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

>>Um, Jamie Sharath with Liga data, I'm primarily on the delivery side of the house, but I also support our new business teams. I'd like to spend a minute really just kind of telling you about, uh, uh, legal data where basically a Silicon valley startup, uh, started in 2014 and, uh, our lead iron, our executive team, basically where the data officers at Yahoo before this, uh, we provide managed data services and we provide products that are focused on telcos. So we have some experience in non telco industry, but our focus for the last seven years or so is specifically on telco. So again, something over 200 employees, we have a global presence in north America, middle east Africa, Asia, and Europe. And we have folks in all of those places. Uh, I'd like to call your attention to the, uh, the middle really of the screen there. >>So here is where we have done some partnership with Cloudera. So if you look at that, you can see we're in Holland and, uh, Jamaica, and then a lot to throughout Africa as well. Now the data fabric is the product that we're talking about. And the data fabric is basically a big data type of data warehouse with a lot of additional functionality involved. The data fabric is comprised of, uh, some something called flare, which we'll talk about admitted below there, and then the Cloudera data platform underneath. So this is how we're partnering together. We, uh, we, we have this tool and it's, uh, it's functioning and delivering in something over and up. Oops. So flare now, flare is a piece of that. It's legal data IP. The rest is Cloudera. And what flare does is that basically pulls in data and integrates it to an event streaming, uh, platform. >>It, uh, it is the engine behind the data fabric. Uh, it's also a decisioning platform. So in real time, we're able to pull in data. We're able to run analytics on it and we're able to alert our, do whatever is needed in a real-time basis. Of course, a lot of clients at this point are still sending data in batch. So it handles that as well, but we call that a cut off picture Sanchez. Now Sacho is a very interesting app. It's an AI analytics app for executives. What it is is it runs on your mobile phone. It ties into your data. Now this could be the data fabric, but it couldn't be a standalone product. And basically it allows you to ask, you know, human type questions to say, how are my gross ads last week? How are they comparing against same time last week before that? >>And even the same time 60 days ago. So as an executive or as an analyst, I can pull it up and I can look at it instantly in a meeting or anywhere else without having to think about queries or anything like that. So that's pretty much for us legal data. Now, it really does set the context of where we are. So this is a traditional telco environment. So you see the systems of record and you see the cloud, you see OSS and BSS day. So one of the things that the next step above which calls we call the system of intelligence of the data fabric does, is it mergers that BSS and OSS data. So the longer we have any silos or anything that's separated, it's all coming into one area to allow business, to go in or allow data scientists go in and do that. >>So if you look at the bottom line, excuse me, of the, uh, of the system of intelligence, you can see that flare is the tool that pulls in the data. So it provides even screening capabilities, it preserves entity states, so that you can go back and look at it to the state at any time. It does stream analytics that is as the data is coming in, it can perform analytics on it. And it also allows real-time decisioning. So that's something that, uh, that's something that business users can go in and create a system of, uh, if them's, it looks very much like a graph database where you can create a product that will allow the user to be notified if a certain condition happens. So for instance, a bundle, so a real-time offer or user is fixing to run out of is ongoing and an offer can be sent to him right on the fly. >>And that's set up by the business user as opposed to programmers a data infrastructure. So the fabric has really three areas. That data is persistent, obviously there's the data lake. So the data lake stores that level of granularity that is very deep years and years of history, data scientists love that. And, uh, you know, for a historical record keeping and requirements from the government, that data would be stored there. Then there's also something we call the business semantics layer and the business semantics layer contains something over 650 specific telco KPIs. These are initially from PM forum, but they also are included in, uh, various, uh, uh, mobile operators that we've delivered at. And we've, we've grown that. So that's there for business. The data lake is there for data scientists, analytical stores, uh, they can be used for many different reasons. There are a lot of times RDBMS is, are still there. >>So these, this, this basically platform, this cloud they're a platform can tie into analytical data stores as well via flair access and reporting. So graphic visualizations, API APIs are a very key part of it. A third-party query tools, any kind of grid jewels can be used. And those are the, of course, the, uh, the ones that are highly optimized and allow, you know, search of billions of records. And then if you look at the top, it's the systems of engagement, then you might vote this use cases. So telco reporting, hundreds of KPIs that are, that are generated for users, segmentation, basically micro to macro segmentation, segmentation will play a key role in a use case. We talk about in a minute monetizations. So this helps telco providers monetize their specific data, but monetize it in, okay, how to do they make money off of it, but also how might you leverage this data to, in, in dates with another client? >>So for instance, in some cases where it's allowed a DPI is used and the, uh, fabric tracks exactly where each person goes each, uh, we call it a subscriber, goes within his, uh, um, uh, internet browsing for 5g and, uh, all that data is stored. Uh, whereas you can tell a lot of things where the segment, the profile that's being used and, you know, what are they propensity to buy? Did they spend a lot of time on the Coca-Cola page? There are buyers out there that find that information very valuable, and then there's sideshow. And we spoke briefly about Sacha before that sits on top of the fabric or it's it's alone. >>So, so the story really that we want to tell is, is one, this is, this is one case out of it. This is a CVM type of case. So there was a mobile operator out there that was really offering, you know, packages, whether it's a bundle or whether it's a particular tool to subscribers, they, they were offering kind of an abroad approach that it was not very focused. It was not depending on the segments that were created around the profiling earlier, uh, the subscriber usage was somewhat dated and this was causing a lot of those. Uh, a lot of those offers to be just basically not taken and not, not, uh, uh, there was limited segmentation capabilities really before the, uh, before the, uh, fabric came in. Now, one of the key things about the fabric is when you start building segments, you can build that history. >>So all of that data stored in the data lake can be used in terms of segmentation. So what did we do about that? The, the, the MDNO, the challenge, uh, we basically put the data fabric in and the data fabric was running Cloudera data platform and that, uh, and that's how we team up. Uh, we facilitated the ability to personalize campaign. So what that means is, uh, the segments that were built and that user fell within that segment, we knew exactly what his behavior most likely was. So those recommendations, those offers could be created then, and we enable this in real time. So real-time ability to even go out to the CRM system, again, their further information about that, all of these tools, again, we're running on top of the cloud data platform, uh, what was the outcome? Willie, uh, outcome was that there was a much more precise offer given to the client that is, that was accepted, you know, increase in cross sell and upsell subscriber retention. >>Uh, our clients came back to us and pointed out that, uh, it was 183% year on year revenue increase. Uh, so this is a, this is probably one of the key use cases. Now, one thing to really mention is there are hundreds and hundreds of use cases running on the fabric. And, uh, I would even say thousands. A lot of those have been migrated. So when the fabric is deployed, when they bring the, uh, Cloudera and the legal data solution in there's generally a legacy system that has many use cases. So many of those were, were migrated virtually all of them in pen, on put on the cloud. Uh, another issue is that new use cases are enabled again. So when you get this level of granularity and when you have campaigns that can now base their offers on years of history, as opposed to 30 days of history, the campaigns campaign management response systems, uh, are, are, uh, are enabled quite a bit to do all, uh, to be precise in their offers. Yeah. >>Okay. So this is a technical slide. Uh, one of the things that we normally do when we're, when we're out there talking to folks, is we talk and give an overview and that last little while, and then we give a deep technical dive on all aspects of it. So sometimes that deep dive can go a couple of hours. I'm going to do this slide and a couple of minutes. So if you look at it, you can see over on the left, this is the, uh, the sources of the data. And they go through this tool called flare that runs on the cloud. They're a data platform, uh, that can either be via cues or real-time cues, or it can be via a landing zone, or it can be a data extraction. You can take a look at the data quality that's there. So those are built in one of the things that flare does is it has out of the box ability to ingest data sources and to apply the data quality and validation for telco type sources. >>But one of the reasons this is fast to market is because throughout those 10 or 12 opcos that we've done with Cloudera, where we have already built models, so models for CCN, for air for, for most mediation systems. So there's not going to be a type of, uh, input that we haven't already seen are very rarely. So that actually speeds up deployment very quickly. Then a player does the transformation, the, uh, the metrics, continuous learning, we call it continuous decisioning, uh, API access. Uh, we, uh, you know, for, for faster response, we use distributed cash. I'm not going to go too deeply in there, but the layer and the business semantics layer again, are, are sitting top of the Cloudera data platform. You see the cough, but flu, uh, Q1 on the right as well. >>And all of that, we're calling the fabric. So the fabric is Cloudera data platform and the cloud and flair and all of this runs together. And by the way, there've been many, many, many, many hundreds of hours testing flare with Cloudera and, uh, and the whole process, the results, what are the results? Well, uh, there are, there are four I'm going to talk about, uh, we saw the one for the, it was called my pocket pocket, but it's a CDM type, uh, use case. Uh, the subscribers of that mobile operator were 14 million plus there was a use case for a 24 million plus a year on year revenue was 130%, uh, 32 million plus for 38%. These are, um, these are different CVM pipe, uh, use cases, as well as network use cases. And then there were 44%, uh, telco with 76 million subscribers. So I think that there are a lot more use cases that we could talk about, but, but in this case, this is the ones we're looking at again, 183%. This is something that we find consistently, and these figures come from our, uh, our actual end client. So how do we unlock the full potential of this? Well, I think to start is to arrange a meeting and, uh, it would be great to, to, uh, for you to reach out to me or to Anthony. Uh, we're working in conjunction on this and we can set up a, uh, we can set up a meeting and we can go through this initial meeting. And, uh, I think that's the very beginning. Uh, again, you can get additional information from Cloudera website and from the league of data website, Anthony, that's the story. Thank you. >>Oh, that's great. Jeremy, thank you so much. It's a, it's, it's wonderful to go deep. And I know that there are hundreds of use cases being deployed in MTN, um, but great to go deep on one. And like you said, it can, once you get that sort of architecture in place, you can do so many different things. The power of data is tremendous, but it's great to be able to see how you can, how you can track it end to end from collecting the data, processing it, understanding it, and then applying it in a commercial context and bringing actual revenue back into the business. So there is your ROI straightaway. Now you've got a platform that you can transform your business on. That's, that's, it's a tremendous story, Jimmy, and thank you for your partnership. So, um, that's, uh, that's, that's our story for today, like Jamie says, um, please do fleet, uh, feel free to reach out to us. Um, the, the website addresses are there and our contact details, and we'd be delighted to talk to you a little bit more about some of the other use cases, perhaps, um, and maybe about your own business and, uh, and how we might be able to make it, make it perform a little better.

>>Hi today, I'm going to talk about network analytics and what that means for, for telecommunications as we go forward. Um, thinking about, uh, 5g, what the impact that's likely to have on, on network analytics and the data requirement, not just to run the network and to understand the network a little bit better. Um, but also to, to inform the rest of the operation of the telecommunications business. Um, so as we think about where we are in terms of network analytics and what that is over the last 20 years, the telecommunications industry has evolved its management infrastructure, uh, to abstract away from some of the specific technologies in the network. So what do we mean by that? Well, uh, in the, in the initial, uh, telecommunications networks were designed, there were management systems that were built in, um, eventually fault management systems, uh, assurance systems, provisioning systems, and so on were abstracted away. >>So it didn't matter what network technology had, whether it was a Nokia technology or Erickson technology or Huawei technology or whatever it happened to be. You could just look at your fault management system, understand where false, what happened as we got into the last sort of 10, 15 years or so. Telecommunication service providers become became more sophisticated in terms of their approach to data analytics and specifically network analytics, and started asking questions about why and what if in relation to their network performance and network behavior. And so network analytics as a, as a bit of an independent function was born and over time, more and more data began to get loaded into the network analytics function. So today just about every carrier in the world has a network analytics function that deals with vast quantities of data in big data environments that are now being migrated to the cloud. >>As all telecommunications carriers are migrating as many it workloads as possible, um, to the cloud. So what are the things that are happening as we migrate to the cloud that drive, uh, uh, enhancements in use cases and enhancements and scale, uh, in telecommunications network analytics? Well, 5g is the big thing, right? So 5g, uh, it's not just another G in that sense. I mean, in some cases, in some senses, it is 5g means greater bandwidth, lower latency and all those good things. So, you know, we can watch YouTube videos with less interference and, and less sluggish bandwidth and so on and so forth. But 5g is really about the enterprise and enterprise services. Transformation, 5g is more secure, kind of a network, but 5g is also a more pervasive network 5g, a fundamentally different network topology than previous generations. So there's going to be more masts and that means that you can have more pervasive connectivity. >>Uh, so things like IOT and edge applications, autonomous cars, smart cities, these kinds of things, um, are all much better served because you've got more masks that of course means that you're going to have a lot more data as well. And we'll get to that. The second piece is immersive digital services. So with more masks, with more connectivity, with lower latency with higher man, the potential, uh, is, is, is, is immense for services innovation. And we don't know what those services are going to be. We know that technologies like augmented reality, virtual reality, things like this have great potential. Um, but we, we have yet to see where those commercial applications are going to be, but the innovation and the innovation potential for 5g is phenomenal. Um, it certainly means that we're going to have a lot more, uh, edge devices, um, uh, and that again is going to lead to an increase in the amount of data that we have available. >>And then the idea of pervasive connectivity when it comes to smart, smart cities, uh, autonomous, autonomous currents, um, uh, integrated traffic management systems, um, all of this kind of stuff, those of those kind of smart environments thrive where you've got this kind of pervasive connectivity, this persistent, uh, connection to the network. Um, again, that's going to drive, um, um, uh, more innovation. And again, because you've got these new connected devices, you're going to get even more data. So this rise, this exponential rise in data is really what's driving the change in, in network analytics. And there are four major vectors that are driving this increase in data in terms of both volume and in terms of speed. So the first is more physical elements. So we said already that 5g networks are going to have a different apology. 5g networks will have more devices, more and more masks. >>Um, and so with more physical elements in the network, you're going to get more physical data coming off those physical networks. And so that needs to be aggregated and collected and managed and stored and analyzed and understood when, so that we can, um, have a better understanding as to why things happened the way they do, why the network behaves in which they do in, in, in, in ways that it does and why devices that are connected to the network. And ultimately of course, consumers, whether they be enterprises or retail customers, um, behave in the way they do in relation to their interaction within our edge nodes and devices, we're going to have a, uh, an explosion in terms of the number of devices. We've already seen IOT devices with your different kinds of trackers and, uh, and, and sensors that are hanging off the edge of the network, whether it's to make buildings smarter car smarter, or people smarter, um, in, in terms of having the, the, the measurements and the connectivity and all that sort of stuff. >>So the numbers of devices on the agent beyond the age, um, are going to be phenomenal. One of the things that we've been trying to with as an industry over the last few years is where does the telco network end, and where does the enterprise, or even the consumer network begin. You used to be very clear that, you know, the telco network ended at the router. Um, but now it's not, it's not that clear anymore because in the enterprise space, particularly with virtualized networking, which we're going to talk about in a second, um, you start to see end to end network services being deployed. Um, uh, and so are they being those services in some instances are being managed by the service provider themselves, and in some cases by the enterprise client, um, again, the line between where the telco network ends and where the enterprise or the consumer network begins, uh, is not clear. >>Uh, so, so those edge, the, the, the proliferation of devices at the age, um, uh, in terms of, um, you know, what those devices are, what the data yield is and what the policies are, their need to govern those devices, um, in terms of security and privacy, things like that, um, that's all going to be really, really important virtualized services. We just touched on that briefly. One of the big, big trends that's happening right now is not just the shift of it operations onto the cloud, but the shift of the network onto the cloud, the virtualization of network infrastructure, and that has two major impacts. First of all, it means that you've got the agility and all of the scale, um, uh, benefits that you get from migrating workloads to the cloud, the elasticity and the growth and all that sort of stuff. But arguably more importantly for the telco, it means that with a virtualized network infrastructure, you can offer entire networks to enterprise clients. >>So if you're selling to a government department, for example, is looking to stand up a system for certification of, of, you know, export certification, something like that. Um, you can not just sell them the connectivity, but you can sell them the networking and the infrastructure in order to serve that entire end to end application. You could sentence, you could offer them in theory, an entire end-to-end communications network, um, and with 5g network slicing, they can even have their own little piece of the 5g bandwidth that's been allocated against the carrier, um, uh, and, and have a complete end to end environment. So the kinds of services that can be offered by telcos, um, given virtualize network infrastructure, uh, are, are many and varied. And it's a, it's a, it's a, um, uh, an outstanding opportunity. But what it also means is that the number of network elements virtualized in this case is also exploding. >>That means the amount of data that we're getting on, uh, informing us as to how those network elements are behaving, how they're performing, um, uh, is, is, is going to go up as well. And then finally, AI complexity. So on the demand side, um, while historically, uh, um, network analytics, big data, uh, has been, has been driven by, um, returns in terms of data monetization, uh, whether that's through cost avoidance, um, or service assurance, uh, or even revenue generation through data monetization and things like that. AI is transforming telecommunications and every other industry, the potential for autonomous operations, uh, is extremely attractive. And so understanding how the end-to-end telecommunication service delivering delivery infrastructure works, uh, is essential, uh, as a training ground for AI models that can help to automate a huge amount of telecommunications operating, um, processes. So the AI demand for data is just going through the roof. >>And so all of these things combined to mean big data is getting explosive. It is absolutely going through the roof. So that's a huge thing that's happening. So as telecommunications companies around the world are looking at their network analytics infrastructure, which was initially designed for service insurance primarily, um, and how they migrate that to the cloud. These things are impacting on those decisions because you're not just looking at migrating a workload to operate in the cloud that used to work in the, in the data center. Now you're looking at, um, uh, migrating a workload, but also expanding the use cases in that work and bear in mind, many of those, those are going to need to remain on prem. So they'll need to be within a private cloud or at best a hybrid cloud environment in order to satisfy a regulatory jurisdictional requirements. So let's talk about an example. >>So LGU plus is a Finastra fantastic service provider in Korea. Um, huge growth in that business over the last, uh, over the last 10, 15 years or so. Um, and obviously most people will be familiar with LG, the electronics brand, maybe less so with, uh, with LG plus, but they've been doing phenomenal work. And we're the first, uh, business in the world who launch commercial 5g in 2019. And so a huge milestone that they achieved. And at the same time they deploy the network real-time analytics platform or in rep, uh, from a combination of Cloudera and our partner calmer. Now, um, there were a number of things that were driving, uh, the requirement for it, for the, for the analytics platform at the time. Um, clearly the 5g launch was that was the big thing that they had in mind, but there were other things that re so within the 5g launch, um, uh, they were looking for, for visibility of services, um, and service assurance and service quality. >>So, you know, what services have been launched? How are they being taken up? What are the issues that are arising, where are the faults happening? Um, where are the problems? Because clearly when you launch a new service, but then you want to understand and be on top of the issues as they arise. Um, so that was really, really important. The second piece was, and, you know, this is not a new story to any telco in the world, right. But there are silos in operation. Uh, and so, um, taking advantage of, um, or eliminating redundancies through the process, um, of, of digital transformation, it was really important. And so particular, the two silos between wired and the wireless sides of the business come together so that there would be an integrated network management system, um, for, uh, for LGU plus, as they rolled out 5g. So eliminating redundancy and driving cost savings through the, the integration of the silos is really, really important. >>And that's a process and the people thing every bit, as much as it is a systems and a data thing. So, um, another big driver and the fourth one, you know, we've talked a little bit about some of these things, right? 5g brings huge opportunity for enterprise services, innovation. So industry 4.0 digital experience, these kinds of use cases, um, are very important in the south Korean marketing and in the, um, in the business of LGU plus. And so, uh, um, looking at AI and how can you apply AI to network management? Uh, again, there's a number of use cases, really, really exciting use cases that have gone live now, um, in LG plus since, uh, since we did this initial deployment and they're making fantastic strides there, um, big data analytics for users across LGU plus, right? So it's not just for, um, uh, it's not just for the immediate application of 5g or the support or the 5g network. >>Um, but also for other data analysts and data scientists across the LGU plus business network analytics, while primarily it's primary it's primary use case is around network management, um, LGU plus, or, or network analytics, um, has applications across the entire business, right? So, um, you know, for customer churn or next best offer for understanding customer experience and customer behavior really important there for digital advertising, for product innovation, all sorts of different use cases and departments within the business needed access to this information. So collaboration sharing across the network, the real-time network analytics platform, um, it was very important. And then finally, as I mentioned, LG group is much bigger than just LG plus it's because the electronics and other pieces, and they had launched a major group wide digital transformation program in 2019, and still being a part of that was, well, some of them, the problems that they were looking to address. >>Um, so first of all, the integration of wired and wireless data service data sources, and so getting your assurance data sources, your network, data sources, uh, and so on integrated with is really, really important scale was massive for them. Um, you know, they're talking about billions of transactions in under a minute, uh, being processed, um, and hundreds of terabytes per day. So, uh, you know, phenomenal scale, uh, that needed to be available out of the box as it were, um, real time indicators and alarms. And there was lots of KPIs and thresholds set that, you know, w to make, make it to meet certain criteria, certain standards, um, customer specific, real time analysis of 5g, particularly for the launch root cause analysis, an AI based prediction on service, uh, anomalies and service service issues was, was, was a core use case. Um, as I talked about already the provision of service of data services across the organization, and then support for 5g, uh, served the business service, uh, impact, uh, was extremely important. >>So it's not just understand well, you know, that you have an outage in a particular network element, but what is the impact on the business of LGU plus, but also what is the impact on the business of the customer, uh, from an outage or an anomaly or a problem on, on, on the network. So being able to answer those kinds of questions really, really important, too. And as I said, between Cloudera and Kamarck, uh, uh, and LGU plus, uh, really themselves an intrinsic part of the solution, um, uh, this is, this is what we, we ended up building. So a big complicated architecture space. I really don't want to go into too much detail here. Um, uh, you can see these things for yourself, but let me skip through it really quickly. So, first of all, the key data sources, um, you have all of your wireless network information, other data sources. >>This is really important because sometimes you kind of skip over this. There are other systems that are in place like the enterprise data warehouse that needed to be integrated as well, southbound and northbound interfaces. So we get our data from the network and so on, um, and network management applications through file interfaces. CAFCA no fire important technologies. And also the RDBMS systems that, uh, you know, like the enterprise data warehouse that we're able to feed that into the system. And then northbound, um, you know, we spoke already about me making network analytics services available across the enterprise. Um, so, uh, you know, uh, having both the file and the API interface available, um, for other systems and other consumers across the enterprise is very important. Um, lots of stuff going on then in the platform itself to petabytes and persistent storage, um, Cloudera HDFS, 300 nodes for the, the raw data storage, um, uh, and then, uh, could do for real time storage for real-time indicator analysis, alarm generation, um, uh, and other real time, um, processes. >>Uh, so there, that was the, the core of the solution, uh, spark processes for ETL key quality indicators and alarming, um, and also a bunch of work done around, um, data preparation, data generation for transferal to, to third party systems, um, through the northbound interfaces, um, uh, Impala, API queries, um, for real-time systems, uh, there on the right hand side, and then, um, a whole bunch of clustering classification, prediction jobs, um, through the, uh, the, the, the, the ML processes, the machine learning processes, uh, again, another key use case, and we've done a bunch of work on that. And, um, I encourage you to have a look at the Cloudera website for more detail on some of the work that we did here. Um, so this is some pretty cool stuff. Um, and then finally, just the upstream services, some of these there's lots more than, than, than simply these ones, but service assurance is really, really important. So SQM cm and SED grade. So the service quality management customer experience, autonomous controllers, uh, really, really important consumers of, of the, of the real-time analytics platform, uh, and your conventional service assurance, um, functions like faulted performance management. Uh, these things are as much consumers of the information and the network analytics platform as they are providers of data to the network, uh, analytics >>Platform. >>Um, so some of the specific use cases, uh, that, uh, have been, have been stood up and that are delivering value to this day and lots of more episodes, but these are just three that we pulled out. Um, so first of all, um, uh, sort of specific monitoring and customer quality analysis, Karen response. So again, growing from the initial 5g launch and then broadening into broader services, um, understanding where there are the, where there are issues so that when people complaining, when people have an issue, um, that, um, uh, that we can answer the, the concerns of the client, um, in a substantive way, um, uh, AI functions around root cause analysis or understanding why things went wrong when they went wrong. Um, uh, and also making recommendations as to how to avoid those occurrences in the future. Uh, so we know what preventative measures can be taken. Um, and then finally the, uh, the collaboration function across LGU plus extremely important and continues to be important to this day where data is shared throughout the enterprise, through the API Lira through file interfaces and other things, and through interface integrations with, uh, with upstream systems. >>So, um, that's kind of the, the, uh, real quick run through of LGU plus the numbers are just stave staggering. Um, you know, we've seen, uh, upwards of a billion transactions in under 40 seconds being, um, uh, being tested. Um, and, and we've gone beyond those thresholds now, already, um, and we're started and, and, and, and this isn't just a theoretical sort of a benchmarking test or something like that. We're seeing these kinds of volumes of data and not too far down the track. So, um, with those things that I mentioned earlier with the proliferation of, of, um, of network infrastructure, uh, in the 5g context with virtualized elements, with all of these other bits and pieces are driving massive volumes of data towards the, uh, the, the, the network analytics platform. So phenomenal scale. Um, this is just one example we work with, with service providers all over the world is over 80% of the top 100 telecommunication service providers run on Cloudera. >>They use Cloudera in the network, and we're seeing those customers, all migrating legacy cloud platforms now onto CDP onto the Cloudera data platform. Um, they're increasing the, the, the jobs that they do. So it's not just warehousing, not just ingestion ETL, and moving into things like machine learning. Um, and also looking at new data sources from places like NWTF the network data analytics function in 5g, or the management and orchestration layer in, in software defined networks, network, function, virtualization. So, you know, new use cases coming in all the time, new data sources coming in all the time growth in, in, in, in the application scope from, as we say, from edge to AI. Um, and so it's, it's really exciting to see how the, the, the, the footprint is growing and how, uh, the applications in telecommunications are really making a difference in, in facilitating, um, network transformation. And that's covering that. That's me covered for today. I hope you found that helpful, um, by all means, please reach out, uh, there's a couple of links here. You can follow me on Twitter. You can connect to the telecommunications page, reach out to me directly at Cloudera. I'd love to answer your questions, um, uh, and, uh, and talk to you about how big data is transforming networks, uh, and how network transformation is, is accelerating telcos, uh, throughout >>Jamie Sharath with Liga data, I'm primarily on the delivery side of the house, but I also support our new business teams. I'd like to spend a minute really just kind of telling you about the legal data, where basically a Silicon valley startup, uh, started in 2014, and, uh, our lead iron, our executive team, basically where the data officers at Yahoo before this, uh, we provide managed data services, and we provide products that are focused on telcos. So we have some experience in non telco industry, but our focus for the last seven years or so is specifically on telco. So again, something over 200 employees, we have a global presence in north America, middle east Africa, Asia, and Europe. And we have folks in all of those places, uh, I'd like to call your attention to the, uh, the middle really of the screen there. So here is where we have done some partnership with Cloudera. >>So if you look at that and you can see we're in Holland and Jamaica, and then a lot to throughout Africa as well. Now, the data fabric is the product that we're talking about. And the data fabric is basically a big data type of data warehouse with a lot of additional functionality involved. The data fabric is comprised of, uh, some something called a flare, which we'll talk about in a minute below there, and then the Cloudera data platform underneath. So this is how we're partnering together. We, uh, we, we have this tool and it's, uh, it's functioning and delivering in something over 10 up. So flare now, flare is a piece of that legal data IP. The rest is there. And what flare does is that basically pulls in data, integrates it to an event streaming platform. It's, uh, it is the engine behind the data fabric. >>Uh, it's also a decisioning platform. So in real time, we're able to pull in data. We're able to run analytics on it, and we're able to alert are, do whatever is needed in a real-time basis. Of course, a lot of clients at this point are still sending data in batch. So it handles that as well, but we call that a CA picture Sanchez. Now Sacho is a very interesting app. It's an AI analytics app for executives. What it is is it runs on your mobile phone. It ties into your data. Now this could be the data fabric, but it couldn't be a standalone product. And basically it allows you to ask, you know, human type questions to say, how are my gross ads last week? How are they comparing against same time last week before that? And even the same time 60 days ago. So as an executive or as an analyst, I can pull it up and I can look at it instantly in a meeting or anywhere else without having to think about queries or anything like that. >>So that's pretty much for us at legal data, not really to set the context of where we are. So this is a traditional telco environments. So you see the systems of record, you see the cloud, you see OSS and BSS data. So one of the things that the next step above which calls we call the system of intelligence of the data fabric does, is it mergers that BSS and OSS data. So the longer we have any silos or anything that's separated, it's all coming into one area to allow business, to go in or allow data scientists go in and do that. So if you look at the bottom line, excuse me, of the, uh, of the system of intelligence, you can see that flare is the tools that pulls in the data. So it provides even streaming capabilities. It preserves entity states, so that you can go back and look at it state at any time. >>It does stream analytics that is as the data is coming in, it can perform analytics on it. And it also allows real-time decisioning. So that's something that, uh, that's something that business users can go in and create a system of, uh, if them's, it looks very much like the graph database, where you can create a product that will allow the user to be notified if a certain condition happens. So for instance, a bundle, so a real-time offer or user is succinct to run out of is ongoing, and an offer can be sent to him right on the fly. And that's set up by the business user as opposed to programmers, uh, data infrastructure. So the fabric has really three areas. That data is persistent, obviously there's the data lake. So the data lake stores that level of granularity that is very deep years and years of history, data, scientists like that, uh, and, uh, you know, for a historical record keeping and requirements from the government, that data would be stored there. >>Then there's also something we call the business semantics layer and the business semantics layer contains something over 650 specific telco KPIs. These are initially from PM forum, but they also are included in, uh, various, uh, uh, mobile operators that we've delivered at. And we've, we've grown that. So that's there for business data lake is there for data scientists, analytical stores, uh, they can be used for many different reasons. There are a lot of times RDBMS is, are still there. So these, this, this basically platform, this cloud they're a platform can tie into analytical data stores as well via flair access and reporting. So graphic visualizations, API APIs are a very key part of it. A third-party query tools, any kind of grid tools can be used. And those are the, of course, the, uh, the ones that are highly optimized and allow, you know, search of billions of records. >>And then if you look at the top, it's the systems of engagement, then you might vote this use cases. So teleco reporting, hundreds of KPIs that are, that are generated for users, segmentation, basically micro to macro segmentation, segmentation will play a key role in a use case. We talked about in a minute monetization. So this helps teleco providers monetize their specific data, but monetize it in. Okay, how to, how do they make money off of it, but also how might you leverage this data to engage with another client? So for instance, in some where it's allowed a DPI is used, and the fabric tracks exactly where each person goes each, uh, we call it a subscriber, goes within his, uh, um, uh, internet browsing on the, on the four or 5g. And, uh, the, all that data is stored. Uh, whereas you can tell a lot of things where the segment, the profile that's being used and, you know, what are they propensity to buy? Do they spend a lot of time on the Coca-Cola page? There are buyers out there that find that information very valuable, and then there's signs of, and we spoke briefly about Sanchez before that sits on top of the fabric or it's it's alone. >>So, so the story really that we want to tell is, is one, this is, this is one case out of it. This is a CVM type of case. So there was a mobile operator out there that was really offering, you know, packages, whether it's a bundle or whether it's a particular tool to subscribers, they, they were offering kind of an abroad approach that it was not very focused. It was not depending on the segments that were created around the profiling earlier, uh, the subscriber usage was somewhat dated and this was causing a lot of those. A lot of those offers to be just basically not taken and, and not, not, uh, audited. Uh, there was limited segmentation capabilities really before the, uh, before the, uh, fabric came in. Now, one of the key things about the fabric is when you start building segments, you can build that history. >>So all of that data stored in the data lake can be used in terms of segmentation. So what did we do about that? The, the, the envy and, oh, the challenge this, uh, we basically put the data fabric in and the data fabric was running Cloudera data platform and that, uh, and that's how we team up. Uh, we facilitated the ability to personalize campaign. So what that means is, uh, the segments that were built and that user fell within that segment, we knew exactly what his behavior most likely was. So those recommendations, those offers could be created then, and we enable this in real time. So real-time ability to even go out to the CRM system and gather further information about that. All of these tools, again, we're running on top of the Cloudera data platform, uh, what was the outcome? Willie, uh, outcome was that there was a much more precise offer given to the client that is, that was accepted, no increase in cross sell and upsell subscriber retention. >>Uh, our clients came back to us and pointed out that, uh, it was 183% year on year revenue increase. Uh, so this is a, this is probably one of the key use cases. Now, one thing to really mention is there are hundreds and hundreds of use cases running on the fabric. And I would even say thousands. A lot of those have been migrated. So when the fabric is deployed, when they bring the Cloudera and the legal data solution in there's generally a legacy system that has many use cases. So many of those were, were migrated virtually all of them in pen, on put on the cloud. Uh, another issue is that new use cases are enabled again. So when you get this level of granularity and when you have campaigns that can now base their offers on years of history, as opposed to 30 days of history, the campaigns campaign management response systems, uh, are, are, uh, are enabled quite a bit to do all, uh, to be precise in their offers. Okay. >>Okay. So this is a technical slide. Uh, one of the things that we normally do when we're, when we're out there talking to folks, is we talk and give an overview and that last little while, and then we give a deep technical dive on all aspects of it. So sometimes that deep dive can go a couple of hours. I'm going to do this slide and a couple of minutes. So if you look at it, you can see over on the left, this is the, uh, the sources of the data. And they go through this tool called flare that runs on the cloud. They're a data platform, uh, that can either be via cues or real-time cues, or it can be via a landing zone, or it can be a data extraction. You can take a look at the data quality that's there. So those are built in one of the things that flare does is it has out of the box ability to ingest data sources and to apply the data quality and validation for telco type sources. >>But one of the reasons this is fast to market is because throughout those 10 or 12, uh, opcos that we've done with Cloudera, where we have already built models, so models for CCN, for air for, for most mediation systems. So there's not going to be a type of, uh, input that we haven't already seen are very rarely. So that actually speeds up deployment very quickly. Then a player does the transformations, the, uh, the metrics, continuous learning, we call it continuous decisioning, uh, API access. Uh, we, uh, you know, for, for faster response, we use distributed cash. I'm not going to go too deeply in there, but the layer in the business semantics layer again, are, are sitting on top of the Cloudera data platform. You see the Kafka CLU, uh, Q1, the right as well. >>And all of that, we're calling the fabric. So the fabric is Cloudera data platform and the cloud and flair and all of this runs together. And, and by the way, there've been many, many, many, many hundreds of hours testing flare with Cloudera and, uh, and the whole process, the results, what are the results? Well, uh, there are, there are four I'm going to talk about, uh, we saw the one for the, it was called my pocket pocket, but it's a CDM type, a use case. Uh, the subscribers of that mobile operator were 14 million plus there was a use case for 24 million plus that a year on year revenue was 130%, uh, 32 million plus for 38%. These are, um, these are different CVM pipe, uh, use cases, as well as network use cases. And then there were 44%, uh, telco with 76 million subscribers. So I think that there are a lot more use cases that we could talk about, but, but in this case, this is the ones we're looking at, uh, again, 183%. This is something that we find consistently. And these figures come from our, uh, our actual end client. How do we unlock the full potential of this? Well, I think to start is to arrange a meeting and, uh, it would be great to, to, uh, for you to reach out to me or to Anthony. Uh, we're working at the junction on this, and we can set up a, uh, we can set up a meeting and we can go through this initial meeting. And, uh, I think that's the very beginning. Uh, again, you can get additional information from Cloudera website and from the league of data website, Anthony, that's the story. Thank you. >>No, that's great. Jeremy, thank you so much. It's a, it's, it's wonderful to go deep. And I know that there are hundreds of use cases being deployed in MTN, um, but great to go deep on one. And like you said, it can, once you get that sort of architecture in place, you can do so many different things. The power of data is tremendous, but it's great to be able to see how you can, how you can track it end to end from collecting the data, processing it, understanding it, and then applying it in a commercial context and bringing actual revenue back into the business. So there is your ROI straight away. Now you've got a platform that you can transform your business on. That's, that's, it's a tremendous story, Jamie, and thank you for your part. Sure. Um, that's a, that's, that's our story for today. Like Jamie says, um, please do flee, uh, feel free to reach out to us. Um, the, the website addresses are there and our contact details, and we'd be delighted to talk to you a little bit more about some of the other use cases, perhaps, um, and maybe about your own business and, uh, and how we might be able to make it, make it perform a little better. So thank you.

>> Announcer: From around the globe it's theCUBE with digital coverage of smart data marketplaces brought to you by Io Tahoe. >> Hi everybody, this is Dave Vellante. And welcome back. We've been talking about smart data. We've been hearing Io Tahoe talk about putting data to work and keep heart of building great data outcomes is the Cloud of course, and also Cloud native tooling. Stuti Deshpande is here. She's a partner solutions architect for Amazon Web Services and an expert in this area. Stuti, great to see you. Thanks so much for coming on theCUBE. >> Thank you so much for having me here. >> You're very welcome. So let's talk a little bit about Amazon. I mean, you have been on this machine learning journey for quite sometime. Take us through how this whole evolution has occurred in technology over the period of time. Since the Cloud really has been evolving. >> Amazon in itself is a company, an example of a company that has gotten through a multi year machine learning transformation to become the machine learning driven company that you see today. They have been improvising on original personalization model using robotics to all different women's centers, developing a forecasting system to predict the customer needs and improvising on that and reading customer expectations on convenience, fast delivery and speed, from developing natural language processing technology for end user infraction, to developing a groundbreaking technology such as Prime Air jobs to give packages to the customers. So our goal at Amazon With Services is to take this rich expertise and experience with machine learning technology across Amazon, and to work with thousands of customers and partners to handle this powerful technology into the hands of developers or data engineers of all levels. >> Great. So, okay. So if I'm a customer or a partner of AWS, give me the sales pitch on why I should choose you for machine learning. What are the benefits that I'm going to get specifically from AWS? >> Well, there are three main reasons why partners choose us. First and foremost, we provide the broadest and the deepest set of machine learning and AI services and features for your business. The velocity at which we innovate is truly unmatched. Over the last year, we launched 200 different services and features. So not only our pace is accelerating, but we provide fully managed services to our customers and partners who can easily build sophisticated AI driven applications and utilizing those fully managed services began build and train and deploy machine learning models, which is both valuable and differentiating. Secondly, we can accelerate the adoption of machine learning. So as I mentioned about fully managed services for machine learning, we have Amazon SageMaker. So SageMaker is a fully managed service that are any developer of any level or a data scientist can utilize to build complex machine learning, algorithms and models and deploy that at scale with very less effort and a very less cost. Before SageMaker, it used to take so much of time and expertise and specialization to build all these extensive models, but SageMaker, you can literally build any complex models within just a time of days or weeks. So to increase it option, AWS has acceleration programs just in a solution maps. And we also have education and training programs such as DeepRacer, which are enforces on enforcement learning and Embark, which actually help organization to adopt machine learning very readily. And we also support three major frameworks that just tensive no charge, or they have separate teams who are dedicated to just focus on all these frameworks and improve the support of these frameworks for a wide variety of workloads. And finaly, we provide the most comprehensive platform that is optimized for machine learning. So when you think about machine learning, you need to have a data store where you can store your training sets, your test sets, which is highly reliable, highly scalable, and secure data store. Most of our customers want to store all of their data and any kind of data into a centralized repository that can be treated at the central source of fraud. And in this case from the Amazon Esri data store to build and endurance machine learning workflow. So we believe that we provide this capability of having the most comprehensive platform to build the machine learning workflow from internally. >> Great. Thank you for that. So I wanted, my next question is, this is a complicated situation for a lot of customers. You know, having the technology is one thing, but adoption is sort of everything. So I wonder if you could paint a picture for us and help us understand, how you're helping customers think about machine learning, thinking about that journey and maybe give us the context of what the ecosystem looks like? >> Sure. If someone can put up the belt, I would like to provide a picture representation of how AWS and fusion machine learning as three layers of stack. And moving on to next bill, I can talk about the bottom there. And bottom there as you can see over this screen, it's basically for advanced technologists advanced data scientists who are machine learning practitioners who work at the framework level. 90% of data scientists use multiple frameworks because multiple frameworks are adjusted and are suitable for multiple and different kinds of workloads. So at this layer, we provide support for all of the different types of frameworks. And the bottom layer is only for the advanced scientists and developers who are actually actually want to build, train and deploy these machine learning models by themselves and moving onto the next level, which is the middle layer. This layer is only suited for non-experts. So here we have seen Jamaica where it provides a fully managed service there you can build, tune, train and deploy your machine learning models at a very low cost and with very minimal efforts and at a higher scale, it removes all the complexity, having a thing and guess guesswork from this stage of machine learning and Amazon SageMaker has been the scene that will change. Many of our customers are actually standardizing on top off Amazon SageMaker. And then I'm moving on to the next layer, which is the top most layer. We call this as AI services because this may make the human recognition. So all of the services mentioned here such as Amazon Rekognition, which is basically a deep learning service optimized for image and video analysis. And then we have Amazon Polly, which can do the text to speech from Russian and so on and so forth. So these are the AI services that can be embedded into the application so that the end user or the end customer can build AI driven applications. >> Love it. Okay. So you've got the experts at the bottom with the frameworks, the hardcore data scientists, you kind of get the self driving machine learning in the middle, and then you have all the ingredients. I'm like an AI chef or a machine learning chef. I can pull in vision, speech, chatbots, fraud detection, and sort of compile my own solutions that's cool. We hear a lot about SageMaker studio. I wonder if you could tell us a little bit more, can we double click a little bit on SageMaker? That seems to be a pretty important component of that stack that you just showed us. >> I think that was an absolutely very great summarization of all the different layers of machine unexpected. So thank you for providing the gist of that. Of course, I'll be really happy to talk about Amazon SageMaker because most of our customers are actually standardizing on top of SageMaker. That is spoken about how machine learning traditionally has so many complications and it's very complex and expensive and I traded process, which makes it even harder because they don't know integrated tools or if you do the traditional machine learning all kind of deployment, there are no integrated tools for the entire workflow process and deployment. And that is where SageMaker comes into the picture. SageMaker removes all the heaviness thing and complexities from each step of the deployment of machine learning workflow, how it solves our challenges by providing all of the different components that are optimized for every stage of the workflow into one single tool set. So that models get to production faster and with much less effort and at a lower cost. We really continue to add important (indistinct) leading to Amazon SageMaker. I think last year we announced 50 cubic litres in this far SageMaker being improvised it's features and functionalities. And I would love to call out a couple of those here, SageMaker notebooks, which are just one thing, the prominent notebooks that comes along with easy two instances, I'm sorry for quoting Jarvin here is Amazon Elastic Compute Instances. So you just need to have a one thing deployment and you have the entire SageMaker Notebook Interface, along with the Elastic Compute Instances running that gives you the faster time to production. If you're a machine, if you are a data scientist or a data engineer who worked extensively for machine learning, you must be aware about building training datasets is really complex. So there we have on his own ground truth, that is only for building machine learning training data sets, which can reduce your labeling cost by 70%. And if you perform machine learning and other model technology in general, there are some workflows where you need to do inferences. So there we have inference, Elastic Inference Incense, which you can reduce the cost by 75% by adding a little GP acceleration. Or you can reduce the cost by adding managed squad training, utilizing easy to spot instances. So there are multiple ways that you can reduce the costs and there are multiple ways there you can improvise and speed up your machine, learning deployment and workflow. >> So one of the things I love about, I mean, I'm a prime member who is not right. I love to shop at Amazon. And what I like about it is the consumer experience. It kind of helps me find things that maybe I wasn't aware of, maybe based on other patterns that are going on in the buying community with people that are similar. If I want to find a good book. It's always gives me great reviews and recommendations. So I'm wondering if that applies to sort of the tech world and machine learning, are you seeing any patterns emerge across the various use cases, you have such scale? What can you tell us about that? >> Sure. One of the battles that we have seen all the time is to build scalable layer for any kind of use case. So as I spoke before that as much, I'm really looking to put their data into a single set of depository where they have the single source of truth. So storing of data and any kind of data at any velocity into a single source of would actually help them build models who run on these data and get useful insights out of it. So when you speak about an entry and workflow, using Amazon SageMaker along bigger, scalable analytical tool is actually what we have seen as one of the factors where they can perform some analysis using Amazon SageMaker and build predictive models to say samples, if you want to take a healthcare use case. So they can build a predictive model that can victimize the readmissions of using Amazon SageMaker. So what I mean, to say is, by not moving data around and connecting different services to the same set of source of data, that's tumor avoid creating copies of data, which is very crucial when you are having training data set and test data sets with Amazon SageMaker. And it is highly important to consider this. So the pattern that we have seen is to utilize a central source of depository of data, which could be Amazon Extra. In this scenario, scalable analytical layer along with SageMaker. I would have to code at Intuit for a success story over here. I'm using sandwich, a Amazon SageMaker Intuit had reviews the machine learning deployment time by 90%. So I'm quoting here from six months to one week. And if you think about a healthcare industry, there hadn't been a shift from reactive to predictive care. So utilizing predictive models to accelerate research and discovery of new drugs and new treatments. And you've also observed that nurses were supported by AI tools increase their, their productivity has increased by 50%. I would like to say that one of our customers are really diving deep into the AWS portfolio of machine learning and AI services and including transcribed medical, where they are able to provide some insights so that their customers are getting benefits from them. Most of their customers are healthcare providers and they are able to give some into insights so that they can create some more personalized and improvise patient care. So there you have the end user benefits as well. One of the patterns that I have, I can speak about and what we have seen as well, appearing a predictive model with real time integration into healthcare records will actually help their healthcare provider customers for informed decision making and improvising the personalized patient care. >> That's a great example, several there. And I appreciate that. I mean, healthcare is one of those industries that is just so right for technology ingestion and transformation, that is a great example of how the cloud has really enabled really. I mean, I'm talking about major changes in healthcare with proactive versus reactive. We're talking about lower costs, better health, longer lives is really inspiring to see that evolve. We're going to watch it over the next several years. I wonder if we could close in the marketplace. I've had the pleasure of interviewing Dave McCann, a number of times. He and his team have built just an awesome capability for Amazon and its ecosystem. What about the data products, whether it's SageMaker or other data products in the marketplace, what can you tell us? >> Sure. Either of this market visits are interesting thing. So let me first talk about the AWS marketplace of what, AWS marketplace you can browse and search for hundreds of machine learning algorithms and machine learning, modern packages in a broad range of categories that this company provision, fixed analysis, voice answers, email, video, and it says predictive models and so on and so forth. And all of these models and algorithms can be deployed to a Jupiter notebook, which comes as part of the SageMaker that form. And you can integrate all of these different models and algorithms into our fully managed service, which is Amazon SageMaker to Jupiter notebooks, Sage maker, STK, and even command as well. And this experience is followed by either of those marketplace catalog and API. So you get the same benefits as any other marketplace products, the just seamless deployments and consolidate it. So you get the same benefits as the products and the invest marketplace for your machine learning algorithms and model packages. And this is really important because these can be darkly integrated into our SageMaker platform. And I don't even be honest about the data products as well. And I'm really happy to provide and code one of the example over here in the interest of cooler times and because we are in unprecedented times over here we collaborated with our partners to provide some data products. And one of them is data hub by tablet view that gives you the time series data of phases and depth data gathered from multiple trusted sources. And this is to provide better and informed knowledge so that everyone who was utilizing this product can make some informed decisions and help the community at the end. >> I love it. I love this concept of being able to access the data, algorithms, tooling. And it's not just about the data, it's being able to do something with the data and that we've been talking about injecting intelligence into those data marketplaces. That's what we mean by smart data marketplaces. Stuti Deshpande, thanks so much for coming to theCUBES here, sharing your knowledge and tell us a little bit about AWS. There's a pleasure having you. >> It's my pleasure too. Thank you so much for having me here. >> You're very welcome. And thank you for watching. Keep it right there. We will be right back right after this short break. (soft orchestral music)

>> Live, from Las Vegas! It's theCube! Covering VMworld 2018. Brought to you by VMware and its ecosystem partners. >> Welcome back everyone, it's theCube's live coverage in Las Vegas for VMworld 2018, it's theCube. We got two sets, 24 interviews per day, 94 interviews total. Next three days, we're in day two of three days coverage. It's our ninth year of covering VMworld. It's been great. I'm John Furrier with Dave Vellante, next guest, Cube alumni, number one in the leading boards right now, Sanjay Poonen did a great job today on stage, keynote COO for VMware. Great to have you back. Thanks for coming on. >> John and Dave, you're always so kind to me, but I didn't realize you've been doing this nine years. >> This is our ninth year. >> That's half the life of VMware, awesome. Unreal. Congratulations. >> We know all the stories, all the hidden, nevermind, let's talk about your special day today. You had a really, so far, an amazing day, you were headlining the key note with a very special guest, and you did a great job. I want you to tell the story, who was on, what was the story about, how did this come about? Tech for good, a big theme in this conference has really been getting a lot of praise and a lot of great feedback. Take us through what happened today. >> Well listen, I think what we've been trying to do at VMware is really elevate our story and our vision. Elevate our partnerships, you've covered a lot of the narrative of what we've done with Andy Jessie. We felt this year, we usually have two 90 minute sessions, Day One, Day Two, and it's filled with content. We're technical company, product. We figured why don't we take 45 minutes out of the 180 minutes total and inspire people. With somebody who's had an impact on the world. And when we brainstormed, we had a lot of names suggested, I think there was a list of 10 or 15 and Malala stood out, she never spoke at a tech conference before. I loved her story, and we're all about education. The roots of VMware were at Stamford Campus. Diane Greene, and all of that story. You think about 130 million girls who don't go to school. We want to see more diversity in inclusion, and she'd never spoken so I was like, you know what, usually you go to these tech conferences and you've heard somebody who's spoken before. I'm like, lets invite her and see if she would come for the first time, and we didn't think she would. And we were able to score that, and I was still a little skeptical 'cause you never know is it going to work out or not. So thank you for saying it worked, I think we got a lot of good feedback. >> Well, in your first line, she was so endearing. You asked her what you thought a tech conference, you said too many acronyms. She just cracked the place up immediately. >> And then you heard my response, right? If somebody tells me like that, you tell VMotion wrong she looked at me what? >> Tell them about our story, real quick, our story I want to ask you a point in question. Her story, why her, and what motivated you to get her? >> Those stories, for any of you viewers, you should read the book "I'm Malala" but I'll give you the short version of the story. She was a nine year old in the Pashtun Area of the Swat Valley in Pakistan, and the Taliban setted a edict that girls could not go to school. Your rightful place was whatever, stay at home and become a mom with babies or whatever have you. You cannot go to school. And her father ran a school, Moster Yousafzai, wonderful man himself, an educator, a grandfather, and says know what, we're going to send you to school. Violating this order, and they gave a warning after warning and finally someone shot her in 2012, almost killed her. The bullet kind of came to her head, went down, and miraculously she escaped. Got on a sort of a hospital on a plane, was flown to London, and the world if you remember 2012, the world was following the story. She comes out of this and she's unscathed. She looks normal, she has a little bit of a thing on the right side of her face but her brains normal, everything's normal. Two years later she wins the Nobel Peace Prize. Has started the Malala Fund, and she is a force of nature, an amazing person. Tim Cook has been doing a lot with her in the Malala Fund. I think that actually caught my attention when Tim Cook was working with her, and you know whatever Apple does often gets a little bit of attention. >> Well great job selecting her. How's that relevant to what you guys are doing now, because you guys had a main theme Tech for Good? Why now, why VMware? A lot of people are looking at this, inspired by it. >> There are milestones in companies histories. We're at our 20 year birthday, and I'm sure at people's birthday they want to do big things, right? 20, 30, 40, 50, these decades are big ones and we thought, lets make this year a year to remember in various things we do. We had a 20 year anniversary celebration on campus, we invited Diane Greene back. It was a beautiful moment internally at Vmware during one of our employee meetings. It was a private moment, but just with her to thank her. And man, there were people emotional almost in tears saying thank you for starting this company. A way to give back to us, same way here. What better way to talk about the impact we're having in the community than have someone who is of this reputation. >> Well we're behind your mission 100%, anything you need. We loved the message, Tech for Good, people want to work for a mission driven company. People want to buy >> We hope so. >> from mission driven companies, that stated clear and the leadership you guys are providing is phenomenal. >> We had some rankings that came out around the same time. Fortune ranked companies who are changing the world, and VMware was ranked 17th overall, of all companies in the world and number one in the software category. So when you're trying to change the world, hopefully as you pointed out it's also an attractor of talent. You want to come here, and maybe even attractor of customers and partners. >> You know the other take-away was from the key note was how many Cricket fans there are in the VMworld Community. Of course we have a lot of folks from India, in our world but who's your favorite Cricketer? Was it Sachin Tendulkar? (laughs) >> Clearly you're reading off your notes Dave! >> Our Sonya's like our, >> Dead giveaway! >> Our Sonya's like our Cricket Geek and she's like, ask him about Sachin, no who's your favorite Cricketer, she wants to know. >> Sachin Tendulkar's way up there, Shayuda Free, the person she likes from Pakistan. I grew up playing cricket, listen I love all sports now that I'm here in this country I love football, I love basketball, I like baseball. So I'll watch all of them, but you know you kind of have those childhood memories. >> Sure >> And the childhood memories were like she talk about, India, Pakistan games. I mean this was like, L.A. Dodgers playing Giants or Red Socks, Yankee's, or Dallas Cowboys and the 49ers, or in Germany playing England or Brazil in the World Cup. Whatever your favorite country or team rivalry is, India Pakistan was all there more, but imagine like a billion people watching it. >> Yeah, well it was a nice touch on stage, and I'd say Ted Williams is my favorite cricketer, oh he plays baseball, he's a Red Sock's Player. Alright Sanjay, just cause your in the hot seat, lets get down to business here. Great moment on stage, congratulation. Okay Pat Gelsinger yesterday on the key note talked about the bridges, VMware bridging, connecting computers. One of the highlights is kind of in your wheelhouse, it's in your wheelhouse, the BYOD, Bring Your Own Device bridge. You're a big part of that. Making that work on on the mobile side. Now with Cloud this new bridge, how is that go forward because you still got to have all those table stakes, so with this new bridge of VMware's in this modern era, cloud and multicloud. Cluely validated, Andy Jassy, on stage. Doing something that Amazon's never done before, doing something on premise with VMware, is a huge deal. I mean we think it's a massive deal, we think it's super important, you guys are super committed to the relationship on premises hybrid cloud, multicloud, is validated as far as we're concerned. It's a done deal. Now ball's in your court, how are you going to bring all that mobile together, security, work space one, what's your plan? >> I would say that, listen on as I described in my story today there's two parts to the VMware story. There's a cloud foundation part which is the move the data center to the cloud in that bridge, and then there's the desk job move it to the mobile. Very briefly, yes three years of my five years were in that business, I'm deeply passionate about it. Much of my team now that I put in place there, Noah and Shankar are doing incredible jobs. We're very excited, and the opportunity's huge. I said at my key note of the seven billion people that live in the world, a billion I estimate, work for some company small or big and all of them have a phone. Likely many of those billion have a phone and a laptop, like you guys have here, right? That real estate of a billion in a half, maybe two billion devices, laptops and phones, maybe in some cases laptop, phone, and tablets. Someone's going to manage and secure, and their diverse across Apple, Google, big option for us. We're just getting started, and we're already the leader. In the data center, the cloud world, Pat, myself, Raghu, really as we sat three years ago felt like we shouldn't be a public cloud ourselves. We divested vCloud Air, as I've talked to you on your show before, Andy Jassy is a friend, dear friend and a classmate of mine from Harvard Business School. We began those discussions the three of us. Pat, Raghu, and myself with Andy and his team and as every quarter and year has gone on they become deeper and deep partnerships. Andy has told other companies that VMware Amazon is the model partnership Amazon has, as they describe who they would like to do business more with. So we're proud when they do that, when we see that happen. And we want to continue that. So when Amazon came to us and said listen I think there's an opportunity to take some of our stack and put it on premise. We kept that confidential cause we didn't want it to leak out to the world, and we said we're going to try'n annouce it at either VMworld or re:Invent. And we were successful. A part with these projects is they inevitably leak. We're really glad no press person sniffed it out. There was a lot of speculation. >> Couldn't get confirmation. >> There was a lot of speculation but no one sniffed it out and wrote a story about it, we were able to have that iPhone moment today, I'm sorry, yesterday when we unveiled it. And it's a big deal because RDS is a fast growing business for them. RDS landing on premise, they could try to do on their own but what better infrastructure to land it on than VMware. In some cases would be VMware running on VxRail which benefits Dell, our hardware partners. And we'll continue doing more, and more, and more as customers desire, so I'm excited about it. >> Andy doesn't do deals, as you know Andy well as we do. He's customer driven. Tell me about the customer demand on this because it's something we're trying to get reporting on. Obviously it makes sense, technically the way it's working. You guys and Andy, they just don't do deals out of the blue. There's customer drivers here, what are those drivers? >> Yeah, we're both listening to our customers and perhaps three, four, five years ago they were very focused on student body left, everybody goes public cloud. Like forget your on premise, evaporate, obliterate your data centers and just go completely public. That was their message. >> True, sweep the floor. >> Right, if you went to first re:Invent I was there on stage with them as an SAP employee, that's what I heard. I think you fast forward to 2014, 2015 they're beginning to realize, hey listen it's not as easy. Refactoring your apps, migrating those apps, what if we could bring the best of private cloud and public cloud together enter VMware and Amazon. He may have felt it was harder to have those cultivations of VMware or for all kinds of reasons, like we had vCloud Air and so on and so forth but once we divested that decision culminations had matured between us that door opened. And as that door opened, more culminations began. Jointly between us and with customers. We feel that there are customers who want many of those past type of services of premise. Cause you're building great things, relational database technology, AI, VI maybe. IoT type of technologies if they are landing on premise in an edge-computing kind of world, why not land on VMware because we're the king of the private cloud. We're very happy to those, we progress those discussion. I think in infrastructure software VMware and Amazon have some of the best engineers on the planet. Sometimes we've engineers who've gone between both companies. So we were able to put our engineering team's together. This is a joint engineering effort. Andy and us often talk about the fact that great innovation's built when it's not just Barny go to Marketing and Marketing press releases this. The true joint engineering at a deep level. That's what happened the last several months. >> Well I can tell you right now the commitment I've seen from an executive level and deep technology, both sides are deep and committed to this. It's go big or go home, at least from our perspective. Question I want to ask you Sanjay is you're close to the customer's of VMware. What's the growth strategy? If you zoom out, look down on stage and you got vSAN, NSX at the core, >> vSANjay (laughs) >> How can you not like a product that has my name on it? >> So you got all these things, where's the growth going to come from, the merging side, is the v going to be the stable crown jewels at NSX? How do you guys see the growth, where's it going to come from? >> Just kind of look at our last quarter. I mean if you peel back the narrative, John and Dave, two years ago we were growing single digits. Like low single digits. Two, three percent. That was, maybe the legacy loser description of VMware was the narrative everyone was talking about >> License revenue was flattish right? >> And then now all of sudden we're double digits. 12, 15 sort of in that range for both product revenue. It's harder to grow faster when you're bigger, and what's happened is that we stabilize compute with vSphere in that part and it's actually been growing a little bit because I think people in the VMware cloud provider part of our business, and the halo effect of the cloud meant that as they refresh the servers they were buying more research. That's good. The management business has started to grow again. Some cases double digits, but at least sort of single digits. NSX, the last few order grew like 30, 40%. vSAN last year was growing 100% off a smaller base, this year going 60, 70%. EUC has been growing double digits, taking a lot of share from company's like Citrix and MobileIron and others. And now, also still growing double digits at much bigger paces, and some of those businesses are well over a billion. Compute, management, end-user computing. We talked about NSX on our queue forming called being a 1.4 billion. So when you get businesses to scale, about a billion dollar type businesses and their sort of four, training five that are in that area, and they all get to grow faster than the market. That's the key, you got to get them going fast. That's how you get growth. So we focus on those on those top five businesses and then add a few more. Like VMware Cloud on AWS, right now our goal is customer logo count. Revenue will come but we talked on our earnings call about a few hundred customers of VMware Cloud and AWS. As that gets into the thousands, and there's absolutely that option, why? Because there's 500,000 customers of VMware and two million customers of Amazon, so there's got to be a lot of commonality between those two to get a few thousand. Then we'll start caring about revenue there too, but once you have logos, you have references. Containers, I'd like to see PKS have a few hundred customers and then, we put one on stage today. National Commercial Bank of Jamaica. Fantastic story of PKS. I even got my PKS socks for this interview. (John laughs) >> So that give you a sense as to how we think, there will be four, five that our businesses had scale and then a few are starting to get there, and they become business to scale. The nature of software is we'll always be doing this show because there will be new businesses to talk about. >> Yeah, hardware is easy. Software is hard, as Andy Patchenstien said on theCUBE yesterday. Congratulations Sanjay and all the success, you guys are doing great financially. Products looking really good coming out, the bloom is rising from the fruit you guys have harvested, coming together. >> John if I can say one last thing, I shared a picture of a plane today and I put two engines behind it. There's something I've learned over the last years about focus of a company, and I joked about different ways that my name's are pronounced but at the core of me there's a DNA. I said on stage I'd rather not be known as smart or stupid but having a big heart. VMware, I hope is known by our customers as having these two engines. An engine of innovation, innovating product and a variety of other things. And focused on customer obsession. We do those, the plane will go a long way. >> And it's looking good you guys, we can say we've been to Radio Event, we've been doing a lot of great stuff. Congratulations on the initiative, and a great interview with you today on doing Tech for Good and sharing your story. Getting more exposure to the kind of narratives people want to hear. More women in tech, more girls in tech, more democratization. Congratulations and thanks so much for sharing. >> Thank you John and Dave. >> Appreciate you being here. >> Sanjay Poonen, COO of VMware. Friend of theCUBE, Cube Alumni, overall great guy. Big heart and competitive too, we know that from his Twitter stream. Follow Sanjay on Twitter. You'll have a great time. I'm John Furrier with Dave Vellante, stay with us for more coverage from day two live, here in Las Vegas for VMware 2018. Stay with us. (tech music)

Okay, this presentation includes forward looking statements that are subject to risks and uncertainties. Actual results may differ materially as a result of various risk factors including those described in the 10 k's 10 q's and eight ks. Vm ware files with the SEC, ladies and gentlemen, Sunjay Buddha for the jazz mafia from Oakland, California. Good to be with you. Welcome to late night with Jimmy Fallon. I'm an early early morning with Sanjay Poonen and two are set. It's the first time we're doing a live band and jazz and blues is my favorite. You know, I prefer a career in music, playing with Eric Clapton and that abandoned software, but you know, life as a different way. I'll things. I'm delighted to have you all here. Wasn't yesterday's keynote. Just awesome. Off the charts. I mean pat and Ray, you just guys, I thought it was the best ever keynote and I'm not kissing up to the two of you. If you know pat, you can't kiss up to them because if you do, you'll get an action item list at 4:30 in the morning that sten long and you'll be having nails for breakfast with him but bad it was delightful and I was so inspired by your tattoo that I decided to Kinda fell asleep in batter ass tattoo parlor and I thought one wasn't enough so I was gonna one up with. I love Vm ware. Twenty years. Can you see that? What do you guys think? But thank you all of you for being here. It's a delight to have you folks at our conference. Twenty 5,000 of you here, 100,000 watching. Thank you to all of the vm ware employees who helped put this together. Robin Matlock, Linda, Brit, Clara. Can I have you guys stand up and just acknowledge those of you who are involved? Thank you for being involved. Linda. These ladies worked so hard to make this a great show. Everybody on their teams. It's the life to have you all here. I know that we're gonna have a fantastic time. The title of my talk is pioneers of the possible and we're going to go through over the course of the next 90 minutes or so, a conversation with customers, give you a little bit of perspective of why some of these folks are pioneers and then we're going to talk about somebody who's been a pioneer in the world but thought to start off with a story. I love stories and I was born in a family with four boys and my parents I grew up in India were immensely creative and naming that for boys. The eldest was named Sanjay. That's me. The next was named Santosh Sunday, so if you can get the drift here, it's s a n, s a n s a n and the final one. My parents got even more creative and colon suneel sun, so you could imagine my mother going south or Sunday do. I meant Sanjay you and it was always that confusion and then I come to the United States as an immigrant at age 18 and people see my name and most Americans hadn't seen many Sundays before, so they call me Sanjay. I mean, of course it of sounds like v San, so sanjay, so for all of your V, San Lovers. Then I come to California for years later work at apple and my Latino friends see my name and it sorta sounds like San Jose, so I get called sand. Hey, okay. Then I meet some Norwegian friends later on in my life, nordics. The J is a y, so I get called San Year. Your my Italian friend calls me son Joe. So the point of the matter is, whatever you call me, I respond, but there's certain things that are core to my DNA. Those that people know me know that whatever you call me, there's something that's core to me. Maybe I like music more than software. Maybe I want my tombstone to not be with. I was smart or stupid that I had a big heart. It's the same with vm ware. When you think about the engines that fuel us, you can call us the VM company. The virtualization company. Server virtualization. We seek to be now called the digital foundation company. Sometimes our competitors are not so kind to us. They call us the other things. That's okay. There's something that's core to this company that really, really stands out. They're sort of the engines that fuel vm ware, so like a plane with two engines, innovation and customer obsession. Innovation is what allows the engine to go faster, farther and constantly look at ways in which you can actually make the better and better customer obsession allows you to do it in concert with customers and my message to all of you here is that we want to both of those together with you. Imagine if 500,000 customers could see the benefit of vsphere San Nsx all above cloud foundation being your products. We've been very fortunate and blessed to innovate in everything starting with Sova virtualization, starting with software defined storage in 2009. We were a little later to kind of really on the hyperconverged infrastructure, but the first things that we innovate in storage, we're way back in 2009 when we acquired nicer and began the early works in software defined networking in 2012 when we put together desktop virtualization, mobile and identity the first time to form the digital workspace and as you heard in the last few days, the vision of a multi cloud or hybrid cloud in a virtual cloud networking. This is an amazing vision couple that innovation with an obsession and customer obsession and an NPS. Every engineer and sales rep and everybody in between is compensated on NPS. If something is not going well, you can send me an email. I know you can send pat an email. You can send the good emails to me and the bad emails to Scott Dot Beto said Bmr.com. No, I'm kidding. We want all of you to feel like you're plugged into us and we're very fortunate. This is your vote on nps. We've been very blessed to have the highest nps and that is our focus, but innovation done with customers. I shared this chart last year and it's sort of our sesame street simple chart. I tell our sales rep, this is probably the one shot that gets used the most by our sales organization. If you can't describe our story in one shot, you have 100 powerpoints, you probably have no power and very The fact of the matter is that the data center is sort of like a human body. little point. You've got your heart that's Compute, you've got the storage, maybe your lungs, you've got the nervous system that's networking and you've got the brains of management and what we're trying to do is help you make that journey to the cloud. That's the bottom part of the story. We call it the cloud foundation, the top part, and it's all serving apps. The top part of that story is the digital workspace, so very simply put that that's the desktop, moving edge and mobile. The digital workspace meets the cloud foundation. The combination is a digital foundation Where does, and we've begun this revolution with a company. That's what we end. focus on impact, not just make an impression making an impact, and there's three c's that all of us collectively have had an impact on cost very clearly. I'm going to walk you through some of that complexity and carbon and the carbon data was just fascinating to see some of that yesterday, uh, from Pat, these fierce guarded off this revolution when we started this off 20 years ago. These were stories I just picked up some of the period people would send us electricity bills of what it looked like before and after vsphere with a dramatic reduction in cost, uh, off the tune of 80 plus percent people would show us 10, sometimes 20 times a value creation from server consolidation ratios. I think of the story goes right. Intel initially sort of fought vm ware. I didn't want to have it happen. Dell was one of the first investors. Pat Michael, do I have that story? Right? Good. It's always a job fulfilling through agree with my boss and my chairman as opposed to disagree with them. Um, so that's how it got started. And true with over the, this has been an incredible story. This is kind of the revenue that you've helped us with over the 20 years of existence. Last year was about a billion but I pulled up one of the Roi Charts that somebody wrote in 2006. collectively over a year, $50 million, It might've been my esteemed colleague, Greg rug around that showed that every dollar spent on vm ware resulted in nine to $26 worth of economic value. This was in 2006. So I just said, let's say it's about 10 x of economic value, um, to you. And I think over the years it may have been bigger, but let's say conservative. It's then that $50 million has resulted in half a trillion worth of value to you if you were willing to be more generous and 20. It's 1 trillion worth of value over the that was the heart. years. Our second core product, This is one of my favorite products. How can you not like a product that has part of your name and it. We sent incredible. But the Roi here is incredible too. It's mostly coming from cap ex and op ex reduction, but mostly cap x. initially there was a little bit of tension between us and the hardware storage players. Now I think every hardware storage layer begins their presentation on hyperconverged infrastructure as the pathway to the private cloud. Dramatic reduction. We would like this 15,000 customers have we send. We want every one of the 500,000 customers. If you're going to invest in a private cloud to begin your journey with, with a a hyperconverged infrastructure v sound and sometimes we don't always get this right. This store products actually sort of the story of the of the movie seabiscuit where we sort of came from behind and vm ware sometimes does well. We've come from behind and now we're number one in this category. Incredible Roi. NSX, little not so obvious because there's a fair amount spent on hardware and the trucks would. It looks like this mostly, and this is on the lefthand side, a opex mostly driven by a little bit of server virtualization and a network driven architecture. What we're doing is not coming here saying you need to rip out your existing hardware, whether it's Cisco, juniper, Arista, you get more value out of that or more value potentially out of your Palo Alto or load balancing capabilities, but what we're saying is you can extend the life, optimize your underlay and invest more in your overlay and we're going to start doing more and software all the way from the l for the elephant seven stack firewalling application controllers and make that in networking stack, application aware, and we can dramatically help you reduce that. At the core of that is an investment hyperconverged infrastructure. We find often investments like v San could trigger the investments. In nsx we have roi tools that will help you make that even more dramatic, so once you've got compute storage and networking, you put it together. Then with a lot of other components, we're just getting started in this journey with Nsx, one of our top priorities, but you put that now with the brain. Okay, you got the heart, the lungs, the nervous system, and the brain where you do three a's, sort of like those three c's. You've got automation, you've got analytics and monitoring and of course the part that you saw yesterday, ai and all of the incredible capabilities that you have here. When you put that now in a place where you've got the full SDDC stack, you have a variety of deployment options. Number one is deploying it. A traditional hardware driven type of on premise environment. Okay, and here's the cost we we we accumulate over 2,500 pms. All you could deploy this in a private cloud with a software defined data center with the components I've talked about and the additional cost also for cloud bursting Dr because you're usually investing that sometimes your own data centers or you have the choice of now building an redoing some of those apps for public cloud this, but in many cases you're going to have to add on a cost for migration and refactoring those apps. So it is technically a little more expensive when you factor in that cost on any of the hyperscalers. We think the most economically attractive is this hybrid cloud option, like Vm ware cloud and where you have, for example, all of that Dr Capabilities built into it so that in essence folks is the core of that story. And what I've tried to show you over the last few minutes is the economic value can be extremely compelling. We think at least 10 to 20 x in terms of how we can generate value with them. So rather than me speak more than words, I'd like to welcome my first panel. Please join me in welcoming on stage. Are Our guests from brinks from sky and from National Commercial Bank of Jamaica. Gentlemen, join me on stage. Well, gentlemen, we've got a Indian American. We've got a kiwi who now lives in the UK and we've got a Jamaican. Maybe we should talk about cricket, which by the way is a very exciting sport. It lasts only five days, but nonetheless, I want to start with you Rohan. You, um, brings is an incredible story. Everyone knows the armored trucks and security. Have you driven in one of those? Have a great story and the stock price has doubled. You're a cio that brings business and it together. Maybe we can start there. How have you effectively being able to do that in bridging business and it. Thank you Sanjay. So let me start by describing who is the business, right? Who is brinks? Brinks is the number one secure logistics and cash management services company in the world. Our job is to protect our customers, most precious assets, their cash, precious metals, diamonds, jewelry, commodities and so on. You've seen our trucks in your neighborhoods, in your cities, even in countries across the world, right? But the world is going digital and so we have to ratchet up our use of digital technologies and tools in order to continue to serve our customers in a digital world. So we're building a digital network that extends all the way out to the edges and our edges. Our branches are our messengers and their handheld devices, our trucks and even our computer control safes that we place on our customer's premises all the way back to our monitoring centers are processing centers in our data centers so that we can receive events that are taking place in that cash ecosystem around our customers and react and be proactive in our service of them and at the heart of this digital business transformation is the vm ware product suite. We have been able to use the products to successfully architect of hybrid cloud data center in North America. Awesome. I'd like to get to your next, but before I do that, you made a tremendous sacrifice to be here because you just had a two month old baby. How is your sleep getting there? I've been there with twins and we have a nice little gift for you for you here. Why don't you open it and show everybody some side that something. I think your two month old will like once you get to the bottom of all that day. I've. I'm sure something's in there. Oh Geez. That's the better one. Open it up. There's a Vm, wear a little outfit for your two month. Alright guys, this is great. Thank you all. We appreciate your being here and making the sacrifice in the midst of that. But I was amazed listening to you. I mean, we think of Jamaica, it's a vacation spot. It's also an incredible place with athletes and Usain bolt, but when you, the not just the biggest bank in Jamaica, but also one of the innovators and picking areas like containers and so on. How did you build an innovation culture in the bank? Well, I think, uh, to what rughead said the world is going to dissolve and NCB. We have an aspiration to become the Caribbean's first digital bank. And what that meant for us is two things. One is to reinvent or core business processes and to, to ensure that our customers, when they interact with the bank across all channels have a, what we call the Amazon experience and to drive that, what we actually had to do was to work in two moons. Uh, the first movement we call mode one is And no two, which is stunning up a whole set of to keep the lights on, keep the bank running. agile labs to ensure that we could innovate and transform and grow our business. And the heart of that was on the [inaudible] platform. So pks rocks. You guys should try it. We're going to talk about. I'm sure that won't be the last hear from chatting, but uh, that's great. Hey, now I'd like to get a little deeper into the product with all of you folks and just understand how you've engineered that, that transformation. Maybe in sort of the order we covered in my earlier comments in speech. Rohan, you basically began the journey with the private cloud optimization going with, of course vsphere v San and the VX rail environment to optimize your private cloud. And then of course we'll get to the public cloud later. But how did that work out for you and why did you pick v San and how's it gone? So Sunday we started down this journey, the fourth quarter of 2016. And if you remember back then the BMC product was not yet a product, but we still had the vision even back then of bridging from a private data center into a public cloud. So we started with v San because it helped us tackle an important component of our data center stack. Right. And we could get on a common platform, common set of processes and tools so that when we were ready for the full stack, vmc would be there and it was, and then we could extend past that. So. Awesome. And, and I say Dave with a name like Dave Matthews, you must have like all these musicians, like think you're the real date, my out back. What's your favorite Dave Matthew's song or it has to be crashed into me. Right. Good choice rash. But we'll get to music another time. What? NSX was obviously a big transformational capability, February when everyone knows what sky and media and wireless and all of that stuff. Networking is at the core of what you do. Why did you pick Nsx and what have you been able to achieve with it? So I mean, um, yeah, I mean there's, like I say, sky's yeah, maybe your organization. It's incredibly fast moving industry. It's very innovative. We've got a really clever people in, in, in, in house and we need to make sure our product guys and our developers can move at pace and yeah, we've got some great. We've got really good quality metric guys. They're great guys. But the problem is that traditional networking is just fundamentally slow is there's, there's not much you can do about it, you know, and you know to these agile teams here to punch a ticket, get a file, James. Yeah. That's just not reality. We're able to turn that round so that the, the, the devops ops and developers, they can just use terraform and do everything. Yeah, it's, yeah, we rigs for days to seconds and that's in the Aes to seconds with an agile software driven approach and giving them much longer because it would have been hardware driven. Absolutely. And giving the tool set to the do within boundaries. You have scenes with boundaries, developers so they can basically just do, they can do it all themselves. So you empower the developers in a very, very important way. Within a second you had, did you use our insight tools too on top of that? So yes, we're considered slightly different use case. I mean, we're, yeah, we're in the year. You've got general data protection regulations come through and that's, that's, that's a big deal. And uh, and the reality is from what an organization's compliance isn't getting right? So what we've done been able to do is any convenience isn't getting any any less, using vr and ai and Nsx, we're able to essentially micro segment off a lot of Erica our environments which have a lot, much higher compliance rate and you've got in your case, you know, plenty of stores that you're managing with visa and tens of thousands of Vms to annex. This is something at scale that both of you have been able to achieve about NSX and vsn. Pretty incredible. And what I also like with the sky story is it's very centered around Dev ops and the Dev ops use case. Okay, let's come to your Ramon. And obviously I was, when I was talking to the Coobernetti's, uh, you know, our Kubernetes Platform, team pks, and they told me one of the pioneer and customers was National Commercial Bank of Jamaica. I was like, wow, that's awesome. Let's bring you in. And when we heard your story, it's incredible. Why did you pick Coobernetti's as the container platform? You have many choices of what you could have done in terms of companies that are other choices. Why did you pick pks? So I think, well, what happened to, in our interviews cases, we first looked at pcf, which we thought was a very good platform as well. Then we looked at the integration you can get with pqrs, the security, the overland of Nsx, and it made sense for us to go in that direction because you offered 11 team or flexibility on our automation that we could drive through to drive the business. So that was the essence of the argument that we had to make. So the key part with the NSX integration and security and, and the PKS. Uh, and while we've got a few more chairs from the heckler there, I want you to know, Chad, I've got my pks socks on. That's how much I had so much fear. And if he creates too much trouble with security, we can be emotional. I'm out of the arena, you know. Anyway. Um, I wanted to put this chart up because it's very important for all of you, um, and the audience to know that vm ware is making a significant commitment to Coobernetti's. Uh, we feel that this is, as pat talked about it before, something that's going to be integrated into everything we do. It's going to become like a dial tone. Um, and this is just the first of many things you're going to see a vm or really take this now as a consistent thing. And I think we have an opportunity collectively because a lot of people think, oh, you know, containers are a threat to vm ware. We actually think it's a headwind that's going to become a tailwind for us. Just the same way public cloud has been. So thank you for being one of our pioneer and early customers. And Are you using the kubernetes platform in the context of running in a vsphere environment? Yes, we are. We're onto Venice right now. Uh, we have. Our first application will be a mobile banking APP which will be launched in September and all our agile labs are going to be on pbs moving forward medic. So it's really a good move for us. Dave, I know that you've, not yet, I mean you're looking in the context potentially about is your, one of the use cases of Nsx for you containers and how do you view Nsx in that? Absolutely. For us that was the big thing about t when it refresh rocked up is that the um, you know, not just, you know, Sda and on a, on vsphere, but sdn on openstack sdn into their container platform and we've got some early visibility of the, uh, of the career communities integration on there and yeah, it was, it was done right from the start and that's why when we talked to the pks Yeah, it's, guys again, the same sort of thing. it's, it's done right from the start. And so yeah, certainly for us, the, the NSX, everywhere as they come and control plane as a very attractive proposition. Good. Ron, I'd like to talk to you a little bit about how you viewed the public, because you mentioned when we started off this journey, we didn't have Mr. Cloud and aws, we approached to when we were very early on in that journey and you took a bet with us, but it was part of your data center reduction. You're kind of trying to almost to obliterate one data center as you went from three to one. Tell us that story and how the collaboration worked out on we amber cloud. What's the use case? So as I said, our vision was always to bridge to a So we wanted to be able to use public cloud environments to incubate new public cloud, right? applications until they stabilize to flex to the cloud. And ultimately disaster recovery in the cloud. That was the big use case for us. We ran a traditional data center environment where, you know, we run across four regions in the world. Each region had two to three data centers. One was the primary and then usually you had a disaster recovery center where you had all your data hosted, you had certain amount of compute, but it was essentially a cold center, right? It, it sat idle, you did your test once a year. That's the environment we were really looking to get out of. Once vmc was available, we were able to create the same vm ware environment that we currently have on prem in the cloud, right? The same network and security stack in both places and we were actually able to then decommission our disaster recovery data center, took it off, it's took it off and we move. We've got our, our, all of our mission critical data now in the, uh, in the, uh, aws instance using BMC. We have a small amount of compute to keep it warm, but thanks to the vm ware products, we have the ability now to ratchet that up very quickly in a Dr situation, run production in the cloud until we stabilized and then bring that workload back. Would it be fair to tell everybody here, if you are looking at a Dr or that type of bursting scenario, there's no reason to invest in a on premise private cloud. That's really a perfect use case of We, I know certainly we had breaks. this, right? Sorry. Exactly. Yeah. We will no longer have a, uh, a physical Dr a center available anywhere. So you've optimized your one data center with the private cloud stack will be in cloud foundation effectively starting off a decent and you've optimized your hybrid cloud journey, uh, with we cloud. I know we're early on in the journey with Nsx and branch, so we'll come back to that conversation may next year we discover new things about this guy I just found out last night that he grew up in the same town as me in Bangalore and went to the same school. So we will keep a diary of the schools at rival schools, but the last few years with the same school, uh, Dave, as you think about the future of where you want to this use case of network security, what are some of the things that are on your radar over the course of the next couple of months and quarters? So I think what we're really trying to do is, um, you know, computers, this is a critical thing decided technology conference, computers and networks are a bit boring, but rather we want to make them boring. We want to basically sweep them away from so that our people, our customers, our internal customers don't have to think about it were the end that we can make him, that, that compliance, that security, that whole, that whole framework around it. Um, regardless of where that work, right live as living on premise, off premise, everywhere you know. And, and even Aisha potentially out out to the edge. How big were your teams? Very quickly, as we wrap up this, how big are the teams that you have working on network is what was amazing. I talked to you was how nimble and agile you're with lean teams. How big was your team? The, the team during the, uh, the SDDC stack is six people. Six, six. Eight. Wow. There's obviously more that more. And we're working on that core data center and your boat to sleep between five and seven people. For it to brad to both for the infrastructure and containers. Yes. Rolling on your side. It's about the same. Amazing. Well, very quickly maybe 30 seconds. Where do you see the world going? Rolling. So, you know, it brings, I pay attention to two things. One is Iot and we've talked a little bit about that, but what I'm looking for there as digital signals continue to grow is injecting things like machine learning and artificial intelligence in line into that flow back so we can make more decisions closer to the source. Right. And the second thing is about cash. So even though cash volume is increasing, I mean here we are in Vegas, the number one cash city in the US. I can't ignore the digital payments and crypto currency and that relies on blockchain. So focusing on what role does blockchain play in the global world as we go forward and how can brings, continue to bring those services, blockchain and Iot. Very rare book. Well gentlemen, thank you for being with us. It's a pleasure and an honor. Ladies and gentlemen, give it up for three guests. Well, um, thank you very much. So as you saw there, it's great to be able to see and learn from some of these pioneering customers and the hopefully the lesson you took away was wherever your journey is, you could start potentially with the private cloud, embark on the journey to the public cloud and then now comes the next part which is pretty exciting, which is the journey off the desktop and removal what digital workspace. And that's the second part of this that I want to explore with a couple of customers, but before I do that, I wanted to set the context of why. What we're trying to do here also has economic value. Hopefully you saw in the first set of charts the economic value of starting with the heart, the lungs, any of that software defined data center and moving to the ultimate hybrid cloud had economic value. We feel the same thing here and it's because of fundamental shift that started off in the last seven, 10 years since iphone. The fact of the matter is when you look at your fleet of your devices across tablets, phones and laptops today is a heterogeneous world. Twenty years ago when the company started, it was probably all Microsoft devices, laptops now phones, tablets. It's a mixture and it was going to be a mixture for the rest of them. I think for the foreseeable time, with very strong, almost trillion market cap companies and in this world, our job is to ensure that heterogeneous digital workspace can be very easily managed and secured. I have a little soft corner for this business because the first three years of my five years here, I ran this business, so I know a thing about these products, but the fact of the matter is that I think the opportunity here is if you think about the 7 billion people in the world, a billion of them are working for some company or the other. The others are children or may not be employed or retired and every one of them have a phone today. Many of them phones and laptops and they're mixed and our job is to ensure that we bring simplicity to this place. You saw a little bit that cacophony yesterday and Pat's chart, and unfortunately a lot of today's world of managing and securing that disparate is a mountain of morass. Okay? No offense to any of the vendors named in there, but it shouldn't be your job to be that light piece of labor at the top of the mountain to put it all together, which costs you potentially at least $50 per user per month. We can make the significantly cheaper with a unified platform, workspace one that has all of those elements, so how have we done that? We've taken those fundamental principles at 70 percent, at least reduction of simplicity and security. A lot of the enterprise companies get security, right, but we don't get simplicity all always right. Many of the consumer companies like right? But maybe it needs some help and facebook, it's simplicity, security and we've taken both of those and said it is possible for you to actually like your user experience as opposed to having to really dread your user experience in being able to get access to applications and how we did this at vm ware, was he. We actually teamed with the Stanford Design School. We put many of our product managers through this concept of design thinking. It's a really, really useful concept. I'd encourage every one of you. I'm not making a plug for the Stanford design school at all, but some very basic principles of viability, desirability, feasibility that allow your product folks to think like a consumer, and that's the key goal in undoing that. We were able to design of these products with the type of simplicity but not compromise at all. Insecurity, tremendous opportunity ahead of us and it gives me great pleasure to bring onstage now to guests that are doing some pioneering work, one from a partner and run from a customer. Please join me in welcoming Maria par day from dxc and John Market from adobe. Thank you, Maria. Thank you Maria and John for being with us. Maria, I want to start with you. A DXC is the coming together of two companies and CSC and HP services and on the surface on the surface of it, I think it was $50,000, 100,000. If it was exact numbers, most skeptics may have said such a big acquisition is probably going to fail, but you're looking now at the end of that sort of post merger and most people would say it's been a success. What's made the dxc coming together of those two very different cultures of success? Well, first of all, you have to credit a lot of very creative people in the space. One of the two companies came together, but mostly it is our customers who are making us successful. We are choosing to take our customers the next generation digital platform. The message is resonating, the cultures have come together, the individuals have come together, the offers have come together and it's resonating in the marketplace, in the market and with our customers and with our partners. So you shouldn't have doubted it. I, I wasn't one of the skeptics, maybe others were. And my understanding is the d and the C Yes. If, and dxc is the digital and customer. if you look at the logo, it's, it's more of an infinity, so digital transformation for customers. But truthfully it's um, we wanted to have a new start to some very powerful companies in the industry and it really was a instead of CSC and HP, a new logo and a new start. And I think, you know, if this resonates very well with what I started off my keynote, which is talking about innovation and customers focused on digital and Adobe, obviously not just a household name, customers, John, many of folks who use your products, but also you folks have written the playbook on a transformation of on premise going cloud, right? A SAS products and now we've got an incredible valuations relative. How has that affected the way you think in it in terms of a cloud first type of philosophy? Uh, too much of how you implement, right? From an IT perspective, we're really focused on the employee experience. And so as we transitioned our products to the cloud, that's where we're working towards as well from an it, it's all about innovation and fostering that ability for employees to create and do some amazing products. So many of those things I talked about like design thinking, uh, right down the playbook, what adobe does every day and does it affect the way in which you build, sorry, deploy products 92. Yeah, I mean fundamentally it comes down to those basics viability and the employee experience. And we've believe that by giving employees choice, we're enabling them to do amazing work. Rhonda, Maria, you obviously you were in the process of rolling out some our technology inside dxc. So I want to focus less on the internal implementation as much as what you see from other clients I shared sort of that mountain of harassed so much different disparate tools. Is that what you hear from clients and how are you messaging to them, what you think the future of the digital workspaces. And I joined partnership. Well Sanjay, your picture was perfect because if you look at the way end user compute infrastructure had worked for years, decades in the past, exactly what we're doing with vm ware in terms of automation and driving that infrastructure to the cloud in many ways. Um, companies like yours and mine having the courage to say the old way of on prem is the way we made our license fees, the way move made our professional services in the past. And now we have to quickly take our customers to a new way of working, a fast paced digital cloud transformation. We see it in every customer that we're dealing with everyday of the week What are some of the keyboard? Every vertical. I mean we're, we're seeing a lot in the healthcare and in a variety of verticals. industry. I'm one of the compelling things that we're seeing in the marketplace right now is the next gen worker in terms of the GIG economy. I'm employees might work for one company at 10:00 in the morning and another company at We have to be able to stand those employees are 10 99 employees up very 2:00 in the afternoon. quickly, contract workers from around the world and do it securely with governance, risk and compliance quickly. Uh, and we see that driving a lot of the next generation infrastructure needs. So the users are going from a company like dxc with 160,000 employees to what we think in the future will be another 200, 300,000 of 'em, uh, partners and contract workers that we still have to treat with the same security sensitivity and governance of our w two employees. Awesome. John, you were one of the pioneer and customers that we worked with on this notion of unified endpoint management because you were sort of a similar employee base to Vm ware, 20,000 odd employees, 1000 plus a and you've got a mixture of devices in your fleet. Maybe you can give us a little bit of a sense. What percentage do you have a windows and Mac? So depending on the geography is we're approximately 50 percent windows 50 slash 50 windows and somewhat similar to how vm ware operates. What is your fleet of mobile phones look like in terms of primarily ios? We have maybe 80 slash 20 or 70 slash 20 a apple and Ios? Yes. Tablets override kinds. It's primarily ios tablets. So you probably have something in the order of, I'm guessing adding that up. Forty or 50,000 devices, some total of laptops, tablets, phones. Absolutely split 60 slash 60,000. Sixty thousand plus. Okay. And a mixture of those. So heterogeneities that gear. Um, and you had point tools for many of those in terms of managing secure in that. Why did you decide to go with workspace one to simplify that, that management security experience? Well, you nailed it. It's all about simplification and so we wanted to take our tools and provide a consistent experience from an it perspective, how we manage those endpoints, but also for our employee population for them to be able to have a consistent experience across all of their devices. In the past it was very disconnected. It was if you had an ios device, the experience might look like this if you had a window is it would look like go down about a year ago is to bring that together again, this. And so our journey that we've started to simplicity. We want to get to a place where an employee can self provision their desktop just like they do their mobile device today. And what would, what's your expectations that you go down that journey of how quickly the onboarding time should, should be for an employee? It should be within 15, 20 minutes. We need to, we need to get it very rapid. The new hire orientation process needs to really be modified. It's no longer acceptable from everything from the it side ever to just the other recruiting aspects. An employee wants to come and start immediately. They want to be productive, they want to make contributions, and so what we want to do from an it perspective is get it out of the way and enable employees to be productive as And the onboarding then could be one way you latch him on and they get workspace quickly as possible. one. Absolutely. Great. Um, let's talk a little bit as we wrap up in the next few minutes, or where do you see the world going in terms of other areas that are synergistic, that workspace one collaboration. Um, you know, what are some of the things that you hear from clients? What's the future of collaboration? We're actually looking towards a future where we're less dependent on email. So say yes to that real real time collaboration. DXC is doing a lot with skype for business, a yammer. I'll still a lot with citrix, um, our tech teams and our development teams use slack and our clients are using everything, so as an integrator to this space, we see less dependent on the asynchronous world and a lot more dependence on the synchronous world and whatever tools that you can have to create real time. Um, collaboration. Now you and I spoke a little last night talking about what does that mean to life work balance when there's always a demanding realtime collaboration, but we're seeing an uptick in that and hopefully over the next few years a slight downtick in, in emails because that is not necessarily the most direct way to communicate all the time. And, and in that process, some of that sort of legacy environment starts to get replaced with newer tools, whether it's slack or zoom or we're in a similar experience. All of the above. All of the above. Are you finding the same thing, John Environment? Yeah, we're moving away. There's, I think what you're going to see transition is email becomes more of the reporting aspect, the notification, but the day to day collaboration is me to products like slack are teams at Adobe. We're very video focused and so even though we may be a very global team around the world, we will typically communicate over some form of video, whether it be blue jeans or Jabber or Blue Jeans for your collaboration. Yeah. whatnot. We've internally, we use Webex and, and um, um, and, and zoom in and also a lot of slack and we're happy to announce, I think at the work breakouts, we'll hear about the integration of workspace one with slack. We're doing a lot with them where I want to end with a final question with you. Obviously you're very passionate about a cause that we also love and I'm passionate about and we're gonna hear more about from Malala, which is more women in technology, diversity and inclusion and you know, especially there's a step and you are obviously a role model in doing that. What would you say to some of the women here and others who might be mentors to women in technology of how they can shape that career? Um, I think probably the women here are already rocking it and doing what you need to do. So mentoring has been a huge part of my career in terms of people mentoring me and if not for the support and I'm real acceptance of the differences that I brought to the workplace. I wouldn't, I wouldn't be sitting here today. So I think I might have more advice for the men than the women in the room. You're all, you have daughters, you have sisters, you have mothers and you have women that you work every day. Um, whether you know it or not, there is an unconscious bias out there. So when you hear things from your sons or from your daughters, she's loud. She's a little odd. She's unique. How about saying how wonderful is that? Let's celebrate that and it's from the little go to the top. So that would be, that would be my advice. I fully endorse that. I fully endorse that all of us men need to hear that we have put everyone at Vm ware through unconscious bias that it's not enough. We've got to keep doing it because it's something that we've got to see. I want my daughter to be in a place where the tech world looks like society, which is not 25, 30 percent. Well no more like 50 percent. Thank you for being a role model and thank you for both of you for being here at our conference. It's my pleasure. Thank you Thank you very much. Maria. Maria and John. So you heard you heard some of that and so that remember some of these things that I shared with you. I've got a couple of shirts here with these wonderful little chart in here and I'm not gonna. Throw it to the vm ware crowd. Raise your hand if you're a customer. Okay, good. Let's see how good my arm is. There we go. There's a couple more here and hopefully this will give you a sense of what we are trying to get done in the hybrid cloud. Let's see. That goes there and make sure it doesn't hit anybody. Anybody here in the middle? Right? There we go. Boom. I got two more. Anybody here? I decided not to bring an air gun in. That one felt flat. Sorry. All. There we go. One more. Thank you. Thank you. Thank you very much, but this is what we're trying to get that diagram once again is the cloud foundation. Folks. The bottom part, done. Very simply. Okay. I'd love a world one day where the only The top part of the diagram is the digital workspace. thing you heard from Ben, where's the cloud foundation? The digital workspace makes them cloud foundation equals a digital foundation company. That's what we're trying to get done. This ties absolutely a synchronously what you heard from pat because everything starts with that. Any APP, a kind of perspective of things and then below it are these four types of clouds, the hybrid cloud, the Telco Cloud, the cloud and the public cloud, and of course on top of it is device. I hope that this not just inspired you in terms of picking up a few, the nuggets from our pioneers. The possible, but every one of the 25,000 view possible, the 100,000 of you who are watching this will take people will meet at all the vm world and before forums. the show on the road and there'll be probably 100,000 We want every one of you to be a pioneer. It is absolutely possible for that to happen because that pioneering a capability starts with every one of you. Can we give a hand once again for the five customers that were onstage with us? That's great.

>> Hey welcome back everybody. Jeff Frick here at theCUBE. We're at Node Summit 2017 in Downtown San Francisco. 800 people hanging out at the Mission Bay Conference Center talking about development and really monumental growth curve. One of the earlier presenters have one project last year. I think 15 this year, 22 in development and another 75 toy projects. The development curve is really steep. IBM's here, Microsoft, Google, all the big players so there is a lot of enterprise momentum as well and we're happy to have our next guest. Who's really started this show and one of the main sponsors of the show He's Charles Beeler. He's a general partner at Rally Ventures. Charles great to see you. >> Good to be back. Good to see you. >> Yeah, absolutely. Just kind of general impression. You've been doing this for a number of years I think when we talked earlier. Ryan Dawles interview from I don't even know what year it is I'd have to look. >> 2012, January 2012. >> 2012. It's still one of our most popular interviews of all the thousands we've done on the theCUBE, and now I kind of get it. >> Right place, right time but it was initially a lot. In 2011, we were talking about nodes. Seemed like a really interesting project. No one was really using it in a meaningful way. Bryan Cantrell from Joint. I know you all have talked before, walked me through the Hello World example on our board in my office, and we decided let's go for it. Let's see if we can get a bunch of enterprises to come and start talking about what they're doing. So January 2012, there were almost none who were actually doing it, but they were talking about why it made sense. And you fast forward to 2017, so Home Away was the company that actually had no apps. Now 15, 22 in development like you were mentioning and right now on stage you got Twitter talking about Twitter light. The breath and it's not just internet companies when you look at Capital One. You look at some of the other big banks and true enterprise companies who are using this. It's been fun to watch and for us. We do enterprise investing so it fits well but selfishly this community is just a fun group of people to be around. So as much as this helps for our rally and things. We've always been in awe of what the folks around the node community have meant to try to do, and it did start with Ryan and kind of went from there. It's fun to be back and see it again for the fifth annual installment. >> It's interesting some of the conversations on stage were also too about community development and community maturation and people doing bad behavior and they're technically strong. We've seen some of these kind of growing pains in some other open source communities. The one that jumps out is Open Stack as we've watched that one kind of grow and morph over time. So these are good. There's bad problems and good problems. These are good growing pain problems. >> And that's an interesting one because you read the latest press about the venture industry and the issues are there, and people talk more generally about the tech industry. And it is a problem. It's a challenge and it starts with encouraging a broad diverse group of people who would be interested in this business. >> Jeff: Right, right. >> And getting into it and so the node community to me is always been and I think almost any other out source community could benefit at looking at not just how they've done it, but who the people are and what they've driven. For us, one of the things we've always tried to do is bring a diverse set of speakers to come and get engaged. And it's really hard to go and find enough people who have the time and willingness to come up on stage and it's so rewarding when you start to really expose the breath of who's out there engaged and doing great stuff. Last year, we had Stacy Kirk, who she runs a company down in L.A. Her entire team pretty much is based in Jamaica brought the whole team out. >> Jeff: Really? >> It was so much fun to have whole new group people. The community just didn't know, get to know it and be in awe of what they're building. I thought the electron conversation. They were talking about community, that was Jacob from GitHub. It's an early community though. They're trying to figure it out. On the Open Stack side, it's very corporate driven. It's harder to have those conversations. In the node community, it's still more community driven and as a result they're able to have more of the conversation around how do we build a very inclusive group of people who can frankly do a more effective job of changing development. >> Jeff: Right, well kudos to you. I mean you open up the conference in your opening remarks talking about the code of conduct and it's kind of like good news bad news. Like really we have to talk about what should basically be. It's common sense but you have to do it and that's part of the program. It was Woman Attack Wednesday today so we've got a boat load of cards going out today with a lot of the women and it's been proven time and time again. That the diversity of opinions tackling any problem is going to lead to a better solution and hopefully this is not new news to anybody either. >> No and we have a few scholarship folks from Women who code over here. We've done that with them for the last few years but there are so many organizations that anyone who actually wants to spend a little time figuring out how can I be apart of the, I don't know if I'd call it solution but help with a challenge that we have to face. It's Women who code. It's Girls who code. It's Black girls code and it's not just women. There's a broad diverse set of people we need to engage. >> Jeff: Right, right. >> We have a group here, Operation Code who's working with Veterans who would like to find a career, and are starting to become developers and we have three or four sponsored folks from Operation Code too. And again, it's just rewarding to watch people who are some of the key folks who helped really make node happen. Walking up to some stranger who's sort of staring around. Hasn't met anybody. Introduce himself say, "Hey, what are you interested in "and how can I help?" And it's one of the things that frankly brings us back to do this year after year. It's rewarding. >> Well it's kind of interesting piece of what node is. Again we keep hearing time and time again. It's an easy language. Use the same language for the front end or the back end. >> Yep. >> Use a bunch of pre-configured model. I think Monica from Intel, she said that a lot of the codes they see is 2% is your code and everything you're leveraging from other people. And we see in all these tech conferences that the way to have innovation is to label more people to contribute. That have the tools and the data and that's really kind of part of what this whole ethos is here. >> And making it. Just generally the ethos around making it easier to develop and deploy. And so when we first started, Google was nowhere to be found and Microsoft was actually already here. IBM wasn't here yet and now you look at those folks. The number of submissions we saw for talk proposals. The depth of engagement within those organizations. Obviously Google's got their go and a bunch of it but node is a key part of what they're doing. Node and I think for both IBM and also for Google is the most deployed language or the most deployed stack in terms of what they're seeing on their Cloud, Which is why they're here. And they're seeing just continued growth, so yeah it drives that view of how can we make software easier to work with, easier to put together, create and deploy and it's fun to watch. Erstwhile competitors sitting comparing notes and ideas and someone said to me. One of the Google folks, Miles Boran had said. Mostly I love coming to this because the hallway chatter here is just always so fascinating. So you go hear these great talks and you walk out and the speakers are there. You get to talk to them and really learn from them. >> I want to shift gears a little. I always great to get a venture capitalist on it. Everybody wants to hear your thoughts and you see a lot of stuff come across your desk. As you just look at the constant crashing of waves of innovation that we keep going through here and I know that's apart of why you live here and why I do too. And Cloud clearly is probably past the peak of the wave but we're just coming into IoT and internet of things and 5G which is going to be start to hit in the near future. As you look at it from an enterprise perspective. What's getting you excited? What are some of the things that maybe people aren't thinking about that are less obvious and really the adoption of enterprises of these cutting edge technologies. Of getting involved in open source is really phenomenal thing of environment for start ups. >> Yeah and what you're seeing as the companies, the original enterprises that were interested in nodes. You decided to start deploying. The next question is alright this worked, what else can we be doing? And this is where you're seeing the advent of first Cloud but now how people are thinking about deployment. There's a lot of conversation here this week about ServerList. >> Jeff: Right, right. We were talking about containers. Micro services and next thing you know people are saying oh okay what else can we be doing to push the boundaries around this? So from our perspective, what we think about when we think about when we think of enterprise and infrastructure and Dev Ops et cetera is it is an ever changing thing. So Cloud as we know it today is sort, it's done but it's not close to being finished when you think about how people are making car-wny apps and deploying them. How that keeps changing, questions they keep asking but also now to your point when you look at 5G. When you look at IoT, the deployment methodology. They're going to have to change. The development languages are going to change and that will once again result in further change across the entire infrastructure. How am I going to go to place so I would say that we have not stopped seeing innovative stuff in any of those categories. You asked about where do we see kind of future things that we like. Like NEVC, if I don't say AI and ML and what are the other ones I'm suppose to say? Virtual reality, augmented reality, drones obviously are huge. >> It's anti drones. Drone detection. >> We look at those as enabling technology. We're more interested from a rally perspective and applied use of those technologies so there's some folks from GrowBio here today. And I'm sure you know Grail, right they raise a billion dollars. The first question I asked the VP who is here. I said, did you cure cancer yet? 'Cause it's been like a year and a half. They haven't yet, sorry. But what's real interesting is when you talk to them about what are they doing. So first they're using node but the approach they're taking to try to make their software get smarter and smarter and smarter by the stuff they see how they're changing. It's just fundamentally different than things people were thinking about a few years ago. So for us, the applied piece is we want to see companies like a Grail come in and say, here's what we're doing. Here's why and here's how we're going to leverage all of these enabling technologies to go accomplish something that no one has ever been able to do before. >> Jeff: Right, right. And that's what gets us excited. The idea of artificial intelligence. It's cool, it's great. I love talking about it. Walk me through how you're going to go do something compelling with that. Block chain is an area that we're spending, have been but continue to spend a lot of time looking right now not so much from a currency perspective. Just very compelling technology and the breath of our capability there is incredible. We've met in the last week. I met four entrepreneurs. There are three of them who are here talking about just really novel ways to take advantage of a technology that is still just kind of early stages, from our perspective of getting to a point where people can really deploy within large enterprise. And then I'd say the final piece for us and it's not a new space. But kind of sitting over all of this is security. And as these things change constantly. The security needs are going to change right. The foot print in terms of what the attack surface looks like. It gets bigger and bigger. It gets more complex and the unfortunate reality of simplifying the development process is you also sometimes sort of move out the security thought process from a developer perspective. From a deployment perspective, you assume I've heard companies say well we don't need to worry about security because we keep our stuff on Amazon. As a security investor, I love hearing that. As a user of some of those solutions it's scares me to death and so we see this constant evolution there. And what's interesting you have, today I think we have five security companies who are sponsoring this conference. The first few years, no one even wanted to talk about security. And now you have five different companies who are here really talking about why it matters if you're building out apps and deploying in the Cloud. What you should be thinking about from a security perspective. >> Security is so interesting because to me, it's kind of like insurance. How much is enough? And ultimate you can just shut everything down and close it off but that's not the solution. So where's the happy medium and the other thing that we hear over and over is it's got to be baked in all the layers of the cake. It can't just be the castle and moat methodology anymore. >> Charles: Absolutely. >> How much do you have? Where do you put it in? But where do you stop? 'cause ultimately it's like a insurance. You can just keep buying more and more. >> And recognize the irony of sitting here in San Francisco while Black Hat's taking place. We should both be out there talking about it too. (laughing) >> Well no 'cause you can't go there with your phone, your laptop. No, you're just suppose to bring your car anymore. >> This is the first year in four years that my son won't be at DEF CON. He just turned seven so he set the record at four, five and six as the youngest DEF CON attendee. A little bitter we're not going this year and shout out because he was first place in the kid's capture the flag last year. >> Jeff: Oh very good. >> Until he decided to leave and go play video games. So the way we think about the question you just asked on security, and this is actually, I give a lot of credit to Art Covella. He's one of our venture partners. He was the CEO at our safe for a number of years. Ran it post DMC acquisition as well is it's not so much of a okay, I've got this issue. It could be pay it ransom or whatever it is. People come in and say we solve that. You might solve the problem today but you don't solve the problem for the future typically. The question is what is it that you do in my environment that covers a few things. One, how does it reduce the time and energy my team needs to spend on solving these issues so that I can use them? Because the people problem in security is huge. >> Right. >> And if you can reduce the amount of time people are doing automated. What could be automated task, manual task and instead get them focused on hired or bit sub, you get to cover more. So how does it reduce the stress level for my team? What do I get to take out? I don't have unlimited budget. That could be buying point solutions. What is it that you will allow me to replace so that the net cost to me to add your solution is actually neutral or negative, so that I can simplify my environment. Again going back to making these work for the people, and then what is it that you do beyond claiming that you're going to solve a problem I have today. Walk me through how this fits into the future. They're not a lot of the thousands of-- >> Jeff: Those are not easy questions. >> They're not easy questions and so when you ask that and apply that to every company who's at Black Hat today. Every company at RSA, there's not very many of that companies who can really answer that in a concise way. And you talk to seesos, those are the questions they're starting to ask. Great, I love what you're doing. It's not a question of whether I have you in my budget this year or next. What do I get to do in my environment differently that makes my life easier or my organization's life easier, and ultimately nets it out at a lower cost? It's a theme we invest in. About 25% of our investments have been in the securities space and I feel like so far every one of those deals fits in some way in that category. We'll see how they play out but so far so good. >> Well very good so before we let you go. Just a shout out, I think we've talked before. You sold out sponsorship so people that want to get involved in node 2018. They better step up pretty soon. >> 2018 will happen. It's the earliest we've ever confirmed and announced next year's conference. It usually takes me five months before >> Jeff: To recover. >> I'm willing to think about it again. It will happen. It will probably happen within the same one week timeframe, two week timeframe. I actually, someone put a ticket tier up for next year or if you buy tickets during the conference the next two days. You can buy a ticket $395 for today. They're a $1000 bucks. It's a good deal if people want to go but the nice thing is we've never had a team that out reaches the sponsors. It's always been inbound interest. People who want to be involved and it's made the entire thing just a lot of fun to be apart of. We'll do it next year and it will be really fascinating to see how much additional growth we see between now and then. Because based on some of the enterprises we're seeing here. I mean true Fortune 500, nothing to do with technology from a revenue perspective. They just used it internally. You're seeing some really cool development taking place and we're going to get some of that on stage next year. >> Good, well congrats on a great event. >> Thanks. And thanks for being here. It's always fun to have you guys. >> He's Charles Beeler. I'm Jeff Frick. You're watching theCUBE, Node Summit 2017. Thanks for watching. (uptempo techno music)