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

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante by the dip has been been an effective strategy since the market bottomed in early march last year the approach has been especially successful in tech and even more so for those tech names that one were well positioned for the forced march to digital i sometimes call it i.e remote work online commerce data centric platforms and certain cyber security plays and two already had the cloud figured out the question on investors minds is where to go from here should you avoid some of the high flyers that are richly valued with eye-popping multiples or should you continue to buy the dip and if so which companies that capitalized on the trends from last year will see permanent shifts in spending patterns that make them a solid long-term play hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we shine the spotlight on three companies that may be candidates for a buy the dip strategy and it's our pleasure to welcome in ivana delevco who's the chief investment officer and founder of spear alpha a new research-centric etf focused on industrial technology ivana is a long-time equity analyst with a background in both long and short investing ivana welcome to the program thanks so much for coming on thanks for having me david yeah it's really our pleasure i i want to start with your etf and give the folks a bit more background about you first you know we gotta let people know i'm not an investment pro i'm not an advisor i don't make stock recommendations i don't sell investments so you got to do your own research i have a lot of data so happy to share it but you got to understand your own risks you of course yvonne on the other hand you do offer investment services and so people before investing got to carefully review all the available available investment docs understand what you're getting into before you invest now with that out of the way ivana i have some stats up here on this slide your spear you're a newly launched female lead firm that does deep research into the supply chain we're going to talk about that you try to uncover as i understand it under-appreciated industrial tech firms and some really pretty cool areas that we list here but tell us a little bit more about your background and your etf so thanks for having me david my background is in industrial research and industrial technology investments i've spent the past 15 years covering this space and what we've seen over the past five years is technology changes that are really driving fundamental shifts in industrial manufacturing processes so whether this is 5g connectivity innovation in the software stack increasing compute speeds all of these are major technological advancements that are impacting uh traditional manufacturers so what we try to do is assess speak to these firms and assess who is at the leading and who is at the lagging end of this digital transformation and we're trying to assess what vendors they're using what processes they're implementing and that is how we generate most of our investment ideas okay great and and we show on the bottom of of this sort of intro slide if you will uh so one of the processes that you use and one of the things that that is notable a lot of people compare you uh to kathy woods are investments when you came out uh i think you use a different process i mean maybe there are some similarities in terms of disruption but at the bottom of this slide it shows a mckinsey sort of graphic that that i think informs people as to how you really dig into the supply chain from a research standpoint is that right absolutely so for us it's all about understanding the supply chain going deep in the supply chain and gather data points from primary sources that we can then translate into investment opportunities so if you look at this mckinsey graph uh you will see that there is a lot of opportunity to for these companies to transform themselves both on the front end which means better revenue better products and on their operation side which means lower cost whether it's through better operations or through better processes on the the back end so what we do is we will speak to a traditional manufacturing company and ask them okay well what do you use for better product development and they will give us the name of the firms and give us an assessment of what's the differences between the competitors why they like one versus the other so then we're gonna take the data and we will put it into our financial model and we'll understand the broader market for it um the addressable market the market share that the company has and will project the growth so for these higher growth stocks that that you cover the main alpha generation uh potential here is to understand what the amount of growth these companies will generate over the next 10 to 20 years so it's really all about projecting growth in the next three years in the next five years and where will growth ultimately settle in in the next 10 to 20 years love it we're gonna have a fun conversation because today we're going to get into your thesis for cooper snowflake and z scalar we're going to bring in some of our own data some of our data from etr and and why you think these companies may be candidates for long-term growth and and be buy the dip stock so to do that i hacked up this little comparison slide we're showing here i do this for context our audience knows i'm not a cfa or a valuation expert but we like to do simple comparisons just to give people context and a sense of relative size growth and valuation and so this chart attempts to do that so what i did is i took the most recent quarterly revenue for cooper snowflake and z scalar multiplied it by four to get a run rate we included servicenow in the table just for baseline reference because bill mcdermott as we've reported aspires to make service now the next great enterprise software company alongside with salesforce and oracle and some of the others and and all these companies that we list here that through the three here they aspire to do so in their own domain so we're displaying the market cap from friday morning september 10th we calculated a revenue run rate multiple and we show the quarterly revenue growth and what this data does is gives you a sense of the three companies they're well on their way to a billion dollars in revenue it underscores the relationship between revenue growth and valuation snowflake being the poster child for that dynamic savannah i know you do much more detailed financial analysis but let's talk about these companies in order maybe start with koopa they just crushed their quarter i mean they blew away consensus on the top line what else about the company do you like and why is it on your by the dip list so just to back up david on valuation these companies investors either directly or indirectly value on a dcf basis and what happened at the beginning of the year as interest rates started increasing people started freaking out and once you plug in 100 basis points higher interest rate in your dcf model you get significant price downside so that really drove a lot of the pullback at the beginning of the year right now where we stand today interest rates haven't really moved all that significantly off the bot of the bottom they're still around the same levels maybe a little bit higher but those are not the types of moves that are going to drive significant downside in this stock so as things have stabilized here a lot of these opportunities look pretty attractive on that basis so koopa specifically came out of our um if you go back to that uh the chart of like where the opportunities lie in um in across the manufacturing uh um enterprise koopa is really focused on business pen management so they're really trying to help companies reduce their cost uh and they're a leader in the space uh they're unique uh unique in that they're cloud-based so the feedback we've been hearing from from our companies that use it jetblue uses it train technologies uses it the feedback we've been hearing is that they love the ease of implementation so it's very easy to implement and it drives real savings um savings for these companies so we see in our dcf model we see multiple years of this 30 40 percent growth and that's really driving our price target yeah and we can i can confirm that i mean i mean just anecdotally you know you know we serve a lot of the technology community and many of our clients are saying hey okay you know when you go to do invoicing or whatever you work with procurement it's koopa you know this is some ariba that's kind of the legacy which is sap we'll talk about that a little later but let's talk about snowflake um you know snowflake we've been tracking them very closely we know the management there we've watched them through their last two companies now here and have been following that company early on since since really 2015. tell us why you like snowflake um and and maybe why you think it can continue its rapid growth thanks david so first of all i need to compliment you on your research on the company on the technology side so where we come in is more from understanding where our companies can use soft snowflake and where snowflake can add value so what we've been hearing from our companies is the challenge that they're facing is that everybody's moving to the cloud but it's not as simple as just send your data to the cloud and call aws and they're gonna generate more revenue for your solve your cost problem so what we've been hearing is that companies need to find tools that are easy to use where they can use their own domain expertise and just plug and play so um ansys is one of the companies we covered the dust simulation they've found snowflake to be an extremely useful tool in sales lead generation and within sales crm systems have been around for a while and they're they've really been implemented but analyzing sales numbers is something that is new to this company some some of our companies don't even know what their sales are even when they look back after the quarter is closed so tools like this help um companies do easy analytics and therefore drive revenue and cost savings growth so we see really big runway for for this company and i think the most misunderstood part about it is that people view it as a warehousing data warehousing play while this is all about compute and the company does a good job separating the two and what our their customers like or like the companies that we cover like about it is that it can lower their compute costs um and make it much easier much more easily manageable for them great and we're going to talk about more about each of these companies but let's talk about z-scaler a bit i mean z-scaler is a company we've been very excited about and identified them kind of early on they've definitely benefited from the move to cloud generally and specifically the remote work uh situation with the cyber threats etc but tell us why you like z-scaler so interestingly z-scaler um we like the broader security space um the broader cyber security space and interestingly our companies are not yet spending to the level that is commensurate with the increase in attack rate so we think this is a trend that is really going to accelerate as we go forward um my own board 20 of the time on the last board meeting was spent on cyber security what we're doing and this is a pretty simple operation that that we're running here so you can imagine for a large enterprise with thousands of people all around the world um needing to be on a single simple system z-scaler really fits well here very easy to implement several of our industrial companies use it siemens uses it ge uses it and they've had great great experience with it excellent i just want to take a quick look at how some of these names have performed over the last year and and what if anything this data tells us this is a chart comparing the past 12 months performance of of those four companies uh that we just talked about and we added in you know servicenow z scalar as you can see has outperformed the other despite your commentary on discounted cash flow snowflake is underperformed really precisely for the reasons that you mentioned not to mention the fact that it was pretty highly valued and you can see relative to the nas but it's creeping back lately after very strong earnings even though the stock dropped after it beat earnings because the street wants the cfo to say to guide even higher than maybe as mike scarpelli feels is prudent and you can see cooper has also underperformed relatively speaking i mean it absolutely destroyed consensus this week the stock went up but it's been off with the the weaker market this week i know you like to take a longer term view but but anything you would add here yeah so interestingly both z-scaler and koopa were in the camp of as we went into earnings expectations were already pretty high because few of their competitors reported very strong results so this scalar yesterday their revenue growth was was pretty strong the stock is down today uh and the reason is because people were kind of caught up a little bit in the noise of this quarter growth is 57 last quarter it was 60 like is this a deceleration we don't see it as that at all and the company brought up one point that i thought was extremely interesting which is as their deal sizes are getting larger it takes a little longer time for them to see the revenue come through so it takes a little bit of time to for you to see it into from billings into into revenue same thing with cooper very strong earnings report but i think expectations were already pretty high going into it uh given the service now and um and anna plan as well reported strong results so i think it's all about positioning so we love these setups where you can buy the deep in on this opportunity where like people get caught up in um short-term noise and and it creates good entry points excellent i i want to bring in some data from our partner etr and see if you have any comments ivana so what we're showing here is a two-dimensional chart we like to show this uh very frequently it's based on a survey of between a thousand and fifteen hundred chief information officers and technology buyers every quarter this is from their most recent july survey the vertical axis shows net score which is a measure of spending momentum i mean this it measures the net percentage of customers in the survey that are spending more on a particular product or platform in other words it essentially subtracts the percentage of customers spending less from those spending more which yields a net score it's more granular than that but basically that's what it does the horizontal axis is market share or pervasiveness in the data set it's not revenue market share like you get from idc it's it's a mention market share and now that red dotted line at the 40 percent mark on the vertical represents an elevated level in other words anything above 40 percent we consider notable and we've plotted our three by the dip companies and included some of their competitors for context and you can see we added salesforce servicenow and oracle and that orange ellipse because they're some of the bigger names in the software business so let's take these in alphabetical order ivana starting with koopa in the blue you can see we plotted them next to sap's ariba and you can see cooper has stronger spending momentum but not as much presence in the market so to me my influence is oh that's an opportunity for them to steal share more modern technology you know more facile and of course oracle has products in this space but the oracle dot includes all oracle products not just the procurement stuff but uh maybe your thoughts on this absolutely i love this chart i think that's your spot on this would be the same way i would interpret the chart where um increased spending momentum is is a sign of the company providing products that people like and we we expect to see cooper's share grow market share grow over time as well so let's come back to the chart and i want to i want to really point out the green ellipse this is the data zone if you will uh and we're like a broken record on this program with snowflake has performed unbelievably well in net score and spending momentum every quarter the dtr has captured enough end sample in its survey holding near or above 80 percent its net score consistently is has been up there and we've plotted data bricks in that zone it's been expected right that data bricks is going to do an ipo this year late last month company raised 1.6 billion in a private round so i guess that was either a strategy to delay the ipo or raise a bunch more cash and give late investors a low risk bite at the apple you know pre-ipo as we saw with snowflake last year what we didn't plot here are some of snowflake's biggest competitors ivana who also happen to be their partners most notably the big cloud players all who have their own database offerings aws microsoft and google now you've said snowflake is much more than a database company i wonder if you could add some color here yeah that's a very good point david uh basically the the driver of the thesis in snowflake is all about acceleration and spending and what we are seeing is the customers that are signed up on their platform today they're not even spending they're probably spending less than five percent of what they can ultimately spend on this product and the reason is because they don't yet know what the ultimate applications are for this right so you're gonna start with putting the data in a format you can use and you need to come up with use cases or how are you actually going to use this data so back to the example that i gave with answers the first use case that they found was trying to optimize leads there could be like 100 other use cases and they're coming up with with those on a daily basis so i would expect um this score to keep keep uh keep up pretty high or or go even higher as we as people figure out how they can use this product you know the buy-the-dip thesis on snowflake was great last quarter because the stock pulled back after they announced earnings and when we reported we said you know mike the the company see well cleveland research came out remember they got the dip on that and we looked at the data and we said mike scarpelli said that you know we're going to probably as a percentage of overall customers decelerate the net net new logos but we're going deeper into the customer base and that's exactly what's happening with with snowflake but okay let's bring up the slide again last but not least the z scaler we love z scalar we named z scaler in 2019 as an emerging four-star security company along with crowdstrike and octa and we said these three should be on your radar and as you see we've plotted z scalar with octa who with its it's its recent move into to converging identity and governance uh it gets kind of interesting uh we plotted them with palo alto as well another cyber security player that we've covered extensively we love octa in addition to z-scaler we great respect for palo alto and you'll note all of them are over that 40 percent line these are disruptors they're benefiting well not so much palo alto they're more legacy but the the other two are benefiting from that shift to work from home cloud security modern tech stack uh the acquisition that octa-made of of of auth0 and again z scalar cloud security getting rid of a lot of hardware uh really has a huge tailwind at its back if on a zscaler you know they've benefited from the huge my cloud migration trend what are your thoughts on the company so i actually love all three companies that are there right and the point is people are just going to spend more money whether you are on the cloud of the cloud the data centers need more security as well so i think there is a strong case to be made for all three with this scaler the upside is that it's just very easy to use very easy to implement and if you're somebody that is just setting up infrastructure on the cloud there is no reason for you to call any other competitor right with palo alto the case there is that if you have an established um security platfor if you're on their security platform the databa on the data center side uh they they did introduce through several acquisitions a pretty attractive cloud offering as well so they've been gaining share as well in the space and and the company does look pretty attractive on valiation basis so for us cyber security is really all about rising tide lifts all boats here right so you can have a pure play like this scaler uh that benefits from the cloud but even somebody like palo alto is pretty well positioned um to benefit yeah we think so too over a year ago we reported on the valuation divergence between palo alto and fortinet fortinet was doing a better job moving to the cloud and obviously serves more of a mid-market space palo alto had some go-to-market execution challenges we said at the time they're going to get through those and when we talk to chief information security officers palo alto is like the gold standard they're the thought leader they want to work with them but at the same time they also want to participate in some of these you know modern cloud stacks so i we agree there's plenty of room for all three um just to add a bit more color and drill into the spending data a little bit more this slide here takes that net score and shows the progression since january 2019 and you can see a snowflake just incredible in terms of its ability to maintain that elevated net score as we talked about and the table on the insert it shows you the number of responses and all three of these companies have been getting more mentions over time but snowflake and z scale are now both well over 100 n in the survey each quarter and the other notable piece here and this is really important you can see all three are coming out of the isolation economy with the spending uptick nice upticks shown in the most recent survey so that's again another positive but i want to close ivana with kind of making the bull and bear case and have you address really the risks to the buy the dip scenario so look there are a lot of reasons to like these companies we talked about them cooper they've got earnings momentum you know management on the call side had very strong end market demand this the stock you know has underperformed the nasdaq you know this year snowflake and zscaler they also have momentum snowflake get this enormous tam uh although they were punished for not putting a hard number on it which is ridiculous in my opinion i mean the thing is it's huge um the investors were just kind of you know wanting a little binky baby blanket but they all have modern tech in the cloud and really importantly this shows in the etr surveys you know the momentum that they have so very high retention is the other point i wanted to make the very very low churn of these companies however cooper's management despite the blowout quarter they gave kind of underwhelming guidance they've cited headwinds uh they've with the the the lamisoft uh migration to their cloud platform snowflake is kind of like price to perfection so maybe that's an advantage because every every little negative news is going to going to cause the company to dip but it's you know it's pretty high value because salutman and scarpelli everybody expects them to surpass what happened at servicenow which was a rocket ship and it could be all argued that all three are richly priced and overvalued so but ivana you're looking out as you said a couple of years three years maybe even five years how do you think about the potential downside risks in in your by the dip scenario you buy every dip you looking for bigger dips or what's your framework there so what we try to do is really look every quarter the company reports is there something that's driving fundamental change to the story or is it a one-off situation where people are just misunderstanding what the company is reporting so in the case we kind of addressed some of the earnings that that were reported but with koopa we think the man that management is guiding conservatively as they should so we're not very concerned about their ability to execute on on the guidance and and to exceed the guidance with snowflake price to perfection that's never a good idea to avoid a stock uh because it just shows that there is the company is doing a great job executing right so um we are looking for reports like the cleveland report where they would be like negative on the stock and that would be an entry point uh for us so broadly we apply by the deep philosophy but not not if something fundamentally changes in the story and none of these three are showing any signs of fundamental change okay we're going to leave it right there thanks to my guest today ivana tremendous having you would love to have you back great to see you thank you david and def you definitely want to check out sprx and the spear etf now remember i publish each week on wikibon.com and siliconangle.com these episodes they're all available as podcasts all you do is search breaking analysis podcasts you can always connect with me on twitter i'm at d vallante or email me at david.vellante at siliconangle.com love the comments on linkedin don't forget to check out etr.plus for all the survey action this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

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 despite all the chatter about cloud repatriation and the exorbitant cost of cloud computing customer spending momentum continues to accelerate in the post-isolation economy if the pandemic was good for the cloud it seems that the benefits of cloud migration remain lasting in the late stages of covid and beyond and we believe this stickiness is going to continue for quite some time we expect i asked revenue for the big four hyperscalers to surpass 115 billion dollars in 2021 moreover the strength of aws specifically as well as microsoft azure remain notable such large organizations showing elevated spending momentum as shown in the etr survey results is perhaps unprecedented in the technology sector hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll share some some fresh july survey data that indicates accelerating momentum for the largest cloud computing firms importantly not only is the momentum broad-based but it's also notable in key strategic sectors namely ai and database there seems to be no stopping the cloud momentum there's certainly plenty of buzz about the cloud tax so-called cloud tax but other than wildly assumptive valuation models and some pockets of anecdotal evidence you don't really see the supposed backlash impacting cloud momentum our forecast calls for the big four hyperscalers aws azure alibaba and gcp to surpass 115 billion as we said in is revenue this year the latest etr survey results show that aws lambda has retaken the lead among all major cloud services tracked in the data set as measured in spending momentum this is the service with the most elevated scores azure overall azure functions vmware cloud on aws and aws overall also demonstrate very highly elevated performance all above that of gcp now impressively aws momentum in the all-important fortune 500 where it has always showed strength is also accelerating one concern in the most recent survey data is that the on-prem clouds and so-called hybrid platforms which we had previously reported as showing an upward spending trajectory seem to have cooled off a bit but the data is mixed and it's a little bit too early to draw firm conclusions nonetheless while hyperscalers are holding steady the spending data appears to be somewhat tepid for the on-prem players you know particularly for their cloud we'll study that further after etr drops its full results on july 23rd now turning our attention back to aws the aws cloud is showing strength across its entire portfolio and we're going to show you that shortly in particular we see notable strength relative to others in analytics ai and the all-important database category aurora and redshift are particularly strong but several other aws database services are showing elevated spending velocity which we'll quantify in a moment all that said snowflake continues to lead all database suppliers in spending momentum by a wide margin which again will quantify in this episode but before we dig into the survey let's take a look at our latest projections for the big four hyperscalers in is as you know we track quarterly revenues for the hyperscalers remember aws and alibaba ias data is pretty clean and reported in their respective earnings reports azure and gcp we have to extrapolate and strip out all a lot of the the apps and other certain revenue to make an apples-to-apples comparison with aws and alibaba and as you can see we have the 2021 market exceeding 115 billion dollars worldwide that's a torrid 35 growth rate on top of 41 in 2020 relative to 2019. aggressive yes but the data continues to point us in this direction until we see some clearer headwinds for the cloud players this is the call we're making aws is perhaps losing a sharepoint or so but it's also is so large that its annual incremental revenue is comparable to alibaba's and google's respective cloud business in total is business in total the big three u.s cloud companies all report at the end of july while alibaba is mid mid-august so we'll update these figures at that time okay let's move on and dig into the survey data we don't have the data yet on alibaba and we're limited as to what we can share until etr drops its research update on on the 23rd but here's a look at the net score timeline in the fortune 500 specifically so we filter the fortune 500 for cloud computing you got azure and the yellow aws and the black and gcp in blue so two points here stand out first is that aws and microsoft are converging and remember the customers who respond to the survey they probably include a fair amount of application software spending in their cloud answers so it favors microsoft in that respect and gcp second point is showing notable deceleration relative to the two leaders and the green call out is because this cut is from an aws point of view so in other words gcp declines are a positive for aws so that's how it should be interpreted now let's take a moment to better understand the idea of net score this is one of the fundamental metrics of the etr methodology here's the data for aws so we use that as a as a reference point net score is calculated by asking customers if they're adding a platform new that's the lime green bar that you see here in the current survey they're asking are you spending six percent or more in the second half relative to the first half of the year that's the forest green they're also asking is spending flat that's the gray or are you spending less that's the pink or are you replacing the platform i.e repatriating so not much spending going on in replacements now in fairness one percent of aws is half a billion dollars so i can see where some folks would get excited about that but in the grand scheme of things it's a sliver so again we don't see repatriation in the numbers okay back to net score subtract the reds from the greens and you get net score which in the case of aws is 61 now just for reference my personal subjective elevated net score level is 40 so anything above that is really impressive based on my experience and to have a company of this size be so elevated is meaningful same for microsoft by the way which is consistently well above the 50 mark in net score in the etr surveys so that's you can think about it that's even more impressive perhaps than aws because it's triple the revenue okay let's stay with aws and take a look at the portfolio and the strength across the board this chart shows net score for the past three surveys serverless is on fire by the way not just aws but azure and gcp functions as well but look at the aws portfolio every category is well above the 40 percent elevated red line the only exception is chime and even chime is showing an uptick and chime is meh if you've ever used chime every other category is well above 50 percent next net score very very strong for aws now as we've frequently reported ai is one of the four biggest focus areas from a spending standpoint along with cloud containers and rpa so it stands to reason that the company with the best ai and ml and the greatest momentum in that space has an advantage because ai is being embedded into apps data processes machines everywhere this chart compares the ai players on two dimensions net score on the vertical axis and market share or presence in the data set on the horizontal axis for companies with more than 15 citations in the survey aws has the highest net score and what's notable is the presence on the horizontal axis databricks is a company where high on also shows elevated scores above both google and microsoft who are showing strength in their own right and then you can see data iq data robot anaconda and salesforce with einstein all above that 40 percent mark and then below you can see the position of sap with leonardo ibm watson and oracle which is well below the 40 line all right let's look at at the all-important database category for a moment and we'll first take a look at the aws database portfolio this chart shows the database services in aws's arsenal and breaks down the net score components with the total net score superimposed on top of the bars point one is aurora is highly elevated with a net score above 70 percent that's due to heavy new adoptions redshift is also very strong as are virtually all aws database offerings with the exception of neptune which is the graph database rds dynamodb elastic document db time stream and quantum ledger database all show momentum above that all important 40 line so while a lot of people criticize the fragmentation of the aws data portfolio and their right tool for the right job approach the spending spending metrics tell a story and that that the strategy is working now let's take a look at the microsoft database portfolio there's a story here similar similar to that of aws azure sql and cosmos db microsoft's nosql distributed database are both very highly elevated as are azure database for mysql and mariadb azure cash for redis and azure for cassandra also microsoft is giving look at microsoft's giving customers a lot of options which is kind of interesting you know we've often said that oracle's strategy because we think about oracle they're building the oracle database cloud we've said oracle strategy should be to not just be the cloud for oracle databases but be the cloud for all databases i mean oracle's got a lot of specialty capability there but it looks like microsoft is beating oracle to that punch not that oracle is necessarily going there but we think it should to expand the appeal of its cloud okay last data chart that we'll show and then and then this one looks at database disruption the chart shows how the cloud database companies are doing in ibm oracle teradata in cloudera accounts the bars show the net score granularity as we described earlier and the etr callouts are interesting so first remember this is an aws this is in an aws context so with 47 responses etr rightly indicates that aws is very well positioned in these accounts with the 68 net score but look at snowflake it has an 81 percent net score which is just incredible and you can see google database is also very strong and the high 50 percent range while microsoft even though it's above the 40 percent mark is noticeably lower than the others as is mongodb with presumably atlas which is surprisingly low frankly but back to snowflake so the etr callout stresses that snowflake doesn't have a strong as strong a presence in the legacy database vendor accounts yet now i'm not sure i would put cloudair in the legacy database category but okay whatever cloudera they're positioning cdp is a hybrid platform as are all the on-prem players with their respective products and platforms but it's going to be interesting to see because snowflake has flat out said it's not straddling the cloud and on-prem rather it's all in on cloud but there is a big opportunity to connect on-prem to the cloud and across clouds which snowflake is pursuing that that ladder the cross-cloud the multi-cloud and snowflake is betting on incremental use cases that involve data sharing and federated governance while traditional players they're protecting their turf at the same time trying to compete in cloud native and of course across cloud i think there's room for both but clearly as we've shown cloud has the spending velocity and a tailwind at its back and aws along with microsoft seem to be getting stronger especially in the all-important categories related to machine intelligence ai and database now to be an essential infrastructure technology player in the data era it would seem obvious that you have to have database and or data management intellectual property in your portfolio or you're going to be less valuable to customers and investors okay we're going to leave it there for today remember these episodes they're all available as podcasts wherever you listen all you do is search breaking analysis podcast and please subscribe to the series check out etr's website at etr dot plus plus etr plus we also publish a full report every week on wikibon.com and siliconangle.com you can get in touch with me david.velante at siliconangle.com you can dm me at d vallante or you can hit hit me up on our linkedin post this is dave vellante for the cube insights powered by etr have a great week stay safe be well and we'll see you next time you

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 aws is pointing the way to a revolution in system architecture much in the same way that aws defined the cloud operating model last decade we believe it is once again leading in future systems design the secret sauce underpinning these innovations is specialized designs that break the stranglehold of inefficient and bloated centralized processing and allows aws to accommodate a diversity of workloads that span cloud data center as well as the near and far edge hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll dig into the moves that aws has been making which we believe define the future of computing we'll also project what this means for customers partners and aws many competitors now let's take a look at aws's architectural journey the is revolution it started by giving easy access as we all know to virtual machines that could be deployed and decommissioned on demand amazon at the time used a highly customized version of zen that allowed multiple vms to run on one physical machine the hypervisor functions were controlled by x86 now according to werner vogels as much as 30 of the processing was wasted meaning it was supporting hypervisor functions and managing other parts of the system including the storage and networking these overheads led to aws developing custom asics that help to accelerate workloads now in 2013 aws began shipping custom chips and partnered with amd to announce ec2 c3 instances but as the as the aws cloud started to scale they really weren't satisfied with the performance gains that they were getting and they were hitting architectural barriers that prompted aws to start a partnership with anaperta labs this was back in 2014 and they launched then ec2 c4 instances in 2015. the asic in c4 optimized offload functions for storage and networking but still relied on intel xeon as the control point aws aws shelled out a reported 350 million dollars to acquire annapurna in 2015 which is a meager sum to acquire the secret sauce of its future system design this acquisition led to a modern version of project nitro in 2017 nitro nitro offload cards were first introduced in 2013 at this time aws introduced c5 instances and replaced zen with kvm and more tightly coupled the hypervisor with the asic vogels shared last year that this milestone offloaded the remaining components including the control plane the rest of the i o and enabled nearly a hundred percent of the processing to support customer workloads it also enabled a bare metal version of the compute that spawned the partnership the famous partnership with vmware to launch vmware cloud on aws then in 2018 aws took the next step and introduced graviton its custom designed arm-based chip this broke the dependency on x86 and launched a new era of architecture which now supports a wide variety of configurations to support data intensive workloads now these moves preceded other aws innovations including new chips optimized for machine learning and training and inferencing and all kinds of ai the bottom line is aws has architected an approach that offloaded the work currently done by the central processing unit in most general purpose workloads like in the data center it has set the stage in our view for the future allowing shared memory memory disaggregation and independent resources that can be configured to support workloads from the cloud all the way to the edge and nitro is the key to this architecture and to summarize aws nitro think of it as a set of custom hardware and software that runs on an arm-based platform from annapurna aws has moved the hypervisor the network the storage virtualization to dedicated hardware that frees up the cpu to run more efficiently this in our opinion is where the entire industry is headed so let's take a look at that this chart pulls data from the etr data set and lays out key players competing for the future of cloud data center and the edge now we've superimposed nvidia up top and intel they don't show up directly in the etr survey but they clearly are platform players in the mix we covered nvidia extensively in previous breaking analysis and won't go too deep there today but the data shows net scores on the vertical axis that's a measure of spending velocity and then it shows market share in the horizontal axis which is a measure of pervasiveness within the etr data set we're not going to dwell on the relative positions here rather let's comment on the players and start with aws we've laid out aws how they got here and we believe they are setting the direction for the future of the industry and aws is really pushing migration to its arm-based platforms pat morehead at the 6-5 summit spoke to dave brown who heads ec2 at aws and he talked extensively about migrating from x86 to aws's arm-based graviton 2. and he announced a new developer challenge to accelerate that migration to arm instances graviton instances and the end game for customers is a 40 better price performance so a customer running 100 server instances can do the same work with 60 servers now there's some work involved but for the by the customers to actually get there but the payoff if they can get 40 improvement in price performance is quite large imagine this aws currently offers 400 different ec2 instances last year as we reported sorry last year as we reported earlier this year nearly 50 percent of the new ec2 instances so nearly 50 percent of the new ec2 instances shipped in 2020 were arm based and aws is working hard to accelerate this pace it's very clear now let's talk about intel i'll just say it intel is finally responding in earnest and basically it's taking a page out of arm's playbook we're going to dig into that a bit today in 2015 intel paid 16.7 billion dollars for altera a maker of fpgas now also at the 6.5 summit nevin shenoy of intel presented details of what intel is calling an ipu it's infrastructure processing unit this is a departure from intel norms where everything is controlled by a central processing unit ipu's are essentially smart knicks as our dpus so don't get caught up in all the acronym soup as we've reported it's all about offloading work and disaggregating memory and evolving socs system-on-chip and sops system on package but just let this sink in a bit a bit for a moment intel's moves this past week it seems to us anyway are designed to create a platform that is nitro like and the basis of that platform is a 16.7 billion dollar acquisition just compare that to aws's 350 million dollar tuck-in of annapurna that is incredible now chenoy said in his presentation rough quote we've already deployed ipu's using fpgas in a in very high volume at microsoft azure and we've recently announced partnerships with baidu jd cloud and vmware so let's look at vmware vmware is the other you know really big platform player in this race in 2020 vmware announced project monterrey you might recall that it's based on the aforementioned fpgas from intel so vmware is in the mix and it chose to work with intel most likely for a variety of reasons one of the obvious ones is all the software that's running on on on vmware it's been built for x86 and there's a huge install base there the other is pat was heading vmware at the time and and you know when project monterey was conceived so i'll let you connect the dots if you like regardless vmware has a nitro like offering in our view its optionality however is limited by intel but at least it's in the game and appears to be ahead of the competition in this space aws notwithstanding because aws is clearly in the lead now what about microsoft and google suffice it to say that we strongly believe that despite the comments that intel made about shipping fpgas and volume to microsoft that both microsoft and google as well as alibaba will follow aws's lead and develop an arm-based platform like nitro we think they have to in order to keep pace with aws now what about the rest of the data center pack well dell has vmware so despite the split we don't expect any real changes there dell is going to leverage whatever vmware does and do it better than anyone else cisco is interesting in that it just revamped its ucs but we don't see any evidence that it has a nitro like plans in its roadmap same with hpe now both of these companies have history and capabilities around silicon cisco designs its own chips today for carrier class use cases and and hpe as we've reported probably has some remnants of the machine hanging around but both companies are very likely in our view to follow vmware's lead and go with an intel based design what about ibm well we really don't know we think the best thing ibm could do would be to move the ibm cloud of course to an arm-based nitro-like platform we think even the mainframe should move to arm as well i mean it's just too expensive to build a specialized mainframe cpu these days now oracle they're interesting if we were running oracle we would build an arm-based nitro-like database cloud where oracle the database runs cheaper faster and consumes less energy than any other platform that would would dare to run oracle and we'd go one step further and we would optimize for competitive databases in the oracle cloud so we would make oci run the table on all databases and be essentially the database cloud but you know back to sort of fpgas we're not overly excited about about the market amd is acquiring xi links for 35 billion dollars so i guess that's something to get excited about i guess but at least amd is using its inflated stock price to do the deal but we honestly we think that the arm ecosystem will will obliterate the fpga market by making it simpler and faster to move to soc with far better performance flexibility integration and mobility so again we're not too sanguine about intel's acquisition of altera and the moves that amd is making in in the long term now let's take a deeper look at intel's vision of the data center of the future here's a chart that intel showed depicting its vision of the future of the data center what you see is the ipu's which are intelligent nixed and they're embedded in the four blocks shown and they're communicating across a fabric now you have general purpose compute in the upper left and machine intelligent on the bottom left machine intelligence apps and up in the top right you see storage services and then the bottom right variation of alternative processors and this is intel's view of how to share resources and go from a world where everything is controlled by a central processing unit to a more independent set of resources that can work in parallel now gelsinger has talked about all the cool tech that this will allow intel to incorporate including pci and gen 5 and cxl memory interfaces and or cxl memory which are interfaces that enable memory sharing and disaggregation and 5g and 6g connectivity and so forth so that's intel's view of the future of the data center let's look at arm's vision of the future and compare them now there are definite similarities as you can see especially on the right hand side of this chart you've got the blocks of different process processor types these of course are programmable and you notice the high bandwidth memory the hbm3 plus the ddrs on the two sides kind of bookending the blocks that's shared across the entire system and it's connected by pcie gen 5 cxl or ccix multi-die socket so you know you may be looking to say okay two sets of block diagrams big deal well while there are similarities around disaggregation and i guess implied shared memory in the intel diagram and of course the use of advanced standards there are also some notable differences in particular arm is really already at the soc level whereas intel is talking about fpgas neoverse arms architecture is shipping in test mode and we'll have end market product by year end 2022 intel is talking about maybe 2024 we think that's aspirational or 2025 at best arm's road map is much more clear now intel said it will release more details in october so we'll pay attention then maybe we'll recalibrate at that point but it's clear to us that arm is way further along now the other major difference is volume intel is coming at this from a high data center perspective and you know presumably plans to push down market or out to the edge arm is coming at this from the edge low cost low power superior price performance arm is winning at the edge and based on the data that we shared earlier from aws it's clearly gaining ground in the enterprise history strongly suggests that the volume approach will win not only at the low end but eventually at the high end so we want to wrap by looking at what this means for customers and the partner ecosystem the first point we'd like to make is follow the consumer apps this capability the capabilities that we see in consumer apps like image processing and natural language processing and facial recognition and voice translation these inference capabilities that are going on today in mobile will find their way into the enterprise ecosystem ninety percent of the cost associated with machine learning in the cloud is around inference in the future most ai in the enterprise and most certainly at the edge will be inference it's not today because it's too expensive this is why aws is building custom chips for inferencing to drive costs down so it can increase adoption now the second point is we think that customers should start experimenting and see what you can do with arm-based platforms moore's law is accelerating at least the outcome of moore's law the doubling of performance every of the 18 to 24 months it's it's actually much higher than that now when you add up all the different components in these alternative processors just take a look at apple's a5 a15 chip and arm is in the lead in terms of performance price performance cost and energy consumption by moving some workloads onto graviton for example you'll see what types of cost savings you can drive for which applications and possibly generate new applications that you can deliver to your business put a couple engineers in the task and see what they can do in two or three weeks you might be surprised or you might say hey it's too early for us but you'll find out and you may strike gold we would suggest that you talk to your hybrid cloud provider as well and find out if they have a nitro we shared that vmware they've got a clear path as does dell because they're you know vmware cousins what about your other strategic suppliers what's their roadmap what's the time frame to move from where they are today to something that resembles nitro do they even think about that how do they think about that do they think it's important to get there so if if so or if not how are they thinking about reducing your costs and supporting your new workloads at scale now for isvs these consumer capabilities that we discussed earlier all these mobile and and automated systems and cars and and things like that biometrics another example they're going to find their way into your software and your competitors are porting to arm they're embedding these consumer-like capabilities into their apps are you we would strongly recommend that you take a look at that talk to your cloud suppliers and see what they can do to help you innovate run faster and cut costs okay that's it for now thanks to my collaborator david floyer who's been on this topic since early last decade thanks to the community for your comments and insights and hey thanks to patrick morehead and daniel newman for some timely interviews from your event nice job fellas remember i published each week on wikibon.com and siliconangle.com these episodes are all available as podcasts just search for breaking analysis podcasts you can always connect with me on twitter at d vallante or email me at david.velante at siliconangle.com i appreciate the comments on linkedin and clubhouse so follow us if you see us in a room jump in and let's riff on these topics and don't forget to check out etr.plus for all the survey data this is dave vellante for the cube insights powered by etr be well and we'll see you next time

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 the convenience of online access to bank accounts payment apps crypto exchanges and other transaction systems has created enormous risks which the vast majority of individuals either choose to ignore or simply don't understand the internet has become the new private network and unfortunately it's not so private apis scripts spoofing insider crime sloppy security hygiene by users and much more all increase our risks the convenience of cloud-based services in many respects exacerbates the problem but software built in the cloud is a big part of the solution hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll try to raise awareness about a growing threat to your liquid assets and hopefully inspire you to do some research and take actions to lower the probability of you losing thousands hundreds of thousands or millions of dollars let's go back to 2019 in an event that should have forced us to act but for most of us didn't in september of that year jack dorsey's twitter twitter account was hacked the hackers took over his account and posted racial slurs and other bizarre comments before twitter could regain control of the account and assure us that this wasn't a system-wide attack most concerning however was the manner in which the attackers got a hold of dorsey's twitter account they used an increasingly common and relatively easy to execute technique referred to as a sim hijack or a sim swap the approach allows cyber thieves to take control of a victim's phone number now they often will target high-profile individuals like ceos and celebrities to embarrass or harass them but increasingly they're going after people's money of course now just in the past month we've seen a spate of attacks where individuals have lost cash it's a serious problem of increasing frequency so let's talk a little bit about how it works now some of you are familiar with this technique but most people that we talk to either aren't aware of it or aren't concerned you should be in a sim hack like this one documented on medium in may of 2019 four months prior to the dorsey attack the hackers who have many of your credentials that have likely been posted on the dark web they have your email they have your frequently used passwords your phone number your address your mother's maiden name name of your favorite pet and so forth they go in and they spoof a mobile phone carrier rep into thinking that it's you and they convince the agent that they've switched phones or have some other ruse to get a new sim card sent to them or they pay insiders at the phone carrier to steal sim card details hey 100 bucks a card big money now once in possession of the sim card info the attacker now can receive sms messages as part of two-factor authentication systems that are often used to verify identity they can't use face id on mobile but what they can do is go into your web account and change the password or other information the website then sends an sms and now the attacker has the code and is in then the individual can lock you out and steal your money before you even know what hit you all right so what can you do about it first there's no system that is hack proof if the bad guys want to get you and the value is high enough they will get you but that's the key roi what's roi simply put it's a measure of return derived from dividing the value stolen by the cost of getting that value it's benefit divided by cost so a good way to dissuade a criminal is to increase the denominator if you make it harder to steal the value goes down the roi is less here's a layered system shared by jason floyer the son of our very own david floyer smart dna there so we appreciate his contribution to the cube the system involves three layers of protection first you got to think about all the high value online systems that you have here are just a few you got bank accounts you have investment accounts you might have betting sites that has cash in it e-commerce sites and so forth now many of these sites if not most will use sms-based two-factor authentication to identify you now that exposes you to the sim hack the system that jason proposes let's start in the middle of this chart the first thing is you got to acknowledge that the logins that you're using to access your critical systems are already public so the first thing you do is to get a in quotes secure email in other words one that no one knows about and isn't on the dark web find a provider that you trust maybe the one maybe one that doesn't sell ads but that look that's your call or maybe go out and buy a domain and create a private email address now the second step is to use a password manager now for those who don't know what that is you're probably already using one that comes with your chrome browser for example and it remembers your passwords and autofills them now if you on your iphone if you're an iphone user go to settings passwords and security recommendations or if you're on an android phone open your chrome app and go to settings passwords check passwords you're likely to see a number of recommendations as in dozens or maybe even hundreds that have been compromised reuse passwords and or or are the subject of a data breach so a password manager is a single cloud-based layer that works on your laptop and your mobile phone and allows you to largely automate the creation management and maintenance of your online credentials now the third layer here involves an external cloud-based or sometimes app-based two-factor authentication system that doesn't use sms one that essentially turns your phone into a hardware authentication device much like an external device that you would use like a yubikey now that's also a really good idea to use as that third layer that hardware fob so the system basically brings together all your passwords under one roof under one system with some layers that lower the probability of your money getting stolen again it doesn't go to zero percent but it's dramatically better than the protection that most people have here's another view of that system and this venn the password manager in the middle manages everything and yes there's a concern that all your passwords are in one place but once set up it's more secure than what you're likely doing today we'll explain that and it'll make your life a lot easier the key to this system is there's there's a single password that you have to remember for the password manager and it takes care of everything else now for many password managers you can also add a non-sms based third-party two-factor authentication capability we'll come back and talk about that in a moment so the mobile phone here uses facial recognition if it's enabled so it would require somebody they had either have you at gunpoint to use your phone and to stick it in front of your face to get into your accounts or you know eventually they'll become experts at deep fakes that's probably something we're going to have to contend with down the road so it's the desktop or laptop via web access that is of the greatest concern in this use case this is where the non-sms-based third-party two-factor authentication comes into play it's installed on your phone and if somebody comes into your account from an unauthorized device it forces a two-factor authentication not using sms but using a third-party app as you guessed it is running in the cloud this is where the cloud creates this problem but it's also here to help solve this problem but the key is this app it generates a verification code that changes on your phone every 20 seconds and you can't get into the website without entering that auto generated code well normal people can't get in there's probably some other back door if they really want to get you but i think you see that this is a better system than what 99 of the people have today but there's more to the story so just as with enterprise tech and dealing with the problem of ransomware air gaps are an essential tool in com combating our personal cyber crime so we've added a couple of items to jason's slide so the this air gap and the secure password notion what you want to do is make sure that that password manager is strong and it's easy for you to remember it's never used anywhere except for the password manager which also uses the secure email now if you've set up a non s if you've set up a two factor authentication sms or otherwise you're even more protected non-sms is better for the reasons we've described now for your crypto if you got a lot first of all get out of coinbase not only does coinbase gouge you on transaction costs but we'd recommend storing a good chunk of your crypto in an air-gapped vault now what you want to do is you want to make a few copies of this critical information you want to keep your secure password on you in one spot or memorize it but maybe keep a copy in your wallet your physical wallet and put the rest in a fireproof filing cabinet and a safety deposit box and or fire proof lock a lock box or a book in your library but but have multiple copies that somebody has to get to in order to hack you and you want to put also all your recovery codes so when you set all this up you're going to get recovery codes for the password manager in your crypto wallets that you own yeah it gets complicated and it's a pain but imagine having 30 percent or more of your liquid assets stolen now look we've really just scratched the surface here and you you're going to have to do some research and talk to people who have set this stuff up to get it right so figure out your secure email provider and then focus on the password manager now just google it and take your time deciding which one is the best for you here's a sample there are many some are free you know the better ones are for pay but carve out a full day to do research and set up your system take your time and think about how you use it before pulling the trigger on these tools and document everything offline air gap it now the other tooling that you want to use is the non-sms based third-party authentication app so in case you get sim hacked you've got further protection this turns your phone into a secure token generator without using sms unfortunately it's even more complicated because not only are there a lot of tools but not all your financial systems and apps we will support the same two-factor authentication app your password manager for example might only support duo your crypto exchange might support authy but your bank might only support symantec vip or it forces you to have a key fob or use sms so it's it's a mishmash so you may need to use multiple authentication apps to protect your liquid assets yeah i'm sorry but the consequences of not protecting your money and identity are worth the effort okay well i know there's a deviation from our normal enterprise tech discussions but look we're all the cios of our respective home i.t we're the network admin the storage admin the tech support help desk and we're the chief information security officer so as individuals we can only imagine the challenges of securing the enterprise and one of the things we talk about a lot in the cyber security space is complexity and fragmentation it's just the way it is now here's a chart from etr that we use frequently which lays out the security players in the etr data set on two dimensions net score or spending velocity in the vertical axis and market share or pervasiveness within the data set on the horizontal now for change i'm not going to elaborate on any of the specific vendors today you've seen a lot of this before but the chart underscores the complexity and fragmentation of this market and this is just really literally one tiny subset but the cloud which i said at the outset is a big reason that we got into this problem holds a key to solving it now here's one example listen to this clip of dave hatfield the longtime industry exec he's formerly an executive with pure storage he's now the ceo of laceworks lace work a very well-funded cloud-based security company that in our view is attacking one of the biggest problems in security and that's the fragmentation issue that we've often discussed take a listen so at the core of what we do you know you know it's um it's really trying to merge when we look at we look at security as a data problem security and compliance is the data problem and when you apply that to the cloud it's a massive data problem you know you literally have trillions of data points you know across shared infrastructure that we you need to be able to ingest and capture uh and then you need to be able to process efficiently and provide context back to the end user and so we approached it very differently than how legacy approaches have been uh in place you know largely rules-based engines that are written to be able to try and stop the bad guys and they miss a lot of things and so our data-driven approach uh that we patented is called uh polygraph it's it's a security architecture and there are three primary benefits it does a lot of things but the three things that we think are most profound first is it eliminates the need for you know dozens of point solutions um i was shocked when i you know kind of learned about security i was at symantec back in the day and just to see how fragmented this market is it's one of the biggest markets in tech 124 billion dollars in annual spend growing at 300 billion dollars in the next three years and it's massively fragmented and the average number of point solutions that customers have to deal with is dozens like literally 75 is the average number and so we wanted to take a platform approach to solve this problem where the larger the attack service that you put in the more data that you put into our machine learning algorithms the smarter that it gets and the higher the efficacies look hatfield nailed it in our view i mean the cloud and edge explodes the threat surface and this becomes a data problem at massive scale now is lace work going to solve all these problems no of course not but having researched this it's common for individuals to be managing dozens of tools and enterprises as hatfield said 75 on average with many hundreds being common the number one challenge we hear from csos and they'll tell you this is a lack of talent lack of human skills and bandwidth to solve the problem and a big part of that problem is fragmentation multiple apis scripts different standards that are constantly being updated and evolved so if the cloud can help us reduce tooling creep and simplify and automate at scale as the network continues to expand like the universe we can keep up with the adversaries they're never going to get ahead of them so look i know this topic is a bit off our normal swim lane but we think this is so important and no people that have been victimized so we wanted to call your attention to the exposure and try to get you to take some action even if it's baby steps so let's summarize you really want to begin by understanding where your credentials have been compromised because i promise they have been just look at your phone or look into your browser and see those recommendations and you're going to go whoa i got to get on this at least i hope you do that now you want to block out an entire day to focus on this and dig into it in order to protect you or your and your family's assets there's a lot of stake here and look one day is not going to kill you it's worth it then you want to begin building those three layers that we showed you choose a private email that is secure quote-unquote quote-unquote research the password manager that's find the one that's going to work for you do you want one that's web-based or an app that you download how does the password manager authenticate what do the reviews say how much does it cost don't rush into this you may want to test this out on a couple of low risk systems before fully committing because if you screw it up it's really a pain to unwind so don't rush into it then you want to figure out how to use your non-sms based two-factor authentication apps and identify which assets you want to protect you don't want to protect everything do you really care about your credentials on a site where you signed up years ago and never use it anymore it doesn't have any credit cards in it just delete it from your digital life and focus on your financial accounts your crypto and your sites where your credit card or other sensitive information lives and can be stolen also it's important to understand which institutions utilize which authentication methods really important that you make sure to document everything and air gap the most sensitive credentials and finally you're going to have to keep iterating and improving your security because this is a moving target you will never be 100 protected unfortunately this isn't a one-shot deal you're going to do a bunch of work it's hard but it's important work you're going to maintain your password you're going to change them every now and then maybe every few months six months maybe once a year whatever whatever is right for you and then a couple years down the road maybe two or three years down the road you might have to implement an entirely new system using the most modern tooling which we believe is going to be cloud-based or you could just ignore it and see what happens okay that's it for now thanks to the community for your comments and input and thanks again to jason floyer whose analysis around this topic was extremely useful remember i publish each week on wikibon.com and siliconangle.com these episodes are all available as podcasts all you can do is research breaking analysis podcasts or you can always connect on twitter i'm at d vallante or email me at david.velante siliconangle.com of course i always appreciate the comments on linkedin and clubhouse follow me so you're notified when we start a room and riff on these topics don't forget to check out etr.plus for all the survey data this is dave vellante for the cube insights powered by etr be well and we'll see you next time

[Music] from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante uipath's recent 750 million dollar raise at a 35 billion valuation underscores investor enthusiasm for robotic process automation rpa and why not the pandemic has fueled a surge in automation as organizations retool their operations and prepare for a post-covered environment but look reasonable people are asking is this market getting overheated welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll explore the current trends in the rpa market and try to address the question is uipath's value supported by the spending data how will the rpa market evolve from a total available market perspective and where do some of the other players like automation anywhere pega systems and blue prism fit let's first summarize what's new in rpa since we last reported in the space we've all beat to death the positive impact that covet has had on many sectors and rpa is one of the beneficiaries from most if not many organizations if you're not a digital business today you're out of business and replacing labor with software is a major factor in the digital transformations that are taking place now uipath has raised about two billion dollars to date and has a value comparable to that of snowflake at its ipo many are predicting that it will in fact be the snowflake of 2021. look i'm optimistic about the future of uipath but in my opinion the operational excellence that frank slootman and mike scarpelli have brought to snowflake is not nearly as baked as at that at uipath and that that said the market conditions are quite good for uipath right now now while cost reduction is still the main driver for rpa adoptions we're seeing more business productivity use cases that point to a broader automation agenda than simply installing some software robots and point applications to eliminate mundane tasks rather we're seeing a much more holistic thinking in within organizations in a large part driven by the covid slap in the face and given the ceos have now a green light to make big changes that would have been culturally more difficult pre-pandemic to wit we're seeing many more centers of excellence pop up around rpa with a more aggressive agenda than we saw pre-covered before covert these efforts they've been met with a lot more resistance to change than we're seeing today now in the coming decade we expect two major trends to emerge one is a move from this search and destroy mentality toward process transformation to more of an automated approach around discovering candidates for automation second is low code implementations are likely to lead the rise of the so-called citizen developer now capabilities in this regard today are nascent within most products but we believe they will improve steadily over the next several years years and lead to the democracy democratization of rpa so the big question is are we in an rpa bubble or is this really the next big thing this chart depicts our attempt a while back to assess the total available market for robotic process automation and there are a few important points here first our effort was somewhat narrowly focused on rpa tooling but we did try to take into account a broader automation agenda across enterprises we tried to size the move from back office to front office to enterprise-wide automation efforts leading to this buzzword that ultimately gartner created called hyper automation now when we first published this chart we got feedback that we were too conservative and so you know we've thought about what could we be missing and that's depicted there in that red big question mark we've got to do more work on this but looking at the global automation market we see a multi-hundred billion dollar opportunity however that largely focuses on industrial automation versus replacing human tasks with robots process automation is a much smaller piece of that pie and overall these larger figures they also include drones autonomous vehicles and other innovations that rpa may or may not address is it possible that there's an order of magnitude greater opportunity for rpa than we initially thought well here's another way to look at it rpa generally is targeted at larger organizations which can justify the investment with faster returns according to fortune magazine the 500 largest companies in the world generate more than 30 trillion in revenue is it unreasonable to assume that they could spend one percent of revenue on rpa we don't think that's crazy so there very well could be a tam of hundreds of billions of dollars for this market we would say however to attack that opportunity point rpa tools won't get you there but automation platforms very well could in fact that better be the case because with a 35 billion dollar valuation pre-ipo uipath and its peers will need a massive massive market to justify those investments so we'll keep digging into that expanded opportunity to see if it holds water now another way to look at the opportunity is to look at the spending data so let's do that and bring in our etr friends to that discussion as we've reported for many quarters now rpa is one of the top areas in which organizations are investing and you can see that in this chart here this graphic shows net score or spending momentum across the etr taxonomy and you can see we've highlighted rpa which along with machine learning slash ai cloud and containers lead the pack the big four momentum leaders in the only four that consistently over the last several quarters show a net score in the survey above that dotted red forty percent line now remember that net score is a measure of spending velocity based on looking at the percent of customers in the survey that are spending more and subtracting those that are spending less the calculation is a bit more granular than that but you get the point now one of the components of net score is new adoptions and that's part of the spend more equation and this chart shows that only the new adoptions across that taxonomy in those sectors and you can see that rpa and machine learning slash ai top the charts that yellow line shows the january survey results you can see these two sectors are well ahead of others in terms of new spending albeit they are down somewhat from previous quarters a year ago rather but up from the previous quarter now here's another look at the data let's let's really drill into the the rpa sector and look at those components of net score what this chart shows is that granularity along with market share for the past nine surveys i'll explain that the bright green or that lime green on the bars that's new adoptions the forest green is the percent of customers that are spending more on rpa that means they're spending more than five percent the gray depicts flat spending the pink is spending less meaning less than five percent relative to earlier years and the the earlier year and the bright red is replacing the platform we're chucking it out and the net score blue line at the top it nets out the lesses from the moors so you can see very highly elevated for the rpa sector holding firm over time and now even increasing so very very positive now you see that yellow line at the bottom that shows so-called market share which depicts the pervasiveness of rpa within the overall survey relative to other sectors so the steady uptick over time suggests that buyers continue to allocate more and more budget to rpa so very very positive signs there because let's face it the return has been really positive and the mandate for automation thanks to covid is really staring us in the face now let's drill into some of the vendors and see who's winning in the market and maybe who's got less momentum the chart here shows spending momentum or net score over time for the five companies that we're showing now at the top is power automate from microsoft which last year required softer motive and is integrating rpa into its offerings microsoft look they loom large as we've reported and they're everywhere in so many many sectors and rpa is no different the reality is that power automate is not as mature as products from the leaders a classic microsoft 1.0 version if you will but they're in the game now and they cannot be taken lightly we expect microsoft to steadily improve its functionality and integration with the broader microsoft portfolio making it an easy choice for many if not most of microsoft's customers that either want to dabble in rpa or have it as an item in their portfolio you can see uipath has retaken the lead in net score over rival automation anywhere and is showing a nice uptick from last summer's survey as it has made some acquisitions and is moving toward becoming a platform play versus a product play we'd also note three factors that favor uipath in the marketplace first is its simplicity uipath is probably the easiest to adopt second is its emphasis on training and third is the very robust community and ecosystem that it's developed automation anywhere's line is under pressure and we think that's because the company essentially had to do a major product refresh and like any install based migration it's going to slow down momentum and create maybe some friction in the marketplace but we think from a competitive standpoint it was absolutely the right move by aa you've got to bite the bullet invest in the product and grow from there the company also has a really strong ecosystem good engineering and we expect continued improvement for automation anywhere going forward you also see a big uptick for blue prism it's got a mature product and a strong ecosystem as well and we've seen its momentum pop up and down in the survey over the last several quarters and years but they're clearly a solid player in this market they don't have the momentum of a ui path or an automation anywhere they're they're a smaller company but certainly they're a credible player now pega systems is really interesting to us we don't see them as an rpa player per se they're much more focused on a broader business process play include things like crm and intelligent automation in their portfolio rpa is a bundled offering that pega layers into its broader suite and we like what the company has accomplished we're going to come back to them in a moment and talk a little bit more about them and their performance but before we do that let's take another look at the competitive landscape this view is one of our favorites it's that it's that xy view so so we're plotting net score on the y-axis and market share or the pervasiveness within the survey on the x-axis and you can see uipath is they're literally off the charts in the upper right there with because microsoft looming large with its very strong presence and fast adoption of power automate but microsoft ui path automation anywhere in blue prism they all have shared ends or mentions in the survey of more than 50 and net scores over 50 percent so those stand out to us above the rest with uipath as the leader combining both the most significant market momentum and product excellence notwithstanding microsoft's presence again microsoft and their microsoft and we'd be foolish to minimize their their presence in the marketplace now again pega is in the mix they've got a respectable 31 net score but again they're not an rpa specialist and their strategy is paying off in our view the rpa froth combined with pegas history its vision its solid engineering culture and execution are paying off for the company as you can see in these charts so there's charts so what we're showing here is a graph of pegas stock price over the last five years what's most impressive is the strong upward move very very strong since march of last year peg is a billion dollar company been around for a long time but it's growing it's moving it's shifting into a subscription model so it's going through that process of communicating that to wall street i think doing a very very good job of it as it transitions it's transitioning to a recurring revenue stream that's going to have a much more predictable cash flow and profitability impact on the company and you can see its valuation it's at 12 billion it's about 12x revenue it's significantly lower than uipath's most recent value by a factor of roughly 3x but you know presumably that's due to its slower growth rate but pega they've got to love this dynamic because the market's coming to them they've got a mature business that's thriving through a transition to an arr model with solid growth strong customer base and a culture of innovation so really solid job within pega that management is doing in our opinion now let's close by digging into the two pure play leaders uipath and automation anywhere we do this quite frequently in these updates and we'll look at the so-called wheel charts for each company let's start with uipath so this is a pie if you will or wheel breakdown of what we described earlier in net score it's derived from this view by subtracting the reds from the greens several things stand out first you got a nice chunk of new adoptions at 15 percent supported by 56 percent of its customers spending more and only 5 percent spending less than zero percent replacing so that's a very nice picture now let's compare that to automation anywhere and its profile the chart shows the same picture and and even a larger substantially larger new adoptions so that perhaps is is a function of its new platform resonating with customers now automation anywhere's net score is lower than you ui pass owing to a much larger portion of the customer base that has flat spending and a slightly higher replacement figure but both these companies exhibit strong spending patterns in the etr data now we want to share one other data point that stands out in its early days this new relatively new era of rpa we're still there even though rpa has been around for for decades but the point is that large companies have they got a lot of divisions with a lot of buying autonomy within those divisions and as such you're going to see multiple rpa vendors within the account so the question here is okay how are these accounts doing these where they have multiple vendors in the account what stands out in this chart is uipath's performance in shared accounts the chart looks at microsoft power automate and automation anywhere accounts you can see that in the little pull down there in the in the left hand column and so it's it's it's it's microsoft power automate and aaa accounts that also have ui path installed and you can see that little cut on ui path there in the upper middle and there's 149 of those accounts in the etr data set this last quarter and you can see the performance of uipath since covid hit this is very encouraging it speaks to ui past strong go to market and it's really solid land and expand strategy so by no means is this game over for the other players but the etr data continues to support where investors are placing their bets what customers tell us and anecdotal information within the marketplace that that uipath continues to pave the way for a new wave of growth a well-funded automation anywhere is on its tail and these two continue to vie for leadership and are trying to break out from the pack we expect public offerings from both companies within the next 12 to 24 months in fact as you know probably uipath has filed confidentially to do an ipo and has given a time frame i think of 12 to 18 months and they both companies in our view got to get they got a window of 12 to 24 months to go public prior to microsoft getting its product act together and getting to a point where it could really cause some disruption to these respective businesses so anyway i hope this gives you a good snapshot of how we see the marketplace how do you see it please let me know you can dm me at d vallante or comment on my linkedin posts or email me at david.velante at siliconangle.com remember i publish each week on wikibon.com and siliconangle.com and don't forget to check out etr.plus as well all these episodes are available on podcasts wherever you listen thanks for watching this episode of thecube insights powered by etr this is dave vellante wishing you well stay safe and we'll see you next time you

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 as we watch an historic election unfold before our eyes we look back at the early days of the millennium with the memorable presidential race of 2000 that decade of course was defined by 911 which permanently reshaped our thinking and we exited that decade at the tail end of a massive financial crisis only to enter the 2010s with the hope and the momentum of fiscal stimulus a flat globe job growth and very importantly the ascendancy of the cloud cloud computing unquestionably powered the innovation engine over the last 10 years and the pandemic marks a new era where adoption of cloud data and ai have been accelerated by at least two to three years and that's what's going to shape the future of the technology industry and frankly all businesses and organizations hello everyone and welcome to this week's episode of thecube insights powered by etr in this breaking analysis we're going to update you on our latest cloud market share and dig in to some fresh october survey data from our partners over at etr let me start just with a brief summary of the latest action that's going on in cloud now quite interestingly each of the big three cloud players they showed nearly identical year-on-year growth rates in q3 as they did in q2 now we're going to dig into that in a moment but our data suggests that these three companies combined will account for more than 75 billion dollars in infrastructure as a service and platform as a service revenue in 2020 and they're potentially on track to hit 100 billion in 2021. customer survey data indicates that cio's top two infrastructure priorities remain security and cloud migration now that said as we previously reported the cloud it's not immune to the pandemic the remote worker pivot well it's a positive for cloud hasn't completely eradicated certain headwinds now what i mean here is that because the cloud vendors are now so large they're somewhat exposed to the softness in the overall i.t spending climate and also industries that have been hit hardest by the pandemic now would the cloud growth have been better if the pandemic didn't hit we'll never know for sure but our data suggests no covet has definitely been a benefactor to cloud in our view cloud will remain at the center of technological innovation for the foreseeable future the economics of cloud are becoming so compelling that we think the power of the big cloud companies will only increase this decade now importantly we're talking about the costs of running hyper-distributed systems we're not commenting here on what they charge customers that's a different story we believe the cost structure for the hyperscalers is superior to alternative approaches and we believe this advantage will only accelerate over the next several years we also believe that competition is going to continue to drive competitive pricing and innovation all right let's look at our latest market share numbers for the big three this chart shows our estimates of aws azure and the google cloud platform now viewers of this program know that these are is and pass figures and you also know that aws is the only company that provides clean numbers on that sector whereas azure and gcp are estimates that we make based on tidbits of guidance that the companies give us and survey data that we capture and other modeling that we do now as we've said we'll end this year it's about 75 billion in revenue or maybe even a little bit more note that for these three note that we've we've slightly restated some of our earlier estimates for azure to reconcile some differences that we had between constant currency and actual growth we try to keep things in constant currency where possible sorry for that but sometimes that happens azure according to our estimates as we reported last week is now 18 of microsoft's overall revenue number we had it at 19 that last week but when i dug in we made some adjustments so we toned it down a bit aws represents a much smaller percentage of course of amazon's revenues at about 12 percent but it represents 56 percent of amazon's profits gcp on the other hand accounts for less than five percent of google's overall revenue which as we've stated a few weeks ago needs more attention from google but look at the growth rates for these three platforms and the respective size of their is and pass businesses hear all this talk about repatriation i.e that what i mean by that is people go to the cloud but they're unhappy or the bill is too high it's too expensive so then they come back on prem well you just don't see that in the numbers so you gotta be careful when vendor a vendor tries to sell you on that trend i don't buy it except for selective situations now let's bring in some of the etr data and compare the spending momentum for each of the big three you've seen these wheel graphs before they show the breakdown of net score for aws microsoft and google now one note these figures represent these three companies overall within the etr technology taxonomy so for example they don't include amazon's retail business of course but they do include for example microsoft's entire tech portfolio not just the cloud the green portion of the wheel represents increases in spending via new adoptions and increased spending whereas the red sections show decreases via lower spending and defections net score which i've highlighted in the orange is calculated by subtracting the two reds from the two true greens in other words adoptions and increase minus decrease and replacements the takeaway here is these are all pretty strong with aws leading the pack microsoft is exceptionally strong as we pointed out last last week because they're so huge and they still have net scores comparable to aws which is a pure play gcp is a laggard and is showing softness in the data despite a sanguine outlook that we had back in 2019 based on survey data i don't know perhaps google's smaller presence muted their customers ability to take advantage of the platform the thinking there is the customers maybe needed to pivot to the cloud so quickly and aws and azure were the incumbents and that was maybe the most expedient path hence the higher increases in the spend more category but you do see gcp um they had 13 new adoptions which is pretty good so we'll keep looking at that regardless again these are not pure play cloud comparisons but they give a good indication of spending momentum i'd also note that all three show very low defections well each is showing solid increases in new adoptions especially google as i mentioned so that's kind of interesting to see but again google much much smaller you would expect that now i want to turn our attention to one of the hottest areas in cloud which is serverless and this is a pure play comparison so serverless let me start there it's a strange term because it's not really accurate but it's stuck serverless computing is a model where the cloud platform dynamically delivers services as the application requires so so you don't have to configure the compute and the containers for example rather when an application needs resources it goes and gets them and you only pay for when the services are actually invoked and in use so it's really good for workloads that spin up and spin down very frequently it kind of reminds me in concept anyway of the component tree that we saw in the days of soa if you remember that services oriented architecture but now this is cloud it's cloud native it's a whole new world and it's increasingly a popular model and as we'll show in a moment there's a lot of spending momentum in this area but before we do that i want to share some comments made by andy jassy a while back about serverless take a listen it's a good question and you know i really the comment i made was really about um directionally what amazon would do you know in this in the very earliest days of aws jeff used to say a lot if i were starting amazon today i'd have built it on top of aws we didn't have all the capability and all the functionality at that very moment but he knew what was coming and he saw what people were still able to accomplish even with where the services were at that point i think the same thing is true here with lambda which is i think if amazon were starting today it's a given they would build it on the cloud and i think with a lot of the applications that comprise amazon's consumer business we would build those on on our serverless capabilities now now lambda of course jesse referring to lambda that's amazon's serverless offering and if you think about amazon's retail business and take for example the frequent spin up and spin down of resources for something like black monday serverless would be a much more cost effective approach same for a managed data warehouse service for example where you know you don't want to pay for the compute if it's idle the app just calls for the compute when it's needed so it's a very popular model and it's got increased momentum today and you see that in this slide it shows the net score breakdown for serverless for azure aws is lambda which is again is their serverless offering and google cloud functions again you're shipping functions to the application that's why it's called functions look at the net scores azure functions nearly 70 aws at 65 google again lagging and that's a bit of a concern because this is a really really hot space all right let's move on and look at the competitive landscape as we like to do often and update you on that this xy graph is one of our favorites and it shows net score or spending momentum on the vertical axis and market share on the horizontal market share is a measure of pervasiveness in the data set in the upper right you also see a table that ranks each vendor my net score and it includes the shared n in other words the number of mentions in this sector for each vendor now you can you can see up top in the middle i've selected on the cloud computing category so this represents only the cloud businesses for each of these players there's a little bit of nuance here and that we've selected on microsoft azure there's a category in the etr taxonomy for that and we're comparing that with aws overall so there's there are things in the aws overall number that fit into the other parts of the taxonomy like maybe ai collaboration etc whereas azures and gcp are just the cloud segments so i i know it's a bit strange because aws is all cloud but don't get caught up in the taxonomical nuance the point is it's good to be azure in aws it's shown there when you look at the upper right of the chart here they stand out and they stand alone in cloud leadership google cloud is they have nice elevated levels but they're much much smaller they don't have the presence in the market now look at that hybrid cloud zone emerging we've talked about this sometimes in the past and and i want to call it vmware cloud on aws red hat open shift and vmware cloud itself like vmware cloud foundation and their other cloud services all of these appear to be gaining traction and you can see in the number of occurrences in the upper right that shared end that i talked about we're starting to see real numbers that are meaningful in this space vmware cloud on aws for example has a net score of 53 percent with 116 accounts within that total respondent sample that you see there in the middle left of 1438 that's how many cios and technology buyers responded to the etr survey in october you look at open shift at 45 net score and that's with 82 accounts now openshift is in beta with what looked to be some really strong offerings on aws and you can see for context i've added dell emc's cloud offerings hpe's cloud offerings and the oracle cloud and ibm cloud and also rackspace dell actually pretty strong with a net score of 20 and 185 shared accounts much much higher than dell overall which is kind of in the red zone oracle ibm you see those rackspace you know organizing not killing it rackspace is kind of in the big negative so that's a concern but anyway we'd like for these guys we'd like to see the data match the marketing rhetoric for the the guys that are in the red and look alibaba is starting to to show up in the server there's only 26 shared ends but we thought we'd we'd put it in there those three key points again aws and microsoft keep on trucking google needs to do better hybrid is becoming real and that bodes well for multi-cloud and the legacy on-prem guys they got a lot of work to do they're under a lot of pressure the pivot to cloud has not been easy for them uh and it's still a case where they're i've talked about this a lot they're they're declines in their on-premises offerings they're not being offset by the new stuff the cloud momentum all right i want to close out by sharing some of the conversations and thoughts that we've had in the community around sas and its impact on cloud we really have been focusing on ias and pass of the sas layer obviously up the stack so let me first share that there's a lot of talk around and has been for years about aws they're slowing growth rates and whether or not they'll have to enter the sas market to expand their total available market and i've said consistently while i never say never about aws i don't think so at least not yet this chart plots the big three cloud players note aws is a bigger piece of this pie now that i've turned off the cloud computing filter and i know more nuances but the data wonks will will find you know see this and they'll ask me about it this is all of aws portfolio and again it's only the microsoft azure portfolio so you see it aws now overtakes azure on the x-axis i.e market share now we've plotted some of the major sas vendors and you can see servicenow and salesforce both very large and they have really strong spending momentum and servicenow's you know pushing 100 billion dollars in market value they've surpassed workday quite some time ago workday's got less presence but they've got really really solid net score and i got to say i'm impressed with sap despite some of the earnings challenges that they've been having they're right up there with splunk and tableau splunk has softened in recent surveys and i've i've also plotted in there netsuite and oracle fusion which are just okay and that is i think for now anyway aws is going to position as the best place and the most friendly and highest quality cloud in which to run your sas for example workday runs on aws aws is salesforce's preferred infrastructure platform so my premise here is just like retail companies might want not want to run on aws a number of sas companies that compete with microsoft they might think twice about running on azure so aws would be better off for now trying to attract those sas players and drive their services and sticking to infrastructure and the pass layer snowflake is actually kind of interesting and i've added them for context because their netscore is always kind of a bellwether it's really off the charts and they're an isv running on the cloud they're different from some of the other sas players and the snowflake is a database okay and most of snowflake's business runs on aws and aws competes with snowflake with redshift but aws has the best cloud and drives a lot of business for snowflake and vice versa so it's kind of interesting snow snowflake to redshift and a much smaller example is kind of like netflix to amazon prime video to compete they both thrive so i think aws is going to continue to grow by attracting sas players as the preferred platform and they'll also attract developers and try to disrupt sas players like servicenow which runs on its own cloud i remember years ago david floyer and i said that servicenow was it was awesome but at some point its infrastructure cost structure its infrastructure cost structure is going to be less competitive than those companies that are running on hyperscale clouds certainly the hyperscale clouds themselves and servicenow they have this multi-instance architecture which just can't easily port over to the cloud but it can charge a lot which it does now at some point some sharp developers are going to look at all this and say whoa see that service now i can build this for less and they'll attack servicenow and their seat base license model maybe with the consumption pricing model and a platform that's perhaps or a set of services that are perhaps less expensive you're seeing this to a you know a certain degree with like elastic inside the application performance management space so there's some some things to watch there but there are those who firmly believe that aws will and must enter the sas space directly we talked last week about how beneficial microsoft's application business is for azure and what a flywheel that is but for me i think we're not there yet let's give it some time i think maybe four to five years before aws may even start to think about filling some of the space up the stack now maybe they'll find some unique opportunities to do that for instance at the edge but i think that's way off okay so bottom line it's good to be in tech these days it's even better to be in the cloud and it's best if you're aws and microsoft and i don't see that changing for a while now remember these episodes are all available as podcasts wherever you listen i publish each week on wikibon.com and siliconangle.com you can get in touch with me through email it's david at siliconangle.com feel free to dm me on twitter at d vallante i post on linkedin love your comments there thank you and don't forget to check out etr plus for all the survey action thanks for watching this episode of thecube insights powered by etr this is dave vellante stay safe stay sane and we'll see you next time you

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 cios in the most recent september etr spending survey tell us that they expect a slight sequential improvement in q4 spending relative to q3 but still down four percent from q4 2019 so this picture is still not pretty but it's not bleak either to whit firms are adjusting to the new abnormal and are taking positive actions that can be described as a slow thawing of the deep freeze hello everyone this is dave vellante and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we're going to review fresh survey data from etr and provide our outlook for both q4 of 2020 and into 2021. now we're still holding at our four to five percent decline in tech spending for 2020 but we do see light at the end of the tunnel with some cautions specifically more than a thousand cios and it buyers have we've surveyed expect tech spending to show a slight upward trend of roughly two percent in 2021. this is off of a q4 decline of 4 relative to q4 2019 but i would put it this way a slightly less worse decline sequentially from q3 last quarter we saw a 5 decline in spending okay so generally more of the same but things seem to be improving again with caveats now in particular we'll show data that suggests technology project freezes are slowly coming back and we see remote workers returning at a fairly significant rate however executives expect nearly double the percentage of employees working remotely in the midterm and even long term than they did pre-covert that suggests that the work from home trend is not cyclical but showing signs of permanence and why not cios report that on balance productivity has been maintained or even improved during covit now of course this all has to be framed in the context of the unknowns like the fall and even winter surge what about fiscal policy there's uncertainty in the election social unrest all right so let's dig into some of the specifics of the etr data now i mentioned uh the number of respondents at over a thousand i have to say this was predominantly a us-based survey so it's it's 80 sort of bias to the u.s and but it's also weighted to the big spenders in larger organizations with a nice representation across industries so it's good data here now you can see here the slow progression of improvement relative to q3 which as i said was down five percent year-on-year with the four percent decline expected in q4 now etr is calling for a roughly four percent decline for the year you know i've been consistently in the four to five percent decline range and agree with that outlook and you can see cios are planning for a two percent uptick in 2021 as we said at the open now in our view this represents some prudent caution and i think there's probably some upside but it's a good planning assumption for the market overall in my view now let's look at some of the actions that organizations are taking and how that's changed over time you can see here that organizations they're slowly releasing that grip on tech spending overall you know still no material change in employees working from home or traveling we can see that hiring freezes are down that's that's positive in the green as our new i.t deployment freezes and a slight uptick in acceleration of new deployments now as well you see fewer companies are planning layoffs and while small the percent of companies adding head count has doubled from last quarter's you know minimal number all right so this is based on survey data at the end of the summer so it reflects that end of summer sentiment so we got to be a little bit cautious here and i think cios are you know by nature cautious on their projections of two percent up in 2021. now importantly remember this does not get us back to 20 20 19 spending levels so we may be seeing a kind of a long slow climb out of this you know tepid market maybe 2022 gets back over 2019 before we start to see sustained growth again and remember these recoveries are rarely smooth they're not straight lines so you got to expect some choppiness with you know some pockets of opportunity which we'll discuss here in this slide we're showing the top areas that respondents cited as spending priorities for q4 and into 2021 so the chart shows the ratings based on a seven-point scale and these are the top spending initiatives heading into the year end now as we've been saying for the better part of a decade cyber security is a do-over and i've joked you know if it ain't broke don't fix it well coven broke everything and cyber is an area that's seeing long-term change in my opinion endpoint security identity access management cloud security security as a service these are all trends that we're seeing as really major waves as a result of covid now it's coming at the expense of large install bases of things like traditional hardware-based firewalls and we've talked about this a lot in previous segments cloud migration is interesting and i really think it needs some interpretation i mean nobody likes to do migrations so i would suggest this includes things like i have a bunch of people answering phones and offices or i had and then overnight boom the offices are closed so i needed a cloud-based solution i didn't just lift and ship my shift my entire phone routing system you know from the office into the cloud but i probably pivoted to a cloud solution to support those work from home employees now my guess is i think that would be included in these responses i mean i do know an example of an insurance company that did migrate its claims application to the cloud during coven but this was something that they were you know planning to do pre-covered and i guess the point here is twofold again like i said migrations are hairy nobody wants to do them and i think this category really means i'm increasing my use of the cloud so i'm kind of migrating my my operations over time to the cloud all right look at collaboration no shocker here we've pounded you know zoom and webex to death analytics is really interesting we have talked extensively uh and have been covering snowflake and we pointed out that there's a new workload that has emerged in the cloud it's not just snowflake you know there are others aws redshift google with bigquery and and others but snowflake is the off the charts you know hot ipo and so we we talk a lot about it but it relates to this easy setup and access to a data layer with having you know requisite security and governance and this market is exploding adding ai on top and really doing this in the cloud so you can scale it up or down and really only pay for what you need that's a real benefit to people compare that to the traditional edw snake swallowing a basketball i got to get every new intel chip you're not dialing up down down you're over provisioning and half the time you're not using you know half most of the time you're not utilizing what you've paid for all right look at networking you know traffic patterns changed overnight with covet ddos attacks are up 25 to 40 percent uh since coven cyber attacks overall are up 400 percent this year so these all have impacts on the network machine learning and ai i talked about a little bit earlier about that but organizations are realizing that infusing ai into the application portfolio it's becoming really an imperative much more important as the automation mandate that we've talked about becomes more acute people you can't scale humans at this at the pace of technology so automation becomes much more important that of course leads us to rpa now you might think rpa should be a higher priority but i think what's happening here is i t organizations they were scrambling to plug holes in the dike rpa is somewhat more strategic and planful our data suggests that rpa remains one of the most elevated spending categories in terms of net score etr's measure of spending momentum so this means way more people are spending more than spending less in the rpa category so it really has a lot of legs in fact with the exception of container orchestration i think rpa is a sector that has the highest net score i think you'll see that in the upcoming surveys it's as high or even higher than ai i think it's higher than cloud it's just that it remember this is an it survey and a lot of the rpa stuff is going on at the business level but it had to keep the ship afloat when coveted hit which somewhat shifted priorities but but rpa remains strong now let's go back uh to the work from home trend for a moment i know it's been been played out and kind of beat on really heavily covered but i got to tell you etr was the very first on this trend it was way back in march and the data here is instructive it shows that the percentage of employees working from home prior to cor covid currently working from home the percent expected in six months and then those expected essentially permanently and this is primarily work from home versus yeah i don't work a day or two per week it's really the the five day a week i i work remotely as you can see only 16 percent of employees were working from home pre pandemic whereas more than 70 percent are at home today and cios they actually see a meaningful decline in that number over the next six months you know we'll see based on how covid comes back and you know this fall and winter surge and how will that will affect these plans but look what it does long term it settles in at like 34 percent that's double pre-covet so really a meaningful and permanent impact is expected from the isolation economy that we're in today and again why not look at this data it shows the distribution of productivity improvements so that while 23 of respondents said work from home productivity impacts were neutral nearly half i think it was 48 if you add up those bars on the right nearly half are seeing productivity improvements well less than 30 percent see a decline in productivity and you can see the etr quants they peg the average gain at between three and five percent that's pretty significant now of course not everyone can work from home if you're working at a restaurant you really you know unless you're in finance you really can't work from home but we're seeing in this digital economy with cloud and other technologies that we actually can work from pretty much anywhere in the world and many employees are going to look at work from home options as a benefit you know it was just a couple years ago remember that we were talking about companies like ibm and yahoo who mandated coming into the office i mean that was like 2017 2018 time frame well that trend is over now let me give you a quick preview of some of the other things that we're seeing and what the etr data shows now let me also say i'm just scratching the surface here etr has deep deep data cuts they have the sas platform allows you to look at the data all different ways and if you're not working with them you should be because the data gets updated so frequently every quarter there's new data there's drill down surveys and it's forward-looking so you know a lot of the survey data or a lot of the data that we use market share data and other data are sort of looking back you know you use your sales data your sales forecast that's obviously forward-looking but but the etr survey data can actually give an observation space outside of your sales force and no i'm not getting paid by etr but but it's been such a valuable resource i want to make it available and make the community aware of it all right so let's do a little speed round on on some of the the vendors of interest that we've talked about in the last several segments last couple years actually many years decade anyway start with aws aws continues to be strong but they they have less momentum than microsoft this is sort of a recurring pattern here but aws churn is low low low not a lot of people leaving the aws platform despite what we hear about this repatriation trend data warehousing is a little bit soft whereas we see snowflake very very strong but aws share is really strong inside of large companies so cloud and teams and security are strong from microsoft whereas data warehouse and ai aren't as robust as we've seen before but but microsoft azure cloud continues to see a little bit more momentum than aws so we'll watch that next quarter for aws earnings call now google has good momentum and they're steady especially in cloud database ai and analytics we've talked a lot about how google's behind the big two but nonetheless they're showing good good momentum servicenow very low churn but they're kind of hitting the law of large numbers still super strong in large accounts but not the same red hot hat red hot momentum as we've seen in the past octa is showing continued momentum they're holding you know close to number one or that top spot in security that we talked about last time no surprise given the increased importance of identity access management that we've been talking about so much crowdstrike last survey in july they showed some softness despite a good quarter and and we we're seeing continued to sell it to deceleration in the survey now that's from extremely elevated levels but it's significantly down from where crowdstrike was at the height of the lockdown i mean we like the sector of endpoint security and crowdstrike is definitely a leader there and you know well-managed company company but you know maybe they got hit with uh with you know a quick covet injection with with a step up function that's maybe moderating somewhat you know maybe there's some competition you know vmware freezing the market with carbon black i i really don't see that i think it's it's it's you know maybe there's some survey data isn't reflective of of what what crowdstrike is seeing we're going to see in the upcoming earnings release but it's something that we're watching very closely you know two survey snapshots with crowdstrike being a little bit softer it doesn't make a sustained trend but we would have liked to seen you know a little bit stronger this this quarter the data's still coming in so we'll see sale point is one we focused on recently and we see very little negative in their numbers so they're holding solid z scalar showing pretty strong momentum and while there was some concern last survey within large organizations it seemed that might have been a survey anomaly because z scalar they had a strong quarter a good outlook and we're seeing a strong recovery in the most recent data so it also looks like z z scaler is pressuring some of palo alto network's dominance and momentum heading into the quarter so we'll pay close attention to that we've said we like palo alto networks but they're so big uh they've got some exposures but they can offset those you know and they're doing a better job in cloud with their pricing models and sort of leaning into some of the the market waves uh sale point appears to be holding serve you know heading into the fourth quarter snowflake i mean what can we say it continues to show some of the strongest spending momentum going into q4 and into 2021 no signs of slowing down they're going to have their first earnings reports coming up you know in a few months so i i got to believe they got it together and and they're going to be strong reports uipath and momentum is is slowing down a bit but existing customers keep spending with ui path and there's very few defections so it looks like their land and expand is working pretty well automation anywhere continues to be strong despite comments about the sector earlier which showed you know maybe it wasn't as high a priority some other sectors but as i said you know it's still really really strong strong in terms of momentum and automation anywhere in uipath they continue to battle it out for the the top spot within the data set within the automation data set well i should say within rpa i mean companies like pega systems have a broader automation agenda and we really like their strategy and their execution databricks you know hot company once a hot company and still hot but we're seeing a little bit of a deceleration in the survey even though new customer acquisition is quite strong put it this way databricks is strong but not the off the chart outperformer that it used to be this is how etr frame that their analysis so i want to obviously credit that to them datadog showing the most strength in the application performance management or monitoring sector whichever you prefer but generally the the net scores in that sector as we talked about last week they're not great as a sector when you compare it to other leading sectors like cloud or automation rpa as an example container orchestration you know apm is kind of you know significantly lower it's not it's not as low as some of the on-prem on-prem infrastructure or some of the on-prem software but you know given datadog's high valuation it's somewhat of a concern so keep an eye on that mongodb you know they got virtually no customer churn but they're losing some momentum in terms of net score in the survey which is something we're keeping an eye on and a big downtick in in large organization acquisitions within the data so in other words they had a lot of new acquisitions within large companies but that's down now again that could be anomalies in the data i don't want to you know go to the bank on that necessarily but that's something to watch zoom they keep growing but etr data cites a churn of actually up to seven percent due to some security concerns so that was widely reported in the press and somewhere slower velocity for zoom overall due to possible competition from microsoft teams but i tell you it has an amazing stat that etr threw out pre-cove at zoom penetration in the education vertical was 15 today it's over 80 percent wowza cisco cisco's core is weak as we've said you've seen that in their earnings numbers it's it's there's softness there but security meraki those are two areas that remain strong same kind of similar story to last quarter survey pure storage you know they're the the high flyer they're like the one-eyed man in the land of the the storage blind so storage you know not a great market we've talked about that we've seen some softness in the the data set from uh in pure storage and really often sympathy with the generally back burner storage market you know again they they still outperforming their peers but we've seen slower growth rates there in the in in the survey and that's been reflected in their earnings uh so we've been talking about that for a while really keeping an eye on on on pure they made some acquisitions trying to expand their market enough said about that rubric rubric's interesting they kind of were off the charts in a couple surveys ago and they really come off of those highs you know anecdotally we're hearing some concerns in in the market it's hard to tell the private company cohesity has overtaken rubric and spending momentum now for the second quarter in a row you know they're still not as prevalent in the data set we'd like to see more ends from cohesity remember this is sort of a random sample across multiple industries we let the or etr lets the the respondents tell them what they're buying and what they're spending on you know but because cohesity has the highest net score relative to to compares like rubric like veeam you know i even threw in when i looked at nutanix pure dell emcs vxrail those are not direct competitors but they're you know kind of quasi compares if you will new relic they're showing some concerning trends on churn and the company is way off its 2018 momentum highs in the survey and we talked about this last week some of the challenges new relic is facing but we like their tech the nrdb is purpose-built for monitoring and performance management and we feel like you know they can retain their leadership if they can can pull it together we talked about elliott management being in there so that's something that we're watching red hat is showing strength in open shift really really strong ibm you know services exposure uh it's it's not the greatest business in the world right now at the same time there's there's crosswinds there at the same time people you know need some services and they need some help there but the certainly the outsourcing business so there's you know countervailing you know crosswinds uh within ibm but openshift bright spot i i think you know when i look at at the the red hat acquisition yeah 34 billion but but it's it's pretty obvious why ibm made that move um but anyway ibm's core business continues to be under under pressure that's why red hat is such an important component which brings me to vmware vmware has been an execution machine they had vmworld this past week uh we talked last month about the strength of vmware cloud on aws and it's still strong and and vmware cloud portfolio with vmware cloud foundation and other offerings but other than tanzu vmware is in this october survey of the first first look shows some deceleration really across the board you know one potential saving grace etr shared with me is that the fortune 500 spending for vmware is stronger so maybe on a spend basis when i say stronger stronger stronger than the mean so maybe on a spend basis vmware is okay but there seems to be some potential exposure there you know we won't know for sure until late next year uh how the dell reshuffle is going to affect them but it's going to be interesting to see how dell restructures vmware's balance sheet to get its own house in order and remember dell wants to get to investment grade for its own balance sheet yet at the same time it wants to keep vmware at investment grade but the interesting thing to watch is what impact that's going to have on vmware's ability to fund its future and we're not going to know that for a long long time but you know we'll keep an eye on on those developments now dell for its part showing strength and work from home and also strengthen giant public and privates which is a bellwether in the etr data set uh you know these are huge private companies for example uh koch industries would be one you know massive private companies mars would be another example not necessarily that they're the ones responding although my guess is they are it's it's anonymous but actually etr actually knows and they can identify who those bell weathers are and it's been a it's been a predictor of performance for the last you know better part of a decade so we'll see vxrail is strong um you know servers and storage they're they're still muted relative to last year but not really down from july so you know holding on dell holding on to it to to a tepid spending outlook they got such huge exposure on-prem you know so on balance i wouldn't expect you know a barn burner out of dell you know but they got a big portfolio and they've got a lot of a lot of options there and remember they still have the the still have they have a pc uh business unlike hpe which i'll talk about in in in a moment talk about now aruba is the bright spot for hpe but servers and storage those seem to be off you know similar to dell uh but but but maybe even down further i think you know dell is kind of holding relative to last quarter survey you know down from earlier this year and certainly down from from last year uh but hpe seems to be on a steeper downward trajectory uh in storage and service from the survey you know we'll see again you know one one snapshot quarter this is not a trend to make uh but again storage looks particularly soft which is a bit of a concern and we saw that you know in hpe's numbers you know last quarter um customer acquisition is strong for nutanix but overall spending is decelerating versus a year ago levels uh we know about the 750 million dollar injection uh from from bain capital basically you know in talking to bain what essentially they're doing is they they're betting on upside in the hyper-converged marketplace it's true that from a penetration standpoint there's a long long way to go and it's also true that nutanix is shifting from a you know perpetual model you know boom by the the capex to a in an annual occurring revenue model and they kind of need a bridge of cash to sort of soften that blow we've seen companies like tableau make that transition adobe successfully made that transition splunk is in that transition now and it's you know kind of funky for them but at any rate you know within that infrastructure software and virtualization sectors you know nutanix is showing some softness but in things like storage actually nutanix looking pretty strong very strong actually so again this theme of of these crosswinds uh supporting some companies whereas they're exposed in other areas you certainly see that with large companies and and nutanix looks like it's got some momentum in some areas and you know challenges in in others okay so that's just a quick speed dating round with some of the vendor previews for the upcoming survey so i just want to summarize now and we'll wrap so we see overall tech spending off four to five percent in 2020 with a slightly less bad slightly less bad q4 sequentially relative to q3 all this is relative to last year so we see continued headwinds coming into 2021 expect low single-digit spending growth next year let's call it two percent and there are some clear pockets of growth taking advantage of what we see is a more secular work from home trend particularly in security although we're watching some of the leaders shift positions cloud despite the commentary earlier remains very very strong aws azure google red hat open shift serverless kubernetes analytic cloud databases all very very strong automation also stands out as as a a priority in what we think is the coming decade with an automation mandate and some of the themes we've talked about for a long time particularly the impact of cloud relative to on-prem you know we don't see this so-called repatriation as much of a trend as it is a bunch of fun from on-prem vendors that don't own a public cloud so just you just don't see it i mean i'm sure there are examples of oh we did something in the cloud we lifted and shifted it didn't work out we didn't change our operating model okay but the the number of successes in cloud is like many orders of magnitude you know greater than the numbers of failures on the plus side however the for the on-prem guys the hybrid and multi-cloud spaces are increasingly becoming strategic for customers so that's something that i've said for a long time particularly with multi-cloud we've kind of been waiting it's been a lot of vendor power points but that really we talked to customers now they're hedging their bets in cloud they're they're putting horses for courses in terms of workloads they're they're they're not betting their business necessarily on a single cloud and as a result they need security and governance and performance and management across clouds that's consistent so that's actually a a really reasonable and significant opportunity for a lot of the on-prem vendors and as we've said before they're probably not necessarily going to trust the cloud players the public cloud players to deliver that they're going to want somebody that's cloud agnostic okay that's it for this week remember all these episodes are available as podcasts wherever you listen so please subscribe i publish weekly on wikibon.com and siliconangle.com and don't forget to check out etr.plus for all the survey action and the analytics these guys are amazing i always appreciate the comments on my linkedin posts thank you very much you can dm me at d vallante or email me at david.volante at siliconangle.com and this is dave vellante thanks for watching this episode of cube insights powered by etr be well and we'll see you next time you