live from Barcelona Spain it's the cube covering Cisco live 2020s brought to you by Cisco and its ecosystem partners welcome back to Barcelona Spain everybody this is the cube the leader and live tech coverage and we're here day one for us at Cisco live Barcelona even though we did a little preview game preview yesterday my good friend kena Reilly is here he's the director of customer experience at Cisco and he's joined by Kyle winters Technical Marketing engineer for the customer experience technology and transformation group it's six to go guys great to see you thanks for coming on and you know we love talking customer experience Cisco is a it's a big company big portfolio and a lot of complexity for clients and so bring it all together and customer experience is very important can't we have it a conversation with Alastair early today and he was talking about Cisco's commitment from the top chuck Robbins on down to really improve that customer experience bring essentially a digital virtual experience to your customers and you guys obviously fit into that right absolutely so about two years ago when Chuck brought in Maria Martinez that was the first step into really pushing Cisco to focus more on successful outcomes for customers so we had already always sold that way but with the complexity of technology and how fast technology is moving accelerating value realization for customers has never been bigger especially in the security space because as we've talked before you know with everything that goes on today and the fact that the bad guys are trying to get data faster quicker and different getting the technology in play operational and production it has never been more important and we're gonna dig in with Kyle with some detail and double click into the lifecycle specifically and the different points of that journey but that's really important for any customer experience is really understanding that lifecycle that maturity model can you talk about that a little bit yeah so so with us you know we've been at it for about six years when we started as Lancope so we've got a great model and you know our approach to getting outcomes for customers is completely in line with with the strategy of our products and technologies and all security so it's really important that you align with that strategy because salespeople sell and they sell you the what we sell the how we're gonna get you and so you have to understand what it is that customers need and how that technology maps because you don't want a shelf where and you don't want products or technology sitting there waiting to be implemented because you know these days especially with the move to the cloud it's got to get up and running you know within an hour so our model has always been that way we built our model with customer first and so we are you know we are the security experts we're the trusted security adviser so when we go in and work with customers we completely know exactly those outcomes that they need and with all the sort of technologies and products that we have not only with stealthWatch but the other products that sent ulema tree to us we have in Kyle will talk about how our service is completely aligned with those outcomes and the journeys that we will take our customers on yes a faster adoption means faster time to value obviously let's focus in on stealthWatch Kenneth you came in with the stealthWatch acquisitions been very successful I mean Cisco security business grew 22% last quarter we'll talk more about the sort of umbrella but let's drill in with Kyle to stealthWatch services specifically maybe you could sort of take us through you know at a high level what what the areas are and then we can sort of follow up on yeah yes so so our customer maturity model when it comes to services there's kind of three different stages to it it starts with the visibility stage so we have services around being able to deploy an operational I stealthWatch will bring in our best practices and help customers get up to speed and using the system quickly and efficiently from there we also have services around detection capabilities so being able to use automation and integrations to further the detection capabilities of stealthWatch things like being able to classify host groups through automation from source like IP address management systems things like asset discovering classification service that helped drive segmentation efforts all of these things help improve the behavioral algorithms and processes that stealthWatch is using to detect these threats in real time and then from there we have an integration stage as well - which is all about bridging the gap between stealthWatch and the rest of not only Cisco's portfolio but the entirety of our customer security portfolio as well and some of those services include things like sim integrations being able to integrate stealthWatch with Splunk we have services such as our proxy integration service as well a lot of different types of services that we're able to help get our customers to the next stage with their stealth watch environments I got a lot of questions yeah we could get to it and you guys could take it by stage so yes the sort of visibility that's where you start that's when you do the discovery right so what what are you discovering how do you actually do that discovery so a lot of that is about making sure that we've got all the flow and telemetry that we need from the various different sources of our network coming into stealthWatch feeding into the processes and algorithms that are going on there so a lot of things is not only net flow data but getting ice integrated in there as well being able to pull that user attribution data and being able to find sources of data where we maybe can convert it into net flow if it's not already net flow and be able to ingest that data as well we also in that space typically to help set up customers with a lot of different best practices that kind of get them operationalized very quickly and things like being able to build custom reports and dashboards for them will work through them which is kind of understanding the system from a base level to more of a professional fully operational level a lot of times we come in during the stage two and customers don't even understand what's going on in their network they're seeing things that maybe they've never seen before one stealthWatch turns on a great example actually as we were at a large financial firm and we were able within 30 minutes of being on site with them through our services team we were able to identify rogue DNS servers unsecured telnet going on sequel injections suspicious SMB and that's the sage traffic this is all just within 30 minutes of us coming on there and taking a look at this stuff you don't even want to look at sometimes yeah so who's doing this can I mean is this sort of all automated you've got professionals sort of overseeing it in our society yeah so the team that we have the technology transformation team when we've talked about it before that team is kind of on the bleeding edge of helping customers and you know a lot of these services that that Kyle talked about is we are building services that customers are consuming based on their needs today and that's why the team is very flexible we build you know a lot of these integrations with those requirements in mind and then we take those and we can scale that so these are all field engineers we have developers so in in essence it is like a mini development team that goes out and works on the specific things that customers need to protect themselves okay and my understanding is there's a there's an ongoing learning with the customers and a it's a transfer of knowledge from day one right there the customer is with you on this in each of these phases and you're sort of learning as they go along and that's sort of part of the transfer of knowledge it's I would say even a tool a transfer knowledge too because we're teaching them our best practices and how to best be successful with these systems but we also learn from them what's going on what are the trends that they're seeing how can we help get them to the next stage and that's where our technology and transformation group comes and they're able to be on the cutting edge here the problems that the customers are talking about and be able to take stealthWatch to the next level okay let's dig it to the detection phase so this is where you're classifying things like host groups etc I'm interested in how that happens is that you know it used to be you'd get everybody in a room you start drawing pictures and that just doesn't scale it's too complicated today so can you auto classify stuff how does that all work and use them oh yeah genius math to do that so so traditionally the the you know the MIT's a manual effort to classify your whole group somebody who's very familiar with the network comes in and they say okay these are the DNS servers these are the web servers these are this network scanners oh oh today but the problem is that today's networks are so dynamic and fluid that what the network looks like today is not necessarily going to be the same tomorrow so there needs to be that relief from the analyst to be able to come in there needs to be that automation that they can go in each day and know that their system is going to be classified accurately and meaningfully that way the behavioral detection that is built into stealthWatch is also driven and accurate and meaningful - so we have this service so for example our host group automation service and through that we're able to pull in telemetry and data from various different sources such as IP address management systems cmdbs we can do threat feeds as well external threat feeds and we're able to drive the classification based off of the metadata that we see from these different sources so we're able to write different types of automation rules that essentially pull this data in detect the different patterns that we're seeing with that metadata and then drive that classification stealthWatch that way when you come in that next day you know that your network scanners are gonna be classified as Network scanners and your web servers are gonna be web servers etc etc so you you have that integrity of data coming in every single day yeah so a lot of different data sources data quality obviously really important I mean you'd love it if somebody had like you know a single CMDB from ServiceNow boom and pop it right in but that's not always the case we never always the case there's always a challenge and that's where kind of our services engineers come in they're able to work through these different environments and understand what the main admit what the metadata is where we need to go and how we need to classify and driving the classification from there so it does require a little bit of a human element on the front-end but once we get it worked out it can be fully automated you know there's lots of different sources and the quality of the data is not always there we've seen for example customers who have Excel spreadsheets and everything is just you're all over the place and we have to figure out a way to work with that and that's part of what our engineer success is so before we get to the integration piece can you been following this industry for for a while um security is really exciting space it's growing like crazy it's really hard I did a braking analysis piece you know a few weeks ago just talking about the fragmentation in the business you see startups coming out like crazy big valuations at the same time you see companies like Cisco with big portfolios yeah you mentioned Splunk before and they've kind of become a gold standard for for log files but very complex and you talk to security practitioners and they'll tell you our number one problem is just skillsets so get you know paint a picture of what's going on in the security world and what's in the house cisco is trying to address that so the security teams the analysts all the way up the management chain to the sea so they're under tremendous pressure their businesses are growing and so when their businesses are growing the sort of a tax base is growing and the business is growing faster than they can protect it so with the sort of increase in the economy more money more investment to build more point products so you've got a very stressed team a lot of turnover skill sets aren't great and what do we do as an industry we just give them more technology right more tools more tools complexity avalanche ok they're buried all right so we feel and we've made great strides within the security group within Cisco is we're taking the products that we have and we're integrating them under one platform so that it is in a bunch of point products and so that the that's what everybody else is doing I mean the other guys are acquiring companies then they're trying to integrate those because the customers are saying I don't need another point protocol yeah yeah it's too much so you know with us that's the way we approach it and now with the platform that's going to be launching this year the cisco threat response that we've launched you're gonna see later on in this year that we will be selling and positioned in implementing the entire platform yeah so I have a stat I came up with this and my one of my analyses it was the the worldwide economy is like 86 trillion and we spent about 0.014 percent on security so we're barely scratching the surface so this sort of tools avalanche probably isn't gonna change though integration becomes an extremely important aspect of the customer journeys and it's through that and to continue on that point you just made as well - I believe in our Cisco cybersecurity report from 2017 only fifty four six percent or fifty seven percent of actual threats are being investigated remediated so there's always that need to kind of help build bridge that gap make it easier for people to understand these threats and and mitigate and prioritize know what to go after right which part the integration exactly so we do have a lot of different integration services as well - for example I mentioned our sim integration service one thing that we can really do that's really awesome with that is we're able to deploy for example with Splunk a full-fledged stealthWatch for Splunk application that allows you to utilize stealth watches capabilities directly inside of Splunk without having to actually store an index any data inside of Splunk so all these api's are on demand inside of this app and available throughout the rest of the Splunk capabilities as well so you can extend it into other search reporting correlate that against other sets of data that you have and Splunk you can do quite a bit with it we also have other ways absolutely advantage of that is just obviously integration you're not leaving the environment plus its cost you're saving customers money a lot of a lot of customers kind of see their sim as a single pane of glass so being able to bring that stealthWatch value into that single pane is a huge win for our customers not to mention that reduction in licensing costs as well we have other ways to that we can reduce licensing costs some customers like to send their flow data into their sim for deeper analytics and long-term retention and we have a service we call it our flow adapter service and through this service we're essentially able to take buy flow off of the stealthWatch flow collectors and the buy flow is essentially when the raw net flow hits the stealthWatch flow collectors it's coming from multiple different routers and switches on the network this is gets converted into bi flow which is bi-directional deduplicated stitched together flow records so right there by sending that data into a sim or a data Lake as opposed to ronette flow we see data reduction cost anywhere from 15 to 80% depending on how the customers network is architected great any any favorite customer examples you have that you can share where ya guys have gone in you know provided these services and and it's had an outcome that got the customer excited or you found some bad guys or there's one that's one of my favorites so we have this service we call it our asset discovering classification service and I mentioned the host tree of automation service that's if you have some sort of authoritative source we can pull that information in but if a customer doesn't have that authoritative source they don't know what's on their network and a lot of times too they want to do a segmentation effort they're undergoing network segmentation but they need to understand what's on their network how these devices are communicating and that's where our asset discovery classification service comes in we're able to pull in telemetry not just from stealthWatch but other sources such as ice tetration Active Directory I Pam's again as well and we're able to essentially profile these different devices based off of the nature of their behavior so we were at a kind of a large technology company and we were essentially in this effort trying to segment their security cameras and upon segmenting their security cameras we were able to build this report where we can see the security camera and how its communicating with the other parts of the network and we noticed that there was essentially two IP addresses from inside of their network that were accessing all these different security cameras but they were not authorized to so with this service we were able to see that these different these two hosts were unauthorized actually accessing these devices that got reported up through the management chain and ultimately those two employees were no longer at that technology permanence that was discovered nice to love it alright bring us on we're here in the dev net zone sort of all about hit for structures code and software and and and and talk a little bit about the futures where you see this all going yeah so for us for Cisco security the future is really bright we've either built or acquired a portfolio that the customers really need that get absolute outcomes that customers need and through the customer experience organization certainly stealthWatch is fitting into the broader play to to get customers who have all those technologies get that operational and get them success so when we talked last summer I told you the jury was still out we would see how the journeys gonna go and the journey has started it has gotten much better since the summer and this year I think we're gonna be doing some great things for our customers just we can't get in too much of the business but stealthWatch customers are still expanding because I think we told you last time customers can never get enough stealthWatch okay the attack surface is too big right so so we we feel really good about that and the other technologies that they're building really fit into what customers need we're going to the cloud so they're gonna be able to consume cloud on-prem hybrid protect networks the campus protect their cloud infrastructure so we're really checking a lot of boxes in our group brings it all together and takes all the complexity out of that for customers just to get them the outcomes that I named us Cisco is one of my four star security companies for 2020 okay based on spending data that we share from our friends at ETR and the reason was because cisco has both a large presence in the market and but also you have spending momentum I mentioned 22% you know growth last quarter and the security business but you've also got the expertise you put your money where your mouth is you know the big portfolio which helps if you can bring it together and do these types of integrations it simplifies the customers environment and so that's a winner in my book so I named you along with some other high fliers right you know and you see some really interesting startups coming out and probably acquisition targets probably something that aren't your radar but guys thanks so much for coming on the cube thank you thank you I keep it right there everybody we'll be back with our next guest is a Dave Volante for the cubes 2 min Amanda John Faria are also in the house at Cisco live Barcelona right back

>>Okay. We're back live here. The velocity conference is Santa Clara live. This is the cube Silicon angle's flagship program. We go out to the events, restrict the signal from the noise. I'm John furry, the founder of Silicon angle. And our next guest is CEO of guarantee a data, his here in the cube. Welcome to the cube. Thank you. It's great to be here. We are at the velocity conference, which is really the intersection of infrastructure and application development, kind of in a holistic way, full stack new technologies. Um, so first tell us a little about your company and what you guys are doing here at velocity. >>Well, we deal with a new type of database, which took the developers community by storm. This is a no sequel in memory database called Radis where this is very, very fast. You know, it, it processes hundreds of thousand transactions per second at sub milliseconds. And this is all about performance. So velocity is the right right place to be when you deal with Radis. >>So why, why red is, first of all, is taking everyone by storm. We use it, um, great technology. Um, why, why, why is it so popular? >>Well is, has many attractive datatypes and commands, which are very useful in many, many use cases today for almost any application. So that's why, you know, developers really love it, >>The in-memory database. So we cover a lot of storage, SSDs and infrastructure. Um, SSDs had brought up, uh, with flash, a whole nother level of caching on the level for storage area networks really exploded open source scale-out. Um, but people still need the real fast, low latency data, no doubt. And that's where in memory, but developers don't need to be storage gurus to do that. So is that an area that you guys are? >>Yes, definitely. The basic idea is to provide developers what they need in terms of database needs, without all the hassle of, you know, operating those databases. So with our products, which with our product, which is called the Radice cloud right now, this product is provided as a fully managed hosting service over various clouds and platforms as a service. So with this product, the user does not need to do anything, simply send your data and forget about it. We take care of scalability, high availability, stabilizing performance, and all the ops. >>So one of the things about the web that's really challenging it's asynchronous, right? So persistence is a really big thing. How do you guys look at that channel? >>Okay. We have built a whole suite of high availability provisions for Radis. First of all, you can with a click of a button with a checkbox, you can replicate your data set within the same data center, uh, and when a node fails, and this is something which happens in the cloud almost everyday, we immediately, uh, switch your data to the, to the replica and, uh, you are up and running without any, any problem whatsoever. So this is one thing we recently last week, we announced another layer, which is multi a Z replication, which means that you can with a click of a button, replicate your data set to another data center. So if the entire data center fails, we immediately use the replica in the other data center, the backup replica. And again, you're up and running without any interruption. >>This really is a value proposition. That's as a dream scenario for developers with dealing with the cloud. I mean, because your alternative is to provision bare metal, exact load Linux systems >>Administrator. This is crazy. I mean, >>Oh, and cuing too, is another another issue. I mean, how do you know? So if I'm going to manage large volumes of data set to say that, um, my side becomes popular, my application becomes popular because, uh, someone shitted, virally, I want to have that queuing and that persistence that's really, really important. I might not have the time to provision a new server, a new database. So what you're saying is if I get this right with Reddis cloud, I can spin up in dynamically handle that those kinds of replication and persistence >>Over, you know, basic red. Is that right? Absolutely. Absolutely. You know, our native red is the open source is basically, uh, limited in scalability. You cannot grow beyond the single master server. Now the community is working for a while and something which is called Reddis cluster, which is supposed to solve all that. However, this is taking for a very long time with our Reddis cloud, you can grow your dataset from megabytes to Jigga bytes, to terabytes and even more, and all that is done in a fully automated manner without you do not need to deal with nodes, clustering, scaling, stuff like that. And while supporting all the data types and commands of Radis, which is really, really unique. >>Yeah. I mean, I got to say, you know, one of the challenges with the cloud is orchestration, right? And so that's one element. So automation has been a big problem for folks on premise on large enterprises and application developers. The other challenge has been real time. So a lot of apps need to have real time, like no JS or things of that nature. So how does a developer, I'm a developer and I'm, I want real time. I want persistence. And I want to have the flexibility to, to, to just push code and everything take care of itself. How does Reddis help me there? >>Well, red is, as I said, is the fastest data store available today, much faster than anything else. Like, you know, people talk to them about HANA SAP HANA, uh, red is, is, uh, 10 X, you know, in terms of speed, we are talking about hundreds of thousands transactions at sub-millisecond latencies. Whenever you want performance, whenever you need performance, the best database for that is rarely snow. >>Okay. So I got to ask you the question, first of all, big fan, really glad you're here in the cube. So we like, we like what you're doing, um, for the folks that don't understand what you guys are doing or are red or new to Retis. Why is it so good? Why is it so popular and what, what benefits does it provide the developer and say a business that wants to use that? >>I would say use cases, use cases, use cases whenever, whenever you, whenever you need a job management, for example, you know, signaling inside your, your, your application. So platforms such as sidekick, sidekicks, you know, et cetera, use Radice whenever you need, uh, stuff like, uh, Twitter type functionality, you know, followers, et cetera. You have a built in clone within radius for that whenever you need, uh, you know, uh, fast analytics, there is nothing better than red is caching, you know, already since replacing Memcached totally today, new apps, uh, page ranks, post ranks, you know, stuff like that. All these are great use cases for remedies. And if you, you know, in any one of those various is the best for that. Yeah. >>Well, congratulations, really like what you guys are doing. Um, and you're at the show here. What are you showing here at velocity? Again? Congratulations on your success. Well-deserved reticence is really becoming the standard. What, what are you guys doing here at velocity and what are you guys showing? >>We demonstrate, uh, first of all, the service we demonstrate the performance. You can, you know, if you have a minute drop over to our booth next door here, and we show the great performance, you know, we are showing hundred thousands of transactions, you know, with large databases in sub-millisecond latencies. This is, you know, this is real life and we are demonstrating our high availability with multi a Z replication and instant out of fail over. >>Okay, well, we are here with Ofer B gal with the system guarantee, a system data, um, Reddis cloud, great product, congratulations on your success. Thanks for coming inside the cube. This is the velocity conference. This is the kind of technology folks that velocity is about the loss of these, the intersection between a, almost a systems view of user experience, user design with cloud and infrastructure or dev ops, whatever you want to call it, we'll figure out a word for it, but it's really kind of coming together. I guess we call it velocity conference. This is the modern infrastructure that a lot of the web-scale companies or hyperscale companies are using and developers, developers who are small-scale today. We'll be, we'll be big scale. We'll use things like redness. This is what it's all about. This is the Silicon ankles flagship program. Go to youtube.com/looking angled to watch the videos go to siliconangle.com to get, to see the blog posts and coverage. We'll be right back with our next guest. After the short break.

>> From theCUBE studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Snowflake Summit 2022 underscored that the ecosystem excitement which was once forming around Hadoop is being reborn, escalated and coalescing around Snowflake's data cloud. What was once seen as a simpler cloud data warehouse and good marketing with the data cloud is evolving rapidly with new workloads of vertical industry focus, data applications, monetization, and more. The question is, will the promise of data be fulfilled this time around, or is it same wine, new bottle? Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this "Breaking Analysis," we'll talk about the event, the announcements that Snowflake made that are of greatest interest, the major themes of the show, what was hype and what was real, the competition, and some concerns that remain in many parts of the ecosystem and pockets of customers. First let's look at the overall event. It was held at Caesars Forum. Not my favorite venue, but I'll tell you it was packed. Fire Marshall Full, as we sometimes say. Nearly 10,000 people attended the event. Here's Snowflake's CMO Denise Persson on theCUBE describing how this event has evolved. >> Yeah, two, three years ago, we were about 1800 people at a Hilton in San Francisco. We had about 40 partners attending. This week we're close to 10,000 attendees here. Almost 10,000 people online as well, and over over 200 partners here on the show floor. >> Now, those numbers from 2019 remind me of the early days of Hadoop World, which was put on by Cloudera but then Cloudera handed off the event to O'Reilly as this article that we've inserted, if you bring back that slide would say. The headline it almost got it right. Hadoop World was a failure, but it didn't have to be. Snowflake has filled the void created by O'Reilly when it first killed Hadoop World, and killed the name and then killed Strata. Now, ironically, the momentum and excitement from Hadoop's early days, it probably could have stayed with Cloudera but the beginning of the end was when they gave the conference over to O'Reilly. We can't imagine Frank Slootman handing the keys to the kingdom to a third party. Serious business was done at this event. I'm talking substantive deals. Salespeople from a host sponsor and the ecosystems that support these events, they love physical. They really don't like virtual because physical belly to belly means relationship building, pipeline, and deals. And that was blatantly obvious at this show. And in fairness, all theCUBE events that we've done year but this one was more vibrant because of its attendance and the action in the ecosystem. Ecosystem is a hallmark of a cloud company, and that's what Snowflake is. We asked Frank Slootman on theCUBE, was this ecosystem evolution by design or did Snowflake just kind of stumble into it? Here's what he said. >> Well, when you are a data clouding, you have data, people want to do things with that data. They don't want just run data operations, populate dashboards, run reports. Pretty soon they want to build applications and after they build applications, they want build businesses on it. So it goes on and on and on. So it drives your development to enable more and more functionality on that data cloud. Didn't start out that way, you know, we were very, very much focused on data operations. Then it becomes application development and then it becomes, hey, we're developing whole businesses on this platform. So similar to what happened to Facebook in many ways. >> So it sounds like it was maybe a little bit of both. The Facebook analogy is interesting because Facebook is a walled garden, as is Snowflake, but when you come into that garden, you have assurances that things are going to work in a very specific way because a set of standards and protocols is being enforced by a steward, i.e. Snowflake. This means things run better inside of Snowflake than if you try to do all the integration yourself. Now, maybe over time, an open source version of that will come out but if you wait for that, you're going to be left behind. That said, Snowflake has made moves to make its platform more accommodating to open source tooling in many of its announcements this week. Now, I'm not going to do a deep dive on the announcements. Matt Sulkins from Monte Carlo wrote a decent summary of the keynotes and a number of analysts like Sanjeev Mohan, Tony Bear and others are posting some deeper analysis on these innovations, and so we'll point to those. I'll say a few things though. Unistore extends the type of data that can live in the Snowflake data cloud. It's enabled by a new feature called hybrid tables, a new table type in Snowflake. One of the big knocks against Snowflake was it couldn't handle and transaction data. Several database companies are creating this notion of a hybrid where both analytic and transactional workloads can live in the same data store. Oracle's doing this for example, with MySQL HeatWave and there are many others. We saw Mongo earlier this month add an analytics capability to its transaction system. Mongo also added sequel, which was kind of interesting. Here's what Constellation Research analyst Doug Henschen said about Snowflake's moves into transaction data. Play the clip. >> Well with Unistore, they're reaching out and trying to bring transactional data in. Hey, don't limit this to analytical information and there's other ways to do that like CDC and streaming but they're very closely tying that again to that marketplace, with the idea of bring your data over here and you can monetize it. Don't just leave it in that transactional database. So another reach to a broader play across a big community that they're building. >> And you're also seeing Snowflake expand its workload types in its unique way and through Snowpark and its stream lit acquisition, enabling Python so that native apps can be built in the data cloud and benefit from all that structure and the features that Snowflake is built in. Hence that Facebook analogy, or maybe the App Store, the Apple App Store as I propose as well. Python support also widens the aperture for machine intelligence workloads. We asked Snowflake senior VP of product, Christian Kleinerman which announcements he thought were the most impactful. And despite the who's your favorite child nature of the question, he did answer. Here's what he said. >> I think the native applications is the one that looks like, eh, I don't know about it on the surface but he has the biggest potential to change everything. That's create an entire ecosystem of solutions for within a company or across companies that I don't know that we know what's possible. >> Snowflake also announced support for Apache Iceberg, which is a new open table format standard that's emerging. So you're seeing Snowflake respond to these concerns about its lack of openness, and they're building optionality into their cloud. They also showed some cost op optimization tools both from Snowflake itself and from the ecosystem, notably Capital One which launched a software business on top of Snowflake focused on optimizing cost and eventually the rollout data management capabilities, and all kinds of features that Snowflake announced that the show around governance, cross cloud, what we call super cloud, a new security workload, and they reemphasize their ability to read non-native on-prem data into Snowflake through partnerships with Dell and Pure and a lot more. Let's hear from some of the analysts that came on theCUBE this week at Snowflake Summit to see what they said about the announcements and their takeaways from the event. This is Dave Menninger, Sanjeev Mohan, and Tony Bear, roll the clip. >> Our research shows that the majority of organizations, the majority of people do not have access to analytics. And so a couple of the things they've announced I think address those or help to address those issues very directly. So Snowpark and support for Python and other languages is a way for organizations to embed analytics into different business processes. And so I think that'll be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most people in the organization are not analysts. They're doing some line of business function. They're HR managers, they're marketing people, they're sales people, they're finance people, right? They're not sitting there mucking around in the data, they're doing a job and they need analytics in that job. >> Primarily, I think it is to contract this whole notion that once you move data into Snowflake, it's a proprietary format. So I think that's how it started but it's usually beneficial to the customers, to the users because now if you have large amount of data in paket files you can leave it on S3, but then you using the Apache Iceberg table format in Snowflake, you get all the benefits of Snowflake's optimizer. So for example, you get the micro partitioning, you get the metadata. And in a single query, you can join, you can do select from a Snowflake table union and select from an iceberg table and you can do store procedure, user defined function. So I think what they've done is extremely interesting. Iceberg by itself still does not have multi-table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache Iceberg in a raw format, they don't have it, but Snowflake does. So the way I see it is Snowflake is adding more and more capabilities right into the database. So for example, they've gone ahead and added security and privacy. So you can now create policies and do even cell level masking, dynamic masking, but most organizations have more than Snowflake. So what we are starting to see all around here is that there's a whole series of data catalog companies, a bunch of companies that are doing dynamic data masking, security and governance, data observability which is not a space Snowflake has gone into. So there's a whole ecosystem of companies that is mushrooming. Although, you know, so they're using the native capabilities of Snowflake but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other like relational databases, you can run these cross platform capabilities in that layer. So that way, you know, Snowflake's done a great job of enabling that ecosystem. >> I think it's like the last mile, essentially. In other words, it's like, okay, you have folks that are basically that are very comfortable with Tableau but you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency. To Sanjeev's point, and I think part of it, this kind of plays into it is what makes this different from the Hadoop era is the fact that all these capabilities, you know, a lot of vendors are taking it very seriously to put this native. Now, obviously Snowflake acquired Streamlit. So we can expect that the Streamlit capabilities are going to be native. >> I want to share a little bit about the higher level thinking at Snowflake, here's a chart from Frank Slootman's keynote. It's his version of the modern data stack, if you will. Now, Snowflake of course, was built on the public cloud. If there were no AWS, there would be no Snowflake. Now, they're all about bringing data and live data and expanding the types of data, including structured, we just heard about that, unstructured, geospatial, and the list is going to continue on and on. Eventually I think it's going to bleed into the edge if we can figure out what to do with that edge data. Executing on new workloads is a big deal. They started with data sharing and they recently added security and they've essentially created a PaaS layer. We call it a SuperPaaS layer, if you will, to attract application developers. Snowflake has a developer-focused event coming up in November and they've extended the marketplace with 1300 native apps listings. And at the top, that's the holy grail, monetization. We always talk about building data products and we saw a lot of that at this event, very, very impressive and unique. Now here's the thing. There's a lot of talk in the press, in the Wall Street and the broader community about consumption-based pricing and concerns over Snowflake's visibility and its forecast and how analytics may be discretionary. But if you're a company building apps in Snowflake and monetizing like Capital One intends to do, and you're now selling in the marketplace, that is not discretionary, unless of course your costs are greater than your revenue for that service, in which case is going to fail anyway. But the point is we're entering a new error where data apps and data products are beginning to be built and Snowflake is attempting to make the data cloud the defacto place as to where you're going to build them. In our view they're well ahead in that journey. Okay, let's talk about some of the bigger themes that we heard at the event. Bringing apps to the data instead of moving the data to the apps, this was a constant refrain and one that certainly makes sense from a physics point of view. But having a single source of data that is discoverable, sharable and governed with increasingly robust ecosystem options, it doesn't have to be moved. Sometimes it may have to be moved if you're going across regions, but that's unique and a differentiator for Snowflake in our view. I mean, I'm yet to see a data ecosystem that is as rich and growing as fast as the Snowflake ecosystem. Monetization, we talked about that, industry clouds, financial services, healthcare, retail, and media, all front and center at the event. My understanding is that Frank Slootman was a major force behind this shift, this development and go to market focus on verticals. It's really an attempt, and he talked about this in his keynote to align with the customer mission ultimately align with their objectives which not surprisingly, are increasingly monetizing with data as a differentiating ingredient. We heard a ton about data mesh, there were numerous presentations about the topic. And I'll say this, if you map the seven pillars Snowflake talks about, Benoit Dageville talked about this in his keynote, but if you map those into Zhamak Dehghani's data mesh framework and the four principles, they align better than most of the data mesh washing that I've seen. The seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace and governance. Those are the seven pillars that he talked about in his keynote. All data, well, maybe with hybrid tables that becomes more of a reality. Global architecture means the data is globally distributed. It's not necessarily physically in one place. Self-managed is key. Self-service infrastructure is one of Zhamak's four principles. And then inherent governance. Zhamak talks about computational, what I'll call automated governance, built in. And with all the talk about monetization, that aligns with the second principle which is data as product. So while it's not a pure hit and to its credit, by the way, Snowflake doesn't use data mesh in its messaging anymore. But by the way, its customers do, several customers talked about it. Geico, JPMC, and a number of other customers and partners are using the term and using it pretty closely to the concepts put forth by Zhamak Dehghani. But back to the point, they essentially, Snowflake that is, is building a proprietary system that substantially addresses some, if not many of the goals of data mesh. Okay, back to the list, supercloud, that's our term. We saw lots of examples of clouds on top of clouds that are architected to spin multiple clouds, not just run on individual clouds as separate services. And this includes Snowflake's data cloud itself but a number of ecosystem partners that are headed in a very similar direction. Snowflake still talks about data sharing but now it uses the term collaboration in its high level messaging, which is I think smart. Data sharing is kind of a geeky term. And also this is an attempt by Snowflake to differentiate from everyone else that's saying, hey, we do data sharing too. And finally Snowflake doesn't say data marketplace anymore. It's now marketplace, accounting for its application market. Okay, let's take a quick look at the competitive landscape via this ETR X-Y graph. Vertical access remembers net score or spending momentum and the x-axis is penetration, pervasiveness in the data center. That's what ETR calls overlap. Snowflake continues to lead on the vertical axis. They guide it conservatively last quarter, remember, so I wouldn't be surprised if that lofty height, even though it's well down from its earlier levels but I wouldn't be surprised if it ticks down again a bit in the July survey, which will be in the field shortly. Databricks is a key competitor obviously at a strong spending momentum, as you can see. We didn't draw it here but we usually draw that 40% line or red line at 40%, anything above that is considered elevated. So you can see Databricks is quite elevated. But it doesn't have the market presence of Snowflake. It didn't get to IPO during the bubble and it doesn't have nearly as deep and capable go-to market machinery. Now, they're getting better and they're getting some attention in the market, nonetheless. But as a private company, you just naturally, more people are aware of Snowflake. Some analysts, Tony Bear in particular, believe Mongo and Snowflake are on a bit of a collision course long term. I actually can see his point. You know, I mean, they're both platforms, they're both about data. It's long ways off, but you can see them sort of in a similar path. They talk about kind of similar aspirations and visions even though they're quite in different markets today but they're definitely participating in similar tam. The cloud players are probably the biggest or definitely the biggest partners and probably the biggest competitors to Snowflake. And then there's always Oracle. Doesn't have the spending velocity of the others but it's got strong market presence. It owns a cloud and it knows a thing about data and it definitely is a go-to market machine. Okay, we're going to end on some of the things that we heard in the ecosystem. 'Cause look, we've heard before how particular technology, enterprise data warehouse, data hubs, MDM, data lakes, Hadoop, et cetera. We're going to solve all of our data problems and of course they didn't. And in fact, sometimes they create more problems that allow vendors to push more incremental technology to solve the problems that they created. Like tools and platforms to clean up the no schema on right nature of data lakes or data swamps. But here are some of the things that I heard firsthand from some customers and partners. First thing is, they said to me that they're having a hard time keeping up sometimes with the pace of Snowflake. It reminds me of AWS in 2014, 2015 timeframe. You remember that fire hose of announcements which causes increased complexity for customers and partners. I talked to several customers that said, well, yeah this is all well and good but I still need skilled people to understand all these tools that I'm integrated in the ecosystem, the catalogs, the machine learning observability. A number of customers said, I just can't use one governance tool, I need multiple governance tools and a lot of other technologies as well, and they're concerned that that's going to drive up their cost and their complexity. I heard other concerns from the ecosystem that it used to be sort of clear as to where they could add value you know, when Snowflake was just a better data warehouse. But to point number one, they're either concerned that they'll be left behind or they're concerned that they'll be subsumed. Look, I mean, just like we tell AWS customers and partners, you got to move fast, you got to keep innovating. If you don't, you're going to be left. Either if your customer you're going to be left behind your competitor, or if you're a partner, somebody else is going to get there or AWS is going to solve the problem for you. Okay, and there were a number of skeptical practitioners, really thoughtful and experienced data pros that suggested that they've seen this movie before. That's hence the same wine, new bottle. Well, this time around I certainly hope not given all the energy and investment that is going into this ecosystem. And the fact is Snowflake is unquestionably making it easier to put data to work. They built on AWS so you didn't have to worry about provisioning, compute and storage and networking and scaling. Snowflake is optimizing its platform to take advantage of things like Graviton so you don't have to, and they're doing some of their own optimization tools. The ecosystem is building optimization tools so that's all good. And firm belief is the less expensive it is, the more data will get brought into the data cloud. And they're building a data platform on which their ecosystem can build and run data applications, aka data products without having to worry about all the hard work that needs to get done to make data discoverable, shareable, and governed. And unlike the last 10 years, you don't have to be a keeper and integrate all the animals in the Hadoop zoo. Okay, that's it for today, thanks for watching. Thanks to my colleague, Stephanie Chan who helps research "Breaking Analysis" topics. Sometimes Alex Myerson is on production and manages the podcasts. Kristin Martin and Cheryl Knight help get the word out on social and in our newsletters, and Rob Hof is our editor in chief over at Silicon, and Hailey does some wonderful editing, thanks to all. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis Podcasts. I publish each week on wikibon.com and siliconangle.com and you can email me at David.Vellante@siliconangle.com or DM me @DVellante. If you got something interesting, I'll respond. If you don't, I'm sorry I won't. Or comment on my LinkedIn post. Please check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time. (upbeat music)

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 data mesh is a new way of thinking about how to use data to create organizational value leading edge practitioners are beginning to implement data mesh in earnest and importantly data mesh is not a single tool or a rigid reference architecture if you will rather it's an architectural and organizational model that's really designed to address the shortcomings of decades of data challenges and failures many of which we've talked about on the cube as important by the way it's a new way to think about how to leverage data at scale across an organization and across ecosystems data mesh in our view will become the defining paradigm for the next generation of data excellence hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we welcome the founder and creator of data mesh author thought leader technologist jamaak dagani shamak thank you for joining us today good to see you hi dave it's great to be here all right real quick let's talk about what we're going to cover i'll introduce or reintroduce you to jamaac she joined us earlier this year in our cube on cloud program she's the director of emerging tech at dot works north america and a thought leader practitioner software engineer architect and a passionate advocate for decentralized technology solutions and and data architectures and jamaa since we last had you on as a guest which was less than a year ago i think you've written two books in your spare time one on data mesh and another called software architecture the hard parts both published by o'reilly so how are you you've been busy i've been busy yes um good it's been a great year it's been a busy year i'm looking forward to the end of the year and the end of these two books but it's great to be back and um speaking with you well you got to be pleased with the the momentum that data mesh has and let's just jump back to the agenda for a bit and get that out of the way we're going to set the stage by sharing some etr data our partner our data partner on the spending profile and some of the key data sectors and then we're going to review the four key principles of data mesh just it's always worthwhile to sort of set that framework we'll talk a little bit about some of the dependencies and the data flows and we're really going to dig today into principle number three and a bit around the self-service data platforms and to that end we're going to talk about some of the learnings that shamak has captured since she embarked on the datamess journey with her colleagues and her clients and we specifically want to talk about some of the successful models for building the data mesh experience and then we're going to hit on some practical advice and we'll wrap with some thought exercises maybe a little tongue-in-cheek some of the community questions that we get so the first thing i want to do we'll just get this out of the way is introduce the spending climate we use this xy chart to do this we do this all the time it shows the spending profiles and the etr data set for some of the more data related sectors of the ecr etr taxonomy they they dropped their october data last friday so i'm using the july survey here we'll get into the october survey in future weeks but about 1500 respondents i don't see a dramatic change coming in the october survey but the the y-axis is net score or spending momentum the horizontal axis is market share or presence in the data set and that red line that 40 percent anything over that we consider elevated so for the past eight quarters or so we've seen machine learning slash ai rpa containers and cloud is the four areas where cios and technology buyers have shown the highest net scores and as we've said what's so impressive for cloud is it's both pervasive and it shows high velocity from a spending standpoint and we plotted the three other data related areas database edw analytics bi and big data and storage the first two well under the red line are still elevated the storage market continues to kind of plot along and we've we've plotted the outsourced it just to balance it out for context that's an area that's not so hot right now so i just want to point out that these areas ai automation containers and cloud they're all relevant to data and they're fundamental building blocks of data architectures as are the two that are directly related to data database and analytics and of course storage so it just gives you a picture of the spending sector so i wanted to share this slide jamark uh that that we presented in that you presented in your webinar i love this it's a taxonomy put together by matt turk who's a vc and he called this the the mad landscape machine learning and ai and data and jamock the key point here is there's no lack of tooling you've you've made the the data mesh concept sort of tools agnostic it's not like we need more tools to succeed in data mesh right absolutely great i think we have plenty of tools i think what's missing is a meta architecture that defines the landscape in a way that it's in step with organizational growth and then defines that meta architecture in a way that these tools can actually interoperable and to operate and integrate really well like the the clients right now have a lot of challenges in terms of picking the right tool regardless of the technology they go down the path either they have to go in and big you know bite into a big data solution and then try to fit the other integrated solutions around it or as you see go to that menu of large list of applications and spend a lot of time trying to kind of integrate and stitch this tooling together so i'm hoping that data mesh creates that kind of meta architecture for tools to interoperate and plug in and i think our conversation today around self-subjective platform um hopefully eliminate that yeah we'll definitely circle back because that's one of the questions we get all the time from the community okay let's review the four main principles of data mesh for those who might not be familiar with it and those who are it's worth reviewing jamar allow me to introduce them and then we can discuss a bit so a big frustration i hear constantly from practitioners is that the data teams don't have domain context the data team is separated from the lines of business and as a result they have to constantly context switch and as such there's a lack of alignment so principle number one is focused on putting end-to-end data ownership in the hands of the domain or what i would call the business lines the second principle is data as a product which does cause people's brains to hurt sometimes but it's a key component and if you start sort of thinking about it you'll and talking to people who have done it it actually makes a lot of sense and this leads to principle number three which is a self-serve data infrastructure which we're going to drill into quite a bit today and then the question we always get is when we introduce data meshes how to enforce governance in a federated model so let me bring up a more detailed slide jamar with the dependencies and ask you to comment please sure but as you said the the really the root cause we're trying to address is the siloing of the data external to where the action happens where the data gets produced where the data needs to be shared when the data gets used right in the context of the business so it's about the the really the root cause of the centralization gets addressed by distribution of the accountability end to end back to the domains and these domains this distribution of accountability technical accountability to the domains have already happened in the last you know decade or so we saw the transition from you know one general i.t addressing all of the needs of the organization to technology groups within the itu or even outside of the iit aligning themselves to build applications and services that the different business units need so what data mesh does it just extends that model and say okay we're aligning business with the tech and data now right so both application of the data in ml or inside generation in the domains related to the domain's needs as well as sharing the data that the domains are generating with the rest of the organization but the moment you do that then you have to solve other problems that may arise and that you know gives birth to the second principle which is about um data as a product as a way of preventing data siloing happening within the domain so changing the focus of the domains that are now producing data from i'm just going to create that data i collect for myself and that satisfy my needs to in fact the responsibility of domain is to share the data as a product with all of the you know wonderful characteristics that a product has and i think that leads to really interesting architectural and technical implications of what actually constitutes state has a product and we can have a separate conversation but once you do that then that's the point in the conversation that cio says well how do i even manage the cost of operation if i decentralize you know building and sharing data to my technical teams to my application teams do i need to go and hire another hundred data engineers and i think that's the role of a self-serve data platform in a way that it enables and empowers generalist technologies that we already have in the technical domains the the majority population of our developers these days right so the data platform attempts to mobilize the generalist technologies to become data producers to become data consumers and really rethink what tools these people need um and the last last principle so data platform is really to giving autonomy to domain teams and empowering them and reducing the cost of ownership of the data products and finally as you mentioned the question around how do i still assure that these different data products are interoperable are secure you know respecting privacy now in a decentralized fashion right when we are respecting the sovereignty or the domain ownership of um each domain and that leads to uh this idea of both from operational model um you know applying some sort of a federation where the domain owners are accountable for interoperability of their data product they have incentives that are aligned with global harmony of the data mesh as well as from the technology perspective thinking about this data is a product with a new lens with a lens that all of those policies that need to be respected by these data products such as privacy such as confidentiality can we encode these policies as computational executable units and encode them in every data product so that um we get automation we get governance through automation so that's uh those that's the relationship the complex relationship between the four principles yeah thank you for that i mean it's just a couple of points there's so many important points in there but the idea of the silos and the data as a product sort of breaking down those cells because if you have a product and you want to sell more of it you make it discoverable and you know as a p l manager you put it out there you want to share it as opposed to hide it and then you know this idea of managing the cost you know number three where people say well centralize and and you can be more efficient but that but that essentially was the the failure in your other point related point is generalist versus specialist that's kind of one of the failures of hadoop was you had these hyper specialist roles emerge and and so you couldn't scale and so let's talk about the goals of data mesh for a moment you've said that the objective is to extend exchange you call it a new unit of value between data producers and data consumers and that unit of value is a data product and you've stated that a goal is to lower the cognitive load on our brains i love this and simplify the way in which data are presented to both producers and consumers and doing so in a self-serve manner that eliminates the tapping on the shoulders or emails or raising tickets so how you know i'm trying to understand how data should be used etc so please explain why this is so important and how you've seen organizations reduce the friction across the data flows and the interconnectedness of things like data products across the company yeah i mean this is important um as you mentioned you know initially when this whole idea of a data-driven innovation came to exist and we needed all sorts of you know technology stacks we we centralized um creation of the data and usage of the data and that's okay when you first get started with where the expertise and knowledge is not yet diffused and it's only you know the privilege of a very few people in the organization but as we move to a data driven um you know innovation cycle in the organization as we learn how data can unlock new new programs new models of experience new products then it's really really important as you mentioned to get the consumers and producers talk to each other directly without a broker in the middle because even though that having that centralized broker could be a cost-effective model but if you if we include uh the cost of missed opportunity for something that we could have innovated well we missed that opportunity because of months of looking for the right data then that cost parented the cost benefit parameters and formula changes so um so to to have that innovation um really embedded data-driven innovation embedded into every domain every team we need to enable a model where the producer can directly peer-to-peer discover the data uh use it understand it and use it so the litmus test for that would be going from you know a hypothesis that you know as a data scientist i think there is a pattern and there is an insight in um you know in in the customer behavior that if i have access to all of the different informations about the customer all of the different touch points i might be able to discover that pattern and personalize the experience of my customer the liquid stuff is going from that hypothesis to finding all of the different sources be able to understanding and be able to connect them um and then turn them them into you know training of my machine learning and and the rest is i guess known as an intelligent product got it thank you so i i you know a lot of what we do here in breaking it house is we try to curate and then point people to new resources so we will have some additional resources because this this is not superficial uh what you and your colleagues in the community are creating but but so i do want to you know curate some of the other material that you had so if i bring up this next chart the left-hand side is a curated description both sides of your observations of most of the monolithic data platforms they're optimized for control they serve a centralized team that has hyper-specialized roles as we talked about the operational stacks are running running enterprise software they're on kubernetes and the microservices are isolated from let's say the spark clusters you know which are managing the analytical data etc whereas the data mesh proposes much greater autonomy and the management of code and data pipelines and policy as independent entities versus a single unit and you've made this the point that we have to enable generalists to borrow from so many other examples in the in the industry so it's an architecture based on decentralized thinking that can really be applied to any domain really domain agnostic in a way yes and i think if i pick one key point from that diagram is really um or that comparison is the um the the the data platforms or the the platform capabilities need to present a continuous experience from an application developer building an application that generates some data let's say i have an e-commerce application that generates some data to the data product that now presents and shares that data as as temporal immutable facts that can be used for analytics to the data scientist that uses that data to personalize the experience to the deployment of that ml model now back to that e-commerce application so if we really look at this continuous journey um the walls between these separate platforms that we have built needs to come down the platforms underneath that generate you know that support the operational systems versus supported data platforms versus supporting the ml models they need to kind of play really nicely together because as a user i'll probably fall off the cliff every time i go through these stages of this value stream um so then the interoperability of our data solutions and operational solutions need to increase drastically because so far we've got away with running operational systems an application on one end of the organization running and data analytics in another and build a spaghetti pipeline to you know connect them together neither of the ends are happy i hear from data scientists you know data analyst pointing finger at the application developer saying you're not developing your database the right way and application point dipping you're saying my database is for running my application it wasn't designed for sharing analytical data so so we've got to really what data mesh as a mesh tries to do is bring these two world together closer because and then the platform itself has to come closer and turn into a continuous set of you know services and capabilities as opposed to this disjointed big you know isolated stacks very powerful observations there so we want to dig a little bit deeper into the platform uh jamar can have you explain your thinking here because it's everybody always goes to the platform what do i do with the infrastructure what do i do so you've stressed the importance of interfaces the entries to and the exits from the platform and you've said you use a particular parlance to describe it and and this chart kind of shows what you call the planes not layers the planes of the platform it's complicated with a lot of connection points so please explain these planes and how they fit together sure i mean there was a really good point that you started with that um when we think about capabilities or that enables build of application builds of our data products build their analytical solutions usually we jump too quickly to the deep end of the actual implementation of these technologies right do i need to go buy a data catalog or do i need you know some sort of a warehouse storage and what i'm trying to kind of elevate us up and out is to to to force us to think about interfaces and apis the experiences that the platform needs to provide to run this secure safe trustworthy you know performance mesh of data products and if you focus on then the interfaces the implementation underneath can swap out right you can you can swap one for the other over time so that's the purpose of like having those lollipops and focusing and emphasizing okay what is the interface that provides a certain capability like the storage like the data product life cycle management and so on the purpose of the planes the mesh experience playing data product expense utility plan is really giving us a language to classify different set of interfaces and capabilities that play nicely together to provide that cohesive journey of a data product developer data consumer so then the three planes are really around okay at the bottom layer we have a lot of utilities we have that mad mac turks you know kind of mad data tooling chart so we have a lot of utilities right now they they manage workflow management you know they they do um data processing you've got your spark link you've got your storage you've got your lake storage you've got your um time series of storage you've got a lot of tooling at that level but the layer that we kind of need to imagine and build today we don't buy yet as as long as i know is this linger that allows us to uh exchange that um unit of value right to build and manage these data products so so the language and the apis and interface of this product data product experience plan is not oh i need this storage or i need that you know workflow processing is that i have a data product it needs to deliver certain types of data so i need to be able to model my data it needs to as part of this data product i need to write some processing code that keeps this data constantly alive because it's receiving you know upstream let's say user interactions with a website and generating the profile of my user so i need to be able to to write that i need to serve the data i need to keep the data alive and i need to provide a set of slos and guarantees for my data so that good documentation so that some you know someone who comes to data product knows but what's the cadence of refresh what's the retention of the data and a lot of other slos that i need to provide and finally i need to be able to enforce and guarantee certain policies in terms of access control privacy encryption and so on so as a data product developer i just work with this unit a complete autonomous self-contained unit um and the platform should give me ways of provisioning this unit and testing this unit and so on that's why kind of i emphasize on the experience and of course we're not dealing with one or two data product we're dealing with a mesh of data products so at the kind of mesh level experience we need a set of capabilities and interfaces to be able to search the mesh for the right data to be able to explore the knowledge graph that emerges from this interconnection of data products need to be able to observe the mesh for any anomalies did we create one of these giant master data products that all the data goes into and all the data comes out of how we found ourselves the bottlenecks to be able to kind of do those level machine level capabilities we need to have a certain level of apis and interfaces and once we decide and decide what constitutes that to satisfy this mesh experience then we can step back and say okay now what sort of a tool do i need to build or buy to satisfy them and that's that is not what the data community or data part of our organizations used to i think traditionally we're very comfortable with buying a tool and then changing the way we work to serve to serve the tool and this is slightly inverse to that model that we might be comfortable with right and pragmatists will will to tell you people who've implemented data match they'll tell you they spent a lot of time on figuring out data as a product and the definitions there the organizational the getting getting domain experts to actually own the data and and that's and and they will tell you look the technology will come and go and so to your point if you have those lollipops and those interfaces you'll be able to evolve because we know one thing's for sure in this business technology is going to change um so you you had some practical advice um and i wanted to discuss that for those that are thinking about data mesh i scraped this slide from your presentation that you made and and by the way we'll put links in there your colleague emily who i believe is a data scientist had some really great points there as well that that practitioners should dig into but you made a couple of points that i'd like you to summarize and to me that you know the big takeaway was it's not a one and done this is not a 60-day project it's a it's a journey and i know that's kind of cliche but it's so very true here yes um this was a few starting points for um people who are embarking on building or buying the platform that enables the people enables the mesh creation so it was it was a bit of a focus on kind of the platform angle and i think the first one is what we just discussed you know instead of thinking about mechanisms that you're building think about the experiences that you're enabling uh identify who are the people like what are the what is the persona of data scientists i mean data scientist has a wide range of personas or did a product developer the same what is the persona i need to develop today or enable empower today what skill sets do they have and and so think about experience as mechanisms i think we are at this really magical point i mean how many times in our lifetime we come across a complete blanks you know kind of white space to a degree to innovate so so let's take that opportunity and use a bit of a creativity while being pragmatic of course we need solutions today or yesterday but but still think about the experiences not not mechanisms that you need to buy so that was kind of the first step and and the nice thing about that is that there is an evolutionary there is an iterative path to maturity of your data mesh i mean if you start with thinking about okay which are the initial use cases i need to enable what are the data products that those use cases depend on that we need to unlock and what is the persona of my or general skill set of my data product developer what are the interfaces i need to enable you can start with the simplest possible platform for your first two use cases and then think about okay the next set of data you know data developers they have a different set of needs maybe today i just enable the sql-like querying of the data tomorrow i enable the data scientists file based access of the data the day after i enable the streaming aspect so so have this evolutionary kind of path ahead of you and don't think that you have to start with building out everything i mean one of the things we've done is taking this harvesting approach that we work collaboratively with those technical cross-functional domains that are building the data products and see how they are using those utilities and harvesting what they are building as the solutions for themselves back into the back into the platform but at the end of the day we have to think about mobilization of the large you know largest population of technologies we have we'd have to think about diffusing the technology and making it available and accessible by the generous technologies that you know and we've come a long way like we've we've gone through these sort of paradigm shifts in terms of mobile development in terms of functional programming in terms of cloud operation it's not that we are we're struggling with learning something new but we have to learn something that works nicely with the rest of the tooling that we have in our you know toolbox right now so so again put that generalist as the uh as one of your center personas not the only person of course we will have specialists of course we will always have data scientists specialists but any problem that can be solved as a general kind of engineering problem and i think there's a lot of aspects of data michigan that can be just a simple engineering problem um let's just approach it that way and then create the tooling um to empower those journalists great thank you so listen i've i've been around a long time and so as an analyst i've seen many waves and we we often say language matters um and so i mean i've seen it with the mainframe language it was different than the pc language it's different than internet different than cloud different than big data et cetera et cetera and so we have to evolve our language and so i was going to throw a couple things out here i often say data is not the new oil because because data doesn't live by the laws of scarcity we're not running out of data but i get the analogy it's powerful it powered the industrial economy but it's it's it's bigger than that what do you what do you feel what do you think when you hear the data is the new oil yeah i don't respond to those data as the gold or oil or whatever scarce resource because as you said it evokes a very different emotion it doesn't evoke the emotion of i want to use this i want to utilize it feels like i need to kind of hide it and collect it and keep it to myself and not share it with anyone it doesn't evoke that emotion of sharing i really do think that data and i with it with a little asterisk and i think the definition of data changes and that's why i keep using the language of data product or data quantum data becomes the um the most important essential element of existence of uh computation what do i mean by that i mean that you know a lot of applications that we have written so far are based on logic imperative logic if this happens do that and else do the other and we're moving to a world where those applications generating data that we then look at and and the data that's generated becomes the source the patterns that we can exploit to build our applications as in you know um curate the weekly playlist for dave every monday based on what he has listened to and the you know other people has listened to based on his you know profile so so we're moving to the world that is not so much about applications using the data necessarily to run their businesses that data is really truly is the foundational building block for the applications of the future and then i think in that we need to rethink the definition of the data and maybe that's for a different conversation but that's that's i really think we have to converge the the processing that the data together the substance substance and the processing together to have a unit that is uh composable reusable trustworthy and that's that's the idea behind the kind of data product as an atomic unit of um what we build from future solutions got it now something else that that i heard you say or read that really struck me because it's another sort of often stated phrase which is data is you know our most valuable asset and and you push back a little bit on that um when you hear people call data and asset people people said often have said they think data should be or will eventually be listed as an asset on the balance sheet and i i in hearing what you said i thought about that i said well you know maybe data as a product that's an income statement thing that's generating revenue or it's cutting costs it's not necessarily because i don't share my my assets with people i don't make them discoverable add some color to this discussion i think so i think it's it's actually interesting you mentioned that because i read the new policy in china that cfos actually have a line item around the data that they capture we don't have to go to the political conversation around authoritarian of um collecting data and the power that that creates and the society that leads to but that aside that big conversation little conversation aside i think you're right i mean the data as an asset generates a different behavior it's um it creates different performance metrics that we would measure i mean before conversation around data mesh came to you know kind of exist we were measuring the success of our data teams by the terabytes of data they were collecting by the thousands of tables that they had you know stamped as golden data none of that leads to necessarily there's no direct line i can see between that and actually the value that data generated but if we invert that so that's why i think it's rather harmful because it leads to the wrong measures metrics to measure for success so if you invert that to a bit of a product thinking or something that you share to delight the experience of users your measures are very different your measures are the the happiness of the user they decrease lead time for them to actually use and get value out of it they're um you know the growth of the population of the users so it evokes a very different uh kind of behavior and success metrics i do say if if i may that i probably come back and regret the choice of word around product one day because of the monetization aspect of it but maybe there is a better word to use but but that's the best i think we can use at this point in time why do you say that jamar because it's too directly related to monetization that has a negative connotation or it might might not apply in things like healthcare or you know i think because if we want to take your shortcuts and i remember this conversation years back that people think that the reason to you know kind of collect data or have data so that we can sell it you know it's just the monetization of the data and we have this idea of the data market places and so on and i think that is actually the least valuable um you know outcome that we can get from thinking about data as a product that direct cell an exchange of data as a monetary you know exchange of value so so i think that might redirect our attention to something that really matters which is um enabling using data for generating ultimately value for people for the customers for the organizations for the partners as opposed to thinking about it as a unit of exchange for for money i love data as a product i think you were your instinct was was right on and i think i'm glad you brought that up because because i think people misunderstood you know in the last decade data as selling data directly but you really what you're talking about is using data as a you know ingredient to actually build a product that has value and value either generate revenue cut costs or help with a mission like it could be saving lives but in some way for a commercial company it's about the bottom line and that's just the way it is so i i love data as a product i think it's going to stick so one of the other things that struck me in one of your webinars was one of the q a one of the questions was can i finally get rid of my data warehouse so i want to talk about the data warehouse the data lake jpmc used that term the data lake which some people don't like i know john furrier my business partner doesn't like that term but the data hub and one of the things i've learned from sort of observing your work is that whether it's a data lake a data warehouse data hub data whatever it's it should be a discoverable node on the mesh it really doesn't matter the the technology what are your your thoughts on that yeah i think the the really shift is from a centralized data warehouse to data warehouse where it fits so i think if you just cross that centralized piece uh we are all in agreement that data warehousing provides you know interesting and capable interesting capabilities that are still required perhaps as a edge node of the mesh that is optimizing for certain queries let's say financial reporting and we still want to direct a fair bit of data into a node that is just for those financial reportings and it requires the precision and the um you know the speed of um operation that the warehouse technology provides so i think um definitely that technology has a place where it falls apart is when you want to have a warehouse to rule you know all of your data and model canonically model your data because um it you have to put so much energy into you know kind of try to harness this model and create this very complex the complex and fragile snowflake schemas and so on that that's all you do you spend energy against the entropy of your organization to try to get your arms around this model and the model is constantly out of step with what's happening in reality because reality the model the reality of the business is moving faster than our ability to model everything into into uh into one you know canonical representation i think that's the one we need to you know challenge not necessarily application of data warehousing on a node i want to close by coming back to the issues of standards um you've specifically envisioned data mesh to be technology agnostic as i said before and of course everyone myself included we're going to run a vendor's technology platform through a data mesh filter the reality is per the matt turc chart we showed earlier there are lots of technologies that that can be nodes within the data mesh or facilitate data sharing or governance etc but there's clearly a lack of standardization i'm sometimes skeptical that the vendor community will drive this but maybe like you know kubernetes you know google or some other internet giant is going to contribute something to open source that addresses this problem but talk a little bit more about your thoughts on standardization what kinds of standards are needed and where do you think they'll come from sure i mean the you write that the vendors are not today incentivized to create those open standards because majority of the vet not all of them but some vendors operational model is about bring your data to my platform and then bring your computation to me uh and all will be great and and that will be great for a portion of the clients and portion of environments where that complexity we're talking about doesn't exist so so we need yes other players perhaps maybe um some of the cloud providers or people that are more incentivized to open um open their platform in a way for data sharing so as a starting point i think standardization around data sharing so if you look at the spectrum right now we have um a de facto sound it's not even a standard for something like sql i mean everybody's bastardized to call and extended it with so many things that i don't even know what this standard sql is anymore but we have that for some form of a querying but beyond that i know for example folks at databricks to start to create some standards around delta sharing and sharing the data in different models so i think data sharing as a concept the same way that apis were about capability sharing so we need to have the data apis or analytical data apis and data sharing extended to go beyond simply sql or languages like that i think we need standards around computational prior policies so this is again something that is formulating in the operational world we have a few standards around how do you articulate access control how do you identify the agents who are trying to access with different authentication mechanism we need to bring some of those our ad our own you know our data specific um articulation of policies uh some something as simple as uh identity management across different technologies it's non-existent so if you want to secure your data across three different technologies there is no common way of saying who's the agent that is acting uh to act to to access the data can i authenticate and authorize them so so those are some of the very basic building blocks and then the gravy on top would be new standards around enriched kind of semantic modeling of the data so we have a common language to describe the semantic of the data in different nodes and then relationship between them we have prior work with rdf and folks that were focused on i guess linking data across the web with the um kind of the data web i guess work that we had in the past we need to revisit those and see their practicality in the enterprise con context so so data modeling a rich language for data semantic modeling and data connectivity most importantly i think those are some of the items on my wish list that's good well we'll do our part to try to keep the standards you know push that push that uh uh movement jamaica we're going to leave it there i'm so grateful to have you uh come on to the cube really appreciate your time it's just always a pleasure you're such a clear thinker so thanks again thank you dave that's it's wonderful to be here now we're going to post a number of links to some of the great work that jamark and her team and her books and so you check that out because we remember we publish each week on siliconangle.com and wikibon.com and these episodes are all available as podcasts wherever you listen listen to just search breaking analysis podcast don't forget to check out etr.plus for all the survey data do keep in touch i'm at d vallante follow jamac d z h a m a k d or you can email me at david.velante at siliconangle.com comment on the linkedin post this is dave vellante for the cube insights powered by etrbwell and we'll see you next time you

>>From the cube studios in Palo Alto in Boston. It's the cube covering the IBM thing brought to you by IBM. We're back and this is Dave Vellante and you're watching the cube and we're covering wall-to-wall the IBM 2020 I think digital experience. Rob Thomas is here. He's the senior vice president of clouds and data. Right. Warm rub. Always a pleasure to see you. I wish you were face to face, but Hey, we're doing the best we can. As you say, doing the best we can. Great to see you Dave. Hope family safe, healthy, happy as best you can be. Yeah. Ditto. You back out your Robin. Congratulations on on the new role, you and the cube. We've been riding this data wave for quite some time now. It's really been incredible. It really is. And last year I talked to you about how clients, we're slowly making progress on data strategy, starting to experiment with AI. >>We've gotten to the point now where I'd say it's game on for AI, which is exciting to see and that's a lot of what the theme of this year's think is about. Yeah, and I definitely want to dig into that, but I want to start by asking you sort of moves that you saw you're in there seeing your clients make with regard to the cobot night covert 19 crisis. Maybe how you guys are helping them in very interested in what you see as sort of longterm and even, you know, quasi permanent as a result of this. I would first say it this way. I don't, I'm not sure the crisis is going to change businesses as much as it's going to be accelerating. What would have happened anyway, regardless of the industry that you're in. We see clients aggressively looking at how do we get the digital faster? >>How do we automate more than we ever have before? There's the obvious things like business resiliency and business continuity, managing the distributed workforce. So to me, what we've seen is really about, and acceleration, not necessarily in a different direction, but an acceleration on. The thing is that that we're already kind of in the back of their minds or in the back of their plans now that as we'll come to the forefront and I'm encouraged because we see clients moving at a rate and pace that we'd never seen before that's ultimately going to be great for them, great for their businesses. And so I'm really happy to see that you guys have used Watson to really try to get, you know, some good high fidelity answers to the citizens. I wonder if you could explain that initiative. Well, we've had this application called Watson assistant for the last few years and we've been supporting banks, airlines, retailers, companies across all industries and helping them better interact with our customers and in some cases, employees. >>We took that same technology and as we saw the whole covert 19 situation coming, we said, Hey, we can evolve Watson assistant to serve citizens. And so it started by, we started training the models, which are intent based models in Watson assistant on all the publicly available data from the CDC as an example. And we've been able to build a really powerful virtual agent to serve really any citizen that has questions about and what they should be doing. And the response has been amazing. I mean, in the last two weeks we've gone live with 20 organizations, many of which are state and local governments. Okay. Also businesses, the city of Austin children's healthcare of Atlanta. Mmm. They local governments in Spain and Greece all over the world. And in some instances these clients have gotten live in less than 24 hours. Meaning they have a virtual agent that can answer any question. >>They can do that in less than 24 hours. It's actually been amazing to see. So proud of the team that built this over time. And it was kind of proof of the power of technology when we're dealing with any type of a challenge. You know, I had a conversation earlier with Jamie Thomas about quantum and was asking her sort of how your clients are using it. The examples that came up were financial institutions, pharmaceutical know battery manufacturers, um, airlines. And so it strikes me when you think about uh, machine intelligence and AI, the type of AI that you're yeah, at IBM is not consumer oriented AI. It's really designed for businesses. And I wonder if you could sort of add some color to that. Yeah, let's distinguish the difference there. Cause I think you've said it well consumer AI is smart speakers things in our home, you know, music recommendations, photo analysis and that's great and it enriches all of our personal lives. >>AI for business is very different. This is about how do you make better predictions, how do you optimize business processes, how do you automate things that maybe your employees don't want to do in the first time? Our focus in IBM as part of, we've been doing with Watson is really anchoring on three aspects of AI language. So understanding language because the whole business world is about communication of language, trust meaning trusted AI. You understand the models, you understand the data. And then third automation and the whole focus of what we're doing here in the virtual think experience. It's focused on AI for automation. Whether that's automating business processes or the new announcement this week, which is around automating AI opera it operations for a CIO. You, you've talked the years about this notion of an AI ladder. You actually, I actually wrote a book on it and uh, but, but it's been hard for customers to operationalize AI. >>Mmm. We talked about this last year. Thanks. What kind of progress, uh, have we made in the last 12 months? There's been a real recognition of this notion that your AI is only as good as your data. And we use the phrase, there's no AI without IAA, meaning information architecture, it's all the same concept, which is that your data, it has to be ready for AI if you want to too get successful outcomes with AI and the steps of those ladders around how you collect data, how you organize data, how you analyze data, how you infuse that into your business processes. seeing major leaps forward in the last nine months where organizations are understanding that connection and then they're using that to really drive initiatives around AI. So let's talk about that a little bit more. This notion of AI ops, I mean it's essentially the take the concept of dev ops and apply it to the data pipeline if you will. >>Everybody, you know, complains, you know, data scientists complained that all, they spent all their time wrangling data, improving data quality, they don't have line of sight across their organization with regard to other data specialists, whether it's data engineers or even developers. Maybe you could talk a little bit more about that announcement and sort of what you're doing in that area. Sure. So right. Let me put a number on it because the numbers are amazing. Every year organizations lose 2016 point $5 billion of revenue because of outages in it system. That is a staggering number when you think about it. And so then you say, okay, so how do you break down and attack that problem? Well, do you have to get better at fixing problems or you have to get better at avoiding problems altogether. And as you may expect, a little bit of both. You, you want to avoid problems obviously, but in an uncertain world, you're always going to deal with unforeseen challenges. >>So the also the question becomes how fast can you respond and there's no better use of AI. And then to do, I hope you like those tasks, which is understanding your environment, understanding what the systems are saying through their data and identifying issues become before they become outages. And once there is an outage, how do you quickly triage data across all your systems to figure out where is the problem and how you can quickly address it. So we are announcing Watson AI ops, which is the nervous system for a CIO, the manager, all of their systems. What we do is we just collect data, log data from every source system and we build a semantic layer on top that. So Watson understands the systems, understands the normal behavior, understands the acceptable ranges, and then anytime something's not going like it should, Watson raises his hand and says, Hey, you should probably look at this before it becomes a problem. >>We've partnered with companies like Slack, so the UI for Watson AI ops, it's actually in Slack so that companies can use and employees can use a common collaboration tool too. Troubleshoot or look at either systems. It's, it's really powerful. So that we're really proud of. Well I just kind of leads me to my next question, which I mean, IBM got the religion 20 years ago on openness. I mean I can trace it back to the investment you made and Lennox way back when. Um, and of course it's a huge investment last year in red hat, but you know, open source company. So you just mentioned Slack. Talk about open ecosystems and how that it fits into your AI and data strategy. Well, if you think about it, if we're going to take on a challenge this grand, which is AI for all of your it by definition you're going to be dealing with full ecosystem of different providers because every organization has a broad set of capabilities we identified early on. >>That means that our ability to provide open ecosystem interoperability was going to be critical. So we're launching this product with Slack. I mentioned with box, we've got integrations into things like PagerDuty service now really all of the tools of modern it architecture where we can understand the data and help clients better manage those environments. So this is all about an open ecosystem and that's how we've been approaching it. Let's start, it's really about data, applying machine intelligence or AI to that data and about cloud for scale. So I wonder what you're seeing just in terms of that sort of innovation engine. I mean obviously it's gotta be secure. It's, it seems like those are the pillars of innovation for the next 10 plus years. I think you're right. And I would say this whole situation that we're dealing with has emphasized the importance of hybrid deployment because companies have it capabilities on public clouds, on private clouds, really everywhere. >>And so being able to operate that as a single architecture, it's becoming very important. You can use AI to automate tasks across that whole infrastructure that makes a big difference. And to your point, I think we're going to see a massive acceleration hybrid cloud deployments using AI. And this will be a catalyst for that. And so that's something we're trying to help clients with all around the world. You know, you wrote in your book that O'Reilly published that AI is the new electricity and you talked about problems. Okay. Not enough data. If your data is you know, on prem and you're only in the cloud, well that's a problem or too much data. How you deal with all that data, data quality. So maybe we could close on some of the things that you know, you, you talked about in that book, you know, maybe how people can get ahold of it or any other, you know, so the actions you think people should take to get smart on this topic. >>Yeah, so look, really, really excited about this. Paul's the capitalists, a friend of mine and a colleague, we've published this book working with a Riley called the a ladder and it's all the concepts we talked about in terms of how companies can climb this ladder to AI. And we go through a lot of different use cases, scenarios, I think. Yeah. Anybody reading this is going to see their company in one of these examples, our whole ambition was to hopefully plant some seeds of ideas for how you can start to accelerate your journey to AI in any industry right now. Well, Rob, it's always great having you on the cube, uh, your insights over the years and you've been a good friend of ours, so really appreciate you coming on and, uh, and best of luck to you, your family or wider community. I really appreciate it. Thanks Dave. Great to be here and again, wish you and the whole cube team the best and to all of our clients out there around the world. We wish you the best as well. All right. You're watching the cubes coverage of IBM think 20, 20 digital, the vent. We'll be right back right after this short break. This is Dave Volante.

>> Narrator: Live from San Diego, California it's theCUBE covering Cisco Live, US, 2019. Brought to you by Cisco and its eco system partners. >> Welcome back to San Diego everybody. This is theCUBE the leader in live tech coverage, My name is Dave Vellante, Stu Miniman is here, Lisa Martin as well but we've got a very special guest now Ken O'Reilly my good friend is here. He's the director of customer experience for Cisco Stealthwatch. Kenny great to see you thanks for coming on. >> Well, thanks for having me, Dave. Good seeing you as well. >> Yes so customer experience, people think about customer experience and security it's not always great right? It's a challenging environment they're constantly sort of chasing their tails it's like the arms race with the bad guy so what is customer experience all about in the context of security? >> So our number one goal for our security customers is to accelerate their value realization so our challenge is to make sure that they get the value out of the product that they're buying because every minute of every day the bad guys are trying to get their assets and their IP and when they buy a technology the quicker you can get it up and running and protect the better it is for our customer. >> So how do you measure like value? It's like reducing the amount of data that you're exposed to losing? Is it increasing the cost of the bad guys getting in? 'cause if I'm a bad guy and it costs me more to get in I would maybe go somewhere else, how do you measure that? >> Right so, you're right, so our whole product strategy is to increase the cost for the bad guy to get the IP or the assets and so for us we have to understand what the value proposition is for our product so that the customers can realize that value, so whether it's tryna help them with the use cases or operationalize the product or in our case what we try to do we have both network users and security users we try to get both groups to adopt the technology and then expand it from there, operation centers to the guys that are doing the thread hunting to the investigations et cetera. So that's how we sort of gauge the value is the number of people that are using the technology and the number of use cases that are actually implemented. >> So we've been talking about security all week Stealthwatch obviously you know one of the flagship products Cisco security business grew 21% last quarter so that's kind of an interesting stat services is 25% of the companies revenue so you're the intersection of two pretty important places for Cisco so specifically when you come into a customer engagement who are you engaging with is it a multidisciplinary are you primarily dealing with the SecOps group or do you touch other parts of the organization? >> Yeah, so typically when a company's looking, it's usually they're looking for network visibility so we're dealing with the network architecture teams and they typically bring in the security architects 'cause today they're working hand in hand, and then from there that's where we say preach the gospel of Stealthwatch we always say you can never have enough Stealthwatch okay? Because you can never have enough visibility 'cause once you turn the lights on and they can see what's going on in their network it's very illuminating for them and then they realize the challenges that they have and what they have to do to protect their assets. >> Yeah I joked at Google Cloud Next it's like the cockroaches all scrambling you know for the corners when you turn the lights on and Stealthwatch at its core is you don't need a lot of fancy AI even though you can apply fancy AI but you start with the basics right? What do ya got, where are the gaps okay, so now once it's exposed what do you do with that information is the customer experience group come in and help implement it faster? That's part of the value so time to value to that? >> So time to value with our experts of course we understand the space we understand our product we understand the challenge and of course our network and security customers are overwhelmed you know the stat that they throw out there is that our large customers have anywhere from 50-100 security products so how do you stand out? So as a vendor our number one goal is to build that relationship with the customer to become the trusted security advisor so we know better than anybody how to get that value how to get it quickly and you know the number one problem that they have Dave is how to operationalize all these tools 'cause Stealthwatch sits in the middle we're a big integration platform we take data, telemetry, NetFlow from a lot of different products and we bring that data together to figure out, to help that customer figure out how to make sense of it update their policies create better policies and really tighten up their security posture. >> Okay so they might like to reduce the number of tools but they really can't right? 'cause their using 'em and so what you do is you bring in a layer to help manage that. >> Absolutely. >> But you're also solving a problem just in terms of exposing gaps and then do you also have tooling to fill those gaps? Or is that partners tools is that Stealthwatch? >> So we have our own what we call integration platform where we have a platform that helps integrate other, not only other Cisco security technologies into our platform but other security technologies as well outside of Cisco so you know it's a platform that we've built it's part of our customer experience sort of tool set but it's a tool set unlike anybody else ever has so that along with what we do with the DevNet group we've built our own set of API's to integrate in with the product API's so we can pump data out to data lakes we can pump data out to SIMS like Splunk and some of the others so you know that's where we are we're a solutions group that's what we do we work on the solutions, long term value you know we work on the lifecycle sort of value chain with customers. We're there with 'em the whole time you know our goal; retention, we want them to renew which means they're investing in us again and of course as Cloud, as their infrastructure is moving the the Cloud and our technologies are moving to the Cloud we have to be there to help them get through all those technology challenges. >> So the pricing model is a subscription model is that right? >> Yeah. >> Or can be or? >> Yes, well we call it term all right? But it's essentially subscription we have switched over the last 18 months from a perm to a term based model. >> Which I mean Chuck Robbins in the conference calls in the earnings calls talks about the importance of you know increasingly having a rateable model and recognizing subscription, so when you say a term so I got to what, sign up for a year, two years, three years or something like that? >> We like three yep. >> So who doesn't right? Okay so you sign up for three years but the price book says monthly I'm sure right so you (laughs) make it look smaller, but it makes sense though because you're not going to start stop, start stop with your security, you really want to get success out of it so you got to have some kind of commitment, let's talk a little bit more about the analytics side of it and how you're applying machine intelligence I mean there's always been some form of analytics largely for reporting and things of that nature but now it's getting more automated so take us on that analytics journey Stealthwatch has been around for what five years? >> 15 yeah over 15 years. >> 15? >> Ken: Yes, yes, yes. >> Oh wow maybe I just found out about it five years ago. >> (laughs) right yeah, not but I mean-- >> Dave: Take us back five years. >> Five years? So the big thing for us in the data that we collect is context. Right so you've talked to TK about the more context you can add to that data the better you are at analyzing that data so for us that's one of the things that we do we add a lot of context to that data through ICE so identity information, what kind of assets they are and that's where we get to through our tools add more context so that our analytical engines so like the cognitive thread analytics, the encrypted thread analytics that we have, that they're able to analyze that data a lot better and that's what we've been doing now for the past three plus years since we were acquired by Cisco is to find a way to add more context to the data so that helps our analytics become much more effective. >> And you can interact with through API's say for instance Splunk you mentioned that so you got that data that you can operate on do you see a point where the machines are actually going to plug the holes? I mean are we on the cusp of that? In other words you see a gap >> Right. >> Dave: Today a human has to take action correct? >> Yes, right, right, right. >> Do you see a point maybe it's two, three, five 10 years but are we going to get to that point? >> I think so down the line I mean because we've seen as we've been able to get better visibility and better context about that data we can make better decisions through the machine all right? So it doesn't take an army of people to read the matrix right, we're getting better at you know synthesizing that matrix down you take our network segmentation capabilities that we've built as part of the Stealthwatch customer experience team we can get to well over 90% identification of the assets on the network which is a lot better than anybody else in the industry all right? So we're getting there and through sort of the final stages of reading that metrics, reading the matrix we're getting to the point where we understand a lot more what's on peoples networks what those assets are. >> So as a security practitioner how do you think we're doing as an industry? I mean I used to go back every year and say okay how much was spent on security? are we more secure, less secure? And it felt like you know as data grew it felt like we were getting more and more and more exposed you've seen the stats where when a company gets infiltrated it takes on average you know 250 days for them to realize they've been infiltrated is that changing, are we getting better as an industry? >> I think in Cisco we are because of the products that we have in that integrated architecture so when we first joined three years ago that was the drum beat and now today we integrate with ICE we're going to integrate with next generation firewall through the integration of the sort of analytics that we've got in the Cloud that's happening right? And we're trying to integrate with other products but you know you go down on the floor and you see the number of point products that is a nightmare for our customers so for us through the customer experience in our organization we're there to take that complexity out and bring all of those technologies together and when you get to that point then you're really making progress with a customer, a customer that's got 50-100 products in the mix that's a recipe for disaster and if it's still like that five years from now customers are still going to be challenged. >> So a big part of your customer experience mission is simplification, speed time, time to value. >> Yes. >> Raise the cost to the bad guys and then do it all over again. >> Yeah, yeah it's just rinse and repeat and that's a life cycle journey and that's what we take our customers through right. >> Now I noticed you have on your phone you got the Bruins logo. >> That's right, right here proud. >> So big game tomorrow any predictions? >> 4-3 in overtime Bruins. >> Oh my God I don't think my heart could take that. >> Could you not take that Dave? It's going to be an overtime game. >> Well it's you know it's rare to have a game seven in any, at the very final one, a lot of game sevens but not to win it all I think the last time at Boston was 1984. >> Ken: Is that right? >> Yeah it's been a long time, so you know I'm excited. >> I know you are (laughs) that's right. >> Warriors fans too we got that thing going out I mean I don't know for all you hoop fans out there so, >> Hopefully there's a game seven for that as well. >> Yeah let's go right, why not? >> Why not, game seven all round. >> All right so Chara is going to play with his broken jaw or whatever's going on. >> Matt Grzelcyk I hope is back. >> Dave: Yeah that would be key. >> That would be key yeah so, >> Dave: sure up the defense >> That's right. (crosstalk) >> Ken: He's a plus minus leader Chara. >> Oh yeah. >> That's right all time. >> Even though we give him a lot of grief. (laughter) he may look slow but he's all time plus minus leader. >> All right Kenny hey thanks so much-- >> All right Dave thanks for having me on all right go Bruins. >> All right keep it right there everybody go Bruins we will be right back Dave Vellante, Stu Miniman and Lisa Martin we're live from Cisco Live in San Diego you're watching theCUBE. (electronic jingle)

[Music] hi I'm Peter Burroughs welcome to another cube conversation from our beautiful Studios here in palo alto california as we do with every cube conversation we come up with a great topic and we find someone who really understands it so they can talk about it we capture them for you so you can learn something about some of the new trends and changes in the industry and we've doing that today too the topic that we're talking about is how do you do a better job of mapping the costs that are being generated by the cloud or that informations coming out of cloud suppliers related to what you're using with the actual business activities that generate the differential capabilities that customers are looking for that's a tough tough challenge and to understand that better we're talking with junior Stormin who's a co-founder of cloud ability Jaron welcome to the cube thanks Peter good to be here so so let's talk about first who are you yeah so I am co-founder of cloud ability and credibility is focused around improving the unit economics of cloud spend so our customers tend to be those who are spending large amounts in AWS or Azure or GC P and we take their billing data their utilization data various metadata about their business and do machine learning and data science on top of it to help them get better visibility into sort of where that spend is going how they're using it but more importantly to give them some controls around how they want to optimize an optimize doesn't necessarily mean save money in a cloud world because most companies who are moving into cloud very heavily are doing that for the innovation for the speed so they can deliver you know better data faster but it's really about fine-tuning the conversation say okay here we want to save money here we want to move faster here we want to focus on quality and really providing a way for the the various groups that aren't normally talking the finance teams with the engineering teams with the procurement teams all these groups to come together and be able to take executive input to say okay how do we want to operate and how do we one improve those your economics as we go well I want to start with just quick comment on this notion of Union I can when people here historically hear the notion of unit unit economics they think of you know increasing scale so the average cost per unit goes down yeah I think you're talking about more than that right are you really also talking about a mapping of what spend is generating to the business activities that actually generate value and ensuring that you get the differential or the optimized Union economics unit cost yeah oh so the mapping is actually really interesting ly challenging in cloud it's hard enough in traditional IT if you look at somebody like AWS they have two hundred thousand SKUs different products you can buy and they now bill at a second level resolution so what this means is you've got all these engineers out there using cloud in a very good way to move quickly and of 8/2 little more features and they kind of have an unlimited credit card that they can go spend on as quickly as they need and they never see the statements they never see the bills and the other side you've got finance teams procurement teams who've sort of lost control of traditionally the power of the PIO that they have to actually rein that in and they're they're struggling just to understand what is the spend and then to the mapping question how do i allocate these hundreds and millions of charges that i have this month into cost centers and business units and getting that sorted in a world where engineers are focused on moving fast or not they're not tagging things based on cost and are typically so once you get that sort of mapping aspect sorted to the next point you brought as is in bringing the business value so how do we start to relate that back there's a concept a lot of you know IT has been a cost center and now it's sexual driver of value in a world where businesses are increasingly delivering their value through software so we need to start tying the spending mapping into the business and then tying that to the value delivered a great example of this I was sitting last week with one of the largest cloud spenders in the world there have been you know nine figures with their primary vendor and in the conversation with the executives we realized that nobody was looking at both sides of that equation you had the the finance people who were saying hey we're tracking the cost and we think I was happening there and then you had the the revenue generators looking at the money coming anyhow the cloud people with that but there wasn't this centralized view to say alright we want to have a conversation about what value I were getting to spend and the question that always comes up what that is I was doing the right amount well let me build on that because it's seat because IT is historically and this is one of the things that we've been doing over the last few years IT has historically done things on a project level yes all right so we had waterfall development we tried to change that with agile we had you know buy the hardware upfront and then deploy the application on a cloud changes that so this project orientation has led to a set of decisions about finance at the moment that the business asides to do it we've changed the practices that we use at a development level we've changed the practices that we use at an asset level is it now time to change the practices that we use at a finance level is that really kind of what's going on here it is that the project analogy is good because what we're seeing is they're shifting from a project basis to a productive basis and products that deliver value increasingly if you think about the change that's happened with DevOps it in the scene and cloud companies are delivering more of their value through software and they're not just using IT for internal projects right it's actually the driver of business how we interact with Airlines and banks and all these things so that's the shift to say okay now we've gotten good at DevOps moving fast and we've gotten good at deploying and building better data stores now we need to bring in this new discipline and the discipline is what the market is calling fin ops which essentially is combining financial financial operations but you simply combine technology applied specifically do a cloud roll and it only can really happen in cloud it can't happen in data centers because data centers have fixed spending right you have to wait to get resources once you make the investment it's a sunk cost there's months of lead time cloud introduced the removal of constraints which means you can get whatever you want as quickly as you want and DevOps meant it's all automated so instead of your collection of 60 servers you've got thousands that are coming up and down all the time so what you don't have to do is bring in all these groups engineers have to think about cost as a new efficiency metric they have to think about the impact of their business at this code this confirmation template they just wrote is going to have and the finance teams have to shift from this mode of I'm under report retro actively and at a quarterly granularity sixty days after it happened and block investment to be I'm going to partner with these teams report in a real-time fashion give them the visibility help forecast and actually bring them together to make better business decisions about the cloud spend so cloud has allowed development to alter practically agile has been around for a long time before the cloud predates the cloud but it became practical and almost demanded as a consequence of what you could do with cloud so cloud change development through agile it changed infrastructure management through DevOps where now you're you're deploying software infrastructure of code and know as code and what you're saying is the third leg of that stool cloud is now changing how you do financial management of technology financial management of IT and we're calling that fin ops yeah and you you you can't really have fin ops without cloud or without DevOps and if you have the two together you alter we need this new set of it's a new operating model the reason this has come to a head of late is you know if you look at going to the Amazon riemeck conferences a few years back it was like well how much is cloud gonna be a thing and okay clouds now gonna be a thing when's it gonna happen now it's about the how and how do we do this better cloud is hitting for the material spend levels now at bigger organizations I mean the you know see the the cloud projections where it's going I think it's now 360 billion the next few years and we're seeing CFO's at public companies look to say okay it's not my biggest line-item yet but it's the most variable and fastest growing cogs expense so it's actually start to affect our margins we needed a new set of process used to actually manage this so one of the things that's coming to market is this new group called the phenoms Foundation which is a non-profit trade association that initially has a few dozen of some of the largest cloudspinners of the world there's the Spotify as the alaskans the nation why it's Autodesk's and they've all come together as a set of best practice practitioners to start to codify this into something that can be you know scaled out in organizations so that group is gonna be putting out a user conference around this area there's a new o'reilly book that's coming out the end of the year that's going to be sort of the treatise and all this stuff pulled together because what we found in you know me is in code ability in the last eight years we bring in technology and platform to show the recommendations of visibility how to do this but the real challenge companies run into is they don't have the internal expertise their finance teams understand what they need to the engineers don't and so you know they came to us last year saying can you help figure out the processes can you educate us and that's really where you know the spin offs foundation is growing bringing together those people to define those processes so the the impact of cloud on each of these different groups on the development group on the infrastructure team and now on the finance team the interest the developer groups I think some of them resisted it but generally speaking it's gone okay and and eventually tooling from a variety different players came along that made it easy to enact best practices and software development through an agile mechanism in the last few years after significant battles within infrastructure teams about whether or not they were going to use software as code we've seen new products new tooling that has facilitated the adoption of those practices what kind of tooling are we going to see introduced that facilitates thin ops so that finance teams procurement teams move from a project orientation to a strategic management of the resource orientation I mean I think the first is on the engineering side is seeing costs become a first-class citizen of an efficiency metric that they need to look at so you know in their build processes baked in the CI CD looking to see am I properly sizing my compute request for the workload that it needs there's some research research just came out showing that I think it's like 80 percent of the market is not using the best discounting options the cloud providers offer you hear these horror stories it's too expensive we said overspend that's not actually a problem with the cloud providers that's a problem with the enterprises not using the tools offered the discounts the reserved ences the infrequent access door exactly so I think at the end of the day it's the first step in this is getting those checks in place to say are we using the things that help drive the right cost for our needs and the other side of that the finance team is really changing the way that they are interacting with their technology teams becoming partners becoming advocates in this versus a passive you know retroactive reporter down the line and this enables these sort of micro optimization discussions that can happen where data center world we bought it some cots is sitting there odd world we can make decisions today that impact you know the business tomorrow so let me make sure I got this so I have a client who who I was having a conversation with them they told me that their their Amazon there AWS bill is 87 gigabytes mm-hmm not that monthly that's 87 pages that's 87 gigabyte yeah so we get we bring this 87 gigabytes in and it's a story about what I consume out of Amazon it's not a story what my business utilizes to achieve its objectives so we're now entering into a world where we're trying to introduce those financial visit that financial visibility into how that spend can be mapped to what the business does so the finance group can look at a common notion of truth and the IT group can look at a combination of troop application owners can look at a common notion of truth and that's what is fin ops is providing if I got that right yeah absolutely and the eighty-seven gigabyte example is the exactly reason why it is fin option not just cloud financial management you can't have a person with a spreadsheet looking at that and trying to make decisions about it right it has to be automated its IT finances code it's got to be baked into the processes you know we we've seen organizations that have hundreds of millions of individual charges hitting them in a consumption based manner the other thing that's come in with the fin ops as a core tenant is we're now seeing a decentralization of accountability for that spend so if you look at the big cloud spenders out there maybe spending tens or hundreds mils a year some of them have thousands of cloud environments gone is the day of we have a centralized Group begins to say we're gonna turn this off turn this off we want to give each of those teams the ability to see there's just their portion of that bill in the right mapped way as you said and to be able to take actions on the back of that so that's changed and they you know you run it you maintain it you understand which shutdown what has sort of come back to the old centralized model is this notion and this is where procurements job is shifted to largely of we deal still want to centralize the rate reduction so engineers you go use less right essentially finance team procurement work together with the cloud vendors to get the best possible rates through reserved instances can be reduced discounts you know volume discounts negotiated rates whatever it is and they become sort of strategic sourcing just say you're gonna use whatever you're going to use and you're gonna watch that to make sure you're using the right amount will targets threshold we're gonna make sure we get the best rate and that's sort of the two sides of the coin well very importantly procurement has always been organized on episodic purchases where the whole point is to bring the price point down and now we're talking about a continuous services where you were literally you're literally basing your business on capabilities provided by a third party and that is a very very very different relation just-in-time purchasing right and it's and it's a new supply chain management process where you have so many SKU options and you are making these purchase decisions sometimes thousands a day and that impacts everything down the road excellent gr storm and co-founder of cloud ability talking about Finn ops and cloud abilities role in helping businesses map the cloud spend to their business activities for a better more optimal views of how they get what they need out of their cloud expenditures Jr thanks very much for being on the connects here and once again I'm Peter burrows and thanks for listening to this acute conversation until next time [Music] you

>> Live, from Barcelona Spain, it's theCUBE, covering Cisco Live! Europe. Brought to you by Cisco and its ecosystem partners. >> Welcome back to theCUBE, here at Cisco Live! 2019 in Barcelona, Spain, I'm Stu Miniman, happy to welcome to the program a first-time guest, but someone I've known for many years, Jason Edelman, who is the founder of Network to Code. Jason, great to see you, and thanks for joining us. >> Thank you for having me, Stu. >> Alright, Jason, let's first, for our audiences, this is your first time on the program, give us a little bit about your background, and what led to you being the founder of Network to Code. >> Right, so my background is that of a traditional network engineer. I've spent 10+ years managing networks, deploying networks, and really, acting in a pre-sales capacity, supporting Cisco infrastructure. And it was probably around 2012 or 13, working for a large Cisco VAR, that we had access to something called Cisco onePK, and we kind of dove into that as the first SDK to control network devices. We have today iPhone SDKs, SDKs for Android, to program for phone apps, this was one of the first SDKs to program against a router and a switch. And that, for me, was just eye-opening, this is kind of back in 2013 or so, to see what could be done to write code in Python, Seer, Java, against network devices. Now, when this was going on, I didn't know how to code, so I kind of used that as the entrance to ramp up, but that was, for me, the pivot point. And then, the same six-week period, I had a demo of Puppet and Ansible automated networking devices, and so that was the pivot point where it was like, wow, realizing I've spent a career architecture and designing networks, and realizing there's a challenge in operating networks day to day. >> Yeah, Jason, dial back. You've some Cisco certifications in your background? >> Sure, yes, CCIE, yeah. >> Yeah, so I think back, when this all, OpenFlow, and before we even called it Software-Defined Networking, you were blogging about this type of stuff. But, as you said, you weren't a coder. It wasn't your background, you were a network guy, and I think the Network to Code, a lot of the things we've been looking at, career-wise, it's like, does everyone need to become coders? How will the tools mature? Give us a little bit about that journey, as how you got into coding and let's go from there. >> Yeah, it was interesting. In 2010, I started blogging OpenFlow-related, I thought it was going to change the world, saw what NICRO was doing at the time, and then Big Switch at the time, and I just speculated and blogged and really just envisioned this world where networks were different in some capacity. And it took a couple years to really shed light on management and operations of networking, and I made some career shifts. And I remember going back to onePK, at the time, my manager then, who is now our CEO at Network to Code, he actually asked, well, why don't you do it? And it was just like, me? Me, automate our program? What do you mean? And so it was kind of like a moment for me to kind of reflect on what I can do. Now, I will say I don't believe every network engineer should know how to code. That was my on-ramp because of partnership with Cisco at the time, and learning onePK and programming languages, but that was for me, I guess, what I needed as that kick in the butt to say, you know what? I am going to do this. I do believe in the shift that's going to happen in the next couple years, and that was where I kind of just jumped in feet first, and now we are where we are. >> Yeah, Jason, some great points there. I know for myself, I look at, Cisco's gone through so much change. A year ago, up on stage, Cisco's talking about their future is as a software company. You might not even think of us as networking first, you will talk to us about software first. So that initial shift that you saw back in 2010, it's happening. It's a different form than we might have thought originally, and it's not necessarily a product, but we're going through that shift. And I like what you said about how not everybody needs to code, but it's this change in paradigms and what we need to do are different. You've got some connections, we're here in the DevNet Zone. I saw, at the US show in Orlando last year, Network to Code had a small booth, there were a whole bunch of startups in that space. Tell us how you got involved into DevNet, really since the earliest days. >> Yes, since the early days, it was really pre-DevNet. So the emergence of DevNet, I've seen it grow into, the last couple years, Cisco Live! And for us, given what we do at Network to Code, as a network-automation-focused company, we see DevNet in use by our clients, by DevNet solutions and products, things like, mentioned yesterday on a panel, but DevNet has always-on sandboxes, too. One of the biggest barriers we've seen with our clients is getting access to the right lab gear on getting started to automate. So DevNet has these sandboxes always on to hit Nexus API or Catalyst API, right? Things like that. And there's really a very good, structured learning path to get started through DevNet, which usually, where we intersect in our client engagement, so it's kind of like post-DevNet, you're kind of really showing what's possible, and then we'll kind of get in and craft some solutions for our clients. >> Yeah, take us inside some of your clients, if you can. Are most of them hitting the API instead of the COI now when they're engaging? >> Yeah, it's actually a good question. Not usually talked about, but the reality is, APIs are still very new. And so we actively test a lot of the newer APIs from Cisco, as an example. IOS XE has some of the best APIs that exist around RESTCONF, NETCONF, modeled from the same YANG models, and great APIs. But the truth is that a lot of our clients, large enterprises that've been around for 20+ years, the install base is still largely not API-enabled. So a lot of the automation that we do is definitely SSH-based. And when you look at what's possible with platforms, if it is something like a custom in Python, or even an ANSEL off the shelf, a lot of the integrations are hidden from the user, so as long as we're able to accomplish the goal, it's the most important thing right now. And our clients' leaderships sometimes care, and it's true, right? You want the outcome. And initially, it's okay if we're not using the API, but once we do flip that switch, it does provide a bit more structure and safety for automating. But the install base is so large right now that, to automate, you have to use SSH, and we don't believe in waiting 'til every device is API-enabled because it'll just take a while to turn that base. >> Alright, Jason, a major focus of the conference this year has been around multi-cloud. How's that impacting your business and your customers? >> So, it's in our path as a company. Right now, there's a lot of focus around multi-cloud and data center, and the truth is, we're doing a lot of automation in the Campus networking space. Right, automating networks to get deployed in wiring closets and firewalls and load balancers and things like that. So from our standpoint, as we start planning with our clients, we see the services that we offer really port over to multi-cloud and making sure that with whatever automation is being deployed today, regardless of toolset, and look at a tool chain to deploy, if it's a CI/CD Pipeline for networking, be able to do that if you're managing a network in the Campus, a data center network, or multi-cloud network, to make sure we have a uniform-looking field to operations, and doing that. >> Alright, so Jason, you're not only founder of your company, you're also an author. Maybe tell us about the, I believe it's an update, or is it a new book, that recently got out. >> Yes, I'm a co-author of a book with Matt Oswalt and Scott Lowe, and it's an O'Reilly book that was published last year. And look, I'm a believer in education, and to really make a change and change an industry, we have to educate, and I think the book, the goal was to play a small part in really bringing concepts to light. As a network engineer by trade, there's fundamental concepts that network engineers should be aware of, and it could be basics and a lot of these, it could be Python or Jinja templating in YAML and Git and Linux, for that matter. It's just kind of providing that baseline of skills as an entrance into automation. And once you have the baseline, it kind of really uncovers what's possible. So writing the book was great. Great opportunity, and thank you to Matt and Scott for getting involved there. It really took a lot of the work effort and collaborated with them on it. >> Want to get your perception on the show, also. Education, always a key feature of what happens at the show. Not far from us is the Cisco bookshop. I see people getting a lot of the big Cisco books, but I think ten years ago, it was like, everybody, get my CCIE, all my different certifications updated, here. Here in the DevNet Zone, a lot of people, they're building stuff, they're building new pieces, they're playing in the labs, and they're doing some of these environments. What's your experience here at the show? Anything in particular that catches your eye? >> So, I do believe in education. I think to do anything well, you have to be educated on it. And I've read Cisco Press books over the years, probably a dozen of them, for the CCIE and beyond. I think when we look at what's in DevNet, when we look at what's in the bookstore, people have to immerse themselves into the technology, and reading books, like the learning labs that are here in the DevNet Zone, the design sessions that are right behind us. Just amazing for me to have seen the DevNet Zone grow to be what it is today. And really the goal of educating the market of what's possible. See, even from the start, Network to Code, we started as doing a lot of training, because you really can't change the methodology of network operations without being aware of what's possible, and it really does kind of come back to training. Whatever it is, on-demand, streaming, instructor-led, reading a book. Just glad to see this happen here, and a lot more to do around the industry, in the space around community involvement and development, but training, a huge part of it. >> Alright, Jason, want to give you the final word, love the story of network engineer gone entrepreneurial, out of your comfort zone, coding, helping to build a business. So tell us what you see, going forward. >> So, we've grown quite a bit in the past couple years. Right now, we're over 20 engineers strong, and starting from essentially just one a couple years ago, was a huge transformation, and seeing this happen. I believe in bringing on A-players to help make that happen. I think for us as a business, we're continuing to grow and accelerating what we do in this network automation space, but I just think, one thought to throw out there is, oftentimes we talk about lower-level tools, Python, Git, YAML, a lot of new acronyms and buzzwords for network engineers, but also, the flip side is true, too. As our client base evolves, and a lot of them are in the Fortune 100, so large clients, looking at consumption models of technology's super-important, meaning is there ITSM tools deployed today, like a ServiceNow, or Webex teams, or Slack for chat integration. To really think through early on how the internal customers of automation will consume automation, 'cause it really does us no good, Cisco, vendors, or clients no good, if we deploy a great network automation platform, and no one uses it, because it doesn't fit the culture of the brand of the organization. So it's just, as we continue to grow, that's really what's top of mind for us right now. >> Alright, well Jason, congratulations on everything that you've done so far, wish you the best of luck going forward, and thank you so much, of course, for watching. We'll have more coverage, three day, wall-to-wall, here at Cisco Live! 2019 in Barcelona. I'm Stu Miniman, and thanks for watching theCUBE. (electronic music)

>> Live from Orlando, Florida, it's theCUBE covering Microsoft Ignite. Brought to you by Cohesity and theCUBE's ecosystem partners. >> Welcome back, everyone, to theCUBE's live coverage of Microsoft Ignite. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman. We're joined by Patrick O'Reilly of O'Reilly Venture Partners based in San Francisco. Thanks so much for coming on theCUBE, Patrick. >> Thanks for having me. >> So, you are a serial entrepreneur now working as a VC, what are you doing here? Tell us why you came to Ignite. >> Yeah, well selfishly on the VC side we have a few of our portfolio companies here that have booths, and I wanted to kind of hear what people are asking, you know, why they're interested in the companies and how we're framing, you know, those companies to the end users. I think these type of events are really good to unlock hidden potential, or things that people can tell you that you wouldn't actually have thought about, yeah. >> Yeah, so Patrick, you know, I've known you for a number of years. Usually see you at the opensource shows. Microsoft, you know, publicly very embracing opensource. You know, they love Linux, partnering with Red Hat, even you know, partnering is a lot of things that Microsoft does. They were working with VMware. What's your viewpoint as to how you see Microsoft and the opensource world, and how about this ecosystem? Is this a vibrant ecosystem that, you know, VCs are investing in, or is it just that there's companies of yours that, you know, this is part of the story. >> No, and I think historically we've had the, you know, build versus buy, you know, kind of way of looking at it, but when I typically think of Microsoft, it's more people building glue, you know, code to kind of connect things together, and you tend to have blinders on and not think about what opensource components you can use. You know, you look for like what company has a solution you can buy, or license or OEM, and I think that's changing, you know, over time. You know, Microsoft does an amazing job with developers of giving them very easy to understand languages and amazing tooling, and along with that the documentation and the training, so I kind of felt like you came into development one of two ways. You either were like on the Microsoft track and using the cookie cutter approach, you know, to doing things and getting certified on something, or you were opensource, you learned the scripting language and you just looked at what you can cobble together in the opensource world, and there wasn't a lot of crosspollination, but now I see that those walls kind of dissolving. People are willing to mix and match. >> Yeah, it's interesting, you know, some places I've seen Microsoft, a lot in the Kubernetes show, so you know, first got to know you you were at Kismetic, you know, really the first company around Kubernetes that we knew. You know, I know you're doing a lot of different things but we love your viewpoint on, you know, anything on Microsoft in that space, as well as just what you've seen, you know, as a watcher of the Kubernetes space these days. >> Yeah, I mean I've been... You know, if I step back from Kubernetes, you know, back to like the Apache Mesos and the Mesosphere days, you know, if you rewind all the way back there you kind of had to do a lot of education of like, "What do you mean 'containerization?' "I have VMs, why do I need containers?" And now that we've gotten past that and people actually understand the value of containers, like having an orchestration system in place that works and works with everything, you know, is obviously more important than ever, and it's... I really credit the CNCF and the Linux Foundation for what they've done to kind of bring standards around Kubernetes and shepherd the project, and I think that, you know, the fairly recent announcement from Google that they're fully trusting, you know, CNCF to be the shepherd of that is huge, and it gives a framework for people, like Gabe at Microsoft, to work with, you know, some of the staff at Google, and like, in a collaborative way and move it forward for everyone, and I think, you know, historically containers made a ton of sense on Linux, but now that we have Windows server, you know, supporting containers and theCUBE working, you know, on Windows, I think in the 111... Or sorry, 113 release we'll have full Windows server, you know, support in Kubernetes, like that'll be huge. And just a quick aside, like the reason I even kind of honed in on containers and thought it was interesting is the average server utilization is still so low, but we're not really trained as technologists to care about that, and you know, we're really good at building data centers and tucking them off in places where no one sees, but when the average server's taking like... It's like running a hairdryer on high, you know, for electricity and then they run so hot you have to cool it. Like, we're really not helping the environment, so I think if we can move towards containerization, move towards efficient utilization of our hardware, you know, it'll be better for everyone, not just this ecosystem, so... >> So, talk to, tell our viewers a little bit about your portfolios and your portfolio companies that are here, and how they fit into the ecosystem. >> Yeah, so the one I'm most excited about, or shouldn't probably say it that way, I'll reframe that-- >> Can't have favorites, they're all your babies. (laughs) >> Yeah, they're all my babies. (laughs) >> But Ziften Technologies is great. I think their integration with the Windows, the vendor ATP, you know, advanced threat protection, you know, tool is great. They focus on the Mac and the Linux components and give you that same kind of pane of glass on the Microsoft side to see those endpoints, and like their utilization of AI, like they have an upcoming release where they're using AI to do things, and traditionally in that space it's been like the AB vendors, you know, doing everything and you had kind of, "Here's our signatures, "we're going to scan against those signatures," and it's a creative use of AI now to, like, look for just anomaly detections. These are the things we haven't seen before. Not sure what it is but it looks abnormal, and those are the kind of like spin-outs of companies that I'm looking for, too. Like I want to see people doing more meaningful things, you know, with AI. I think if we look at Azure and what they're offering now, like I don't need to have a bunch of data scientists at my startup. I can implement computer vision just using what off-the-shelf components, you know, from Microsoft and you know, Azure. I can do video indexing, you know, using their services. Like, if I rewind just back three years I would've had to have a team of like four data scientists. They'd be reading whitepapers, they'd be implementing code that like sort of half works, and they would probably take half a year to train some models to get, like, moderate results, and now in a matter of minutes, you know, I can use this off-the-shelf stuff. >> Yeah, it's fascinating, I think back to, you know, we were pretty early at theCUBE at watching the whole big data trend, and back then it was like, "Okay, we're going to "take that two-year project and you know, "drive it down to six months," and now we talk in the AI space is, you know, how can we drive that down even more. In big data there was concern, everything seemed to be custom. In AI we're starting to get to more templatized solutions, rolling out for a lot of industries, and it feels like it's taking off a lot faster than that space is, and I know there's a lot of investment going on in the space, and a lot there, so... Anything in particular, you know, what excites you, what makes a good, you know, AI investment versus, you know, there's just so much happening out there. >> Well, you know, I... I struggle with the name AI a little bit. >> Yeah, no, no, I understand, yeah. >> I'm working on a talk, and you know, I kind of like don't, I don't enjoy the artificial aspect of it because it's really just intelligence, and you know, right now it's a buzzword people are throwing into everything when really they mean, "We use an algorithm." (laughs) You know, it's not truly AI, but when we get to cognition we get, you know, to, you know, someday if we have quantum supremacy we'll have, you know, systems that actually can maybe have a consciousness, you know, and decide things. That's where I'm interested, I'm looking... Like on the devops side I'm looking for people using AI to get away with repetitive tasks. Like I would love to see, you know, someone have a system where it's like, "Hey, we've noticed, you know, 90 times "this week this guy's done this exact "same thing, you know, 99% the same way." Like, let's automate that away. You know, we've been really good in the space to kind of treat infrastructure like code, you know, and be able to tear things up. Like I mean, I've been incredibly excited to see, like just in my career, how we went from, "Okay, you're going to do something meaningful on the web. "You need to build a data center. "You need to, you know, get a bunch of servers, racks," and then you pay all this equipment and oh, by the way, 18 months from now it's going to be obsolete and you're going to have to spend money again, to where now I can just, you know, get some credits to start up in the cloud, you know, try things out and do like really meaningful things. So, just looking for anyone on AI that's going to do something that moves the needle. >> Yeah, now that, yeah, just on the terminology piece, I've lived through the cloud wars and the argument over what was and what isn't, so it's just, you know, the shorthand for this wave that we have there, where AI or ML, or you know, IBM has some interesting terms that they want to call it. We understand that there's intelligence that I can do with software, a lot of machine-to-machine things that are going on, and it's not a lot of, you know, shouldn't be a lot of heavy lifting by people to go in there. Oh, wait, I can train something, I can learn what's happening, so... >> Well, I wanted to ask when... I'm sure a lot of entrepreneurs ears are pricking up when they hear that you want to make these meaningful investments. What is it that you look for in a company, is it... In terms of the leadership team, in terms of any track record, what sort of makes your eyes light up? >> So, I try to go to as many conferences as I can, because I feel that's where, you know, the hallway track and I can meet people. I can see, you know, their talks, see what they're passionate about, so what I'm really looking for is investing more in the people than in the idea, because startups can always pivot, and you look at some of the greatest companies out there, they were pivots from, you know, a slightly different model and they realized that, "Oh, we should go chase down this other thing." So, to me, I'm looking for people that are doing something exciting where they are already, looking to make the leap. You know, for example, like you know, the Spinnaker team or people that do something, you know, like... You know, like if etcd wanted to move off and be a separate company, like things like that where they've done something, they've proven it, and now they want to go start a company around it, and I think right off the bat, like if you've built some interesting technology that people are starting to use you have a decent revenue stream just from support, you know, of that and helping those end users, and I think, you know, with O'Reilly we do something a little different than other people. Like I focus mostly on seed investment, very early stage. Our typical check size is around $500k, and I actually allow people to take us off the cap table and just pay us back. Like you know, I've done nine startups in my career, and it's... Fundraising is one of those things where you only get good at it once you don't need it anymore, (chuckles) and I felt the pain of being on that side of the desk and I want to be in the position where, you know, we can write the checks and not try to, like, have a lot of governance, not try to take a board seat, not give you down pressure, you know, on what you're doing but really be additive. I think moving forward I would love to be in the position where we can help incubate, you know, a lot of companies because we've found that, you know, you all kind of go through, every company goes through the same process like, "Now, we need a real CFO because "we need financial projections." Like, being able to, like, provide those services for portfolio companies where they don't have to go spend their resources chasing that down. >> I'm curious how much some of the big players, or just the gravity of what's happening in the space that you're looking at, so obviously we're here at the Microsoft show, but Google, Amazon, a lot of activity going on and we can call it AI or what you will, VMware even, Oracle, SalesForce, how much of the big players defining and you have to build around them, versus you know, we look at Kubernetes is supposed to make things independent, to be able to be opensource and be able to build solutions, you know, regardless of what platform they're on. >> Yeah, I mean, I think we're living in a world where people have a lot of choice, you know, and we look at even, like we take the example of cloud providers. Like, as long as I don't get vendor lock in and use, you know, their specific features, like I can move around to different cloud providers, I can now say I want to negotiate a better price here and migrate over, and I think just with any of the technologies, like trying to work in ways where companies can work together and be additive, I think that's where we actually move, you know, move down the field. I don't know what analogy's appropriate to use, but you know, I feel like there's a lot of really interesting stuff that we should be doing, and making... Every company doing a slightly different version of the same thing I don't think, you know, makes sense. Like, you know, even silly things like as we mature. Like, you know, back in the day everyone used to have broadcast television. We built all these antennas, we got all this range, you know, and then we moved to digital and we didn't need those antennas, we didn't need that range, so they started decommissioning them, but then companies came along and they're like, "Well, wait, now we have this "unlicensed spectrum we can use." So, now they're using it for internet. You know, you can get 20 megabit connectivity out to a rural farm where now they can put some cheap IoT sensors, and like, do really meaningful things with low cost technologies, like those are the things I'm interesting in. You know, so kids that want to cobble together, you know, IoT sensors and come up with a way to use, you know, what they have in rural areas, and like, and have technology actually help people in a meaningful way, and I think those are a lot of very viable startups, you know, in that space. I do think we live in a world where every company's going to end up graduating into one of the camps, be it, you know, SalesForce, Google, you know, Microsoft, but in that innovation spike, like when they're first starting improving out the companies I think they have a ton of choice, you know. >> You described a very beneficent approach to how you think about VC. Do you think, how would you describe the VC landscape right now? You said you want to be able to just incubate great ideas and help these young companies when they are not good at fundraising and they don't have the smooth, slick deck that will really impress the bigger VC firms. I mean, how, what's wrong with the VC landscape today and what else are you doing to make it better? >> Well, I think the incentives are a little off. You know, I can speak for myself, like when I was... You know, when I was looking to raise VC money and my previous companies, like you know, you get these great offers from people, but then you talk to other entrepreneurs and you're like, you know, I'm not going to call anyone out by name, but you're like, "Well, how is this VC's firm served you," and you start hearing of ways that it was additive, but also kind of put undue pressure on them, or they say things like, "Well, we really didn't "need to raise that round then. "We could've done bridge financing "or we could've figured out how to get a MVP product "out there and brought in some revenue." So, I just think it's the ultrahigh returns that VCs are looking for, and the promises that those VCs are making to their LPs, (chuckles) you know, in their funds to outperform everyone else, and you know, everyone talks to everyone, right? So, if anything's meaningful out there looking for investment kind of the back channel is very vibrant and it's dog-eat-dog, and some of it, I kind of reckon it to, you know, your alma mater, like where you went to school. Like, you know, if you're an MIT person, like MIT's the best place in the world. You know, if you're, you know, some other school, they're the best place in the world, and the VCs tend to kind of, like, fall in those camps, and what I'm looking to do-- >> And those are real biases that impact women and underrepresented minorities, to their detriment. >> Yeah, and you know, and that's the thing I've struggled with, too, when you look at the... Like, let's take Andreessen, you know, for example and you look at the portfolio companies, like you know, you kind of become locked into that ecosystem. Like if you want to go, you know, if I'm on Mesosphere and I want to go partner with someone that's not under that, or they have a company in that portfolio that does similar things, you're going to be pressured into working with the portfolio company over going off and maybe choosing the better, you know, choice for the industry, so I'd like to see, you know, those things change. >> Right, and so, Patrick, we talked a little bit about Ziften, security endpoint, you know, really hot space. I want to give the opportunity, other companies you have here that we should check out. >> Yeah, so we work closely with the team at Turbonomic. I think, you know, what they've done over time, you know, is amazing. I love products where you can just bolt it in and within a short period of time you're getting value. Like, you know, stepping back and just saying one thing about Ziften, like I think it's amazing, because I come from a software development, you know, background, and one thing as a software developer I've always found fascinating is like when you come in wearing the developer hat they give you the keys to the kingdom. They're like, "Oh, here's root access to the servers, "here's where all of our data is, "here's how you do a snapshot of production "to, you know, test it, you know, in staging," and I've always thought that it was a tremendous amount of risk, and you know, on average a company can be hacked for up to 100 days before they even realize that they've had a breach, and like, any kind of company, you know, be it Ziften or anyone in that space, that can showcase that to you. Like, you know, raise up things that you weren't aware of, you know, is really interesting, and then, you know, to the, like, Nico and Turbonomics and the things that they're doing there. Like, to actually get the most out of what you already have, like that's huge to me, because one of the, you know, one of the things I see in cloud computing that we didn't necessarily have, you know, directly owned physical infrastructure is it's almost too easy to spin things up. You know, you've got the guy clicking through the UIs like, "Oh, this instance looks great. "Oh, and it says it's only be $140 this month," and then they end up spinning up 1,000 of those, you know? (laughs) You get that first sticker shock of, like, here's that $250,000 bill that month, (chuckles) you know, for cloud, and companies like Turbonomics can, like, avoid you, you know, making those mistakes. >> Great, Patrick, thank you so much for coming on theCUBE. It was really fun talking. >> Yeah. >> We could talk to you for hours. >> Thanks for having me, I appreciate it. >> I'm Rebecca Knight for Stu Miniman. We will have more from theCUBE's live coverage of Microsoft Ignite coming up in just a little bit. (techy music)

>> Live from New York, it's theCUBE covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. (techy music) >> Hello, everyone, welcome to this CUBE special presentation here in New York City for CUBENYC. I'm John Furrier with Dave Vellante. This is our ninth year covering the big data industry, starting with Hadoop World and evolved over the years. This is our ninth year, Dave. We've been covering Hadoop World, Hadoop Summit, Strata Conference, Strata Hadoop. Now it's called Strata Data, I don't know what Strata O'Reilly's going to call it next. As you all know, theCUBE has been present for the creation at the Hadoop big data ecosystem. We're here for our ninth year, certainly a lot's changed. AI's the center of the conversation, and certainly we've seen some horses come in, some haven't come in, and trends have emerged, some gone away, your thoughts. Nine years covering big data. >> Well, John, I remember fondly, vividly, the call that I got. I was in Dallas at a storage networking world show and you called and said, "Hey, we're doing "Hadoop World, get over there," and of course, Hadoop, big data, was the new, hot thing. I told everybody, "I'm leaving." Most of the people said, "What's Hadoop?" Right, so we came, we started covering, it was people like Jeff Hammerbacher, Amr Awadallah, Doug Cutting, who invented Hadoop, Mike Olson, you know, head of Cloudera at the time, and people like Abi Mehda, who at the time was at B of A, and some of the things we learned then that were profound-- >> Yeah. >> As much as Hadoop is sort of on the back burner now and people really aren't talking about it, some of the things that are profound about Hadoop, really, were the idea, the notion of bringing five megabytes of code to a petabyte of data, for example, or the notion of no schema on write. You know, put it into the database and then figure it out. >> Unstructured data. >> Right. >> Object storage. >> And so, that created a state of innovation, of funding. We were talking last night about, you know, many, many years ago at this event this time of the year, concurrent with Strata you would have VCs all over the place. There really aren't a lot of VCs here this year, not a lot of VC parties-- >> Mm-hm. >> As there used to be, so that somewhat waned, but some of the things that we talked about back then, we said that big money and big data is going to be made by the practitioners, not by the vendors, and that's proved true. I mean... >> Yeah. >> The big three Hadoop distro vendors, Cloudera, Hortonworks, and MapR, you know, Cloudera's $2.5 billion valuation, you know, not bad, but it's not a $30, $40 billion value company. The other thing we said is there will be no Red Hat of big data. You said, "Well, the only Red Hat of big data might be "Red Hat," and so, (chuckles) that's basically proved true. >> Yeah. >> And so, I think if we look back we always talked about Hadoop and big data being a reduction, the ROI was a reduction on investment. >> Yeah. >> It was a way to have a cheaper data warehouse, and that's essentially-- Well, what did we get right and wrong? I mean, let's look at some of the trends. I mean, first of all, I think we got pretty much everything right, as you know. We tend to make the calls pretty accurately with theCUBE. Got a lot of data, we look, we have the analytics in our own system, plus we have the research team digging in, so you know, we pretty much get, do a good job. I think one thing that we predicted was that Hadoop certainly would change the game, and that did. We also predicted that there wouldn't be a Red Hat for Hadoop, that was a production. The other prediction was is that we said Hadoop won't kill data warehouses, it didn't, and then data lakes came along. You know my position on data lakes. >> Yeah. >> I've always hated the term. I always liked data ocean because I think it was much more fluidity of the data, so I think we got that one right and data lakes still doesn't look like it's going to be panning out well. I mean, most people that deploy data lakes, it's really either not a core thing or as part of something else and it's turning into a data swamp, so I think the data lake piece is not panning out the way it, people thought it would be. I think one thing we did get right, also, is that data would be the center of the value proposition, and it continues and remains to be, and I think we're seeing that now, and we said data's the development kit back in 2010 when we said data's going to be part of programming. >> Some of the other things, our early data, and we went out and we talked to a lot of practitioners who are the, it was hard to find in the early days. They were just a select few, I mean, other than inside of Google and Yahoo! But what they told us is that things like SQL and the enterprise data warehouse were key components on their big data strategy, so to your point, you know, it wasn't going to kill the EDW, but it was going to surround it. The other thing we called was cloud. Four years ago our data showed clearly that much of this work, the modeling, the big data wrangling, et cetera, was being done in the cloud, and Cloudera, Hortonworks, and MapR, none of them at the time really had a cloud strategy. Today that's all they're talking about is cloud and hybrid cloud. >> Well, it's interesting, I think it was like four years ago, I think, Dave, when we actually were riffing on the notion of, you know, Cloudera's name. It's called Cloudera, you know. If you spell it out, in Cloudera we're in a cloud era, and I think we were very aggressive at that point. I think Amr Awadallah even made a comment on Twitter. He was like, "I don't understand "where you guys are coming from." We were actually saying at the time that Cloudera should actually leverage more cloud at that time, and they didn't. They stayed on their IPO track and they had to because they had everything betted on Impala and this data model that they had and being the business model, and then they went public, but I think clearly cloud is now part of Cloudera's story, and I think that's a good call, and it's not too late for them. It never was too late, but you know, Cloudera has executed. I mean, if you look at what's happened with Cloudera, they were the only game in town. When we started theCUBE we were in their office, as most people know in this industry, that we were there with Cloudera when they had like 17 employees. I thought Cloudera was going to run the table, but then what happened was Hortonworks came out of the Yahoo! That, I think, changed the game and I think in that competitive battle between Hortonworks and Cloudera, in my opinion, changed the industry, because if Hortonworks did not come out of Yahoo! Cloudera would've had an uncontested run. I think the landscape of the ecosystem would look completely different had Hortonworks not competed, because you think about, Dave, they had that competitive battle for years. The Hortonworks-Cloudera battle, and I think it changed the industry. I think it couldn't been a different outcome. If Hortonworks wasn't there, I think Cloudera probably would've taken Hadoop and making it so much more, and I think they wouldn't gotten more done. >> Yeah, and I think the other point we have to make here is complexity really hurt the Hadoop ecosystem, and it was just bespoke, new projects coming out all the time, and you had Cloudera, Hortonworks, and maybe to a lesser extent MapR, doing a lot of the heavy lifting, particularly, you know, Hortonworks and Cloudera. They had to invest a lot of their R&D in making these systems work and integrating them, and you know, complexity just really broke the back of the Hadoop ecosystem, and so then Spark came in, everybody said, "Oh, Spark's going to basically replace Hadoop." You know, yes and no, the people who got Hadoop right, you know, embraced it and they still use it. Spark definitely simplified things, but now the conversation has turned to AI, John. So, I got to ask you, I'm going to use your line on you in kind of the ask-me-anything segment here. AI, is it same wine, new bottle, or is it really substantively different in your opinion? >> I think it's substantively different. I don't think it's the same wine in a new bottle. I'll tell you... Well, it's kind of, it's like the bad wine... (laughs) Is going to be kind of blended in with the good wine, which is now AI. If you look at this industry, the big data industry, if you look at what O'Reilly did with this conference. I think O'Reilly really has not done a good job with the conference of big data. I think they blew it, I think that they made it a, you know, monetization, closed system when the big data business could've been all about AI in a much deeper way. I think AI is subordinate to cloud, and you mentioned cloud earlier. If you look at all the action within the AI segment, Diane Greene talking about it at Google Next, Amazon, AI is a software layer substrate that will be underpinned by the cloud. Cloud will drive more action, you need more compute, that drives more data, more data drives the machine learning, machine learning drives the AI, so I think AI is always going to be dependent upon cloud ends or some sort of high compute resource base, and all the cloud analytics are feeding into these AI models, so I think cloud takes over AI, no doubt, and I think this whole ecosystem of big data gets subsumed under either an AWS, VMworld, Google, and Microsoft Cloud show, and then also I think specialization around data science is going to go off on its own. So, I think you're going to see the breakup of the big data industry as we know it today. Strata Hadoop, Strata Data Conference, that thing's going to crumble into multiple, fractured ecosystems. >> It's already starting to be forked. I think the other thing I want to say about Hadoop is that it actually brought such great awareness to the notion of data, putting data at the core of your company, data and data value, the ability to understand how data at least contributes to the monetization of your company. AI would not be possible without the data. Right, and we've talked about this before. You call it the innovation sandwich. The innovation sandwich, last decade, last three decades, has been Moore's law. The innovation sandwich going forward is data, machine intelligence applied to that data, and cloud for scale, and that's the sandwich of innovation over the next 10 to 20 years. >> Yeah, and I think data is everywhere, so this idea of being a categorical industry segment is a little bit off, I mean, although I know data warehouse is kind of its own category and you're seeing that, but I don't think it's like a Magic Quadrant anymore. Every quadrant has data. >> Mm-hm. >> So, I think data's fundamental, and I think that's why it's going to become a layer within a control plane of either cloud or some other system, I think. I think that's pretty clear, there's no, like, one. You can't buy big data, you can't buy AI. I think you can have AI, you know, things like TensorFlow, but it's going to be a completely... Every layer of the stack is going to be impacted by AI and data. >> And I think the big players are going to infuse their applications and their databases with machine intelligence. You're going to see this, you're certainly, you know, seeing it with IBM, the sort of Watson heavy lift. Clearly Google, Amazon, you know, Facebook, Alibaba, and Microsoft, they're infusing AI throughout their entire set of cloud services and applications and infrastructure, and I think that's good news for the practitioners. People aren't... Most companies aren't going to build their own AI, they're going to buy AI, and that's how they close the gap between the sort of data haves and the data have-nots, and again, I want to emphasize that the fundamental difference, to me anyway, is having data at the core. If you look at the top five companies in terms of market value, US companies, Facebook maybe not so much anymore because of the fake news, though Facebook will be back with it's two billion users, but Apple, Google, Facebook, Amazon, who am I... And Microsoft, those five have put data at the core and they're the most valuable companies in the stock market from a market cap standpoint, why? Because it's a recognition that that intangible value of the data is actually quite valuable, and even though banks and financial institutions are data companies, their data lives in silos. So, these five have put data at the center, surrounded it with human expertise, as opposed to having humans at the center and having data all over the place. So, how do they, how do these companies close the gap? How do the companies in the flyover states close the gap? The way they close the gap, in my view, is they buy technologies that have AI infused in it, and I think the last thing I'll say is I see cloud as the substrate, and AI, and blockchain and other services, as the automation layer on top of it. I think that's going to be the big tailwind for innovation over the next decade. >> Yeah, and obviously the theme of machine learning drives a lot of the conversations here, and that's essentially never going to go away. Machine learning is the core of AI, and I would argue that AI truly doesn't even exist yet. It's machine learning really driving the value, but to put a validation on the fact that cloud is going to be driving AI business is some of the terms in popular conversations we're hearing here in New York around this event and topic, CUBENYC and Strata Conference, is you're hearing Kubernetes and blockchain, and you know, these automation, AI operation kind of conversations. That's an IT conversation, (chuckles) so you know, that's interesting. You've got IT, really, with storage. You've got to store the data, so you can't not talk about workloads and how the data moves with workloads, so you're starting to see data and workloads kind of be tossed in the same conversation, that's a cloud conversation. That is all about multi-cloud. That's why you're seeing Kubernetes, a term I never thought I would be saying at a big data show, but Kubernetes is going to be key for moving workloads around, of which there's data involved. (chuckles) Instrumenting the workloads, data inside the workloads, data driving data. This is where AI and machine learning's going to play, so again, cloud subsumes AI, that's the story, and I think that's going to be the big trend. >> Well, and I think you're right, now. I mean, that's why you're hearing the messaging of hybrid cloud and from the big distro vendors, and the other thing is you're hearing from a lot of the no-SQL database guys, they're bringing ACID compliance, they're bringing enterprise-grade capability, so you're seeing the world is hybrid. You're seeing those two worlds come together, so... >> Their worlds, it's getting leveled in the playing field out there. It's all about enterprise, B2B, AI, cloud, and data. That's theCUBE bringing you the data here. New York City, CUBENYC, that's the hashtag. Stay with us for more coverage live in New York after this short break. (techy music)

(bright music) >> Hi, I'm Peter Burris. Welcome to another CUBE Conversation from our beautiful studios here in Palo Alto, California. As we do with every CUBE Conversation, we come up with a great topic and we find someone who really understands it so they can talk about it. We capture them for you so you can learn something about some of the new trends and changes in the industry, and we're doing that today too. The topic that we're talking about is, how do you do a better job of mapping the costs that are being generated by the cloud. Well that information's coming out of cloud suppliers related to what you're using with the actual business activities that generate the differential capabilities that customers are looking for. That's a tough, tough challenge, and to understand that better, we're talking with J.R. Storment, who's a co-founder of Cloudability. J.R, welcome to the CUBE. >> Thanks Peter, good to be here. >> So let's talk about... First, who are you? >> Yeah, so I'm co-founder of Cloudability, and Cloudability is focused around improving the unit economics of cloud spend, so our customers tend to be those who are spending large amount in AWS or Azure or GCP. And we take their billing data, their utilization data, various meta data about their business and do machine learning and data science on top of it to help them get better visibility into where that spend is going, how their using it, but more importantly to give them some controls around how they want to optimize. And optimize doesn't necessarily mean save money in a cloud world. Cause most companies who are moving into cloud very heavily are doing that for the innovation, for the speed, so they can deliver better data faster. But it's really about fine-tuning the conversation. Say, "Okay, here we want to save money. "Here we want to move faster. "Here we want to focus on quality." And really providing a way for the various groups that aren't normally talking, the finance teams with the engineering teams with the procurement teams, all these groups to come together, and be able to take executive input to say, "Okay, how do we want to operate? "And how do we want to improve those unit economics as we go?" >> Well, I want to start with just a quick comment on this notion of unit economics. Cause when people historically hear the notion of unit economics, they think of increasing scale so the average cost per unit goes down. But I think you're talking about more than that, right? Aren't you really also talking about a mapping of what spend is generating to the business activities that actually generate value and ensuring that you get the differential or the optimized unit economics or unit cost? >> Yeah, so the mapping is actually really interestingly challenging in cloud. It's hard enough in traditional IT. If you look at somebody like AWS, they have 200,000 SKUs, different products you can buy. And they now bill at a second level resolution. So what this means is you've got all these engineers out there using cloud in a very good way to move quickly, innovate, include more features. And they kind of have an unlimited credit card that they can go spend on as quickly as they need. And they never see the statements. They never see the bills. And the other side, you've got finance teams, procurement teams who've sort of lost control of traditionally the power of the PO that they have to rein that in. And they're struggling just to understand what is the spend. And then to the mapping question, how do I allocate these hundreds of millions of charges that I have this month into cost centers and business units, and getting that sorted in a world where engineers are focused on moving fast. They're not tagging things based on cost center typically. So once you get that sort of mapping aspect sorted to the next point you brought is is then bring in the business value. So how do we start to relate that back. There's a concept a lot of you know, IT has been a cost center, and now it's actual driver of value in a world where businesses are increasingly delivering their value through software. So we need to start tying the spending, mapping of the business and then tying that to the value delivered. A great example of this, I was sitting last week with one of the largest cloud spenders in the world. And they're up in, you know, nine figures with their primary vendor. And in the conversation with the executives, we realized that nobody was looking at both side of that equation. You had the finance people who were saying, "Hey, we're tracking the costs, "and we're figuring out what's happening there." And then you have the revenue generators looking at the money coming in, you know the cloud people with that. But there wasn't this centralized view to say, "Alright, we want to have a conversation about what value are we getting out of this spend." And the question that always comes up with that is are we spending the right amount? I don't know. >> Let me build on that, because IT is historically, and this is one of the things that we've been doing over the last few years, IT has historically done things at a project level. Alright, so we had waterfall development. We tried to change that with Agile. We had buy the hardware upfront and then deploy the application on it, cloud changes that. So this project orientation has led to a set of decisions about finance at the moment that the business decides to do it. We've changed the practices that we use at a development level. We've changed the practices that we use at an asset level. Is it now time to change the practices that we use at a finance level? Is that really kind of what's going on here? >> It is, the project analogy is good. Because what we're seeing is they're shifting from a project basis to a product basis, and products that deliver value. Increasingly if you think about the change that's happened with DevOps in the scene and cloud, companies are delivering more of their value through software, and they're not just using IT for internal projects, right. It's actually the driver of business. It's how we interact with airlines and banks and all these things. So that's the shift to say, okay, now we gotten good at DevOps moving fast, and we've gotten good at deploying and building better data stores. Now we need to bring in this new discipline. And the discipline is what the market is calling FinOps, which essentially combining financial operations. You're essentially combining-- >> Applied to a technology world. >> Applied specifically to a cloud world. And it can only really happen in cloud. It can't happen in data centers. Because data centers have fixed spending, right? You have to wait to get resources. Once you make the investment, it's a sum cost. There's months of lead time. Cloud introduced the removal of constraints, which means you can get whatever you want as quickly as you want. And DevOps meant it's all automated. So instead of your collection of 60 servers, you've got thousands that are coming up and down all the time. So what you now have to do is bring in all these groups. Engineers have to think about cost as a new efficiency metric. They have to think about the impact on their business that this code, this confirmation template they just wrote is going to have. And the finance teams have to shift from this mode of "I'm going to report retroactively at a quarterly granularity, "60 days after it happened and block investment" to be "I'm going to partner with these teams. "Report in a real-time fashion. "Give them the visibility and help forecast. "Actually bring them together and make better business decisions about the cloud spend." >> So cloud has allowed development to alter practically, I mean Agile has been around for a long time, pre-dates the cloud, but it became practical and almost demanded as a consequence of what you could do with cloud. So cloud changed development through Agile. It changed infrastructure management through DevOps. Where now you're deploying software infrastructure as code. And what you're saying is the third leg of that stool, cloud is now changing how you do financial management of technology, financial management of IT. And we're calling that FinOps. >> You can't really have FinOps without cloud or without DevOps, and if you have the two together, you ultimately need this new set of, it's a new operating model. The reason this has sort of come to a head of late is if you look at going to the Amazon re:Invent conferences a few years back, it was like well how much is cloud going to be a thing. And okay, cloud's not going to be a thing. When's it going to happen? Now it's about the how and how do we do this better. Cloud is hitting sort of material spend levels now at big organizations. You always see the cloud projections where it's going, I think it's now 360 billion in the next few years. And we're seeing CFOs at public companies look to say, "Okay, it's not my biggest line item yet. "But it's the most variable and fastest growing "cogs expense, so it's actually "starting to affect our margins. "We need a new set of processes to actually manage this." So one of the things that's coming to market is this new group called the FinOps Foundation, which is a non-profit trade association that initially has a few dozen of some of the largest cloud spenders in the world. There's the Spotifys, the Laciens, the Nationwides, the Autodesks. And they've all come together as a set of best practice practitioners to start to clarify this into something that can be scaled out in organizations. So that group is going to be putting out a user conference around this area. There's a new O'Reilly book that's coming out the end of the year that's going to be sort of the treatise and all this stuff pulled together. Because what we found and you know me, as in Cloudability in the last eight years, we bring in technology and platform to show the recommendations of visibility and how to do this, but the real challenge companies run into is they don't have the internal expertise. Their finance teams understand what they need to. The engineers don't. And so they came to us last year saying, "Can you help figure out the processes? "Can you educate us?" And that's really where this FinOps Foundation has grown out of, of bringing together those people to define those processes. >> So the impact of cloud on each of these different groups, the development group, on the infrastructure team, and now on the finance team. The developer groups, some of them resisted it. But generally speaking, it's gone okay. And eventually tooling from a variety of different players came along that made it easy to enact best practices in software development through an Agile mechanism. In the last few years after significant battles within infrastructure teams about whether or not they were going to use software as code. We've seen new products, new tooling that has facilitate the adoption of those practices. What kind of tooling are we going to see introduced that facilitates FinOps, so that finance teams, procurement teams move from a project orientation to a strategic management of a resource orientation? >> I mean I think the first is on the engineering side is seeing cost become a first class citizen of an efficiency metric that they need to look at. So you know in their build processes baked in the CICD, looking to see am I properly sizing my compute request for the workload that it needs. There's some research that just came out showing that, I think it's 80% of the market is not using the best discounting options that cloud providers offer. You hear these horror stories. Cloud's too expensive, we overspend. That's not actually a problem with the cloud provider. That's a problem with the enterprises not using the tools that offer the discounts, the reservances, the infrequent access. >> Caveat emptor. >> Exactly, so I think at the end of the day, the first step in this is getting those checks in place to say, "Are we using the things that help drive the right cost for our needs?" And on the other side of that, the finance teams really changing the way that they are interacting with the technology teams. Becoming partners, becoming advocates in this versus a passive, retroactive reporter down the line. And this enables these sort of micro-optimization discussions that can happen where data center world, we bought it at some cost, it's sitting there, cloud world, we can make decisions today that impact the business tomorrow. >> So let me make sure I got this. So I have a client who I was having a conversation with him. They told me that their Amazon, their AWS bill, is 87 gigabytes monthly. Not some 87 pages. That's 87 gigabytes. So we bring this 87 gigabytes in, and it's a story about what I consume out of Amazon. It's not a story of what my business utilizes to achieve its objectives. So we're now entering into a world where we're trying to introduce that financial visibility into how that spend can be mapped to what the business does. So the finance group can look at a common notion of truth. And the IT group can look at a common notion of truth. Application owners can looks at a common notion of truth. And that's what FinOps is providing. Have I got that right? >> Absolutely, and the 87 gigabytes example is the exact reason why it is FinOps, and not just cloud financial management. You can't have a person with a spreadsheet looking at data and trying to make decisions about it, right? It has to be automated. It's IT finances code. It's got to be baked into the processes. We've seen organizations that have hundreds of millions of individual charges hitting them in a consumption based manner. The other thing that's come in with the FinOps as a core tenet is we're now seeing a decentralization of accountability for that spend. So if you look at the big cloud spenders out there who are maybe spending tens or hundreds of millions a year, some of them have thousands of cloud environments. Gone is the day of we have a centralized group getting to say, "We're going to turn this off, turn this off." We want to give each of those teams the ability to see just their portion of that bill in the right mapped way, as you said, and to be able to take actions on the back of that. So that's changed in the you know, you run it, you maintain it, you understand what's shut down. What has sort of come back to the old centralized model is this notion, and this is where procurement's job has shifted to largely, of we do still want to centralize the rate reduction. So engineers, you go use less, right? Essentially, finance teams, procurement work together with the cloud vendors to get the best possible rates through reserved instances, can be deduced discounts, you know volume discounts, negotiated rates, whatever it is. And they become sort of strategic sourcing. To say you're going to use whatever you're going to use, and you're going to watch that to make sure you're using the right amount with target thresholds. We're going to make sure we get the best rate for it. And that's sort of the two sides of the coin. >> Well, very important, procurement has always been organized on episodic purchases, where the whole point is to bring the price point down. And now we're talking about a continuous service, where you are literally basing your business on capabilities provided by a third party. And that is a very, very, very different relationship. >> It's just in time purchasing. And it's a new supply-chain management process, where you have so many SKU options, and you are making these purchase decisions, sometimes thousands a day, and that impacts everything down the road. >> Excellent. J.R. Storment, co-founder of Cloudability, talking about FinOps and Cloudability's role in helping businesses map their cloud spend to their business activities for better, more optimal views of how they get what they need out of their cloud expenditures. J.R., thank you very much for being on the CUBE. >> Thanks, Peter. >> And once again, I'm Peter Burris. And thanks for listening to this CUBE Conversation. Until next time.

>> Announcer: Live from Las Vegas, it's theCUBE, covering AWS Reinvent 2017, presented by AWS, intel, and our ecosystem of partners. >> Well, welcome back. We're live here in Las Vegas at Reinvent. AWS putting on it's annual show, and you might notice the volume's gone up a little bit around here. Well, it's 5 o'clock reception time here, so the show floor has a little different vibe to it, you might say, right now. Justin Warren, John Walls, you kind of feel it, don't you, right now? >> Oh yeah, there is an energy just sort of vibrating around. I can feel the energy lifting as the booze starts to flow some more. >> Energy's a good way to put it. >> Yeah. >> Right. We're with W. Curtis Preston, who is the chief technical architect at Druva, and Curtis, thanks for being with us. >> Glad to be here. >> Do you feel a vibe, too? >> I feel the vibe. I feel the vibe standing out in the big line to get in here. >> Yeah, in here. >> And now we're in here, it's yeah, it's a lot of people. >> By the way, for those of you at home not familiar, you were named, this year, on the Deloitte Technology Fast 500 list, 175. >> Hey. >> Quite an honor. >> I assume you're talking about Druva, not me personally. >> Well, yeah, Druva, not you. Although maybe you did, I don't know. >> Yeah, I don't think. >> But that's quite an accomplishment, though. And quite an honor for the company. I mean, tell a little bit about that, about that process, and what do you think that means? What's that stamp of approval for what you guys are doing? >> Well, I think it's just, you know, like a lot of those lists, it's a recognition of the position that we're holding, right? I mean, Druva historically is really well known for their protection of endpoints and SaaS applications. They're expanding into data center and Cloud protection, but I think they're absolutely recognized as the leader in the protection of endpoints. >> Okay, so characterize the Cloud work you guys are doing. Like you said, this is a new move for you, I mean relatively new move, but the market's driving that way, right? >> Yeah. >> People starting to nod their head, and they're thinking, yep, this is where we need to be. >> Yeah, yep. >> So, what has been your strategy then, as far as facilitating what's no longer a trend, it's a way of life. >> Yeah, so I'd say first off, we are definitely unlike a lot of other players. We are a Cloud first company, in that, it's not a strategy, it's a way of life, so our entire application is built in and for the cloud, and by that I mean that it takes advantage of everything that the Cloud offers, right? And when you look at specifically AWS, a lot of backup software products use, well, they all use some kind of database, some kind of catalog to keep track of all the backups. And all of those catalogs, all of those databases, whether it's SQL Server or Db2 or Oracle, they all have scalability limits. We chose to use DynamoDB, which is an incredibly scalable no-SQL database. It's built and available in Amazon as a service, and then all of our products all run in Amazon, right? And so, we can scale both up and down to meet the requirements of a customer. So if we get a new customer. We had a customer that I can't mention by name, but they're a large company that started out with what we consider a small installation of about 10,000 laptops. And that was nice. And then it went well. And then there was a ransomware scare, and so they said, you know what, we're gonna go ahead and do everything. And so suddenly we needed to do 10 times as many laptops. Well, because of the way AWS is, we could scale both the database, the compute, and the storage all instantly to meet the demands of that client. And then once that's done, scale it back down to get back to a state of normal, right? So, for us the Cloud is sort of the core of who we are, and then the only expansion for us is actually protecting the Cloud. So, we've always used the Cloud as our destination, but now our newest offering, Apollo, actually is designed to protect starting with AWS and then expanding into the rest of the Amazon, well, I should say starting with EC2, and then eventually expanding into the rest of the AWS world. >> All right, so, with the tradition of endpoint protection and... >> You're gonna have to speak up, it's really loud in here. >> It's really loud, I'll make sure I'm yelling. So with the heritage that you've got of backing up endpoints and being able to protect endpoints, and now you're moving to protect Cloud workloads, as you say, you've got this Cloud heritage, but you're now looking at protecting workloads that live in the Cloud, what are some of the things that Druva's bringing from that endpoint knowledge that applies to those Cloud type workloads? >> Well I think the idea is that, you know, one of the things about the Cloud, people sort of view, I think there's steps of people using AWS, right? They sort of experiment, and they try out this and that, but once somebody really understands like we did, the things you can do when you can scale your VMs instantly and limitlessly, and your storage and your compute and your databases, once they go down that route, I think the fact that we, it's not necessarily the history of the endpoint itself, but the infrastructure that we built in order to protect those endpoints is already totally scalable and ready to meet the needs of however big of a workload that you'll put in AWS. >> Yeah, I often like to say that Cloud is a state of mind, so if you've already got that state of mind that I want to run my workloads in a Cloud-like way, well I want to be able to protect them in a Cloud-like way, and it sounds like that's really what you're trying to nail there. >> Yeah, and it's a big, because any like, I can look out and see multiple backup products available, and there's a lot of good backup products here. And any of them can run in the cloud, right? You can create a Linux VM or a Windows VM and install your backup software, but it's not going to magically become more scalable because you're running it in Amazon, right? So, designing the product for Amazon and that scalable way of doing things, that's why we talk about being Cloud native. >> Yeah, so how are you attracting customers who would have traditionally thought of you as an endpoint company. It's like, now you're actually saying, look we have these different offerings. So how are you starting to talk to those different kind of customers. How are you finding them and what is it that you're finding resonates with them as compared to some of the other options that they might have? >> Yeah, so as you probably know, I've been in the backup space now for, quarter of a century... (clearing throat) >> Literally wrote the book. >> Literally wrote the book, right? It's on O'Reilly. (laughing) Oreilly.com. >> We'll give you a plug later. >> Don't worry. >> Yeah, yeah, yeah. >> Literally wrote the book. >> Yeah, one thing I can say, there's a couple of things I can say about backups in general, in the average data center. One is, everybody hates their backup software. Right? Like, nobody likes it because it's so hard, right? It's so hard to configure, and using disc as a mechanism instead of tape as a primary mechanism, it's made things better, but it hasn't really solved it, right? It's still this really difficult to manage. There's this massive amount of infrastructure that has to be put in place to do all of that. And because that's so hard and it's so error prone and you're invisible or you're in trouble. No one cares about the millions of backups you get right, only the one restore you got wrong. And so what that translates into is the other truth, which is nobody wants to be the backup guy, right? >> I mean the way I got my first job in backups 24 years ago was a guy named Ron Rodriguez did not want to be the backup guy. >> Curtis, you're it. >> Yeah, you're it. And I within two months, had my first major failure as the backup guy for a 35 billion dollar company, and I thought I was done, I thought I was fired, like so many other backup people, and somehow just accidentally I ended up staying, and so what happens is, it's so hard. So, to go to your question, well what if it was simple, right? The situation is, the current system's not scalable. You're always buying another media server, you're always buying another tape driver, you're always buying another dedupe box. You know, you're always out of something, right? I remember having to go to my boss and being out of tapes. This is, you know, back when tapes were a thing. And I remember saying, "hey, I'm out of tapes." and she was like, we don't have budget." She's like, "what are our choices?" and I go, well, I can stop the backups. She's like, "that's not funny." I'm like, "that's our choice." Right? >> I have so much capacity here. >> These are our choices, right? And so she gave me the tapes that I needed, right? And so it's not scalable, the current system. You're always in need of some piece. It's also super expensive, right? And it's super hard. So we try to be the opposite of that. We try to be scalable, simple, and you know save people money. Right? I know we have a 4S thing. >> It's right there on the tip of your tongue. >> It's right there on the tip of my tongue. But basically we try to be the opposite of everything that backups are. So the big thing is, it's way easier. Just put a piece of software and magic happens, right? And if you're large enough data center that you need to do what we call seeding, where you have to use sneakernet to get the data to us, we have a system for that. If you have a large enough system where the RTO is not going to be able to be met by a copy that's on the other side of the internet, then we have a caching appliance that goes onsite to provide fast recovery. So it's like it's super simple, way less expensive. And I do mean way less expensive. I've seen some TCOs where we compete against other companies, we're even less expensive than renewing what you have, let alone going and buying. >> John: Replacing. >> And replacing it with something, because that happens all the times. Because people are always swapping their backup software, because the problem has got to be the backup software. Right? And I think in the end, it is, right? But, it's because that core architecture, that core way we've done backups, has essentially stayed the same since before I started. All we did was we changed tape to disc, right? And we introduced dedupe, which was great, but there's this technology that we call dedupe, that is really hard when you do it on the backend. You know, there's a company here who makes a lot of money on selling those appliances, right? Except it's really hard to do that, and so it's really expensive to do that. And then you gotta pay for one here and you gotta pay for one over there. With us, you don't buy that. You just go straight to us, and then because we're in AWS, it's already in three locations, right? And it's already offsite. >> Well Curtis, they said 24 years ago it wasn't gonna last, but it did. You made it, congratulations. >> Thanks. >> We appreciate the time here. >> Thanks. >> Thanks for being here with us on theCUBE and onto 175th. Next year who knows where you're going, right? >> Who knows where we're going. >> Excellent, Curtis Preston, joining us here from Druva. Back with more live from Las Vegas. We are at Reinvent at AWS. Back with more in a bit.

(light adventurous music) >> Hello everyone, welcome to theCUBE Studios here in Palo Alto. I'm John Furrier, the co-host of theCUBE, co-founder of SiliconANGLE Media. This is our CUBE Conversation Thought Leader Thursday and I'm here with Val Bercovici, who's the founder and CEO of a new startup called PencilDATA. Val, CUBE alumni, been on many times with NetApp and then a variety of other great startups, but now you're doing your own thing around cryptocurrency, blockchain, enterprise-like technical infrastructure. You've been a CTO, now entrepreneur, founder and CEO of PencilDATA. Congratulations, you're on the crypto wave, this wave is coming. >> I believe it's here. >> It's here. >> Timing couldn't be better. >> So, I interviewed Dr. Jian Wang who's the chairman of Alibaba's technology steering committee, also the founder of Alibaba Cloud, just recently in China. Presented by Intel, plug for Intel there, thanks Intel for supporting theCUBE. He said to me, and I put the clip out on Twitter, natively on the video clip, which was, I asked him about blockchain, you know China, they blocked the ICOs, he said, "Blockchain is fundamental, part of the Internet. "It's as fundamental as TCP/IP was." This is the nuance that is attracting a lot of tier one entrepreneurs. Obviously the money side is hyped up beyond all recognition right now. As Don Klein on our team was saying, "It's melting up in terms of hype." But this really speaks to the transformation of the web, and the Internet now, the web is the Internet, from distributed and decentralized. This is a big sea change. Kind of building on the fundamentals of the internet, formerly called the information superhighway, before the web came along, but the web was designed to withstand nuclear disaster, be resilient, be decentralized. >> It reminds me of Back to the Future in many, many ways, because if you're as old as we are, you remember those DARPA origins of the Internet and exactly that decentralized nature, and we've gone away from that, right? As Tim Berners-Lee brought on the HTTP protocol, we've had web protocols, and as major, the FANG vendors have really dominated their usage of that existing layer of technology we've gone away, we've gone to a very, very centralized approach, which as we're seeing with the tech hearings this week, carries all sorts of risks, it's not just business and legal and political. >> And you're referring to the senate hearings, where Facebook, Google, or Alphabet, and Twitter were in front of the senate committee, you're going to tell them about the Russians, the Russian political thing, but they're bringing up the issue of the role of these mega platforms that have all this data and the problem is that this is not what the users bargained for. I mean, I use Facebook as a free app, I love Facebook, Facebook, we love you, WhatsApp here and there, and Instagram, but you know, my bargain was simple. I'll use your free app and I'll let you use some of my data but now you're making billions, $10 billion quarter, fake news has infiltrated the country, I have a poor user experience every day, it's getting worse and worse, a lot of hate and division. This is not what I bargained for. >> Val: Exactly. >> So the world's kind of revolting against these mega-siloed platforms. >> That's the risk of having such centralized control of the technology. If you remember in the old days when Microsoft's dominance was rising, all you had to do was target Windows as a virus platform and you're able to impact thousands of businesses, even in the early Internet days, within hours. And it's the same thing happening right now, there's a weaponization of these social media platforms and Google's search engine technology and so forth. It's the same side effect now, the centralization of that control is the problem. One of the reasons I love the blockstack technology, and blockchain in general is the ability to decentralize these things right now, and the most passionate thing I care about nowadays is being driven out of Europe, where they have a lot more maturity in terms of handling these new scenarios. >> You mean the tech being driven out of Europe. >> The laws. >> The laws, okay. >> Being driven out of Europe. >> Be specific, we'd like an example. >> The major deadline that's coming up in May 25th of 2018 is GDPR, General Data Protection Regulation, where European citizens now in any company, American or otherwise, catering to European citizens, has to respond to things like the right to be forgotten request. You've got 24 hours, as a global corporation with European operations, to respond to European citizens', EU citizens', right to be forgotten request, where all the personally identifiable information, the PII, has to be removed and an audit trail, proving it's been removed, has to be gone from two, three hundred internal systems within 24 hours. And this has teeth by the way, it's not like the $2.7 billion fine that Google just flipped away casually, this has up to 4% of your global profits per incident where you don't meet that requirement. >> Well you bring up a good point, the GDPR is a good one, it has teeth and it's kind of in the weeds with the folks who might not know that regulation, but really it's about the privacy and the rights of the individual. But coming back to Facebook, to connect another dot is, what we're seeing with Facebook, Twitter, and Alphabet with the senate hearings is, and this is why the industry and the media is crumbling, publications are dying, the newspapers, the media's changing, is because knowing your customer is a really important thing. The people who want to be served need to have a closed loop with the publication, and these platforms are bogarting all the data, and so the right of the customer, the users are suffering, and that's what people are generally talking about. You know, personally, a guy can rent a truck and go mow people down in Manhattan, we should know who these people are, like the neighbors, so I think there's going to be a trend towards knowing who your neighbor is, knowing who the customers are, at a level that's not scary privacy violation, but we're going to know who the crazies are, we're going to know what's going on and then that's kind of out there, that's kind of my general feeling. But now, getting back to the impact. GDPR, these big mega platforms where the users are at the center of the value proposition, really comes down to the shift in user expectations around a decentralized Internet. That means agile goes to a whole other level. If I'm a user and I say, "Hey Facebook, "delete my digital exhaust or digital footprints "from Facebook over the past 10 years." I mean, that's hard to do. >> That's hard for them. >> That's not, technically is a really serious problem. >> And it's actually not just a technology challenge, I always love to go back to Conway's Law in these discussions, the org chart, you know, how information, infrastructure is budgeted for, and managed through various different departments within any large enterprise, data-savvy or not, is a challenge, as is coordinating these efforts, actually going beyond the talking phase, towards implementing a master data model. Those are the main challenges right now, and it's a movement that I believe now has political strength to actually migrate across the pond. Over here as well there's a groundswell movement called Digital Sovereignty as a response to GDPR in Europe, where people are realizing that they have the right to be sovereign over their data, their digital exhaust, their digital footprints online and that's a two-way street. You want and demand control over your data, but on the other hand your identity, which you control, has to be authentic as opposed to a fake identity, and your reputation has to be out there as well. >> These signals and these trends you were just referring to, to me are just like little tremors of the tectonic plates that are going to be changing, because if you look at the major shift in technology, let's take blockchain for instance, and look at the impact of a decentralized internet, now global, immutability with the ability now for more agile capability and not just permanent, "I want to erase things" that you're talking about, but three, the younger generation, if we look at what the young kids are doing, I have four kids, my oldest is 22, it's a gaming culture, right? It's a gaming culture, they're online all the time. They're not old like us, my son's like, "Dad, Google Search is for old people." I mean, that's a general sentiment, over-categorizing, but a combination of the new user experience, this younger generation, entrepreneurs and users, and these tremors we're seeing in the marketplace, signaling that, "hey Facebook, you might be too big for your britches," or, "hey Twitter, you got a bot problem, "hey all you gamers using Twitch," this is now a signal, where is it leading to? And where does blockchain in particular impact it? Because this is kind of where everything's converging to. >> So what I'd like to say right now is, you've got Marc Andreessen's premise that software is eating the world. If you extend that, data is feeding it, blockchain is valuing it, and it's AI that's automating it. So in my mind, particularly in my experience earlier this year in the AI industry, you realize that AI today really boils down to machine learning, which in itself boils down to deep learning, which boils down to data, your access to data. Professor Andrew Wang did this at the recent O'Reilly conference up in the city, he got up and lectured as the keynote instead of sharing slides and his number one, two, and three advice to everyone in the audience was, get the right datasets to train your model. If you don't have that you don't have a differentiated business, and that's what inspired PencilDATA, is my encountering of the cold start AI problem where the IP's in a public domain, public datasets are ubiquitous which is fantastic for academics, but as a business you can't differentiate unless you have access to the right datasets to train your models more specifically. >> Okay, as the founder and CEO of PencilDATA, that's your new startup, let's get into some of the reasons why you're starting it. What problem are you attacking? Obviously a pencil, I can see pencil and you erase things, it's got data... >> The internet is no longer written in ink, that's the premise. Now with Pencil you can erase some data. >> Well blockchain is immutable, so this is conflicting in my mind. Help me kind of rationalize this. The benefit of blockchain is everything's permanent, if you're on-chain as they say. >> Exactly. >> If you're off-chain, you could do some things. Is that kind of what we're getting at? >> We're mixing the best of both. So our premise is that again, whether you're an organization or an individual, you need to have, to survive in a new digital economy, control over your data. The blockchain part of it is the visibility side. If you don't know who's doing what to your data, you're far less likely to share it. And once you know who's doing what to your data, in an immutable blockchain, with a detailed audit trail, with strong authentication, of literally who's doing what to your data, gives you that visibility. Then you do what modern asset managers do. You can't really value an asset until you fully control it. And our premise is, you can't control something until you can take it back. So the notion of PencilDATA is the ability to go on-chain for the visibility and off-chain for managing data in encrypted containers, and if a data owner or publisher doesn't like how the subscriber's consuming their data, they have the power to revoke all downloaded copies. >> So is this kind of like a shadow blockchain model? I'm trying to find a mental model because I remember the old days back, I was breaking into the industry in the late '80s, early '90s, WORM drives, write once, read many. And you write it once, it's a laser, it was optical drives at the time. Also, demilitarized zones in networking was an area where there was a safe harbor kind of thing, where people could play around. What metaphor, what mental model can people take away from some of the things that you're trying to solve? Is it like a DMZ, is it like a-- >> The implementation's a lot like a DMZ and the business challenge and opportunity is that there's a lot of tension between protecting data, because we have an epidemic of data breaches right now, I think you're foolish if you're assuming that you haven't been breached yet but you might be, because everyone has been breached, personally and organizationally, so we have to deal with the rising need to protect data more and more. But at the same time, you can't stay in business if you don't optimize the monetization of the data you have. And so PencilDATA walks that fine line between letting you do both, letting you not just protect infrastructure, that's a whole other industry that we're not involved in, but literally protect data at the data level. If you look up terms like crypto anchor you'll see some of the technologies we're taking advantage of there. But being able to monetize data by unlocking all that latent value of data hidden behind firewalls. If you use a physics analogy of potential and kinetic energy, applied to data behind firewalls, there's hundreds of billions of dollars of value in latent data basically, potential data hiding behind firewalls, and when you can safely share it, give the owners control they've never had before, then you expose the value of that data for the first time. >> Alright, so let's take us through where you're at. Obviously super exciting, you're leveraging the blockchain and you've got an ICO, initial coin offering coming up but you're not just doing that for the sake of doing, there's a lot of scams out there, you're taking a little bit more of a pragmatic approach. Give us the status because you're the founder and CEO, what's the makeup of the team, how big are you guys, what are you guys looking for, obviously you're looking for team members most likely. >> We're looking for developers obviously. >> Where in the process are you? >> We are a two-month-old company. We're at the seed stage. And we've actually assembled a world-class team. You hear that a lot, but I'm really, really proud of the team members we have right now. >> World-class, are they from around the world and then they have class? Define world-class. >> They're worldly, like myself, I travel a lot. (laughter) An example, my chief privacy officer is Sheila Fitzpatrick, she's a worldwide recognized leader in data privacy, she's on many, many privacy boards in the US and EU and so forth, and she now is traveling nonstop lecturing on GDPR, itself specifically. She's one of those recognized-- >> Should you see yourself as a solution for GDPR, because that's, again, it does have teeth, I'll just say that we've been reporting on this through Wikibon, our research team as well as theCUBE, it comes up all the time and there's heavy fines associated with it, so it's not like- >> GDPR is the perfect use case because on the one hand, we have that audit trail that proves what you're doing with data. On the other hand we have a kill switch, that revocable use clause for data where you can literally comply with GDPR in minutes or seconds, as opposed to take a full 24 hours to scour database and delete selected records. >> Alright, so what about the product? Give us an example of the product. Will you be, first of all that's right around the corner, it's next year. >> Val: Yeah. >> I think it was a March or April's timeframe, I don't have the exact date but it's pretty soon. >> Public beta before the end of this year, version 1.0 first of second quarter next year. >> For you guys, PencilDATA. >> Yes. >> Clients, are you working with anyone right now, you have a handful? >> So we've actually got really interesting distribution partnerships that we're not in a position to announce right now but the top-tier brand name enterprise cloud vendors, both on the SaaS and infrastructure and database side, they're lining up to work with us. Because we're enabling amazing use cases in healthcare and life sciences, the ability to selectively share patient data with insurers, with healthcare providers, clinical trials now to share more information through differential privacy and collectively have more data to be processed and analyzed. Use cases are just off the charts. >> Well you know we go to all the big data shows, we're horizontally scaled on the event site circuit, but this is the number one thing that comes up, I want to move from batch marketing, batch process, batch business to real-time business, speed is essential, but it's always been a conflict between, how do I enable data to move really fast and be available for applications but protecting the privacy. >> Yeah. >> Do you solve that problem, is that something that you see yourselves solving? >> We aren't necessarily innovating on speed, of data movement, it's going to be a SaaS service. >> So it's availability model. >> It's availability of data that's really never been shared before and I think that's the key here, is we know there's a lot of value locked up behind corporate firewalls. The irony is, we don't even have to sell this outside firewalls initially, when you go to any medium-to-large size enterprise that has more than one site or more than one department, Sales doesn't trust Marketing and vice versa, Engineering doesn't trust Customer Support, neither of the four of them trust each other, so we're actually going to enable more data shared within an enterprise at first. >> So that's a starting point for you guys. >> That's a starting point, that's the easiest low-hanging fruit sale we have. >> Well PencilDATA, it's great stuff, Val, congratulations on that startup. I mean, you've got a world-class management team, and this kind of brings up a point that I've been banging on theCUBE pretty much every time I go out I'll talk about blockchain and ICO because you know, theCUBE is a very decentralized audience and that's a value that we're looking at as well with blockchain. I've got to ask you the personal question, from your own personal perspective, experience, executive and CTO, why is blockchain attracting so many A players? Because you're seeing a lot of what I call A players, entrepreneurs, technical geeks, really jumping into this because they can see it, they can smell the opportunity, and also, it also attracts the scammers as well, but specifically, why are these A players coming in? Is it, what are you hearing, what's the general vibe, what's the anecdotal reason? >> So as you said earlier on, it's a fundamental evolution of the core internet as a technology, as fundamental as HTTP and web was on top of TCP/IP back 20 years ago, but it's got that rare combination of not only being a technical innovation that empowers new use cases on the web, on the internet, it's also got immediate, amazing business applications as a store of value initially, as an actual valuation of various business processes, or datasets in my case, as an ability to exchange that value so transparently, so, in such a friction-less liquid manner, those are some of the amazing innovations it brings to the table and I think the most important thing is not to think of this as being able to do digital transformation or faster analog, it's about completely reimagining the exchange of value, measurement of value, and new kinds of businesses that just weren't possible before. >> And at all points of the stack, not the low levels and at the application level, the business logic, and to the geek side, right? >> Absolutely. >> You agree. I mean, that's great and as you know, theCUBE is looking at a blockchain ICO on down the horizon so keep an eye out for that, CUBEcoins could be in everyone's future, so we're super excited like you. >> I'm looking forward to your presale, just like I'm looking forward to mine. (laughing) >> Well, we'll see. But the bottom line is that this is what the reality is, you know, reimagining the applications is what people are thinking and I think people should beware of the scams out there, and then final question I want to ask you is, obviously we're both in the community together, with our teams. Share your perspective on the ecosystem, because obviously decentralization will change the nature of traditional ecosystems. >> Very much so. >> What's your vision on how the ecosystem will evolve, and how big is it now relative to these early markets? >> We're actually starting to enter the middle innings of the cloud game, if you will, we're seeing a very good maturity, a good diversification of profitable earnings and outcomes for the major cloud players, so I think we've gone well down the cloud path so far. But the decentralized world is in its infancy. It's embryonic right now. And I've always been a proponent of the multi-cloud environment and a multi-cloud world, and decentralization fundamentally is based on and depends on a multi-cloud, not just multi-region, but multi-data-center-in-a-closet scenario as well, to be able to actually have a democratic model for determining where the value is, where the value isn't, blockchain node style. And that is incredibly exciting to me, because that really cements this rebalancing of the pendulum between core and edge in terms of where processing and value happens. >> Yeah, and value exchange obviously now, markup links are becoming the du jour way to exchange value, users are in control, infrastructure equilibrium is interesting. Great stuff. And I'll say, perfect storm for innovation. The waves are coming. (laughing) >> You know, one thing I've learned over the years is, the innovation, change never stops. There's always an opportunity to innovate, and that's what I love about this movement. >> Blockchain, ICO, PencilDATA, check 'em out, Val Bercovici, founder and CEO, great friend of theCUBE, also really strong CTO, check these guys out. This wave of innovation around blockchain ICOs and infrastructure, reimagining, the future is here upon us at theCUBE, be right back with more, thanks for watching. (electronic music)

>> Narrator: Live from San Francisco, it's TheCUBE. Covering Samsung Developer Conference 2017 brought to you by Samsung. (techno music) >> Hello there and welcome to the special exclusive coverage of Samsung Developer Conference 2017 here at the Moscone West in San Francisco, TheCUBE's coverage. I'm John Furrier, the co-founder of SiliconANGLE Media and co-host of TheCUBE. We're here with Leah Hunter: author, thought leader, covers technology design, women in tech, a variety of things author at O'Reilly's Safari Books, Fast Company, Forbes, among a lot of other things you've done. Welcome to TheCUBE conversation here at the Samsung Developer Conference. >> Thank you. I appreciate it. >> So, Samsung obviously is tied with Google. We saw Google onstage. The story we're seeing here emerging is the edge of the network of mobile devices. That means the humans involved. That means the consumer and the technology are intersecting. This has been a big part of TheCUBE coverage, we've been looking at this for a while. We were just in China talking with Alibaba Cloud and the design ethos culture. Not just creating user experience, that's been out for a while, but it's not about speeds and feeds anymore. It's about enabling human interactions, we're seeing some bad stuff now. The fake news, all that bad behavior, but now, all the data's out there. This is a big part of the developer design now coming forward. What's your thoughts? >> Well, there are two ways that I see that playing out really powerfully and that it can play out powerfully. One, ethnography and social science is getting embedded into what people are creating now and I'm thrilled to see that, because we're at the beginning of a lot of new technologies, augmented reality is one of my specializations, and we're, you know, sure, it's been around for 60 years if you're counting that way, 15 really deeply, but we're just at the cusp of it really taking hold for consumers. And there's this opportunity for anyone developing AR specifically to build social science ethnography user research into their team to create things in a way that is, like, start as you mean to go on. We can be wise about what our future world looks like. And the second thing is around art. You know, when I came here and I sat down, you mentioned at Alibaba there had just been a conversation about art. Well, in my latest book I interviewed someone who is an artist. His name is Alex Mayhew, he did a bunch of work with Peter Gabriel, he's a digital artist who just happened to slide into technology. And because his background is in something entirely different, he approaches AR in a really different way. He just did something for an art museum in Ontario that's really fantastic and worth checking out. You can actually look up the exhibit. It's called ReBlink. I'm going to write about it, but it's there now. >> Well, you've been covering technology many ways, now you're onto AR, and also you're seeing the front range if you will of these new concepts. But before you get it there, define what ethnography is for the folks that might not know what it is. (laughs) >> Thank you. I forget, okay, so I define ethnography as kind of like seeing the world like a five year old. There's an author that I love, her name is Keri Smith. She writes children's books. I found the first copy of this at the Teat Museum. It's called How to be a Life Artist. But her books are all about close observation, collecting everything, paying attention to the world, and finding everything interesting. Being curious in the same way you do when you're a five year old. Well, that's essentially what an ethnographer does in a business context. They observe, they interview people, they go around and collect data the same way that anyone who's on the data side is doing it with numbers. They do it with quotes and observation and pictures and then aggregate that into a story. >> That brings up a great conversation we're seeing here at the Samsung conference as a trend, a mega trend if you will, and that is the blending of analog and digital. Or, they say, physical to digital. Whatever they want to call it. Internet of things is the tech buzzword, >> (Leah) Yeah >> Internet of things being the senses on devices, or wearables, or things of that nature. That is defined as the edge of the network. This is the big wave that's forcing things to be different at the tech level. So this is where this blending comes in. It's the consumerization of tech. This is a big part of these consumer companies who have to kind of get their act together on cloud computing, and a lot of tech detail. So it's coming down from the edge, the infrastructures being redefined, or replatformed as we say. How do you view that, and what does your data show for you around how companies are reacting, what are the consumer expectations? >> Well, I'm going to speak to what I'm seeing in the world because I approach the world like an ethnographer. I wander around, and I collect interesting bits of things, kind of like a magpie. >> (John) Yeah. >> One thing that I saw this week, or I saw two things that were very interesting. I was just in New York, and I walked past an area where it was branded Amazon, but it kind of looked like a carnival. And I was like, what is going on here? And basically, Amazon is doing pop-ups, I believe they said in 18 cities, they just started in New York, but it's a pop-up where you can text in, and you can buy an item on Amazon that you can't get anywhere else. In this instance it was a Nintendo. You go and you pick it up in this physical space that kind of operates like a carnival and has circusy lights and beautiful trucks and whatever. But I thought that that was the coolest blend, and they also gave me their marketing materials that kind of looked like a ticket to a carnival. But I liked that, because it was a new way a digital focused company is operating in the physical world, to your point. It's a new way of blending those. And Amazon doesn't necessarily have to do it. It's just smart marketing. But it also shows the way that companies are pushing from the internet into the physical world. Now that's also happening in reverse. There's a company I really like called Shimmy that basically uses Kinect sensors to measure your body and make custom-made swimsuits for women. They're using that digital information and they're sort of, like, pushing it, so, yeah. >> Yeah, this is a big thing, I mean, this is about reimagining the future. And I think developers, this is a developer conference, so they tried out all the shiny new toys, Bixby, which is personalization now, IOT, which is kind of a geeky message, but ultimately the developers and the ecosystem partners of Samsung have to create the future together. So the question for you is around how you see the ecosystems developing. I see developers learning more about the real world. Less being behind the wall, if you will. Being the super geeks coding away. You're seeing developers on the front lines. And I think that's super important. I do want to get it noted here that you got a book coming out. >> Yes. >> So tell us what you're working on, cause it's going to ship in December? >> Yeah, I... >> What is the book about? I mean, obviously it's chroniclizing this new wave. What is the book about? Tell us a little bit about the book you're writing. >> So I wrote a book, my last book was about industrial augmented reality specifically, and it was sponsored by PTC, so you can actually go and find it for free. They wanted something that would work around industrial AR, and I wrote it in editorial independence so it is truly my perspective, but what was interesting about that at the time I wrote it, I discovered industrial AR was the most powerful place to play, because there were real world examples of AR actually helping people. >> John: Yeah. >> Now, I've broadened that look to see okay, Goldman Sachs said that there's going to be all this growth. Are the areas that they're looking at, things like education, real estate, you know, construction, is there actually growth there? So it's a broad look at a AR. And it's on O'Reilly's Safari Books. >> John: Well, that's interesting. One of the things that's interesting, you know, I've seen many waves myself, I've been through a bunch of cycles. It used to be the consumers that would lead the trends. But you're bringing up an interesting point around AI, augmented reality, even virtual reality. The innovations coming from the enterprise side. So, industrial IOT is really hot right now cause people are connecting physical plant and equipment. You see drones and it's mostly about industrial, AR's industrial because the use cases are so obvious. >> That's right. >> Not necessarily the consumer side has it yet. So it's almost flipped the entire world around. >> But, with, you know, Pokemon Go, that did sort of give consumers the scent, a scent of, okay, this is what it is you know, with AR kit, it hasn't completely lived up to our expectations, but there has been a flurry of activity around people experimenting to see how it can be applied in a consumer way. And, frankly, you know, there are people like DHL who are starting to roll it out in a way that is somewhere between industrial use and consumer in a broad way. So it's moving there. It is nowhere near ready for it yet. >> Leah Hunter here. A thought leader, writer, author, and a new book coming out. I'll give you the final word. What are you up to? What are you going to do after this event? What's next for you? What's the next couple months look like? Obviously, you've got to jam hard on the book, get that done, what else you working on? >> (laughs) I'm an interesting person to ask that question. I produce a television show called Created Here. I'm flying to Austin after this to interview artists and musicians and shoot our next episode of the show. Then we're going to LA and then New York. >> And where are you based out of? >> Me? >> Yeah. >> San Francisco, New York, a little bit Paris, and some New Orleans. >> You're on the plane a lot. >> I am. I like my life. >> Well, you've got a great life, and obviously great work you're doing. Come by TheCUBE studio in Palo Alto, give us an update on what your findings are as you go get that new perspective of art, artistry, artisans are really going to be the craft, we believe that TheCUBE will be the future of intersecting with technology. More exclusive coverage here in Moscone West in San Francisco, this is the Cube's coverage of Samsung Developer Conference. We'll be right back with more coverage after this short break.