. We've got Stepan Pushkarev CTO, CEO, Co-Founder of Provectus. Welcome to theCUBE. And Russ Lamb, eCommerce Retail Data Engineering Lead at PepsiCo, customer story. Gentlemen, thanks for coming on theCUBE. >> Great to be here, John. >> Yeah, thanks for having us. >> I love the practical customer stories because it brings everything to life. This show is about the future, but it's got all the things we want, we love: machine learning, robotics, automation. If you're in DevOps, or you're in data engineering, this is the world of automation. So what's the relationship? You guys, you're a customer. Talk about the relationship between you guys. >> Sure, sure. Provectus as a whole is a professional services firm, premier, a AWS partner, specializing in machine learning, data, DevOps. PepsiCo is our customer, our marquee customer, lovely customer. So happy to jointly present at this re:Invent, sorry, re:MARS. Anyway, Russ... >> I made that mistake earlier, by the way, 'cause re:Invent's always on the tip of my tongue and re:MARS is just, I'm not used to it yet, but I'm getting there. Talk about what are you guys working together on? >> Well, I mean, we work with Provectus in a lot of ways. They really helped us get started within our e-commerce division with AWS, provided a lot of expertise in that regard and, you know, just hands-on experience. >> We were talking before we came on camera, you guys just had another talk and how it's all future and kind of get back to reality, Earth. >> Russ: Get back to Earth. >> If we're on earth still. We're not on Mars yet, or the moon. You know, AI's kind of got a future, but it does give a tell sign to what's coming, industrial change, full transformation, 'cause cloud does the back office. You got data centers. Now you've got cloud going to the edge with industrial spaces, the ultimate poster child of edge and automation safety. But at the end of the day, we're still in the real world. Now people got to run businesses. And I think, you know, having you here is interesting. So I have to ask you, you know, as you look at the technology, you got to see AI everywhere. And the theme here, to me, that I see is the inflection point driving all this future robotics change, that everyone's been waiting for by the way, but it's like been in movies and in novels, is the machine learning and AI as the tipping point. This is key. And now you're here integrating AI into your company. Tell us your story. >> Well, I think that every enterprise is going to need more machine learning, more, you know, AI or data science. And that's the journey that we're on right now. And we've come a long way in the past six years, particularly with our e-commerce division, it's a really data rich environment. So, you know, going from brick and mortar, you know, delivering to restaurants, vending machines and stuff, it's a whole different world when you're, people are ordering on Amazon every couple minutes, or seconds even, our products. But they, being able to track all that... >> Can you scope the problem statement and the opportunity? Because if I just kind of just, again, I'm not, you're in, it's your company, you're in the weeds, you're at the data, you're everything, But it just seems me, the world's now more integration, more different data sources. You've got suppliers, they have their different IT back ends. Some are in the cloud, some aren't in the cloud. This is, like, a hard problem when you want to bring data together. I mean, API certainly help, but can you scope the problem, and, like, what we're talking about here? >> Well, we've got so many different sources of data now, right? So we used to be relying on a couple of aggregators who would pull all this data for us and hand us an aggregated view of things. But now we're able to partner with different retailers and get detail, granular information about transactions, orders. And it's just changed the game, changed the landscape from just, like, getting a rough view, to seeing the nuts and bolts and, like, all the moving parts. >> Yeah, and you see in data engineering much more tied into like cloud scale. Then you got the data scientists, more the democratization application and enablement. So I got to ask, how did you guys connect? What was the problem statement? How did you guys, did you have smoke and fire? You came in solved the problem? Was it a growth thing? How did this, how did you guys connect as a customer with Provectus? >> Yeah, I can elaborate on that. So we were in the very beginning of that journey when there was, like, just a few people in this new startup, let's call it startup within PepsiCo. >> John: Yeah. >> Calling like a, it's not only e-commerce, it was a huge belief from the top management that it's going to bring tremendous value to the enterprise. So there was no single use case, "Hey, do this and you're going to get that." So it's a huge belief that e-commerce is the future. Some industry trends like from brand-centric to consumer-centric. So brand, product-centric. Amazon has the mission to build the most customer-centric customer company. And I believe that success, it gets a lot of enterprises are being influenced by that success. So I remember that time, PepsiCo had a huge belief. We started building just from scratch, figuring out what does the business need? What are the business use cases? We have not started with the IT. We have not started with this very complicated migrations, modernizations. >> John: So clean sheet of paper. >> Yeah. >> From scratch. >> From scratch. >> And so you got the green light. >> Yeah. >> And the leadership threw the holy water on that and said, "Hey, we'll do this."? >> That's exactly what happened. It was from the top down. The CEO kind of set aside the e-commerce vision as kind of being able to, in a rapidly evolving business place like e-commerce, it's a growing field. Not everybody's figured it out yet, but to be able to change quickly, right? The business needs to change quickly. The technology needs to change quickly. And that's what we're doing here. >> So this is interesting. A lot of companies don't have that, actually, luxury. I mean, it's still more fun because the tools are available now that all the hyper scales built on their own. I mean, back in the day, 10 years ago, they had to build it all, Facebook. You didn't know, I had people on here from Pinterest and other companies. They had to build all of that from scratch. Now cloud's here. So how did you guys do this? What was the playbook? Take us through the AI because it sounds like the AI is core, you know, belief principle of the whole entire system. What did you guys do? Take me through the journey there. >> Yeah. Beyond management decisions, strategic decisions that has been made as a separate startup, whatever- >> John: That's great. >> So some practical, tactical. So it may sound like a cliche, but it's a huge thing because I work with many enterprises and this, like, "center of excellence" that does a nice technology stuff and then looks for the budget on the different business units. It just doesn't go anywhere. It could take you forever to modernize. >> We call that the Game of Thrones environment. >> Yes. >> Yeah. Nothing ever gets done 'till it blows up at the end. >> Here, these guys, and I have to admit, I don't want to steal their thunder. I just want to emphasize it as an external person. These guys just made it so differently. >> John: Yeah. >> They even physically sat in a different office in a WeWork co-working and built that business from scratch. >> That's what Andy Jackson talked about two years ago. And if you look at some of the big successes on AWS, Capital One, all the big, Goldman Sachs. The leadership, real commitment, not like BS, like total commitment says, "Go." But enough rope to give you some room, right? >> Yeah. I think that's the thing is, there was always an IT presence, right, overseeing what we were doing within e-commerce, but we had a lot of freedoms to make design choices, technology choices, and really accelerate the business, focus on those use cases where we could make a big impact with a technology choice. >> Take me through the stages of the AI transformation. What are some of the use cases and specific tactics you guys executed on? >> Well, I think that the supply chain, which I think is a hot topic right now, but that was one use case where we're using, like, data real time, real time data to inform our sales projections and delivery logistics. But also our marketing return on investment, I feel like that was a really interesting, complex problem to solve using machine learning, Because there's so much data that we needed to process in terms of countries, territories, products, like where do you spend your limited marketing budget when you have so many choices, and, using machine learning, boil that all down to, you know, this is the optimal choice, right now. >> What were some of the challenges and how did you overcome them in the early days to get things set up, 'cause it takes a lot of energy to get it going, to get the models. What were some of the challenges and how did you overcome them? >> Well, I think some of it was expertise, right? Like having a partner like Provectus and Stepan really helped because they could guide us, Stepan could guide us, give his expertise and what he knows in terms of what he's seen to our budding and growing business. >> And what were the things that you guys saw that you contributed on? And was there anything new that you had to do together? >> Yeah, so yeah. First of all, just a very practical tip. Yes, start with the use cases. Clearly talk to the business and say, "Hey, these are the list of the use cases" and prioritize them. So not with IT, not with technology, not with the migration thing. Don't touch anything on legacy systems. Second, get data in. So you may have your legacy systems or some other third party systems that you work with. There's no AI without data. Get all the pipelines, get data. Quickly boat strap the data lake house. Put all the pipelines, all the governance in place. And yeah, literally took us three months to get up and running. And we started delivering first analytical reports. It's just to have something back to business and keep going. >> By the way, that's huge, speed. I mean, this is speed. You go back and had that baggage of IT and the old antiquated systems, you'd be dragging probably months. Right? >> It's years, years. Imagine you should migrate SAP to the cloud first. No, you don't do don't need to do that. >> Pipeline. >> Just get data. I need data. >> Stream that data. All right, where are we now? When did you guys start? I want to get just going to timeline my head 'cause I heard three months. Where are we now? You guys threw it. Now you have impact. You have, you have results. >> Yeah. I mean that for our marketing ROI engine, we've built it and it's developed within e-commerce, but we've started to spread it throughout the organization now. So it's not just about the digital and the e-commerce space. We're deploying it to, you know, regionally to other, to Europe, to Latin America, other divisions within PepsiCo. And it's just grown exponentially. >> So you have scale to it right now? >> Yeah. Well- >> How far are you in now? What, how many years, months, days? >> E-commerce, the division was created six years ago, which is, so we've had some time to develop this, our machine learning capabilities and this use case particular, but it's increasingly relevant and expansion is happening as we speak. >> What are you most proud of? You look back at the impact. What are you most proud of? >> I think the relationship we built with the people, you know, who use our technology, right. Just seeing the impact is what makes me proud. >> Can you give an example without revealing any confidential information? >> Yeah. Yeah. I mean, there was an example from my talk about, I was approached recently by our sales team. They were having difficulty with supply chain, monitoring our fill rate of our top brands with these retailers. And they come up to me, they have this problem. They're like, "How do we solve it?" So we work together to find a data source, just start getting that data in the hands of people who can use it within days. You know, not talking like a long time. Bring that data into our data warehouse, and then surface the data in a tool they can use, you know, within a matter of a week or two. >> I mean, the transformation is just incredible. In fact, we were talking on theCUBE earlier today around, you know, data warehouses in the cloud, data meshes of different pros and cons. And the theme that came out of that conversation was data's a product now. >> Yes. >> Yes. >> And what you're kind of describing is, just gimme the product or find it. >> Russ: Right. >> And bring it in with everything else. And there's some, you know, cleaning and stuff people do if they have issues with that. But, if not, it's just bring it in, right? It's a product. >> Well, especially with the data exchanges now. AWS has a data exchange and this, I think, is the future of data and what's possible with data because you don't have to start from, okay, I've got this Excel file somebody's been working with on their desktop. This is a, someone's taken that file, put it into a warehouse or a data model, and then they can share it with you. >> John: So are you happy with these guys? >> Absolutely, yeah. >> You're actually telling the story. What was the biggest impact that they did? Was it partnering? Was it writing code, bringing development in, counseling, all the above, managed services? What? >> I think the biggest impact was the idea, you know, like being able to bring ideas to the table and not just, you know, ask us what we want, right? Like I think Provectus is a true partner and was able to share that sort of expertise with us. >> You know, Andy Jackson, whenever I interview on theCUBE, he's now in charge of all Amazon. But when he was at (inaudible). He always had to use their learnings, get the learnings out. What was the learnings you look back now and say, Hey, those were tough times. We overcome them. We stopped, we started, we iterated, we kept moving forward. What was the big learning as you look back, some of the key success points, maybe some failures that you overcome. What was the big learnings that you could share with folks out there now that are in the same situation where they're saying, "Hey, I'd rather start from scratch and do a reset." >> Yeah. So with that in particular, yes, we started this like sort of startup within the enterprise, but now we've got to integrate, right? It's been six years and e-commerce is now sharing our data with the rest of the organization. How do we do that, right? There's an enterprise solution, and we've got this scrappy or, I mean, not scrappy anymore, but we've got our own, you know, way of doing. >> Kind of boot strap. I mean, you were kind of given charter. It's a start up within a big company, I mean- >> But our data platform now is robust, and it's one of the best I've seen. But how do we now get those systems to talk? And I think Provectus has came to us with, "Here, there's this idea called data mesh, where you can, you know, have these two independent platforms, but share the data in a centralized way. >> So you guys are obviously have a data mesh in place, big part of the architecture? >> So it is in progress, but we know the next step. So we know the next step. We know the next two steps, what we're going to do, what we need to do to make it really, to have that common method, data layer. between different data products within organization, different locations, different business units. So they can start talking to each other through the data and have specific escalates on the data. And yeah. >> It's smart because I think one of the things that people, I think, I'd love to get your reaction to this is that we've been telling the story for many, many years, you have horizontally scalable cloud and vertically specialized domain solutions, you need machine learning that's smart, but you need a lot of data to help it. And that's not, a new architecture, that's a data plane, it's control plane, but now everyone goes, "Okay, let's do silos." And they forget the scale side. And then they go, "Wait a minute." You know, "I'm not going to share it." And so you have this new debate of, and I want to own my own data. So the data layer becomes an interesting conversation. >> Yeah, yes. Meta data. >> Yeah. So what, how do you guys see that? Because this becomes a super important kind of decision point architecturally. >> I mean, my take is that there has to be some, there will always be domains, right? Everyone, like there's only so much that you can find commonality across, like in industry, for example. But there will always be a data owner. And, you know, kind of like what happened with rush to APIs, how that enabled microservices within applications and being sharing in a standardized way, I think something like that has to happen in the data space. So it's not a monolithic data warehouse, it's- >> You know, the other thing I want to ask you guys both, if you don't mind commenting while I got you here, 'cause you're both experts. >> We just did a showcase on data programmability. Kind of a radical idea, but like data as code, we called it. >> Oh yeah. >> And so if data's a product and you're acting on, you've got an architecture and system set up, you got to might code it's programmable. You need you're coding with data. Data becomes like a part of the development process. What do you guys think of when you hear data as code and data being programmable? >> Yeah, it's a interesting, so yeah, first of all, I think Russ can elaborate on that, Data engineering is also software engineering. Machine learning engineering is a software. At the end of the day, it's all product. So we can use different terms and buzz words for that but this is what we have at the end of the day. So having the data, well I will use another buzz word, but in terms of the headless architecture- >> Yes. >> When you have a nice SDK, nice API, but you can manipulate with the data as your programming object to build reach applications for your users, and give it, and share not as just a table in Redshift or a bunch of CSV files in S3 bucket, but share it as a programmable thing that you can work with. >> Data as code. >> Yeah. This is- >> Infrastructure code was a revolution for DevOps, but it's not AI Ops so it's something different. It's really it's data engineering. It's programming. >> Yeah. This is the way to deliver data to your consumers. So there are different ways you can show it on a dashboard. You can show it, you can expose it as an API, or you can give it as an object, programmable interface. >> So now you're set up with a data architecture that's extensible 'cause that's the goal. You don't want to foreclose. You must think about that must keep you up at night. What's going to foreclose that benefit? 'Cause there's more coming. Right? >> Absolutely. There's always more coming. And I think that's why it's important to have that robust data platform to work from. And yeah, as Stepan mentioned, I'm a big believer in data engineering as software engineering. It's not some like it's not completely separate. You have to follow the best practices software engineers practice. And, you know, really think about maintainability and scalability. >> You know, we were riffing about how cloud had the SRE managing all those servers. One person, data engineering has a many, a one to many relationships too. You got a lot going on. It's not managing a database. It's millions of data points and data opportunity. So gentlemen, thanks for coming on theCUBE. I really appreciate it. And thanks for telling the story of Pepsi. >> Of course, >> And great conversation. Congratulations on this great customer. And thanks for >> coming on theCUBE. >> Thanks, thank you. Thanks, Russ, would you like to wrap it up with the pantry shops story? >> Oh, yeah! I think it will just be a super relevant evidence of the agility and speed and some real world applicable >> Let's go. Close us out. >> So when, when the pandemic happened and there were lockdowns everywhere, people started buying things online. And we noticed this and got a challenge from our direct to consumer team saying, "Look, we need a storefront to be able to sell to our consumers, and we've got 30 days to do it." We need to be able to work fast. And so we built not just a website, but like everything that behind it, the logistics of supply chain aspects, the data platform. And we didn't just build one. We built two. We got pantry shop.com and snacks.com, within 30 days. >> Good domains! >> The domain broker was happy on that one. Well continue the story. >> Yeah, yeah. So I feel like that the agility that's required for that kind of thing and the like the planning to be able to scale from just, you know, an idea to something that people can use every day. And, and that's, I think.- >> And you know, that's a great point too, that shows if you're in the cloud, you're doing the work you're prepared for anything. The pandemic was the true test for who was ready because it was unforeseen force majeure. It was just like here it comes and the people who were in the cloud had that set up, could move quickly. The ones that couldn't. >> Exactly. >> We know what happened. >> And I would like to echo this. So they have built not just a website, they have built the whole business line within, and launched that successfully to production. That includes sales, marketing, supply chain, e-commerce, aside within 30 days. And that's just a role model that could be used by other enterprises. >> Yeah. And it was not possible without, first of all, right culture. And second, without cloud Amazon elasticity and all the tools that we have in place. >> Well, the right architecture allows for scale. That's the whole, I mean, you did everything right at the architecture that's scale. I mean, you're scaling. >> And we empower our engineers to make those choices, right. We're not, like, super bureaucratic where every decision has to be approved by the manager or the managers manager. The engineers have the power to just make good decisions, and that's how we move fast. >> That's exactly the future right there. And this is what it's all about. Reliability, scale agility, the ability to react and have applications roll out on top of it without long timeframes. Congratulations. Thanks for being on theCUBE. Appreciate it. All right. >> Thank you. >> Okay, you're watching theCUBE here at re:MARS 2020, I'm John Furrier. Stay tuned. We've got more coverage coming after this short break. (upbeat music)

>> From "theCUBE" studios in Palo Alto, in Boston, bringing you data driven insights from "theCUBE" and ETR. This is "Breaking Analysis" with Dave Vellante. >> There are very few political issues that get bipartisan support these days, nevermind consensus spanning geopolitical boundaries. But whether we're talking across the aisle or over the pond, there seems to be common agreement that the power of big tech firms should be regulated. But the government's track record when it comes to antitrust aimed at big tech is actually really mixed, mixed at best. History has shown that market forces rather than public policy have been much more effective at curbing monopoly power in the technology industry. Hello, and welcome to this week's "Wikibon CUBE" insights powered by ETR. In this "Breaking Analysis" we welcome in frequent "CUBE" contributor Dave Moschella, author and senior fellow at the Information Technology and Innovation Foundation. Dave, welcome, good to see you again. >> Hey, thanks Dave, good to be here. >> So you just recently published an article, we're going to bring it up here and I'll read the title, "Theory Aside, Antitrust Advocates Should Keep Their "Big Tech" Ambitions Narrow". And in this post you argue that big sweeping changes like breaking apart companies to moderate monopoly power in the tech industry have been ineffective compared to market forces, but you're not saying government shouldn't be involved rather you're suggesting that more targeted measures combined with market forces are the right answer. Can you maybe explain a little bit more the premise behind your research and some of your conclusions? >> Sure, and first let's go back to that title, when I said, theory aside, that is referring to a huge debate that's going on in global antitrust circles these days about whether antitrust should follow the traditional path of being invoked when there's real harm, demonstrable harm to consumers or a new theory that says that any sort of vast monopoly power inevitably will be bad for competition and consumers at some point, so your best to intervene now to avoid harms later. And that school, which was a very minor part of the antitrust world for many, many years is now quite ascendant and the debate goes on doesn't matter which side of that you're on the questions sort of there well, all right, well, if you're going to do something to take on big tech and clearly many politicians, regulators are sort of issuing to do something, what would you actually do? And what are the odds that that'll do more good than harm? And that was really the origins of the piece and trying to take a historical view of that. >> Yeah, I learned a new word, thank you. Neo-brandzian had to look it up, but basically you're saying that traditionally it was proving consumer harm versus being proactive about the possibility or likelihood of consumer harm. >> Correct, and that's a really big shift that a lot of traditional antitrust people strongly object to, but is now sort of the trendy and more send and view. >> Got it, okay, let's look a little deeper into the history of tech monopolies and government action and see what we can learn from that. We put together this slide that we can reference. It shows the three historical targets in the tech business and now the new ones. In 1969, the DOJ went after IBM, Big Blue and it's 13 years later, dropped its suit. And then in 1984 the government broke Ma Bell apart and in the late 1990s, went after Microsoft, I think it was 1998 in the Wintel monopoly. And recently in an interview with tech journalist, Kara Swisher, the FTC chair Lena Khan claimed that the government played a major role in moderating the power of tech giants historically. And I think she even specifically referenced Microsoft or maybe Kara did and basically said the industry and consumers from the dominance of companies like Microsoft. So Dave, let's briefly talk about and Kara by the way, didn't really challenge that, she kind of let it slide. But let's talk about each of these and test this concept a bit. Were the government actions in these instances necessary? What were the outcomes and the consequences? Maybe you could start with IBM and AT&T. >> Yeah, it's a big topic and there's a lot there and a lot of history, but I might just sort of introduce by saying for whatever reasons antitrust has been part of the entire information technology industry history from mainframe to the current period and that slide sort of gives you that. And the reasons for that are I think once that we sort of know the economies of scale, network effects, lock in safe choices, lot of things that explain it, but the good bit about that is we actually have so much history of this and we can at least see what's happened in the past and when you look at IBM and AT&T they both were massive antitrust cases. The one against IBM was dropped and it was dropped in as you say, in 1980. Well, what was going on in at that time, IBM was sort of considered invincible and unbeatable, but it was 1981 that the personal computer came around and within just a couple of years the world could see that the computing paradigm had change from main frames and minis to PCs lines client server and what have you. So IBM in just a couple of years went from being unbeatable, you can't compete with them, we have to break up with them to being incredibly vulnerable and in trouble and never fully recovered and is sort of a shell of what it once was. And so the market took care of that and no action was really necessary just by everybody thinking there was. The case of AT&T, they did act and they broke up the company and I would say, first question is, was that necessary? Well, lots of countries didn't do that and the reality is 1980 breaking it up into long distance and regional may have made some sense, but by the 1990 it was pretty clear that the telecom world was going to change dramatically from long distance and fixed wires services to internet services, data services, wireless services and all of these things that we're going to restructure the industry anyways. But AT& T one to me is very interesting because of the unintended consequences. And I would say that the main unintended consequence of that was America's competitiveness in telecommunications took a huge hit. And today, to this day telecommunications is dominated by European, Chinese and other firms. And the big American sort of players of the time AT&T which Western Electric became Lucent, Lucent is now owned by Nokia and is really out of it completely and most notably and compellingly Bell Labs, the Bell Labs once the world's most prominent research institution now also a shell of itself and as it was part of Lucent is also now owned by the Finnish company Nokia. So that restructuring greatly damaged America's core strength in telecommunications hardware and research and one can argue we've never recovered right through this 5IG today. So it's a very good example of the market taking care of, the big problem, but meddling leading to some unintended consequences that have hurt the American competitiveness and as we'll talk about, probably later, you can see some of that going on again today and in the past with Microsoft and Intel. >> Right, yeah, Bell Labs was an American gem, kind of like Xerox PARC and basically gone now. You mentioned Intel and Microsoft, Microsoft and Intel. As many people know, some young people don't, IBM unwillingly handed its monopoly to Intel and Microsoft by outsourcing the micro processor and operating system, respectively. Those two companies ended up with IBM ironically, agreeing to take OS2 which was its proprietary operating system and giving Intel, Microsoft Windows not realizing that its ability to dominate a new disruptive market like PCs and operating systems had been vaporized to your earlier point by the new Wintel ecosystem. Now Dave, the government wanted to break Microsoft apart and split its OS business from its application software, in the case of Intel, Intel only had one business. You pointed out microprocessors so it couldn't bust it up, but take us through the history here and the consequences of each. >> Well, the Microsoft one is sort of a classic because the antitrust case which was raging in the sort of mid nineties and 1998 when it finally ended, those were the very, once again, everybody said, Bill Gates was unstoppable, no one could compete with Microsoft they'd buy them, destroy them, predatory pricing, whatever they were accusing of the attacks on Netscape all these sort of things. But those the very years where it was becoming clear first that Microsoft basically missed the early big years of the internet and then again, later missed all the early years of the mobile phone business going back to BlackBerrys and pilots and all those sorts of things. So here we are the government making the case that this company is unstoppable and you can't compete with them the very moment they're entirely on the defensive. And therefore wasn't surprising that that suit eventually was dropped with some minor concessions about Microsoft making it a little bit easier for third parties to work with them and treating people a little bit more, even handling perfectly good things that they did. But again, the more market took care of the problem far more than the antitrust activities did. The Intel one is also interesting cause it's sort of like the AT& T one. On the one hand antitrust actions made Intel much more likely and in fact, required to work with AMD enough to keep that company in business and having AMD lowered prices for consumers certainly probably sped up innovation in the personal computer business and appeared to have a lot of benefits for those early years. But when you look at it from a longer point of view and particularly when look at it again from a global point of view you see that, wow, they not so clear because that very presence of AMD meant that there's a lot more pressure on Intel in terms of its pricing, its profitability, its flexibility and its volumes. All the things that have made it harder for them to A, compete with chips made in Taiwan, let alone build them in the United States and therefore that long term effect of essentially requiring Intel to allow AMD to exist has undermined Intel's position globally and arguably has undermined America's position in the long run. And certainly Intel today is far more vulnerable to an ARM and Invidia to other specialized chips to China, to Taiwan all of these things are going on out there, they're less capable of resisting that than they would've been otherwise. So, you thought we had some real benefits with AMD and lower prices for consumers, but the long term unintended consequences are arguably pretty bad. >> Yeah, that's why we recently wrote in Intel two "Strategic To Fail", we'll see, Okay. now we come to 2022 and there are five companies with anti-trust targets on their backs. Although Microsoft seems to be the least susceptible to US government ironically intervention at this this point, but maybe not and we show "The Cincos Comas Club" in a homage to Russ Hanneman of the show "Silicon Valley" Apple, Microsoft, Google, and Amazon all with trillion dollar plus valuations. But meta briefly crossed that threshold like Mr. Hanneman lost a comma and is now well under that market cap probably around five or 600 million, sorry, billion. But under serious fire nonetheless Dave, people often don't realize the immense monopoly power that IBM had which relatively speaking when measured its percent of industry revenue or profit dwarf that of any company in tech ever, but the industry is much smaller then, no internet, no cloud. Does it call for a different approach this time around? How should we think about these five companies their market power, the implications of government action and maybe what you suggested more narrow action versus broad sweeping changes. >> Yeah, and there's a lot there. I mean, if you go back to the old days IBM had what, 70% of the computer business globally and AT&T had 90% or so of the American telecom market. So market shares that today's players can only dream of. Intel and Microsoft had 90% of the personal computer market. And then you look at today the big five and as wealthy and as incredibly successful as they've been, you sort of have almost the argument that's wrong on the face of it. How can five companies all of which compete with each other to at least some degree, how can they all be monopolies? And the reality is they're not monopolies, they're all oligopolies that are very powerful firms, but none of them have an outright monopoly on anything. There are competitors in all the spaces that they're in and increasing and probably increasingly so. And so, yeah, I think people conflate the extraordinary success of the companies with this belief that therefore they are monopolist and I think they're far less so than those in the past. >> Great, all right, I want to do a quick drill down to cloud computing, it's a key component of digital business infrastructure in his book, "Seeing Digital", Dave Moschella coined a term the matrix or the key which is really referred to the key technology platforms on which people are going to build digital businesses. Dave, we joke you should have called it the metaverse you were way ahead of your time. But I want to look at this ETR chart, we show spending momentum or net score on the vertical access market share or pervasiveness in the dataset on the horizontal axis. We show this view a lot, we put a dotted line at the 40% mark which indicates highly elevated spending. And you can sort of see Microsoft in the upper right, it's so far up to the right it's hidden behind the January 22 and AWS is right there. Those two dominate the cloud far ahead of the pack including Google Cloud. Microsoft and to a lesser extent AWS they dominate in a lot of other businesses, productivity, collaboration, database, security, video conferencing. MarTech with LinkedIn PC software et cetera, et cetera, Googles or alphabets of business of course is ads and we don't have similar spending data on Apple and Facebook, but we know these companies dominate their respective business. But just to give you a sense of the magnitude of these companies, here's some financial data that's worth looking at briefly. The table ranks companies by market cap in trillions that's the second column and everyone in the club, but meta and each has revenue well over a hundred billion dollars, Amazon approaching half a trillion dollars in revenue. The operating income and cash positions are just mind boggling and the cash equivalents are comparable or well above the revenues of highly successful tech companies like Cisco, Dell, HPE, Oracle, and Salesforce. They're extremely profitable from an operating income standpoint with the clear exception of Amazon and we'll come back to that in a moment and we show the revenue multiples in the last column, Apple, Microsoft, and Google, just insane. Dave, there are other equally important metrics, CapX is one which kind of sets the stage for future scale and there are other measures. >> Yeah, including our research and development where those companies are spending hundreds of billions of dollars over the years. And I think it's easy to look at those numbers and just say, this doesn't seem right, how can any companies have so much and spend so much? But if you think of what they're actually doing, those companies are building out the digital infrastructure of essentially the entire world. And I remember once meeting some folks at Google, and they said, beyond AI, beyond Search, beyond Android, beyond all the specific things we do, the biggest thing we're actually doing is building a physical infrastructure that can deliver search results on any topic in microseconds and the physical capacity they built costs those sorts of money. And when people start saying, well, we should have lots and lots of smaller companies well, that sounds good, yeah, it's all right, but where are those companies going to get the money to build out what needs to be built out? And every country in the world is trying to build out its digital infrastructure and some are going to do it much better than others. >> I want to just come back to that chart on Amazon for a bit, notice their comparatively tiny operating profit as a percentage of revenue, Amazon is like Bezos giant lifestyle business, it's really never been that profitable like most retail. However, there's one other financial data point around Amazon's business that we want to share and this chart here shows Amazon's operating profit in the blue bars and AWS's in the orange. And the gray line is the percentage of Amazon's overall operating profit that comes from AWS. That's the right most access, so last quarter we were well over a hundred percent underscoring the power of AWS and the horrendous margins in retail. But AWS is essentially funding Amazon's entrance into new markets, whether it's grocery or movies, Bezos moves into space. Dave, a while back you collaborated with us and we asked our audience, what could disrupt Amazon? And we came up with your detailed help, a number of scenarios as shown here. And we asked the audience to rate the likelihood of each scenario in terms of its likelihood of disrupting Amazon with a 10 being highly likely on average the score was six with complacency, arrogance, blindness, you know, self-inflicted wounds really taking the top spot with 6.5. So Dave is breaking up Amazon the right formula in your view, why or why not? >> Yeah, there's a couple of things there. The first is sort of the irony that when people in the sort of regulatory world talk about the power of Amazon, they almost always talk about their power in consumer markets, whether it's books or retail or impact on malls or main street shops or whatever and as you say that they make very little money doing that. The interest people almost never look at the big cloud battle between Amazon, Microsoft and lesser extent Google, Alibaba others, even though that's where they're by far highest market share and pricing power and all those things are. So the regulatory focus is sort of weird, but you know, the consumer stuff obviously gets more appeal to the general public. But that survey you referred to me was interesting because one of the challenges I sort of sent myself I was like okay, well, if I'm going to say that IBM case, AT&T case, Microsoft's case in all those situations the market was the one that actually minimized the power of those firms and therefore the antitrust stuff wasn't really necessary. Well, how true is that going to be again, just cause it's been true in the past doesn't mean it's true now. So what are the possible scenarios over the 2020s that might make it all happen again? And so each of those were sort of questions that we put out to others, but the ones that to me by far are the most likely I mean, they have the traditional one of company cultures sort of getting fat and happy and all, that's always the case, but the more specific ones, first of all by far I think is China. You know, Amazon retail is a low margin business. It would be vulnerable if it didn't have the cloud profits behind it, but imagine a year from now two years from now trade tensions with China get worse and Christmas comes along and China just says, well, you know, American consumers if you want that new exercise bike or that new shoes or clothing, well, anything that we make well, actually that's not available on Amazon right now, but you can get that from Alibaba. And maybe in America that's a little more farfetched, but in many countries all over the world it's not farfetched at all. And so the retail divisions vulnerability to China just seems pretty obvious. Another possible disruption, Amazon has spent billions and billions with their warehouses and their robots and their automated inventory systems and all the efficiencies that they've done there, but you could argue that maybe someday that's not really necessary that you have Search which finds where a good is made and a logistical system that picks that up and delivers it to customers and why do you need all those warehouses anyways? So those are probably the two top one, but there are others. I mean, a lot of retailers as they get stronger online, maybe they start pulling back some of the premium products from Amazon and Amazon takes their cut of whatever 30% or so people might want to keep more of that in house. You see some of that going on today. So the idea that the Amazon is in vulnerable disruption is probably is wrong and as part of the work that I'm doing, as part of stuff that I do with Dave and SiliconANGLE is how's that true for the others too? What are the scenarios for Google or Apple or Microsoft and the scenarios are all there. And so, will these companies be disrupted as they have in the past? Well, you can't say for sure, but the scenarios are certainly plausible and I certainly wouldn't bet against it and that's what history tells us. And it could easily happen once again and therefore, the antitrust should at least be cautionary and humble and realize that maybe they don't need to act as much as they think. >> Yeah, now, one of the things that you mentioned in your piece was felt like narrow remedies, were more logical. So you're not arguing for totally Les Affaire you're pushing for remedies that are more targeted in scope. And while the EU just yesterday announced new rules to limit the power of tech companies and we showed the article, some comments here the regulators they took the social media to announce a victory and they had a press conference. I know you watched that it was sort of a back slapping fest. The comments however, that we've sort of listed here are mixed, some people applauded, but we saw many comments that were, hey, this is a horrible idea, this was rushed together. And these are going to result as you say in unintended consequences, but this is serious stuff they're talking about applying would appear to be to your point or your prescription more narrowly defined restrictions although a lot of them to any company with a market cap of more than 75 billion Euro or turnover of more than 77.5 billion Euro which is a lot of companies and imposing huge penalties for violations up to 20% of annual revenue for repeat offenders, wow. So again, you've taken a brief look at these developments, you watched the press conference, what do you make of this? This is an application of more narrow restrictions, but in your quick assessment did they get it right? >> Yeah, let's break that down a little bit, start a little bit of history again and then get to Europe because although big sweeping breakups of the type that were proposed for IBM, Microsoft and all weren't necessary that doesn't mean that the government didn't do some useful things because they did. In the case of IBM government forces in Europe and America basically required IBM to make it easier for companies to make peripherals type drives, disc drives, printers that worked with IBM mainframes. They made them un-bundle their software pricing that made it easier for database companies and others to sell their of products. With AT&T it was the government that required AT&T to actually allow other phones to connect to the network, something they argued at the time would destroy security or whatever that it was the government that required them to allow MCI the long distance carrier to connect to the AT network for local deliveries. And with that Microsoft and Intel the government required them to at least treat their suppliers more even handly in terms of pricing and policies and support and such things. So the lessons out there is the big stuff wasn't really necessary, but the little stuff actually helped a lot and I think you can see the scenarios and argue in the piece that there's little stuff that can be done today in all the cases for the big five, there are things that you might want to consider the companies aren't saints they take advantage of their power, they use it in ways that sometimes can be reigned in and make for better off overall. And so that's how it brings us to the European piece of it. And to me, the European piece is much more the bad scenario of doing too much than the wiser course of trying to be narrow and specific. What they've basically done is they have a whole long list of narrow things that they're all trying to do at once. So they want Amazon not to be able to share data about its selling partners and they want Apple to open up their app store and they don't want people Google to be able to share data across its different services, Android, Search, Mail or whatever. And they don't want Facebook to be able to, they want to force Facebook to open up to other messaging services. And they want to do all these things for all the big companies all of which are American, and they want to do all that starting next year. And to me that looks like a scenario of a lot of difficult problems done quickly all of which might have some value if done really, really well, but all of which have all kinds of risks for the unintended consequence we've talked before and therefore they seem to me being too much too soon and the sort of problems we've seen in the past and frankly to really say that, I mean, the Europeans would never have done this to the companies if they're European firms, they're doing this because they're all American firms and the sort of frustration of Americans dominance of the European tech industry has always been there going back to IBM, Microsoft, Intel, and all of them. But it's particularly strong now because the tech business is so big. And so I think the politics of this at a time where we're supposedly all this great unity of America and NATO and Europe in regards to Ukraine, having the Europeans essentially go after the most important American industry brings in the geopolitics in I think an unavoidable way. And I would think the story is going to get pretty tense over the next year or so and as you say, the Europeans think that they're taking massive actions, they think they're doing the right thing. They think this is the natural follow on to the GDPR stuff and even a bigger version of that and they think they have more to come and they see themselves as the people taming big tech not just within Europe, but for the world and absent any other rules that they may pull that off. I mean, GDPR has indeed spread despite all of its flaws. So the European thing which it doesn't necessarily get huge attention here in America is certainly getting attention around the world and I would think it would get more, even more going forward. >> And the caution there is US public policy makers, maybe they can provide, they will provide a tailwind maybe it's a blind spot for them and it could be a template like you say, just like GDPR. Okay, Dave, we got to leave it there. Thanks for coming on the program today, always appreciate your insight and your views, thank you. >> Hey, thanks a lot, Dave. >> All right, don't forget these episodes are all available as podcast, wherever you listen. All you got to do is search, "Breaking Analysis Podcast". Check out ETR website, etr.ai. We publish every week on wikibon.com and siliconangle.com. And you can email me david.vellante@siliconangle.com or DM me @davevellante. Comment on my LinkedIn post. This is Dave Vellante for Dave Michelle for "theCUBE Insights" powered by ETR. Have a great week, stay safe, be well and we'll see you next time. (slow tempo music)

(calm techno music) >> Hey, welcome to this Cube Conversation. I'm Lisa Martin. I've got two guests here with me. Please Welcome Philipp Niemietz, the intermediate head of the department for the Laboratory of Machine Tools and Production Engineering or WZL. Philipp, welcome to the program. >> Thank you. >> And we have Russ Caldwell here as well, senior product manager at Dell Technologies. Russ, great to see you. >> Thanks for the invite. >> Absolutely. We're going to be talking about how the enhanced video capabilities of Dell EMC's streaming data platform are enabling manufacturing, anomaly detection, and quality control through the use of sensors, cameras, and x-ray cameras. We're going to go ahead, Philipp, and start with you. We're abbreviating the lab as you guys do as WZL. Talk to us about the lab. What types of problems are you solving? >> Yeah, thank you. In the laboratory for machine tools, we are looking at actually all the other problems that arise in production engineering in general. So that's from the actual manufacturing of work pieces and that's getting used in aerospace or automotive industries, and really dig into the specifics of how those metal parts are manufactured, how they are formed, what are the mechanics of this. So this is a very traditional area where we are coming from. We're also looking at like how to manage all those production systems, how to come up with decision-making processes that's moving those engineering environments forward. But in our department, we recently get... 10 years ago... This Industry 4.0 scenario is getting more and more pushed into authentic research. So more and more data is gathered. We have to deal with a lot of data coming from various sources, and how to actually include this in the research, how to derive new findings from this, or even maybe, even physical equations from all the data that we are gathering around this manufacturing technologies. And this is something that we're, from the research perspective, looking at. >> And talk to me about when you were founded. You're based in Germany, but when was the lab founded? >> The lab was founded 100 years ago, about 100 years ago. It's like a very long history. It is the largest institute for production engineering in Germany, or maybe even in Europe. >> Got it. Okay. Well, 100 years. Amazing innovation that I'm sure the lab has seen. Russ, let's go over to you. Talk to us about the Dell EMC streaming data platform or SDP is what referred to it. >> Yeah. Thanks Lisa. So it's interesting that Philipp brings up Industry 4.0 because this is a prime area where the streaming data platform comes into play. Industry 4.0 for manufacturing really kind of encompasses a few things. It's real-time data analysis. It's automation, machine learning. SDP pulls all that together. So it's a software solution from Dell EMC. And one of the ways we make it all happen is we've unified this concept of time in data. Historical data and real-time data are typically analyzed very, very differently. And so we're trying to support Industry 4.0 manufacturing use cases. That's really important, right? Looking at historical data and real-time data, so you can learn from the past, work you've done on the factory floor, and apply that in real-time analytics. And the platform is used to ingest store and analyze data of this real-time and historical data. It leverages a high availability and dynamic scaling with Kubernetes. So that makes it possible to have lot different projects on the platform. And it really offers a lot of methods to automate this high speed and high precision activities that Philipp's talking about here. There's a lot of examples where it comes into play. It's really exciting to work with Philipp and the team there in Germany. But what's great about it is it's a general purpose platform that supports things like construction where they're doing drones with video ingestion, tracking resources on the ground, and things like that. Predictive maintenance and safety for amusement parks, and many other use cases. But with Industry 4.0 and manufacturing, RWTH and Philipp's team has really kind of pushed the boundaries of what's possible to automate and analyze data for the manufacturing process. >> What a great background. So we understand about the lab. We understand about Dell EMC SDP. Philipp, let's go back to you. How was the lab using this technology? >> Yeah, good question. Maybe, going a little bit back to the details of the use case that we are presenting. We started maybe five, six years ago where all this Industry 4.0 was put into research where you wanted to get more data out of the process now. So we started to apply a little census to the machine, starting with the more traditional ones, like energy consumption and some control information that we get from the machine tool itself. But the sensor system are quite like not that complex. And we could deal with the amount of data fairly easy now using just a USB sticks and some local devices, just a storage. But as it's getting more sophisticated, we're getting more sensor data. We're applying new sensor systems with the tool where the extra process is taking place, throughout the year, like delicious information is hidden. So we're getting really close to the process, applying video data, bigger data streams, more sensor data, and even like are not something like an IoT scenarios. We usually have some data points per second, but we're talking here about census that have like maybe a million data points a second now. So every high frequencies that we have to deal with, and of course, then we had to come up with some system that actually have to do this, help to deal with this data. And yeah, use the classic big data stack that we then set up for ourselves in our research facility to deal with this amount of streaming data to then apply historical analysis. Like Russ just talked about on this classic Hadoop data stack where we used Kafka and Storm for ingestion, and then for streaming processing, and Spark for this traditional historical analysis. And actually, this is exactly where the streaming data platform came into play because we had a meeting with one of the techy account at the university. And we were like talking about this. We were having a chat about this problem. And he's like, "Oh, we have something going on in America, in USA with this a streaming data platform. It was still under a code name or something." And then actually, Russ and I got into contact then talking about the streaming data platform, and how we could actually use it, and get getting part. We were taking part in the alpha program, really working with the system with the developers. And it was really an amazing experience. >> Were you having scale problems with the original kind of traditional big data platform that you talked about with Hadoop, Apache, Kafka, Spark? Was that scale issues, performance issues? Is that why you looked to Dell EMC? >> Yeah. There were several issues, like one is the scaling option now. And when we were not always using all of the sensors, we are just using some of the sensors. We're thinking about account process to different manufacturing technologies, different machines that we have in our laboratory so that we can quickly add sensors. They are shut down sensors. Do not have to take care about setting up new workers or stuff so that the work balance is handled. But that's not the only thing. We also had a lot of issues with administrating this Hadoop stacks. It's quite error prone if you do it yourself, like we are still in the university even though we are very big level laboratory. We still have limited resources. So we spend a lot of time dealing with the dev ops of the system. And actually, this is something where on the streaming data platform actually helped us to reduce the time that we invested into this administration processes. We were able to take more time into the analytics, which is actually what we are interested in. And specifically, the point that Russ talked about this unified concept of time, we now can just apply one and that type of analysis on historical and streaming data, and do not have to separate domains that we have to deal with. Now we dealt with Kafka, and Storm on one side, and Spark on the other side. And now, we can just put it into one model and actually reduce the time now to maintain and handle and implement the code. >> The time reduction is critical for the overall laboratory, the workforce productivity of the folks that are using it. Russ, let's go back to you. Tell us about, first of all, how long has the Dell EMC SDP been around? And what are some of the key features that WZL is leveraging that you're also seeing benefit other industries? >> So the product actually officially launched in early 2020. So in the first quarter of 2020. But what Philipp was just talking about, his organization was actually in the alpha and the beta programs earlier than that in 2019. And that's actually where we had a cross-section of very different kinds of companies in all sorts of industries all over the world; in Japan, and Germany, in the US. And that's where we started to see this pattern of commonality of challenges, and how we could solve those. So one of those things we mentioned that unified concept of time is really powerful because with one line of code, you can actually jump to any point on the timeline of your data, whether it's the real-time data coming off of the sensors right now or something minutes, hours, years ago. And so it's really, really powerful for the developers. But we saw the common challenges that Philipp was just talking about everywhere. So the SDP, one of the great things about it is it's a single piece of software that will install, manage, secure, upgrade, and be supported of all the components that you just heard Philipp talking about. So all the pieces for the ingestion, the storage and the analytics are all in there. And that makes it easier to focus on the problem there. There was other common challenges that our customers were seeing as well. Things like this concept of derived streams, so that you can actually bring in raw streams of data, leave it in its raw form because many times, regulatory reasons, audit reasons, you want to not touch that data. But you can create parallel streams of that data that are called derived streams that are versions that you've altered for some consumption or reporting purposes without affecting the others. And that's powerful when you have multiple teams analyzing different data. And then finally, the thing that Philipp mentioned we saw everywhere, which was a unified way to interact with sensors all the same way because there's sensors for IoT sensors, telemetry log files, video, X-ray, infrared, all sorts of things. But being able to simplify that so that the developers and the data scientists can really build models to solve a business problem was really where we started to focus on how we wanted to bring to market the value of SDP. >> So you launched this, right? And you said early 2020, right before the pandemic and all of the chaos that has- >> Don't recommend that by the way. Don't recommend launching into a pandemic. But yes. >> I'm sure that a lot of lessons learned from silver linings, I'm sure. >> That's right. >> But obviously, big challenges there. I'm curious thought if you thought. One of the things that we've learned from the pandemic is that for so many industries, the access to real-time data is no longer just a nice to have. It is a critical differentiator for those that needed to pivot multiple times to survive in the early days to thrive to continue pivoting. I'm curious, what other industries you saw Russ that came to you saying, "All right, guys. We've got challenges here. Help us figure this out."? Give me a snapshot of some of the other industries that were sort of leading Edge last year. >> Sure. There was some surprising ones. I've mentioned it a little bit, but it's interesting you give me a chance to talk about them. 'cause what was also shocking about this was not only that the same problems that I just mentioned happened in multiple industries. It was actually the prevalence of certain kinds of data. So for example, the construction example I gave you where a company was using drones to ingest streaming video as well as Telemetry of all the equipment on the ground. Drones are in all sorts of industries. So it turns out that's a pattern. But even a lower level than just drone data is actually video data or any kind of media data. And so Philipp talked about they're using that kind of data as well in manufacturing. We're seeing video data in every industry combined with other sensor data. And that's what's really surprised us in the beta program. So working with Philipp, we actually altered our roadmap after we launched to realize that we needed to escalate even more features about video analysis and actually be able to take the process even closer to the Edge where the data's being generated. So the other industries, including construction, logistics, medicine, network traffic, all sorts of data, that is a continuous unbounded stream of data falls into the category of being able to be analyzed, stored, playback like a DVR with SDP. >> Playback like a DVR. I like that. Philipp, back over to you. Talk to us about what's next. Obviously, a tremendous amount of innovation in the first 100 years of WZL. Talk to me about what some of the lab's plans are for the future from a streaming data perspective, got a great foundation infrastructure there with Dell EMC. What's next? >> Like we are working together with a large industry consortium, and then we get a lot of information. Not information, but they really want to see that all this big data stuff that's coming into Industry 4.0. And Russ already talked about it. And then, I'm pretty satisfied in having all the data and the data centers that they have, but they want to push it to the Edge. So all the analytics, it's getting more and more to the Edge because they see that the more data you gather, the more data has to be transferred via the network. So we have to come up with ways on, of course, deploy all the model on the Edge, maybe do some analytics on the Edge. I don't know, something like federated learning to see. Maybe you don't even need to transfer the data to the data center. You can start learning approaches on the Edge and combine them with different data sources that are actually sharing the data, which is the specific point in like corporations that want to corporate using the different data sources, but have some privacy issues. So this is something that we are looking into. And also, working like low-code or no-code environments, like different framework that we use here just in our laboratory, but this is also something that we see in the industry. And more and more people have to interact with the data management systems. So they have to somehow get a lower access point than just some pile from script that they need to write. Maybe, they just need drag and drop environment where they can modify some ingestion or some transformation to the data. So they're not always the people and all the data engineers or the computer science experts have to deal with those kind of stuff, and other people can do as well. So this is something that we are looking into this in the next future. But, yeah. But there are a lot of different things, and there's not enough time to talk about all of them. >> So it sounds like an idea to democratize that data to allow more data citizens to leverage that, analyze it and extract value from it because we all know data is oil, it's gold, but only if you can actually get those analysis quickly and make decisions that really affect and drive the business. Russ, last question for you. Talk to us about what you see next coming in the industry. Obviously, launching this technology at a very interesting time, a lot of things have changed in the last year. You've learned a lot. You said you modified the technology based on the WZL implementation. But what are some of the things that you see coming next? >> So it's really interesting 'cause my colleague at Dell constantly reminds me that people develop solutions with the technology they have at the time, right? It's a really obvious statement, but it's really powerful to realize what customers of ours have been doing so far. It's been based on batch tools and storage tools that were available at the time, but weren't necessarily the best match for the problem that we're trying to solve. And the world is moving completely to a real-time view of their data. If you can understand that answer sooner, there's higher value for higher revenue, lower costs, safety, all sorts of reasons, right? To do that, everyone's realizing you can't really count on... Like Philipp, he can't count on moving all the data somewhere else to make that decision, that latency; or sometimes, rules around controlling what data can go. Really, we'll keep it from that. So being able to move code closer to the data is where we see things are really happening. This is actually why the streaming data platform has really focused heavily on Edge implementations. We have SDP Core for the core data center. We also have SDP Edge that runs on single node in three node configurations for a headless environments for all sorts of use cases where you need to move the code and make the decisions right when the data is generated at the sensors. The other things we see happening in the industry that are really important is everything's moving to a fully software-defined solution. This idea of being able to have software-defined stream ingestion, analytics and storage. You can deploy the solution you want in the form factor that you have available at your location is important, right? And so, fully software-defined solutions is really going to be where things are at, and which gives you this kind of cloud-like experience, but you can deploy it anywhere at the Edge, Core or cloud, right? And that's really, really powerful. Philipp picked up on the one that we see a lot of this idea of low-code, no-code whether it's things like node red in the IoT world, where you're being able to stitch together a sequence of functions to answer questions in real time or other more sophisticated tools. That ability to, like you said, democratize what people can do with the data in real time is going to be extremely valuable as things move forward. And then the biggest thing we see that we're really focused on is we need to make it as easy as possible to ingest any kind of data. The more data types that you can bring in, the more problems you can solve. And so bringing on as many on-ramps and connectivity into other solutions is really, really important. And for all that, SDP's team is really focused on trying to prioritize the customers like Philipp's team in the RWTH WZL labs there. But finding those common patterns everywhere so that we can actually kind of make it the norm to be analyzing streaming data, not just historical batch data. >> Right. That's outstanding. As you said, the world is moving to real-time analytics. Real-time data ingestion is absolutely critical on there. Just think of the problems that we don't even know about that we could solve. Guys, thank you for joining me today, talking about what WZL is doing with the Dell EMC streaming data platform, and all the innovations you've done so far, and what's coming in the future. We'll have to catch up in the next six months or so, and see what great progress you've made. Thank you for your time. >> Thanks, Lisa. >> Thank you. >> For my guests, I'm Lisa Martin. You're watching a Cube Conversation. (calm techno music)

>> Narrator: From around the globe, It's the Cube. With digital coverage of AWS reinvent 2020. Sponsored by Intel, AWS, and our commudity partners. >> Okay, Welcome back. You're ready. Jeff Frick here with the cube. We are, coming to you from our Palo Alto studio with our continuing coverage of AWS reinvent 2020 digital this year, like everything in 2020 but we're excited to welcome back to The Cube. He's been on a number of times, he's Russ Currie. The vice president enterprise strategy for Netscout systems. Russ great to see you. >> Great to see you, Jeff. Thank you. >> Absolutely. So before we jump into there's so(laughs), so many things going on in 2020. What I do want to do is, is reflect back a little bit. You were first on The Cube at AWS reinvent 2017. So it's been about three years. And I remember, one of the lines you had said, I believe that was your guys' first, AWS show as well. So I wonder if you could reflect on kind of how the world has changed in terms of your business, and the importance of AWS and public cloud within the infrastructure systems of your clients. >> Yeah, well, it was interesting, right? We were just getting our feet wet at that point, and had just introduced some of our technology for use in AWS, and it was kind of a interesting little adventure. So we were looking at it and saying, okay where's this going to lead us? And ultimately now we're just really waist deep in it, and really having a great partnership with AWS, and delivering new technologies, new capabilities, and our customer base also is becoming so much more reliant on public cloud in particular AWS and the services that they can provide. So as we've gone and they've gone it's been a journey that we've taken together, and it's been quite, fruitful and exciting. >> Right, right. And it really reinforces this concept of I think you'd mentioned it before, a blended, you know kind of a blended infrastructure approach. So there's a lot of conversations about public cloud, hybrid cloud, multicloud, et cetera, et cetera. But at the, at the end of the day from a customer perspective, as you've mentioned it's really kind of a blended network, right. And it's really application centric, and you put the applications where those applications need to be to be the most appropriate, and that might even change over time from, from test dev to really roll out to, to scale. So you're seeing that consistency. Consistency, >> Absolutely. Yeah. The, the blended environment that in it it's so incredibly complex of our customers. As they take a look at the way that the world has changed, right? When we take a look at what has happened with people working remotely, working from home and having to come into access services in such a, a completely blended and hybrid environment as you say, not only the move to the cloud, but the move to Colo, and bringing all of this together for interconnect, it's definitely a complex environment that they have to have their fingers on the pulse of. Right? >> Yep, yep. And then of course there was this little thing that happened this year with COVID. And really right in March, April timeframe light switch moment, everybody worked from home, whether you're ready or not. And that was a very different kind of situation. Cause we had to get people secure and safe, and get them up and operating. So I'm sure you(laugh) saw a lot of interesting stuff at your business there, but I'm even more interested in how that's evolved over time. Here we are at the end of 2020, there's going to be you know, some version of this for the foreseeable future. And a lot of companies are saying that, you know there'll be a lot of, kind of work from anywhere pieces that continue forward. So again, with your customers and looking kind of the change between what happened in the spring, and now what's happening as they really of kind of put in the systems that'll enable them to continue to support, you know people working from anywhere, not even really working from home, but working from anywhere. >> Right. Exactly. I mean, as our customers had to bring up more connectivity, new connectivity, and start to add licenses for virtual desktop or for their VPN connectivity ultimately how they got it done, most of our customers said, you know we're running hot, but stable. And I think that that was, that was great for most folks. But now they're leaning into it and saying, okay how do we continue to make this happen? And how do we provide the visibility that we need to ensure that the services that we're delivering are, making it possible for their users to be productive and successful. A user doesn't want to feel that they're not contributing as much as someone else that may be able to make it into the office. And, it's a, it's a challenging time, but with that being said, technology has really stepped up, and in particular, the way that they're able to stand up services in the cloud, and the automation, and potential cost savings that they get from standing up in the cloud has really been a bood for most of our users. And some of the users, you know, the high end enterprise that we're a little bit slow to adopt, now are just turning it on as fast as they possibly can. >> Yeah, it's pretty wild. And then, we had another representative from Netscout on earlier this year. One of the, the kind of recurring themes that we've seen is you know, changes in the threat landscape. So clearly the increased attack surfaces as more and more people are working from home. They're not working from the secure environment at the office. But you guys notice some interesting things about what's happening, and we've, we've seen a little bit too in terms of kind of, ransomware and the increase in ransomware as a particular type of attack that, that seems to be growing in popularity. And these, these people are a little bit more thorough in the badness that they caused before they, they throw in the ransom request, and that they're looking for a little bit more fundamental disruption to enable them to basically extract that ransom is which they hope to do. >> Yeah. I mean the amount of DDoS attacks that we've seen has just grown incredibly over the past several months. And these extortion attacks they come in and they often hit the customer quickly and hard, and then say, turn it back for a bit and say, pay us, or we're going to shut you down. And they're really coming in more towards the back office aspects of things. So, going in and attacking that part of the business is kind of a new environment for a lot of folks. But one of the other interesting(laughs) challenges here with us is that, oftentimes those extortion notes don't make it through to the people that really need to act on them because they get caught in spam filters or they like so they're finding these DDoS attacks, and don't necessarily understand that they're under an extortion attack. So it's a real challenge for folks. And we've seen a good uptake with our on-prem capabilities to provide that kind of protection, right at the top of the security stack with our Arbor edge defense products. So it's been something that we're trying to get out there and help our customers as much as we can. And even that new, folks. >> Yeah. It's a, it's an interesting environment. And we found out from somebody too that sometimes if you actually pay the bad guys you can be breaking other rules for doing business with countries >> Yeah. >> Or people that we're not supposed to be doing business with. Like, that's the last thing you need to think about when you're trying to get all your data, and your company back online. >> Right, yeah, I mean, are you trying to make sure that you're keeping yourself stood up right? And, it's tough and you know kind of the rule one is never pay the extortion right? But you kind of got to take a look at it and say, hey, you know, what do I do? >> Right, right. So, you guys been around for a while. I wonder if we could dive in a little bit, we're at reinvent. Some of the things you guys are doing specifically on the product side to, basically increase your, your AWS capabilities. >> Sure. Thanks, yeah. We've been working really closely with AWS as they start to roll out new technologies. Last year, we were fundamental in the VPC ingress routing announcement that they have. We've been working with them with their traffic mirroring capabilities. So technology-wise, we keep in close touch with them in terms of everything that they are delivering. But also on the business side of it, we have our networking competency and just last week got our migration competency. So what we're really doing is, trying to both work the technical and the business relationship, as much as we can to try and expand our overall capabilities of book print with AWS. And, having that visibility and being able to kind of provide that same level of control and capability that you had, on-prem in your enterprise network as you move into the public cloud is a great benefit to a lot of our customers. They really have the ability now, to deliver services the way they have been delivering it for years and years. >> Now, what do you mean specifically, when you say migration competency or networking competency? >> So, they have these different competency programs for their technology partners. And the networking competency is, that you've demonstrated capabilities in your ability to provide network monitoring, or network management capabilities, or network connectivity. In the applica--, migration side you've really provided the ability to show that you have the tools, and solution set to drive, and help people become successful migrations into AWS. As you can imagine right now, a lot of folks are just lifting and shifting, putting stuff into AWS as quickly as they can to try and take advantage of the automation and the operational efficiencies that you get when you move into public cloud settings. As you make those migrations, you want to ensure that you're not either leaving something behind, that needed to move with it, or building a dependency onto something that's in the background that's going to have an adverse effect on, user experience. And ultimately, it really all comes down to the user experience that are, delivering to your customers and or your user base. Right? >> Right. Right. So what are the things you talked about in a prior interview was kind of the shifting dynamic in terms of network traffic. As there's more and more, you know kind of SAS based applications, and there's more kind of an application centric, and in this kind of API interface between all the applications that, you know the North-south is still significant, but the growth in the East-west traffic, meaning, you know kind of inside, if you will. And that some of the unique challenges that come from that from kind of a network monitoring. I wonder if you can share a little bit more color on that, as to, and are you continuing to see this increase in East West relative to North-south, and what kind of special opportunities and challenges that that presents? >> Yeah, absolu--. There is an absolute growth in terms of the East-west connectivity and, traffic that exists out there. In particular, when we take a look at the way that people are implementing software defined networks, NSX, for example NSXT has now provided the ability to blend your environment whether you're going to any cloud, any vendor as you move between these environments having that ability to deliver network services under the same framework is really beneficial to our customer base. And we've also been partnering very closely with VM-ware, and a lot of our customers are implementing VMware cloud on AWS. So, they have that ability to stand up services in a consistent manner whether it be in their legacy environments, or into the public cloud environments, and have that same ability to provide visibility down into the East-west traffic so that you can see that. So when you're part of the NSX framework, what you're able to do is really leverage the service framework that they have the service and search it, and be part of the clusters and host groups that are exchanging traffic East-west. And our ability to see into that really exposes chall--, not, exposes challenges but exposes potential issues that(laughs) our customers might be having in delivering high quality services. So that visibility is really what we've been keying on. >> Right. I'm just curious to get your take, you know as people kind of, as you said, make this move to public cloud, and, you know, you talked about wholesale migrations, and wholesale lifts and shifts. You know, there, there's kind of a couple trains of thought. One is, you know, using cloud for just pure economics, and trying to save money, and the flexibility. The second one is, is to is to add this automation as things grow in this, these great opportunities to automate, and try to reduce air. But the third one, right, the big one is to drive innovation, and to unlock innovation enable better innovation, and speed of delivery, and, you know, moving at the speed of business, pick your favorite buzzword. I'm curious whether your customers, as you have you seen them all jumping in? How much of it is still, you know, to save money or to, or to, you know, kind of use the basic, you know cost saving economics versus people really embracing the opportunity to use this as a method to drive innovation, and change within their own business? >> So I, I think the realities of 2020 have been forcing people to look at primarily from operational and cost efficiency perspectives, however with an eye towards innovation, and as they start to get themselves into a, zone where they're comfortable, they look to see how they can leverage the cloud to provide new services, and new ways in which they provide their services, and avail themselves of, the underlying technologies that are there to build something that's new and exciting in their overall portfolio. So, I think that 2021 is probably going to be a little bit more of where can I innovate as opposed to, how do I get there? (Jeff laughs) >> It's probably an unfair question here at 2020 cause priorities certainly got turned upside down in the middle of the year. So maybe, maybe innovation got pushed down a little bit from, you know, let's get people up, let's get people safe, and let's make sure they can access all the systems and all this crazy stuff that we've got available to them from wherever they are. >> Yeah, yeah. >> Not just within the, within the home office. >> I was listening to a, panel from federal government a couple of weeks ago, and it was really the way the they've adopted kind of commercial cha-- commercial capabilities to meet some of these challenges things that they wouldn't normally look at. But now it's a set of innovation that they're looking at, to try and make sure that they can avail themselves of the services that are out there and available in the public cloud. >> Yeah. Well, that's great, Russ. It's great to catch up. I'm sure you must be as amazed as anybody as the rapid acceleration of this, you know since the short time you went to your first re-invent and, >> Yeah. >> And clearly AWS and Amazon generally is an execution we're seeing. So, I think they'll keep doing it. So I think you're, you're probably sitting in a good spot. >> I think so. (Jeff laughs) Thank you. (Russ laughs) >> All right. Thank you Russ for, for stopping by and sharing your insight. Look forward to catching up next time. >> Thanks a lot, Jeff. Really appreciate it. >> Alrighty. He's Russ, I'm Jeff. You're watching The Cube's, continuous coverage of AWS reinvent 2020, the virtual event. Thanks for watching and we'll see you next time. (bright music)

hi everyone this is dave vellante of the cube and i want to share with you an exciting development with some financial support from hpe the cube is hosting exascale day on friday october 16th high performance technical and business communities are coming together to celebrate exascale day now exascale day is happening on october 18th that's 10 18 as in 10 to the power of 18. now on that day we celebrate the scientists and researchers who make breakthrough discoveries with the assistance of some of the largest supercomputers in the world 10 to the power of 18 is a 1 with 18 zeros after that's six commas or seis comas for you russ hannemann fans of silicon valley fame remember he could only get to tres comas and he became suicidal when his net worth dropped below a billion aka dos comas now an exit scale computer exascale supercomputer can do math at the rate of 10 to the power of 18 calculations per second those are those calculations are called flops or floating point operations per second that's a billion calculations per second or exa-flops now we haven't hit that level yet that exit scale level but dollars to donuts we'll buy we will by next year now today we can do header scale computing that's 10 to the power of 15 calculations per second and we entered the petascale era in 2007 before that was the terrascale era it's kind of like dinosaurs which began in the middle of the dot-com boom in 1997. that's 10 to the 12th calculations per second or trillion per second so we can almost get our heads around that and all the way back in 1972 we had the first gigascale computer which was one times ten to the ninth yeah that's more russ hannemann's speed sorry rush you're not invited to at the exascale day party but you are so go to events dot cube365.net slash 10-18 exascale day it's right there in the screen so check it out mark your calendar we'll be sending out notices so don't worry if you're driving right now we have some of the smartest people in the world joining us they're going to share how innovations with supercomputing are changing the world in healthcare space exploration artificial intelligence and these other mind-melting projects we're super excited to be participating in this program we look forward to some great conversations october 16th right before exascale day put on your calendar see you there

>>live from Barcelona, Spain. It's the Cube covering Cisco Live 2020 right to you by Cisco and its ecosystem partners. >>Live on Welcome to the Cube. 2020 is the first Cube segment and session for 2020 next 10 years. This is the 10th year the Cube has been in operation. We're here in Barcelona for Cisco Live, but we're going to spend the next few minutes talking about the Enterprise Tech trends for 2020 and beyond. Really looking back at the past 10 years and then forward 10 years, I'm John Furrier, host of The Cube with Dave Vellante, Stew Minimum and The Cube team. The analysts want to analyze Enterprise tech. You know we love to do that day, but I think more notable is this is our first interview in 2020 for the year. We're kicking off our 10th year as we close down the Cube for 10 year anniversary in May. Quite an evolution. A lot of things we got right with Wiki bond research and the Cuban sites. Ah, lot of things we saw early, and that's the benefit of the Cube. And now, more than ever, it's more complex. It's a lot of noise. A lot of people talking about value propositions here. They're a lot of cloud. I think the reality is set in Cloud is here. It's not a question of why and when it's now. And the impact is just hitting mainstream Tech and Enterprises now leading the category in investments, venture capital, private equity M and A over consumer companies seeing much more focused emphasis on what's going on in the enterprise, which is business. Incredible opportunity ahead. 2020. What's in store? >>Well, you know, the last decade we obviously saw the consumer ization of i t. There was all that social media hype, and I think you're right, John. The enterprises now where the action is. But the last 10 years have been all about Cloud. What got us here to 2020 is not what's going to power through the next 10 years. I think it's not only Cloud, it's cloud plus data, which we definitely bet on it, right. But now the injection of machine intelligence on that data, which, of course, is running in the cloud for scale. So the real big question now is what's gonna happen in the cloud guys Amazon and Azure and clearly have momentum. Google actually beginning to pick it up a little bit, but particularly the case of Amazon who dominated the last decade. Yeah, it's gonna be not as easy for them going forward, You know, everybody now realizes. Wow, they got it right. You said many times they were misunderstood. Well, I think now people are beginning to realize how powerful they are. And the enterprise players have really begun to respond. And they don't like to give up their position to be really interesting to see how that goes. And, of course, you know we're going to talk more about Cisco, but still what? Your thoughts? >>Yeah. So, John, I think some of the things that we looked at as bleeding edge over the last 10 years are becoming a bit more mainstream. The role of the developer. We know the developers, the new king maker. You look where we are in the DEV Net zone. Definite zone. A couple years ago was small and there was people were kind of exciting everything. You look at it today, it looks much more like the regular show. It has really become mainstream. Dave said Cloud Cloud is mainstream developers mainstream that connection between the business enterprise, tech talk about and these other pieces really coming together. That's where the data really is the next fly wheel for what's happening and obviously machine learning the application developer. And still, it's about moving faster that companies are looking to do, and that is what all of the last 10 years has been building for. And now it's the new normal >>great, and I want to get into some of the ways I think when you look at this, because we can always rattle into any kind of technology. But you know, one things that we love to do is look at the ways what waves are going to come where you get your thoughts on that. But I think just let's reflect on what's going on around us right now. The Cube is the 10th year finishing up its 10th year. We're in the media business had a comment from someone here, a distinguished engineer at Cisco said. I can't believe you guys are a technology company. I had tweeted out yesterday on Barcelona about our Cube alumni list. It's turning into an expert network If you look at what's going on with Facebook and with Trump and the impeachment, you're seeing a changing of the guard in the media business. So we as media with Cube, it's looking angle has become interesting, and I think I bring this up because that's kind of out our new model that we've been doing for 10 years. But if you look at how people share information, misinformation, quality information, you're starting to see a paradigm where you don't know what the trust vendor A says they could do this vendor b so they could do that. Amazon says. This Azure says that. So I think the practitioners and consumers of I t in Enterprise Tech, the buyer's Where's the truth day? I mean, the models are completely changing. I've heard comments in the analyst firms are struggling to get modern press outlets are being dwindled down to a handful in the enterprise that new networks are being formed. The expert APS are out there. So this is a tell sign, Yeah, that the world more complex and different than ever before. >>The authentic community doesn't lie right, And your peers at the other day when you have private conversations. That's where the truth comes out. To the extent that you can like to bring that to the Cuban sessions like this, that's really where you say, extract the signal from the noise. We try to do that. We try to do it for 10 years, and I think that's part of the reason why we've been so successful. But at the same time, Look way no were funded by sponsors, which is great. We really appreciate their support, but at the end of the day, we've always gotta put forth what we think is actually happening out there. >>Let's get into some of the ways because it sets the context. So as you have these networks forming, you have cloud technology. You know, Os, I model looks, but I look back at the nineties, and I think this is a proposed to the Cisco show at that time. Dave, During the mini computer wave that set the stage for what became the PC revolution and then ultimately inter networking category, you had proprietary network operating systems, IBM s and a digital equipment corporation deck net, etcetera, etcetera and incomes. The open systems interconnect seven layer stack that changed the industry. In today's world, we have open source, but people are chirping about open core. There seems to be a trend towards proprietary now. Amazon is the big proprietary cloud. >>I don't >>mean proprietary in the sense of you can work with it, but scale is the new proprietary. So you almost have this revert back to old tactics of differentiation, and I think that's not good for customers. I think you look at the customer situation, it creates more complexity. And so I think that's why we're seeing multi Cloud really be a trend, because whoever can connect all the clouds and do that seamlessly is going to win big. And I think that's a TCP I peed like Dynamic >>John. It's a really interesting point because open source in general is more important than ever before. Enterprise companies are contributing. The big vendor community is spending more time on open source than they are on standards anymore. Over. If you look at the big projects out there limits kubernetes like more than half of the contributors have full time jobs. They work for big companies, but as you said, how am I consuming that get hub is a company at the core of open source. But get the platform itself is a proprietary, that open core model that you talked about. And of course, Microsoft built them for a big number. And some people have a little bit concerned >>when might get Lab is there >>and get lab right. Of course, similar they deliver their application itself. Is that open core model so open source is there. Open core is the model that they're doing. Absolutely. It is interesting because, as you said, open source is more pervasive than ever. But I'm consuming it more as >>a service >>from Amazon or from these >>providers face to the bitching and moaning that's going on the open source because there is kind of a lot of chirping going on around. Well, you know, if I build this in the open, is it truly open being co opted by? So the big clouds and you got Microsoft Microsoft Open Office 3 65 That's not gonna go away for the next 10 years. They've SAS ified, their core offering almost like a lock in. I mean, so so it seems to be just >>it smells >>like that old nasty >>habit. Everything we're entering this decade with four trillion years of Amazon hit Trillionaire Club in 2018. Drop town lost Akamas, Russ Hanneman would say, But but Apple, Google, Microsoft and an Amazon they looking vulnerable, don't in the trillions club. But I mean, I would point out, You're saying John, there will be a backlash. Open source Open, open distributed computing tier networks. I don't think I mean history would suggest that these big whales, they're not invulnerable. They can be taken down and open. Source is is one way out? >>Well, it's interesting. One of the things you look at one of the big threat for Cisco for a long time was like, Oh, STN is going to take over what Cisco's doing Well, Cisco still doing just fine with software defined networking and what that having the open compute model for networking is also a threat. If I look at Microsoft, Azure is leveraging their model that the big hyper scaler aren't necessarily coming to Cisco for gear. They're shifting as to where Cisco will be involved. When we talk about cloud models, they're spending much more time up the stack. John in the layer four through seven, they are down in their traditional Vera to three. >>The pressure on these monopolies, historically to continue to perform as public companies, has been enormous, and they get more proprietary to your point, John. And eventually the open markets has hold on. You know that opens up new opportunities. It takes a while, but it's always happened. >>I don't think I think your point about the big incumbent. Players are not going to yield to just being rolled over by the incumbent growing cloud companies. But you cannot deny the fact that, say, Amazon. Dave, I want to get your thoughts on this because what Amazon did to compute change the game in my mind, they completely changed the capabilities. The consumption models, the cost structures. All the economics were changed with compute looking outpost wavelengths. When things are getting in, they have their own networking. So the question is, if you have the cloud ification of the Holy Trinity of infrastructure, which is storage, compute networking. Okay, you can see almost the cloud guys almost changing radically. All three of them computes Already done. Stories is already done. Networking is left, so you have networking battleground because you got to move the packets around. You don't need Mpls route routes because you just go through the cloud. How things are stored data, backup recovery. The list goes on and on. Ultimately, that's the infrastructure as code ethos that's going to change the application environment. So it will. Amazon will Google Will azure commoditized or change networking? >>Yeah. I mean, John, we already see that happening when we came two years ago. One of the challenges for most network engineers is what I need to manage. A large part of it I can't actually touch. I have to rely on third party. It's outside. I don't control it. But if something goes wrong, I'm on the hook for it. And if you go look forward a little bit, you know, if I'm deploying serverless architectures, is their networking involved? Yes. So I know what it is. I know my platform underneath it is going to take care of it, you know, sitting here talking about that transformation of the workforce, Dave, you wrote about it in your piece. That future of work is if you're you know, really, you know, putting together, You know, I'm a CCP my job is being a Cisco certified engineer, and my role will be racking, stacking, configuring and changing and managing those boxes today, it's well, I better get involved in the security side or the application side, because that's where I'm actually connected to the business and the data of things. Because if I'm just concerned about the moving packets around, yeah, there's gonna be either automation or clarification or combination of those things. They take that away from a >>couple thoughts on this. John, you were the very first to report trillion dollar opportunity for Andy Jassy and Amazon, and there was a 35 billion, so they have a long way to go. So I think a big theme for Amazon is gonna be tam. Expansion in one of those areas is, of course, networking, and you've seen the cloud slowly eat away reported this in my Wiki Bond post from the data because slowly eating away over the last 10 years. It's the networking share, and one practitioner said, as we put our data into the cloud, we're going to spend less on traditional networking, so it's clearly a threat. So Cisco, obviously diversifying its portfolio, we're gonna talk about that this week. But but more focused, as we've said do under the leadership of Chuck Robbins than it was. >>Well, Dave, here's a question for you because if you look at enterprise spend, they're increasing their spend on public cloud. But their data center stuff. It has stayed relatively solid. We haven't yet seen the erosion there. So are you saying networking is going to road before the rest of it? Because you know the story of data gravity? What? >>I think you're seeing the networking road not necessarily in terms of shrinking Cisco, although there guiding to a flat to down quarter. But you've certainly seen their growth slowdown, and especially in their core networking space. I mean, they've tried to double down on their switching and routing, and they just made new announcements in that space that John, you know well, but unquestionably the cloud is that it had an impact on Cisco's business. >>Well, 20 point, let's look ahead to the next 10 years. We've got a lot going on, so I think wait and see the big wave. So, to me, the big wave will start David on the ways I think the big wave is value proposition. Is the business model evolution? I think that's going to be a way that will constantly be the North Star or transformation. If whatever people are buying or operating, whether it's their infrastructure or their operating model, it has to have direct contribution to the business model, the company. So I think that's 12 I think AI and data will continue to power a lot of the value. And I think networking is going to be cloud ified. And the impact of that is going to be that as cloud and hybrid computing becomes a technical solution that achieves cheese, the operation model of companies you're going to start to see Multi Cloud emerged as a solution of that meaning Multi cloud isn't a technology. It's an outcome of hybrid combination of cloud. And that's going to change how packets are routed, how packets are networked. I think data ai and a complete transformation of the of the engine of business is gonna happen the next 10 years more than we've ever seen before. And I call this Dev Ops 234 point. Oh, do this. Is it a complete new engine of innovation. Technically, with storage, compute networking, where the application focus is going to be business driven, almost dynamic, almost real time. I think that will be a 10 year horizon. I wrote a Twitter post on this just a few minutes ago, and the lead architect for Azure tweeted back and said late, See, Layton sees never changes >>John. The innovation cocktail, as you said, is, What's the driver going forward, Right? >>Yes, exactly The speed of light. You can't solve that problem without putting points of presence all over, but >>the network architecture is what defines. It's, too, and I've been talking about network automation. We talked about Dev ops. But if you think of hybrid as a technical solution, how you work with public and private premises, Edge is just now a new network configuration that is going to be a very instrumental engineering task, which will actually impact how the software engineers, >>to your point, the latency that's physics and that's the plumbing and the plumbing is going to be there. But I do feel like we're exiting the cloud era into a new era of this innovation cocktail that you talk about the sandwich, which is cloud data plus AI plus digital services. And that's really what we're gonna be talking about 8 to 10 years from now is how organizations are applying those digital services and which companies, whether they're cloud native companies or guys like Cisco and IBM, HP Deli, EMC, how they're leveraging those waves and applying them >>to their business. And I'd be curious. See how the standards evolve around, whether it's de facto standards around interoperability around data, >>and you could look at what's >>happening with data privacy. You start to see the tell signs that data is going to be starting managed, just like packets are managed. It's like a whole interesting dynamic. >>But really what? This is the payoff for what company has been working on to be able to move faster. It was before was okay, used to take 18 months, and now I could do it a few months. But now I can react to that business between the automation, the machine learning, you know, putting together cloud, and you're gonna be able to refocus your workforce to be able to respond to the business and drive new value. >>All right, guys, we got to wrap up guest coming up. Appreciate the commentary. I'll just say that Dave Tesla. You mentioned one of your bringing analysis what Tesla did to the automobile company. I think there's going to be someone in the enterprise that comes out of the woodwork that changes the game on everybody. I think opportunity for that kind of new entrant >>in the same way Amazon. >>Did you think? >>I think Amazon is now an incumbent. I mean, look at the size and scale of it is always an opportunity for that bowl start up company. So it takes a kind of new dynamic electricity with cars, so we'll see. Okay, that's a wrap up. This is a cube conversation here in Barcelona for Cisco Live. I'm John. First Minutemen. Dave Vellante breaking down the Enterprise for the next 10 >>years. Yeah, yeah, yeah, yeah

>> From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone and welcome this week's episode of theCUBE Insights powered by ETR. And welcome to the Trillionaire's Club. In this Breaking Analysis, I want to look at how the big tech companies have really changed the recipe for innovation in the Enterprise. And as we enter the next decade, I think it's important to sort of reset and re-look at how innovation will determine the winners and losers going forward, including not only the sellers of technology but how technology applied will set the stage for the next 50 years of economic growth. Here's the premise that I want to put forth to you. The source of innovation in the technology business has been permanently altered. There's a new cocktail of innovation, if you will, that will far surpass Moore's Law in terms of it's impact on the industry. For 50 years we've marched to the cadence of that Moore's Law, that is the doubling of transistor counts every 18 months, as shown in the left-hand side of this chart. And of course this translated as we know, into a chasing of the chips, where by being first with the latest and greatest microprocessor brought competitive advantage. We saw Moore's Law drive the PC era, the client server era, and it even powered the internet, notwithstanding the effects of Metcalfe's Law. But there's a new engine of innovation or what John Furrier calls the "Innovation Cocktail," and that's shown in the right-hand of this slide where data plus machine intelligence or AI and Cloud are combinatorial technologies that will power innovation for the next 20 plus years. 10 years of gathering big data have put us in a position to now apply AI. Data is plentiful but insights are not and AI unlocks those insights. The Cloud brings three things, agility, scale, and the ability to fail quickly and cheaply. So, it's these three elements and how they are packaged and applied that will in my view determine winners and losers in the next decade and beyond. Now why is this era now suddenly upon us? Well I would argue there are three main factors. One is cheap storage and compute combined with alternative processor types, like GPUs that can power AI. And the era of data is here to stay. This next chart from Dave Moschella's book, "Seeing Digital," really underscores this point. Incumbent organizations born in the last century organized largely around human expertise or processes or hard assets like factories. These were the engines of competitive advantage. But today's successful organizations put data at the core. They live by the mantra of data driven. It is foundational to them. And they organize expertise, processes and people around the data. All you got to do to drive this point home is look at the market caps of the top five public companies in the U.S. Stock Market, Apple, Microsoft, Google, Amazon, and Facebook. I call this chart the Cuatro Comas! as a shout out to Russ Hanneman, the crazy billionaire supporting, was a supporting character in the Silicon Valley series. Now each of these companies, with the exception of Facebook, has hit the trillion dollar club. AWS, like Mr. Hanneman, hit the trillion dollar club status back in September 2018 but fell back down and lost a comma. These five data-driven companies have surpassed big oil and big finance. I mean, the next closest company is Berkshire at 566 billion. And I would argue that if it hadn't been for the fake news scandal, Facebook probably would be right there with these others. Now, with the exception of Apple, these companies, they're not highly valued because of the goods they pump out, rather, and I would argue even in the case of Apple, their highly valued because they're leaders in digital and in the best position to apply machine intelligence to massive stores of data that they've collected. And they have massive scale, thanks to the Cloud. Now, I get that the success of some of these companies is largely driven by the consumer but the consumerization of IT makes this even more relevant, in my opinion. Let's bring in some ETR data to see how this translates into the Enterprise tech world. This chart shows market share from Microsoft, AWS, Apple iPhone, and Google in the Enterprise all the way back to 2010. Now I get that the iPhone is a bit of a stretch here but stick with me. Remember, market share in ETR terms is a measure of pervasiveness in the data set. Look at how Microsoft has held it's ground. And you can see the steady rise of AWS and Google. Now if I superimpose traditional Enterprise players like Cisco, IBM, or Hewlett or even Dell, that is companies that aren't competing with data at the core of their business, you would see a steady decline. I am required to black out January 2020 as you probably remember, but that data will be out soon and made public shortly after ETR exits its self-imposed quiet period. Now Apple iPhone is not a great proxy but Apple, they're not an Enterprise tech company, but it's data that I can show but now I would argue again that Apple's real value and a key determinate of their success going forward, lies in how it uses data and applies machine intelligence at scale over the next decade to compete in apps and digital services, content, and other adjacencies. And I would say these five leaders and virtually any company in the next decade, this applies. Look, digital means data and digital businesses are data driven. Data changes how we think about competition. Just look at Amazon's moves in content, grocery, logistics. Look at Google in automobiles, Apple and Amazon in music. You know, interestingly Microsoft positions this as a competitive advantage, especially in retail. For instance, touting Walmart as a partner, not a competitor, a la Amazon. The point is, that digital data, AI, and Cloud bring forth highly disruptive possibilities and are enabling these giants to enter businesses that previously were insulated from the outsiders. And in the case of the Cloud, it's paying the way. Just look at the data from Amazon. The left bar shows Amazon's revenue. AWS represents only 12% of the total company's turnover. But as you can see on the right-hand side, it accounts for almost half of the company's operating income. So, the Cloud is essentially funding Amazon's entrance into all these other businesses and powering its scale. Now let's bring in some ETR data to show what's happening in the Enterprise in the terms of share shifts. This chart is a double-Y axis that shows spending levels on the left-hand side, represented by the bars, and the average change in spending, represented by the dots. Focus for a second on the dots and the percentages. Container orchestrations at 29% change. Container platforms at 19.7%. These are Cloud-native technologies and customers are voting with their wallets. Machine learning and AI, nearly 18% change. Cloud computing itself still in the 16% range, 10 plus years on. Look at analytics and big data in the double digits still, 10 years into the big data movement. So, you can see the ETR data shows that the spending action is in and around Cloud, AI, and data. And in the red, look at the Moore's Law techs like servers and storage. Now, this isn't to say that those go away. I fully understand you need servers, and storage, and networking, and database, and software to power the Cloud but this data shows that right now, these discreet cocktail technologies are gaining spending momentum. So, the question I want to leave you with is, what does this mean for incumbents? Those that are not digital-natives or not born in the Cloud? Well, the first thing I'd point out is that while the trillionaires, they look invincible today, history suggests that they are not invulnerable. The rise of China, India, open-source, peer-to-peer models, open models, could coalesce and disrupt these big guys if they miss a step or a cycle. The second point I would make is that incumbents are often too complacent. More often than not, in my experience, there is complacency and there will be a fallout. I hear a lot of lip service given to digital and data driven but often I see companies that talk the talk but they don't walk the walk. Change will come and the incumbents will be disrupted and that is going to cause action at the top. The good news is that the incumbents, they don't have to build the tech. They can compete with the disruptors by applying machine intelligence to their unique data sets and they can buy technologies like AI and the Cloud from suppliers. The degree to which they are comfortable buying from these supplies, who may also be competitors, will play out over time but I would argue that building that competitive advantage sooner rather than later with data and learning to apply machine intelligence and AI to their unique businesses, will allow them to thrive and protect their existing businesses and grow. These markets are large and the incumbents have inherent advantages in terms of resources, relationships, brand value, customer affinity, and domain knowledge that if they apply and transform from the top with strong leadership, they will do very, very well in my view. This is Dave Vellante signing out from this latest episode of theCUBE Insights powered by ETR. Thanks for watching everybody. We'll see you next time and please feel free to comment. In my LinkedIn, you can DM me @dvellante and don't forget we turned this into a podcast so check that out at your favorite podcast player. Thanks again.

>> Live from San Diego, California It's the queue covering Sisqo live US 2019 Tio by Cisco and its ecosystem. Barker's >> Welcome Back Here in the San Diego Convention Center. I'm student in my co host, David Dante, and you're watching the Cube, the leader in worldwide Tech coverage, and its Sisqo Live 2019 happening. Welcome back to the program. One of our Cuba, Lem's Russ Curie, who is the vice president Enterprise strategy at Net Scout. It's great to see you. Thanks for joining you guys. Thanks for having me. Alright, we always say, we got a bunch of Massachusetts guys that had to fly all the way across the country to talk to each other really well. So a couple hours for the beast hip, all everybody excited. But a lot of excitement here in the definite zone specifically and Sisqo live overall, 28,000 intended you've been to a lot of customer meetings, gives a little insight. What's been your take away from the show so >> far? I think that there's a lot of energy towards the multi cloud called Deployments in general Security. The whole introduction of Umbrella has got a lot of conversation started. It's amazing the amount of cos you see out there talking about just visibility in general, and that's being one of them as well. So it's been a lot of fun. >> Good show this year, Russ. I've been looking for this conversation. We heard from Chuck Robbins in the keynote. He said The network sees a lot of things, and Cisco says they're going to give customers that visibility. Of course, that ties in a lot, too. What Net scouted love, you know, give us. You know, your thoughts on Multi Cloud. How Cisco doing in the space? And how does Net Scout fit into that whole picture? >> Well, I think that one of things as Chuck talks about that, it's the cloud is the one thing, or the network is the one thing that's common for all. Coming along the devices right? I have. If I go into a different cloud, I have one set a performance metrics I might be able to gather about. You look at what device or an operating system. It's all different. But all the communications on the network T C P I. P is common. That really provides that thread that you're able to provide that level of visibility. So it really becomes one of those things that the network is a unique place to gain perspective on both the performance in the security that we're delivering to our customers. So can >> you just summarize the problem that Net Scout solves for our audience? Sure, I think that primarily it's one of these situations where I've been my own prime environment. It was pretty easy. I had access to everything. I could see what was going on. Quite readily. I started introduced visual ization and now traffic start to move much more East West and became a problem for folks. I think can Cisco recently said 85% of the traffic there seeing on the network is East West traffic, right? And then we moved to the cloud, and it's even more obvious gay that I can't see anything in new ways of network traffic. There typically live in clover and desert starting to address that, but really being able to gain that level of visibility so you can understand exactly what's happening just gaining that perspective. So let's explain it. >> I'm going to stay with the East West north seven metaphor. Why is it easier to get visibility in a column? >> Then? It is a row, I think, because in a column is everything exploding north and self. So you've got everything right there, and usually you have a place where you can look into it. But when you're flat, it starts to become really different you're looking at. But advice is talking to know the devices that don't necessarily have to traverse any part of the network it. Khun, stay within. Ah, hi provides, for example, so providing solutions lawyer game visibility into that environment is really important and the protocols that we use their change a bit so traditional tools don't necessarily fit well. So what's the general solution to >> solving that problem? And then I want to understand the Net Scouts secret sauce. But let's stop. Let's start of high level. How does the industry solved that problem? So the industry >> has been trying to solve that problem mostly by looking at the goodwill of third parties, looking at things like net blower, log events and aggregating that normalizing it. You've had solution sets that looked at network traffic, but it becomes very difficult for a lot of folks to make use of that network traffic, and what we've done is really provide the ability to look into that network. Traffic and gain gather from really anywhere it's deployed whether it's public loud, private cloud, our solution said, That's our secret sauce. Our solution. Second go anyway. >> So so add some color to that in terms of your able to inspect deeper through what just magic software you got. You got a pro you send in so >> well. Actually, we have a device. It's called a SNG, and in the virtual world we use something that we call be stream. In the physical world, we have some that we call in Finnish Stream N. G. And that leverage is a technology that we've developed, called Sai, which is adaptive service intelligence and well, also do is watch all that traffic and build meta data in real time so we can surface key indicators of performance and security events. Get that information up into a collection mechanism that doesn't have to normalize that data. It just looks at it as is way. Build it into a service Contact services context laws uses to see across a multi cloud environment in a single pane of glass. Okay, so one of >> the biggest challenges for customers is that they're changing these environment. It's what happens. Their applications, you know, applications used to be rather self contained. Even the bm They might have moved some, but now we're talking about, you know, micro services, architecture, multi cloud environment. There's there's a lot going on there, you know? What's the impact on that for your world, >> Right? That's been exactly it. Weigh three tier application was kind of pretty straight forward, even though at the point we started introducing, we thought that was a really tough stuff. Now what we're doing, as you say, it's doing micro services architectures, and I might take my presentation layer and put out in the cloud and the public cloud in particular. So I'm closer to the UN user and delivering better high performance capabilities to them lower lately, Auntie and the like and I take my application server and I split that up all over the place, and I might put some in public. Claude. I might put some in private club. I maintain some of it in the legacy. So all that interconnection, all that independency is really, really hard to get your hands around and that complexity. We looked at the street study that said 94% of the 600 respondents said that the the networks are as complex or more complex than they have been two years ago. >> Yeah, that's not surprising, unfortunately to hear that, but you know, when we talk to customers out there, it used to be, you know, the network is something You set it up. You turned all your knobs and then don't breathe on this thing because I've got a just where I want today. It can't be like that. You know, I I we know that it's very dynamic has changed. The message from Cisco has been We need to simplify things and, you know, obviously everybody wants that. But how do you make sure you ensure that application, performance and security, without having the poor admit, have to constantly, you know, be getting tickets in dealing with things >> I think are Solution really provides a common framework for visibility, and that's really what I think is really important. When you're starting to infer based upon different data sets, it becomes very difficult to put your finger on the problem and identified. That's really a problem. And it's trying to blend the organization. Let's sit this concept of the versatile list and trying to make sure that people are more capable in addressing problems in kind of a multi dimensional role that they have now in particular network and security. The organizations, they're trying to come together, God, they rely on different data sense, and that's where it kind of falls apart. If you have a common day to say, you're going to have a better perspective, Okay, >> I was just a front from that application standpoint. How much of this is just giving notification to invisibility? Intuit vs, you know? Is it giving recommendations or even taking actions along those lines? >> Yeah, I think it has. It has to give you recommendations and has to give you pinpoints. You really? You've got to be able to say there here's a problem. This is what you need to do to fix it right? I think what often when I'm talking to folks, I say it's about getting the right information to the right person at the right time to do the right thing If you're able to do that, you're going to be much more effective. Yes. OK, so you've got this early warning system, essentially, hopefully not a tulip. But that's what practitioners want. Tell me something. Tell me. Give me a a gap and tell me the action to take before something goes wrong. Ideally. And so you could do that. You could give them visibility on it, Kind of pinpoint it. And do you see the day, Russ, where you can use machine intelligence toe as Stuart suggesting start to maybe suggest remedial action or even take remedial action? Oh, absolutely. I mean, there are some things that you can really do and do quite well. Walking for security events, for example, is the primary one. We've always had the ideas in place in the early days, a lot of folks who are cautious because they wanted to have a negative impact on the business. But when we take a look at ex filtration and blocking outbound connections, if you know the bad actors and you know the bad addresses, you can stop that before it gets out of your network. So people aren't gonna have that X illustration of your information. >> All right. So, Russ, you've been meeting with a bunch of customers here at the show, What's top of mind for them And if some of the conversation I've been having this week, you know, security, you know, has been climbing that that list for many years now. But in your world, what are some of the top issues? >> Yeah, security, definitely. There's no question. I think it's one of those environments where you can almost never have enough. There is always hungry more and more and better and more accurate solutions. I I think I saw something recently. There was a top 125 security solutions that's like top 120 times really way. Doyle The Town 25 Exactly. And I think I D. C's taxonomy has 73 sub categories to the security. So security is, you know, more than a $500 word. You know, it might be a $5,000 word. It's crazy and same with club, right, because it's not like, you know, in fact, I was talking to someone recently, and it's with the club village Go. It's not a club village. A more This is everything we're doing is the cloud. So it's change in mindset. So it's It's interesting as a cloud universe. So what's next for Net Scout, you know, give us a little road map? What Khun observers expect coming from you guys more significant, pushing the security in particular. One of things we see is that our data set really has the ability to be leverage for both security and performance work. Load sport floats were integrating the products that we bought with the Harbour acquisition we bought over networks. And they have a highly curated threat intelligence feed that we're going to bring in and add to our infinite streams and have the ability to detect problems deep inside the network. You know, it's one of these things the bad actors kind of live off the land. They get in there and they know their way around slowly and methodically and drought dribble information. No. Well, the only way to catch that is like continually monitoring the network. So having that perspective so continuing to grow that out and provide again more of that, eh? I aml approach to understanding and be more predictive when we see things and be able to surf. It's that type of information. Security already used to be activists. And now it's become, you know, high crime even. Yeah, even, you know, nation states, right. And the job of ah of a security technology company is to raise the cost, lower the value right to the hacker, right to the infiltrator so that they go somewhere else. All right. Hey, make it really expensive for them. So either get through. But we ve what's like you get through, make it really hard for them to take stuff out. And that's really what you're doing. >> It was like you made sure to lock the front door now because it stopped them. But, you know, maybe I'll go somewhere else, right? It's a little bit >> different. Preventing you wanna minimize your risk, right? So if you're able to minimize the risk from performance and security problems, it's really all about understanding what you've got, what your assets are protecting them. And then when that someone's trying to look at them stopping it from happening, >> OK, last question I have for you, Russ, is being in this Cisco ecosystem out there. We're watching Cisco go through a transformation become more and more software company now, four years into the Chuckle Robin's era. So you know, how's that going in? What's it mean to partner Francisco today? >> It's going really well, and I think that we adopted a lot of way or adopted a lot of what the Sisko has done as well and really transform Nets go from what was primarily a hardware first company into a software first company. You know, it's kind of I was in a conference once and we were talking about software eating the world, right and but ultimately, its hardware. That's doing the chewing right. So I think it's one of those balancing acts. You know, it's Cisco's still of selling a ton of hardware, but it's a software solution sets so they deploy on their hardware. That makes it happen. And it's similar for us. You know, we're building out software solutions that really address the issues that people have building all these complex environments. All right, >> Russ Curie, congratulations on all the progress there and look forward to keeping up with how Netscape's moving forward in this multi cloud world. Thank you. All right, we'll be back with lots more coverage here from Cisco Live, San Diego for David Dante Obst Amendment. Lisa Martin's also here. Thanks, as always, for watching the Cube.

(energetic music) >> From Washington D.C., It's theCUBE! Covering ScienceLogic Symposium 2019. Brought to you by ScienceLogic. >> Welcome back to TheCUBE's coverage of ScienceLogic Symposium 2019, I'm Stu Miniman, and we're here at the Ritz-Carlton in Washington, D.C. Happy to welcome to the program two first-time guests from ScienceLogic, to my left is Leslie Minnix-Wolfe, who is the Senior Director of Product Marketing. And to her left, is Russ Elsner, who's the Senior Director of Product Strategy. Thank you so much for joining us. >> Thank you sir. >> Good, good to be here. >> All right, so Leslie let's start with you. Talk a lot about the product, a whole lot of announcements, Big Ben on the keynote this morning. Everybody's in, getting a little bit more of injection in the keynote today. Tell us a little bit about your roll, what you work on inside of ScienceLogic. >> Okay, so I am basically responsible for enterprise product marketing. So my job is to spin the story and help our sales guys successfully sell the product. >> All right, and Russ. >> I'm part of the product strategy team. So, I have product management responsibilities. I work a lot with the analytics and applications. And I spend a lot of time in the field with our customers. >> All right so, Leslie let's start with enterprise, the keynote this morning. The themes that I hear at many of the shows, you know we talk about things like digital transformation. But, we know the only constant in our environment is change. You know, it's good. I've actually talked to a couple of your customers and one of them this morning he's like "Look, most people don't like change. "I do, I'm embracing it I'm digging in, It's good." But, you know, we have arguments sometimes in analyst circles. And it's like are customers moving any faster. My peers that have been in the industry longer, they're like, Hogwash Stu. They never move faster they don't want change, we can't get them to move anything. I'm like, come on, if they don't the alternative is often, You're going to be... You know, you're competitors are going to take advantage of data and do things better. So, bring us a little bit of insight as what you're hearing from your customers both here and in your day to day. >> Sure, yeah, change is constant now and so one of the big challenges that our customers are facing is how do I keep up with it. The traditional manual processes that they've had in place for years are just not sufficient anymore. So they're looking for ways to move faster, to automate some of the processes that they've been doing manually. To find ways to free up resources to focus on things that do require a human to be involved. But they really need to have more automation in their day to day operations. >> All right, so Russ when I look at this space you know, tooling, monitoring has been something that in my career, has been a little bit messy. (laughter) Guess a little bit of an understatement even. It's an interesting... When I look at, kind of, that balance between what's happening in the infrastructure space and the application space. I went through, one of your partners over here is like "from legacy to server lists and how many weeks." (laughter) And I'm like okay that sounds good on a slide but, these things take awhile. >> Absolutely. Bring us inside a little bit, kind of the the application space an how that marries with the underlying pieces and monitoring. >> Yeah, you have a lot of transformations happening. There's a lot of new technologies and trends happening. You hear about server lists or containers or microservices. And that does represent a part of the application world. There are applications being written with those technologies. But, one of the things is that those applications don't live in isolation. It's that there part of broader business services and we're not rewriting everything and so the new shiny application and the new framework has to work with the old legacy application. So, a big piece of what we see is how do we collapse those different silos of information? How do we merge that data into something meaningful? You can have the greatest Kubernetes based microservice application but, if it requires a SAP instance it's on PRIM it's on Bare Metal. Those things need to work together. So, how do you work with an environment that's like that? Enterprise, just by it's nature is incredibly heterogeneous, lot's of different technologies and that's not going to change. >> Yeah. It's going to be that way. >> You're preaching to the choir, here. You know, IT it always seems additive the answer is always and. And, unfortunately, nothing ever dies. By the way you want to run that wonderful Kubernetes Docker stuff and everything. I could do it on a mainframe with Z Linux. So, from that environment to the latest greatest hypercloud environment >> Right. Talk a little bit about your customers. Most of them probably have hundreds of applications. They're working through that portfolio. What goes where, how do I manage all of those various pieces, and not kill my staff? (laughter) One of the things we're spending a lot of time with this, is that obviously, we come from a background of infrastructure management. So, we understand the different technologies different layers and the heterogeneous nature and on top of that runs application. So they have their own data and there's APM space. So we're seeing a lot of interest in the work we're doing with taking our view of the infrastructure and marrying it to the application view that we're getting from tools like Appdynamics or Dynatrace or New Relic. And so, we're able to take that data and leverage it on top of the infrastructure to give you a single view which aids in root cause analysis, capacity planning and all the different things that people want to do. Which lead us to automation. So, this idea of merging data from lots of sources is a big theme for us. >> All right so, Leslie who are some of the key constituents that you're talking to, to messaging to. In the industry we talked about silos for so many time. And now it's like oh, we're going to get architects and generalists. And you know cloud changes everything, yes and no. (laughter) We understand where budgets sit for most CIO's today. So, bring us inside what you're seeing. >> Sure. Yeah, we're seeing a tremendous change. Where before we use to talk more to the infrastructure team, to the folks managing the servers, the storage the network. We're really seeing a broader audience. And a multiple constituent. We're looking at directors, VP's, CIO's, CEO's, architects. We're starting to see more people that are tools managers, folks that are involved in the application side of the house. So, it's really diverged. So, you're not going in and talking to one person you're talking to lots of different teams, lots of different organizations that need to work together. To Russ's point in about being able to bring all this data together. As you bring it together, those different stakeholders have more visibility into each others areas. And they also have a better understanding of what the impact is when something goes down in the infrastructure, how it effects the app and vice versa. >> Leslie, the other thing I'm wondering if you can help me squint through, when I looked at the landscape, it's, you know, my ITSM's I've got my logging, I've got all my various tools and silos. When I hear something like, actually, your CEO Dave just said "Oh, we just had a customer that replaced 50 tools." with there it's like, How do you target that? How does a customer know that they have a solution that they have a challenge that you fit, Because, you understand, you can't be all things to all people. You've got certain partners that might claim that kind of thing. >> Right But, where you fit in the marketplace how do you balance that? >> Well, so I think what we're seeing now is that there have been some big players for a long time. What we refer to fondly as the Big Four. And those companies really haven't evolved to the extent that they can support the latest technology. Certainly at the speed with which organizations are adopting them. So, they might be able to support some of the legacy but they've really become so cumbersome, so complicated and difficult to maintain people are wanting to move away from them. I would say five years ago, most organizations weren't willing to move down that path. But with some of the recent acquisitions, The Broadcom acquisition, Microfocus acquisition. You're seeing that more organizations are looking to replace those tools in their entirety. And as a result of that they're looking at how can I minimize my tool set. I'm not going to get rid of everything and only have one vendor. But, how do I pick the right tools and bring them together. And this is one of the areas where we do extremely well in that we can bring in data, we can integrate in other tools, we can give you the full picture. But, we're kind of that hub, that central. And I think we heard that earlier today from Bailey at Cisco, where he talked about ScienceLogic is really the core to their monitoring and management environment, because we're bringing the data and we're feeding the data in to other systems as well as managing it within ScienceLogic. >> Russ, I actually heard, data was emphasized more that I expect. I know enough about the management and monitoring space. We understand data was important to that, I'm a networking guy by background, we've been talking about leveraging the data for network and using some automation and things like that but it's a little bit different. Can you talk some about those relationships to data? We understand data's going to be everywhere and customers actually wrapping my arms around it make sure I can manage it, compliance and to hopefully get value out of that is one of the most important things in today. >> Absolutely, so one of the things we stress a lot when we talk about data, it use to be that data was hard to come by. We were data poor and so how do we get... We don't have a probe there so how do we get this data, Do we need agent? That's different now, data is... We are drowning in data, we have so much data. So, really the key is to give that data context. And so for us that means a lot of structure, and topology and dependencies across the layers of abstraction, across the application. And we think that's really the key to taking this, just vast unstructured mess of data that isn't useful to the business and actually be able to take... Apply analytics, and actually take action, and ultimately drive automation by learning and maintaining that structure in real time automatically, because that's something a human can't do. So, you need machine help, you need to automate that. >> So, Leslie, there was in the keynote this morning that to start discussion of the AI Ops maturity model >> Right >> And one of the things struck me is there was not a single person in the poll that said, yes I've gone fully automated. And first, there's the maturity of the technology, the term and where we are. But, there's also that, let's put it on the table. That fear sometimes, is to "Oh my gosh, the machines are taking our jobs" (laughter) You know, we laugh, but it is something that needs to be addressed. How are you addressing that, Where are your customers with at least that willingness, because I use to run operations for a number of years, and I told my team, look you're going to have more work next year, and you're going to have more things change, so if you can't simplify, automate. Get rid of things, I've got to have somebody helping me, and boy those robots would be a good help there. >> What we're seeing is, I mean let's be real, people don't like to do the mundane tasks, right. So you think about, When you report an issue to the service desk. Do you really want to open that ticket? Do you want to enter in all that information yourself? Do you want to provide all the details that they need in order to help you? No. People don't do it they put in the bare minimum and then what ends up happening is there's this back and forth, as they try collect more information. It's things like that, that you want to automate. You want to be able to take that burden off of the individuals And do the things, or at least allow them to do the things that they really need to do. The things that require their intelligence. So, we can do things like clean up storage disk space when your starting to run out of disk space. Or we can restart a service, or we might apply a configuration change that we know that is inconsistent in environment. So, there's lots of things like that that you can automate without actually replacing the individual. You're just freeing them up to do more high level thinking. >> Russ, anything else along the automation line. Great customer examples or any successes that you've seen that are worth sharing? >> Yeah, automation also comes in the form of connecting the breadcrumbs. So, we have a great example. A customer we worked with, they had an EPM tool, one of the great ones, you know, top of the magic quadrant kind of thing, and it kept on reporting code problems. The applications going down, affecting revenue, huge visibility. And it's saying code problem, code problem ,code problem. But the problem is jumping around. Sometimes it's here, sometimes it's there. So, it seemed like a ghost. So, when we connected that data, the APN data with the V center data and the network data what it turned out was, there was a packet loss in the hypervisor. So, it was actually network outage that was manifesting itself as a code problem, and as soon as they saw that, they said what's causing that network problem? They immediately found a big spike of traffic and were able to solve it. They always had the data. They had the network data, they had the VMware data they had the JVM data. They didn't know to connect the dots. And so, by us putting it right next to each other we connected the dots, and it was a human ultimately that said I know what's wrong, I can fix that. But it took them 30 seconds to solve a problem that they had been chasing after for months. That's a form of automation too is get the information to the human, so that they can make a smart decision. That's automation just as much as rebooting a >> Exactly server or cleaning a disk >> Well right, It's The Hitchhikers Guide to the Galaxy. Sometimes, the answers are easy if I know what question to ask. >> Exactly, yes. (laughter) >> And that's something we've seen from data scientists too. That's what their expertise is, is to help find that. All right, Leslie give us a little view forward. We heard a little bit, so many integrations, the AI ops journey. What should customers be looking for forward? What are they asking you, to help bring them along that journey? >> Oh gosh. They're asking us to make it easier on all counts. Whether it's easier to collect the data, easier to add the context to the data, easier to analyze the data. So, we're putting more and more analytics into our platform. So that their not having to do a lot of the analysis themselves. There's, as you said earlier, there's the folks that are afraid they're going to lose their job because the robots or the machines are taking over. That's not really where I see it. It's just that we're bringing the automation in ways and the analytics in ways that they don't want to have to do, so that they can look at it and solve the really gnarly problems and start focusing on areas that are not necessarily going to be automatable or predictable. It's the things that are unusual that their going to have to get involved in as opposed to the things that are traditional and constant. So, Russ, I'd love for you to comment on the same question. And just a little bit of feedback I got talking to some of the customers is they like directionally where it's going, but the term they through out was dynamic. Because, if you talk about cloud you talk about containers. Down the road things like serverless. It's if it pulls every five minutes it's probably out of date. >> oh, Absolutely. I remember back when we talked big data, real time was one of those misnomers that got thrown out there. Really, what we always said is what real time needs to mean is the data in the right place to the right people to solve the issue >> Absolutely. >> Exactly. So, where do you guys see this directionally, and how do you get more dynamic? >> Well see, dynamic exists in a bunch of different ways. How immediate is the data? How accurate is the dependency map, and that's changing and shifting all the time. So, we have to keep that up to date automatically in our product. It's also the analytics that get applied the recommendations you make. And one of the things you can talk to data scientists and they can build a model, train a model, test a model and find something. But if they find something that was true three weeks ago it's irrelevant. So, we need to build systems that can do this in real time. That they can in real time, meaning, gather data in real time, understand the context in real time, recognize the behavior and make a recommendation or take an action. There's a lot of stuff that we have to do to get there. We have a lot of the pieces in place, it's a really cool time in the industry right now because, we have the tools we have the technology. And it's a need that needs to be filled. That's really where we're spending our energy is completing that loop. Closed loop system that can help humans do their jobs better and in a more automated way. >> Awesome. Well, Leslie and Russ, thanks so much for sharing your visibility into what customers are doing and the progress with your platforms. >> All right, thank you Stu. >> And we'll be back with more coverage here from ScienceLogic Symposium 2019. I'm Stu Miniman, and thank you for watching theCUBE. (energetic music)

>> Live from Las Vegas. It's the Cube. Covering AWS re:Invent 2018. Brought to you by Amazon Web Services, Intel and their ecosystem partners. >> And welcome back to Las Vegas. Good afternoon to you. No matter where you're watching, here in the US, we know it is afternoon. As we wind up our coverage for day three at AWS re:invent here on the Cube. One of seven venues we're in right now that's hosting various satellite events. Right now we're in the Sands Expo. Rebecca Knight, John Walls, with Russ Currie. The vice president of Enterprise Strategy at NetScout. Russ, good to see you sir. >> Nice to see you again. >> I've got to be careful. I've got two Bostonian's of sorts here. >> Sorry, sorry. >> So if I don't get something on the accent, you just let me know (laughs) >> We'll talk amongst ourselves. >> Sorry if your sports inferiority complex is.. We're the champions, whatever >> Russ, if you would, first off, your take about what you're seeing here. Because, here we are, day three. As you know, you've been to a lot of shows. Day three, kind of, usually hits a different gear, right? >> Right >> Slows down a little bit. There's still a lot of excitement here. There's still a lot of people around. This show has a little different vibe to it. >> It really does. It's interesting because it becomes a little bit more serious, I think. At this point, in day three for this show, it's now, people are really kind of, they've been exposed to an awful lot in the last three days. And they're really saying, "Okay, now I really want to understand the nuts and bolts of it", and they're spending a little bit more time sitting and learning, understanding what you have to offer them in terms of your solution sense. So it's been an awful lot of fun. Also we did a couple of speaking engagements, so it's really good getting the folks that went and saw our guys speak and seeing them come into the booth and say, 'I want to talk more about that'. >> Well, I want to talk more about that, so you have a new marketing campaign, but first for the viewers who are not familiar with NetScout, you're a Fortune 500 company, but tell us a little more about who you are and what you do. >> Right, so what we do is we provide visibility into the communications between servers and clients and basically see all of the traffic that traverses the network and whether the network is in a public cloud, private cloud, or an on-prem environment, and by looking at that traffic we're able to understand the performance of the services that are being delivered and ensure the security performance of those servers. But right in that perspective we give IT the tools they need to get quicker to the mean time to knowledge, identifying where a problem might be or where a risk may exist and being able to solve those tough problems. >> So it's been a year since you've been on the Cube, I know the esteemed John Walls interviewed you. >> We go way back, yes. >> What's new this year? What sort of advancements, progressions have you implemented? >> So last year when we came in was really our first entry into the public cloud environment and our first entry into AWS, since then we've got a lot of really good traction, a lot of embracing of our technology, we partnered more closely with AWS and got ourselves onto the marketplace. We also enhanced our partnership with VMware and now a part of their NetX integration so we have high levels of integration into both of those platforms, which of course is strong to this entire audience. We've introduced new features and functionality into our product to be able to provide greater visibility, even going deeper into understanding the way the applications are functioning and also added some more security profiling into our products and being able to identify threats as they come into the enterprise network and also as they go out ,so, it's been interesting, it's been a lot of fun. >> You talk about the hybrid cloud and obviously we're hearing that here, this week right, so AWS is obviously slightly shifting its perspective a bit, you know, in terms of on-prem and dealing with the public cloud as well, mind that merger. Your clients, is their any arm twisting that you still have to do or are people buying into it a little more wholeheartedly now in terms of the public cloud and that you've addressed these security concerns? >> I think actually we've become an enabler in often times for our customers, to move to the cloud with competence, where they were a little bit concerned that as they move into the cloud, what kind of investment in tools are they going to have to make? What are they going to potentially lose as they put their workloads into the cloud ? Do they lose a degree of visibility and control? And what we've been able to do is ensure that they have that same experience no matter where they deploy. We were talking earlier about one of our customers that's in the travel and entertainment business and they have been using our gear on cruise ships and in their on-prem data center, but expanded themselves into AWS to extend their capabilities and provide a better user experience for those on ship and now what they really have was the ability to have visibility from ship to shore to cloud and have that perspective and have the confidence that they're delivering a high quality experience to their customers. >> Ship to shore to cloud, not every company can say that. >> Exactly. >> But speaking of motto's, you have a new marketing campaign, visibility without borders. What does that mean? What are you trying to evoke their with your customers? >> What we we're really trying to look at there is the ability to provide visibility no matter where you're deployed. If you have a deployment in a public cloud environment like AWS, you want to have that same level of visibility in your on-prem environment, you want to have it no matter where you have a workload, wherever you have an instance that you want to manage. You want to be able to have that same perspective, one of the things we talk about a little bit, is the idea that, providing a single pane of glass into the service insurance experience and I think that often times, when people try to get to that single pane of glass, they end up with more of a single glass of pain. (laughing) You know, they're trying to aggregate so much stuff that it really doesn't come together too well. >> Right. >> But because we focus in on the data source itself then it just provides that continuity regardless of where they deploy. >> Alright and the importance of visibility, obviously when you're talking about end to end right now, whether you're on-prem or whether you're in the public cloud you're not particular, right? >> Right. >> As a user, I just want to see my operation from start to finish and I don't care where it is. >> Exactly, providing that end to end perspective and being able to understand how I'm delivering services, what the customer experiences, no matter where I deploy and especially when we look at taking advantage of some of the elastic compute capabilities and the like that exists in the cloud, you want to ensure that you're actually getting what you paid for as well, you want to have those controls in place, knowing that what you're delivering is meaningful and impacting the business in a positive way as opposed to, potentially, in a negative way, and spending too much for something that doesn't improve anything in terms of the customer experience. >> Right. >> So the customers, when they want to talk about return on investment, what excites them most? What kind of things are you showing them that is delighting them? >> Often times it's really about mean time to knowledge, we spend a lot of time pointing fingers at each other when we're trying to solve a problem instead of pointing our fingers at the problem. And that's really what we try to focus in on, really getting down into the real details of why something is not performing properly, not, what might be performing improperly. So being able to really get down to that detail and get the right people working on the right problems, I often talk about it in terms of getting the right information, to the right person at the right time, to do the right thing. And if you're able to do that, you're going to provide a better user experience to your customers. >> You mention that a lot of your personnel, a lot of your folks here have been speaking, talking to various groups, I always find that interesting right, because, it's usually the Q&A. >> Yes. >> That when things pop off. So, if you had to generalize about the kind of feedback you're getting from those sessions in terms of the questions, the concerns, the challenges, what are you hearing from folks out there? >> It's kind of funny, one of the things that we get a lot of times is, "You really can do this?" you know, "Is this real what you're showing us?", it's like, yes, this is actual traffic we're showing you exactly what we're seeing. >> Yeah. >> And then they are often pretty amazed at our ability to bring this all into something that visualizes these complex applications that they're delivering across multiple different environments and they have that ah-ah moment where they go, "Oh gosh I really need this, you know, this is really that end to end view that I've been looking for for so long, but I really can't get what I use, a bunch of disparate tools, to try and bring that together" >> So that means, what you just described, is really the definition of innovation, which is providing customers with things that they want, that they didn't even know they wanted. How do you stay innovative? I mean here we are at AWS, Amazon, one of the most innovative companies on the planet and in the history of industry. >> Absolutely. >> How does a company, you're based in Westford, Massachusetts, how do you stay on the cutting edge? >> We spend an enormous amount of time working with our customers and listening to them in terms of where they're going, what their plans are, what new technologies might they be implementing, what are their major initiatives, we regularly reach out and we have constant contact with them to get that feedback and make sure that we're developing solutions that are meaningful to them, it's really about, not what feature can I deliver but what can I provide as value, that's going to make their lives better. Because, as an IT person, it's a tough job right? You're usually the person that people look at and say "Why isn't this working?". >> Right. >> And not being able to have an answer to that, is not a good position to be in. (laughter) Right, so what we're really trying to do is provide them with that answer and give them that ability to be able to answer the tough questions and solve those tough problems. >> Talking about finger pointing, it happens right? >> It does, you know. So what we're really all about is making sure that they're able to get the problem solved as quickly as possible. One of the interesting things I've been hearing from our customers too is that they're looking to this concept of a versatilist, rather than having just a straight forward specialist coming in and work on problems, having people that are a little bit broader in terms of their capabilities and looking at things not only from the perspective, say I'm a network guy, I'm going to look at the network and the app guys going to look at the app. >> Right. >> You got to cross pollinate a little bit and provide that ability to see both sides of that problem, so that's starting to happen. >> Certainly presents a challenge for your workforce right? All of a sudden, you've got to be a little smarter and where a lot of different hats. >> Exactly. >> Versatilist, I like that. You heard it here first. >> Yeah. >> Well, Russ, if you're going to go and ship the shorter cloud, you let us know? OK? >> OK. (laughing) >> Because we want to take that journey with you, alright? >> Love to. >> Thanks for being with us again, good to see you. >> Thank you, it was a pleasure. >> You bet, safe trip home. Back with more here from AWS re:Invent, you are watching us live on the Cube.

>> LIVE from Las Vegas, it's theCUBE! Covering VMworld 2018. Brought to you by VMware and it's Ecosystem Partners. >> Welcome back to theCUBE's continuing coverage of VMworld 2018! I'm Lisa Martin, finally paired up with Stu Miniman. Hey, Stu! >> Lisa, three days, wall-to-wall coverage and how have you and I not been paired together yet? >> Did you do the scheduling, Stu? >> Um. >> That's okay. I'm glad to be paired up with you. The last interview, saving the best for last. Speaking of the last, we've got two guests, welcoming back some alumni to theCUBE, who also seem to be so busy at VMworld that you come to us as our last guests. I like this tradition. >> You had be the bookend, you know? Got to be the bookend. >> Best for last. >> Exactly. We've got Ajay Patel, SVP of VMware, and Russ Reeder, CEO of OVH US. Welcome, guys! >> Thank you, great to be here. - Thank you. >> Saving the best for last. >> Best for last, you bet. >> So, last year just, yeah, VMworld last year, vCloud Air acquisition by OVH had just happened. Give us an update on what's gone on in the last year and the momentum that that is giving the OVH business in the U.S. >> So, we're super excited to be here on second year as a Diamond Sponsor. We, as OVH Cloud, coming to the U.S. is a great opportunity. OVH is the largest hosting company in Europe. Everyone knows who we are in Europe, we come to the U.S. a year and a half ago, every one's like, who's OVH? We acquire vCloud Air, partnered with VMware, which is old news in Europe. For the past nine years, we've been virtualizing vSphere, seven of those nine we've been the award winning partner in Europe. So coming to America, the best way to really launch with the VMware partnership is to acquire vCloud Air. All of those customers, and brought over those employees, and the best news is that we just launched two months ago, starting to migrate those customers over to OVH Cloud. >> Fantastic. >> It's very exciting. >> Yes. >> Ajay, so, Multi-Cloud being the story of the show, we've seen really the maturation after, you know, we've been tracking this for a lot of years. It was like, okay, do we have the VMware Cloud story? Are we happy with it? Things like that. So first of all, congrats to you and your team. >> Thank you. >> We've had some good proof points, a lot of partners I hear. >> Absolutely. >> I'm joking to you, it's like, yeah, OVH- >> OVH clearly one of the important ones. (laughs loudly) >> So, you know, put this in perspective for us as to, from the vCloud Air world to, you know, we're talking AWS, IBM, OVH and many others. >> Stu, you and I talked about it a couple of times now, this is year number four, so thank you for inviting me, first of all. Our strategy's been consistent. How do we get VMware running on as many destinations as possible? And Hybrid, for us, has been a strategy that's been consistent. Glad the market caught up, even having Andy Jassy talk about moving RDS and making it available to vSphere on-prem is really a sign of maturity that the world is going to be hybrid for a long time. So from a strengths perspective, Hybrid is here to stay and we're really focused on what we've been calling this Cloud Verified Partner. So, OVH is a handful of partners that have reached that highest level achievement of delivering a full-stack VMware CDC and it can have a consistent infrastructure experience that cost customers 10 dollars. So we're at a point where the strategy's being realized. Strategic partners like OVH are delivering a full-stack VMware and customers are seeing the value of delivering Cloud, whether public Cloud or on-prem on vSphere. >> Russ, as I said, multi-cloud, it's matured a bit. You know, one of the big questions we had coming into was that AWS partnership, how much of it is a one-way? Well, things like RDS, really interesting. I've spent a bunch of time digging into it and understanding it. The other thing is, it's as Ajay said, the strategy was VMware everywhere. And partners like yourself, okay, where do we play? You know, public cloud's not the enemy, it's what do we do, what do we partner with? How they're help fitting the landscape as to, you know, how OVH and, you know, how do you play in that larger ecosystem and differentiate and, you know? >> Yeah, I think the third generation of the cloud here coming to multi-cloud is kind of going the first generation of hey, someone needs to do it for me. AWS, I'm going to do it myself. Now, hey, I want to do it myself, but I need multi-cloud. I'm not going to put all my eggs in one basket, I need a true infrastructure partner where I have predictability on billing. I don't have ingress or egress charges. I have a true infrastructure partner with the automation that can scale globally. And so, 20 years ago when we started OVH in Europe, the opportunity there was wide open. Coming here to the U.S. now it's a perfect opportunity in multi-cloud where all customers are saying I need to get out of my closet. I have seven-year-old machines in my Colo facility. I'm all-in-one whether it's AWS, or IBM, or another partner out there, they need to put different workloads where they would work best or DR. So coming in as a true infrastructure player with all of our automation, it's actually perfect timing for OVH to come to the U.S. and laugh OVH Cloud. >> So I'm curious, obviously with the European you have their legacy as we've transitioned and it's a spectrum but from kind of the traditional hosted environment to you're almost fully satisfied when you go this. >> Sure. >> The US, do you still have the spectrum or are you more built the modern with the vCloud Air being the foundation? You know, what spectrum of services are you offering customers? >> We offer the full spectrum. We had the opportunity to take OVH, all of our experience and systems, take the next generation of OVH in Europe, launch it in the U.S. and then bringing that back to Europe. So what we're launching in the U.S. is a full spectrum. The initial launch with VMware, fully hosted suite of the VMware products. So we have the VMware, the vSphere, vSAN, NSX offering that we've just announced. And having nine years of experience with vSphere as a service is a great opportunity to launch that. We also have a public cloud and that's the open source OpenStack public cloud, which is a different unique opportunity for a lot of companies that don't want to go the traditional public cloud. We also, being one of the largest dedicated server providers. It's all built on dedicated server, even server-less compute. And so you have to find a infrastructure partner that doesn't want to provide solutions first, and how do we rack and stack second. We understand the infrastructure and the network globally to help our partner's succeed. >> Ajay, I wonder if you could speak a little bit to the portfolio that your partners get to get access to from VMware. I was just interviewing Milin Desai, who you know oh so well. And the SAS piece is so, you know, it gets lost. You know, infrastructure as a service is one piece, but, you know, it's applications and services and, you know, yeah. >> Yeah, so far our cloud provider partners, what we've done is we introduced something we call Cloud Provider Platform. It gives them all the tools they need to sign up for Cloud, as Russ talked about, in a dedicated cloud. We give you a multi-tenant cloud. We're also, now with our cloud hub announcement, taking the VMware IP Cloud Services and making them available to our partners. And when you think about a partner on MSP, he's no longer just the asset heavy like OVH, but he's also the asset like a DXE. So we're now opening up the aperture for anyone who wants to either build clouds, or use clouds to offer managed services on top. I love the fact that OVH has economics, efficiency, and the customer support with the full VMware value proposition. They've always been the leader in kind of vSphere hosting, now they're offering a full private cloud built on VMware and the managed services go with it. So it's really about that choice, which really uniquely makes a provider program so compelling to our end customers. >> We've heard choice a lot. We hear it, Stu, at every show. Customers need choice, companies like VMware, OVH needs to build for what the customers want, not what you guys all think is great. Another thing that we've heard a lot at this show is that the seamlessness of the message, starting with Pat Gelsinger's keynote on Monday morning with people saying, you know, the structure is in place. I also thought it was one that was very cohesive in terms of the messaging and how the technologies are working together. I'm curious to get your feedback on what are some of the things that you've heard around this show from your customers who need a choice or in multi-cloud environments for many reasons, right? Applications that kind of dictate which direction that needs to go in, or through acquisition and, you know, have multiple cloud solutions. How are they taking this message? Especially with what you're doing with OVH in the U.S. And be able to digest this so they can really figure out, alright, here's what I can do with my infrastructure so that my business succeeds, whether I'm a bank or I'm a hospital. Tell us about that. >> I can go first and then Russ can add. So I think one of the things we've done a really good job this time is clarifying the message. I'm hoping, to the market, we're now becoming a very relevant and strategic platform that spans beyond the traditional VMware data center and hybrid cloud. So the first message is, you know, VMware is providing you the solutions while you're building on VMware or you're building on native clouds. And that CloudHealth acquisition is a good indication of VMware's commitment to kind of pure native public cloud. The second I would say is hybrid and this kind of consistent environment for runtime, if you will, and this hybrid control plane that give people a sense that I will lift and shift my workload first to an OVH and then transform leveraging the power of the public cloud. So it's become very pleasing to say, look, I don't need to change for changes sake, I can move and get economics off a public cloud, a dedicator, or even a pure multi-tenant. But then I can now refactor using public cloud services. So the power of VMware is giving them the flexibility to start a leverage cloud without having to make an upfront investment just for change sake, but more for the business transformation they're trying to drive, right? >> Yeah and so, what we've seen from the OVH side is really coming here and looking at all the partners. So we have Veam for backup, we can no offer Zerto for disaster recovery. Obviously, the VMware partnership we just launched earlier in the week which David Wigglesworth, our chief revenues officer was on talking about our partnership strategy and we have an amazing opportunity to bring partners in. FusionStorm is one of those partners, IT services. So OVH Cloud, we don't compete with our partners, true infrastructure partner with they can leverage our 28 data centers and our 15 terabytes of network and no charges for ingress. So what we're seeing here, our customers are coming and saying, hey, I just used you for DR but I'd like to actually take my on-prem full production system and bring it to the cloud now. So the customer's were migrating. There's more comfort going to the cloud, there's more understanding of the partnership ecosystem, and now instead of just saying, oh, we're going to just put DR or backup, we're going to come and we're going to migrate our entire production system because we've tried it out, our foot's been in the water, and now we're going all-in. So that's exciting and talking to all the customers this week, I love it. It's so exciting to talk to our customers that have migrated to OVH Cloud in the U.S. and now they want to bring over those production workloads. That's where it's really kind of that multi-cloud and I think VMware's been a huge asset to the cloud market in their strategy and a great partner. >> Getting that validation from your customers, the momentum that OVH is carrying is working. You've done a lot of education, especially in the last year. They're getting it and you're seeing your technologies and your partnership validating what it is that they're business needs. >> It's disheartening almost that the technology is in place now. We had to migrate from the vCloud Air into the OVH data center. Those tools, those best practices, those skills now are available to the end customer. So the compelling value here is, you want to take the entire data center and move it to OVH, we know how to do it. We have the tools, the people, the skills. And so just that kind of reference, the ability to say I'm not the first one to do it. It's been done before. That confidence is building in their business now. >> We had the opportunity, I mean, I don't want to say that there was a bleeding edge, but we were on a bleeding edge of HCX and it's working seamlessly now. >> Hybrid Cloud Exchange. >> The extension to bring, without any downtime, from on-prem to over to the cloud with OVH Cloud, or from the vCloud Air cloud over to ours. So it's working and the customer's are super excited, they get that trust. They go back to their management team and say, hey, now it's time to go more. I can go to the cloud and the cost efficiency, the savings, the redundancy of the network and the power, and not all this capex. That's why they're all moving to the cloud now. >> Final thing, you've talked about some good high level things. Any specific customer examples? I know you might not be able to mention names, but, you know, Vertical, or things like that as to how businesses are helping to transform themselves after they've done these sort of solutions. >> Yeah, sure, I mean first of all, it's all about the customer. So we, I can't mention any specific names, we will have some, we filmed some customer testimonials in the booth that we'll be announcing and maybe the next time we can bring customers up here to talk about it. Whether it's really education or high-tech, especially on a high-tech, the tech guys love OVH, right? They really love it. But from an infrastructure provider, people that are looking to lift and shift their existing applications without having to rewrite their applications for a public cloud, that's where OVH really comes into play. I've got all of these systems. I've got VMware on-prem, I need to move it but I don't want to rebuild it. So that's where we see the excitement of, of course I'm going to build some new stuff in the cloud, but how do I take all of my thousands of applications that we have, that we're never going to refactor and just move it over to the cloud to have that security. That's where I think customers are saying, wow, I can't actually do more in the cloud than I thought I could. >> For me, I think, I just walked out of a customer meeting, so I won't name them but just kind of give you a sense of what they're doing. They have four clouds, they believe they have monolithic applications, they don't want to be locked in to a particular cloud, so you're hearing the consistent view is, we're trying to figure out how do we change our development practices. You know, how do we leverage container, whether it's PKS, whether it's payloads. What's my development methodology? How do I make sure that deployment gives me a choice of running across clouds? How should I setup my IT operations to operate in the cloud? So consistency, portability, how do I manage the complexity of running on multiple clouds? What's my cost profile and how do I do it effective? So those are the kinds of questions we're getting. They're starting to look to VMware as a trusted advisor, that safe choice, as we talked about, to say, you know, one thing I can bet on is if I bet on VMware technology, it runs on more clouds than I can, you know, when I need them. It is portable, I can take a workload that traditionally I'd run on different hardware, now we run it on different clouds. So we're seeing a tremendous momentum around this notion of VMware's kind of the pathway or the hybrid control plane that we can bet on. And then partners like OVH, etc. But I have this destination that's safe, that's secure, that's consistent with what they're running today. So pretty exciting in terms of how customers kind of take in the message and start to put it into their strategy as they go forward. >> One more thing I'd like to understand, you talked about the tremendous capabilities that OVH and VMware have together with vCloud Air. You can enable customers to do a lot. To transform IT, to facilitate digital transformation. They're comfortable with this. But one of the things that that absolutely requires is cultural transformation. I'd love to get your final thoughts on how is OVH and VMware together helping your customers to understand and really impact the cultural changes that are need to take advantage, full advantage of the technology? >> That's a great question. Go ahead. >> On our side, what we try to do is, we as a company are going through the same transformation. We're a perpetual company becoming a services company. So the lessons learned, I spent some time where I-CIO actually talks about how we're operating our internal cloud. We're talking about the best practices of how we're moving to a service first mentality. How we're creating a CICD development mentality and practices. How are we leveraging public clouds and how are we managing cost? So those internal lessons learned, we're starting to make available to our customers and our partners. We're also packaging some of the products that we make available to VCP, our provider program. So the products that we're building up much more suited to run a services organization, which when we started five years ago at vCloud Air, we took enterprise products and tried to force fit them, now we're much more delivering our own service, eating our own dog food, if you will, have to incorporate that capability into our platform. So it's a combination of product improvement, best practices and lessons learned that we're making available to the market. >> I can talk from specific example. Acquiring vCloud Air, a great customer base, and all of the personnel from VMware in vCloud Air to come over. So not only was it cultural from a customers perspective, but also from an employee perspective. You build culture on trust. So what's interesting is that our employees and our customers that were over in Europe, in our U.K data centers and our German data centers, they're growing much quicker than the ones over in the U.S. The U.S. after a year of working with us and seeing that, hey, we say we're going to do this and now we're actually doing it, and I've migrated and that was really easy, and I can point and click and actually expand my compute and my storage and add more hosts in a matter of minutes. That builds trust, that has a great culture, and that spreads. So, from an education perspective, we have a lot of higher education customers, and now they're like, I'm going to go talk to this school and this school, and that word of mouth is golden. >> That validation of, we've been in your shoes, VMware has been in our shoes, they've done it successfully. Guys, I wish we had more time, but thanks so much for helping Stu and I wrap up the day on this set. It's great to talk to you both. I think it might be fair to say that we'll probably see you at the next VMworld on day three around the same time. >> Yeah, perfect! - Hopefully earlier. >> Maybe. Ajay, Russ, thank you so much for your time. >> Thank you. - Thank you so much, Lisa. >> For my co-host, Stu Miniman, I'm Lisa Martin. You're watching theCUBE's continuing coverage of VMworld 2018. Stick around, we'll be back to wrap up the show. (electronic music)

from Boston Massachusetts it's the cube covering wtg transform 2018 brought to you by Winslow technology group I'm Stu minimun and you're watching the Q's coverage of wtg transform 2018 happy to welcome to the program got a CIO in the house Russ Roderick who is the with chin bro that's correct how you doing to be here excellent so third year for yourself at the show first first impressions at the show what brings you back yeah third year I've really enjoyed it each of previous years I skipped a year last year travel related issue but I think the layout I like the naming convention change I like everything about it I think this is an excellent program that technology excellent they felt pity on me when they did the name change if you heard because saying the whole user group Dell EMC winslet Technology Group was a bit of a tongue twister but that's you know first world problems so Russ wrote for people that don't know tell us a little bit about chin bro yeah so Tim bro is a construction company headquartered in Pittsfield Maine we're nationwide where we do our projects we're in numerous markets from building infrastructure oil gas chemical industrial manufacturing we have a lot of projects done every year in the hundreds range and we were a little under a billion dollars got about 2,500 team members across the country okay and is the CIO you know how BIG's your team how many locations do you manage and you know tell us yeah let's go there for sure yes so I have 30 person team we have everything from application development to help desk to data analytics business intelligence teams I have infrastructure organization I have field support business analysts and we're a full-service organization all right so restless first start with with chin bro would you know what are some of the biggest challenges you're facing into business what does that term digital transformation resonate with with with your world it does the construction industry is very interesting it's a low margin industry right and so as a result of that we're always always looking to save money we're always looking to be lean we have a major initiative in the company around lean transformation some of that is digital related you know getting off of paper moving into paper forms into electronic forms and then not having to touch the data too many times so we have been moving too for quite a few years now so it's not new to the organization but it is a big challenge okay so absolutely we see that a lot what is the role of IT to the business these days so we have gone from being the elevator music as I like to call it like we're in the background you know we're kind of there to a strategic partner and I've reported from finance and I've moved to into the up to the CFO from CFO excuse me into the CEO role and it's been a great challenge there's some other organizational changes that we're looking at but we're no longer that background music we're very strategic in what we do we actually run several committees for the company looking at project prioritization workflow prioritizations and helping the organization to build an application roadmap new strategy yeah I love that I mean when you talk to us organizationally it says a lot you know under the CFO your call center we are moving under the CEO your strategic to the business right all right you talked about application so let's go there first because that's you know that application modernization is big challenge when I talk about modernization I always say you know application is a long pole in the tabs so what what does that mean to you what what kind of transformations are you going through inside yeah again our industry is dealing with a lot of fragmented software and it's a it's a problem a lot of industries face but ours I feel is we're on the very tail end of the industries that have moved to digital that have moved to modernization of our apps a lot of our products are on-premise our ERP system we've been on for 20 years for example and they've they've modernized we're getting it to mobile our imaging and workflow systems are starting to get more modernized but they're their legacy products and it's difficult when you have to deal with infrastructure that we have in the construction world we don't always have connectivity so we have to have a lot of offline we do all work with Citrix and it can be very problematic yeah you've been doing with the issues that we talked about with edge computing and IOT is you know something you've been living with for years right absolutely so IOT is one of those transformational pieces for us as well you know we have a lot of equipment that's one of the parts of our business you know 25,000 pieces of gear and heavy iron and we don't have a really good solution in place today that we're monitor all of the the meters and the performance and the of those devices so we're looking to IOT to help us get a better picture of that at an aggregate data level okay so I love we started up the stack with the applications let's come down a little bit tell us about your infrastructure what what are you using today how long you been using it for Winslow's in in the mix there's o windows absolutely in the next Dave we've moved from the traditional data center I had a lot of servers and racks and and moved to a V block and the X block with the X rail and our disaster recovery center out in Chicago and we did that because we had you know all the traditional problems of maintenance and and patching and licensing and everything and we never really had to deal with any of the power and heat and energy problems were up in Maine so we don't we're not an issue with cooling but that was a challenge for us having to manage all that stuff so we worked with Winslow and Dell EMC to figure out how do we get into the hyper converge to move away from all those server for administration and management and we did that project a little over a year ago implemented a little year ago and we've seen tremendous opportunities and for how we how we provision equipment and how we manage that need for the applications you know standing up new environments virtualizations really helped us do yeah so did you roll up the VX block and the VX rail together or the were those two sever they were slightly delayed so we did the X block first and then a few months later we did the the VX ray okay so the V block and the VX block after it been around for a number of years that you know 8 10 years at this point VX rails a little bit newer there's some people out there that are like oh wait HCI takes over the whole market tell us why each of those solutions and you know what was it an application price thing what what how did that go yeah we looked at it wasn't necessarily that we were in you know any position where you had to have this massive return on our investment right we built our business case around disaster recovery we our data center our primary or secondary were in the same town you know so we had all those elements of disaster recovery that were a problem for us so when we looked at the solution we wanted to be able to replicate our data more effectively get away from the traditional tape backup models and really help provision the equipment as quickly as we could in the event of you know a disaster and get ourselves recovered business continuity wise so it was a financial decision obviously that we had that we had thing had to pay for itself in which it did but we didn't spend a lot of time really kind of honing in on it has to be an ROI and a payback it was it was really about performance and disaster so is the VIX Bach in the primary and the BX rail and the secondary correct okay really cool on any you know what would you sell your peers is you after you've rolled us out any learnings that you had or the things that you'd go back and you know adjuster tweak no actually we had a really incredible experience working with with Winslow they were able to help us work through all of our any of our production issues very quickly we did replication through zurdo so that's moving our data very efficiently and my my IT team feels very confident that should and we're doing a test later this year but should we have a production incident we'd be able to recover lessons learned you know I think it's it's more about planning your work you know making sure that you've got your activities planned and that you're looking at the operational performance of the applications great all right Russ last question I have for you as a CIO how things been changing yeah you've been in the role for seven years you've moved inside the organization what are some of the things that are changing and some of the things that aren't yeah so the CIO role I believe personally is really kind of gearing towards more of a strategic element you know being part of that business making sure that we're not just providing those generic services of you know ping power and pipe right we're there for the business to understand what their business processes are all about and how they run their workflows and use their applications on a daily basis most of my conversations with my vice-president peers and and business leaders isn't around the technology that's being used yeah they're interested in iPhones and tablet devices but they're really geared at how do I help them solve their business problems and that's where IT is really evolving and the CIO role is doing a lot of that it wraps around that you know the teams you have around you and the strategic elements of the organization and just to follow up on that to do the SAS applications that your business using following under you is there any public cloud in there - what we do we have we have a hybrid cloud solution so we do a lot of on-premise solutions but we have several cloud and they're they're growing as needed but we have a business model that basically says let's look at our infrastructure internally first as a as an employee-owned private company we tend to gravitate towards keeping in-house but we have several business solutions that are in the cloud that are providing value all right well rust pleasure to meet you take so much for you to us and Congrats on everything that Kimbrough is done thank you very much back with lots more coverage here at wtt transform 2018 I'm Stu minimun and thanks so much for watching the Q [Music]