>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle in 2000 >>nine. Hal Varian, Google's chief economist, said that statisticians would be the sexiest job in the coming decade. The modern big data movement >>really >>took off later in the following year. After the Second Hadoop World, which was hosted by Claudette Cloudera in New York City. Jeff Ham Abakar famously declared to me and John further in the Cube that the best minds of his generation, we're trying to figure out how to get people to click on ads. And he said that sucks. The industry was abuzz with the realization that data was the new competitive weapon. Hadoop was heralded as the new data management paradigm. Now, what actually transpired Over the next 10 years on Lee, a small handful of companies could really master the complexities of big data and attract the data science talent really necessary to realize massive returns as well. Back then, Cloud was in the early stages of its adoption. When you think about it at the beginning of the last decade and as the years passed, Maurin Mawr data got moved to the cloud and the number of data sources absolutely exploded. Experimentation accelerated, as did the pace of change. Complexity just overwhelmed big data infrastructures and data teams, leading to a continuous stream of incremental technical improvements designed to try and keep pace things like data Lakes, data hubs, new open source projects, new tools which piled on even Mawr complexity. And as we reported, we believe what's needed is a comm pleat bit flip and how we approach data architectures. Our next guest is Jean Marc de Connie, who is the director of emerging technologies That thought works. John Mark is a software engineer, architect, thought leader and adviser to some of the world's most prominent enterprises. She's, in my view, one of the foremost advocates for rethinking and changing the way we create and manage data architectures. Favoring a decentralized over monolithic structure and elevating domain knowledge is a primary criterion. And how we organize so called big data teams and platforms. Chamakh. Welcome to the Cube. It's a pleasure to have you on the program. >>Hi, David. This wonderful to be here. >>Well, okay, so >>you're >>pretty outspoken about the need for a paradigm shift in how we manage our data and our platforms that scale. Why do you feel we need such a radical change? What's your thoughts there? >>Well, I think if you just look back over the last decades you gave us, you know, a summary of what happened since 2000 and 10. But if even if we go before then what we have done over the last few decades is basically repeating and, as you mentioned, incrementally improving how we've managed data based on a certain assumptions around. As you mentioned, centralization data has to be in one place so we can get value from it. But if you look at the parallel movement off our industry in general since the birth of Internet, we are actually moving towards decentralization. If we think today, like if this move data side, if he said the only way Web would work the only way we get access to you know various applications on the Web pages is to centralize it. We would laugh at that idea, but for some reason we don't. We don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, you know, embrace the distribution of sources of data that they're not just within one part of organization. They're not just within even bounds of organization there beyond the bounds of organization. And then look back and say Okay, if that's the trend off our industry in general, Um, given the fabric of computation and data that we put in, you know globally in place, then how the architecture and technology and organizational structure incentives need to move to embrace that complexity. And to me, that requires a paradigm shift, a full stack from how we organize our organizations, how we organize our teams, how we, you know, put a technology in place, um, to to look at it from a decentralized angle. >>Okay, so let's let's unpack that a little bit. I mean, you've spoken about and written that today's big architecture and you basically just mentioned that it's flawed, So I wanna bring up. I love your diagrams of a simple diagram, guys, if you could bring up ah, figure one. So on the left here we're adjusting data from the operational systems and other enterprise data sets and, of course, external data. We cleanse it, you know, you've gotta do the do the quality thing and then serve them up to the business. So So what's wrong with that picture that we just described and give granted? It's a simplified form. >>Yeah, quite a few things. So, yeah, I would flip the question may be back to you or the audience if we said that. You know, there are so many sources off the data on the Actually, the data comes from systems and from teams that are very diverse in terms off domains. Right? Domain. If if you just think about, I don't know retail, Uh, the the E Commerce versus Order Management versus customer This is a very diverse domains. The data comes from many different diverse domains. And then we expect to put them under the control off a centralized team, a centralized system. And I know that centralization. Probably if you zoom out, it's centralized. If you zoom in it z compartmentalized based on functions that we can talk about that and we assume that the centralized model will be served, you know, getting that data, making sense of it, cleansing and transforming it then to satisfy in need of very diverse set of consumers without really understanding the domains, because the teams responsible for it or not close to the source of the data. So there is a bit of it, um, cognitive gap and domain understanding Gap, um, you know, without really understanding of how the data is going to be used, I've talked to numerous. When we came to this, I came up with the idea. I talked to a lot of data teams globally just to see, you know, what are the pain points? How are they doing it? And one thing that was evident in all of those conversations that they actually didn't know after they built these pipelines and put the data in whether the data warehouse tables or like, they didn't know how the data was being used. But yet the responsible for making the data available for these diverse set of these cases, So s centralized system. A monolithic system often is a bottleneck. So what you find is, a lot of the teams are struggling with satisfying the needs of the consumers, the struggling with really understanding the data. The domain knowledge is lost there is a los off understanding and kind of in that in that transformation. Often, you know, we end up training machine learning models on data that is not really representative off the reality off the business. And then we put them to production and they don't work because the semantic and the same tax off the data gets lost within that translation. So we're struggling with finding people thio, you know, to manage a centralized system because there's still the technology is fairly, in my opinion, fairly low level and exposes the users of those technologies. I said, Let's say warehouse a lot off, you know, complexity. So in summary, I think it's a bottleneck is not gonna, you know, satisfy the pace of change, of pace, of innovation and the pace of, you know, availability of sources. Um, it's disconnected and fragmented, even though the centralizes disconnected and fragmented from where the data comes from and where the data gets used on is managed by, you know, a team off hyper specialized people that you know, they're struggling to understand the actual value of the data, the actual format of the data, so it's not gonna get us where our aspirations and ambitions need to be. >>Yes. So the big data platform is essentially I think you call it, uh, context agnostic. And so is data becomes, you know, more important, our lives. You've got all these new data sources, you know, injected into the system. Experimentation as we said it with the cloud becomes much, much easier. So one of the blockers that you've started, you just mentioned it is you've got these hyper specialized roles the data engineer, the quality engineer, data scientists and and the It's illusory. I mean, it's like an illusion. These guys air, they seemingly they're independent and in scale independently. But I think you've made the point that in fact, they can't that a change in the data source has an effect across the entire data lifecycle entire data pipeline. So maybe you could maybe you could add some color to why that's problematic for some of the organizations that you work with and maybe give some examples. >>Yeah, absolutely so in fact, that initially the hypothesis around that image came from a Siris of requests that we received from our both large scale and progressive clients and progressive in terms of their investment in data architectures. So this is where clients that they were there were larger scale. They had divers and reached out of domains. Some of them were big technology tech companies. Some of them were retail companies, big health care companies. So they had that diversity off the data and the number off. You know, the sources of the domains they had invested for quite a few years in, you know, generations. If they had multi generations of proprietary data warehouses on print that they were moving to cloud, they had moved to the barriers, you know, revisions of the Hadoop clusters and they were moving to the cloud. And they the challenges that they were facing were simply there were not like, if I want to just, like, you know, simplifying in one phrase, they were not getting value from the data that they were collecting. There were continuously struggling Thio shift the culture because there was so much friction between all of these three phases of both consumption of the data and transformation and making it available consumption from sources and then providing it and serving it to the consumer. So that whole process was full of friction. Everybody was unhappy. So its bottom line is that you're collecting all this data. There is delay. There is lack of trust in the data itself because the data is not representative of the reality has gone through a transformation. But people that didn't understand really what the data was got delayed on bond. So there is no trust. It's hard to get to the data. It's hard to create. Ultimately, it's hard to create value from the data, and people are working really hard and under a lot of pressure. But it's still, you know, struggling. So we often you know, our solutions like we are. You know, Technologies will often pointed to technology. So we go. Okay, This this version of you know, some some proprietary data warehouse we're using is not the right thing. We should go to the cloud, and that certainly will solve our problems. Right? Or warehouse wasn't a good one. Let's make a deal Lake version. So instead of you know, extracting and then transforming and loading into the little bits. And that transformation is that, you know, heavy process, because you fundamentally made an assumption using warehouses that if I transform this data into this multi dimensional, perfectly designed schema that then everybody can run whatever choir they want that's gonna solve. You know everybody's problem, but in reality it doesn't because you you are delayed and there is no universal model that serves everybody's need. Everybody that needs the divers data scientists necessarily don't don't like the perfectly modeled data. They're looking for both signals and the noise. So then, you know, we've We've just gone from, uh, et elles to let's say now to Lake, which is okay, let's move the transformation to the to the last mile. Let's just get load the data into, uh into the object stores into semi structured files and get the data. Scientists use it, but they're still struggling because the problems that we mentioned eso then with the solution. What is the solution? Well, next generation data platform, let's put it on the cloud, and we sell clients that actually had gone through, you know, a year or multiple years of migration to the cloud. But with it was great. 18 months I've seen, you know, nine months migrations of the warehouse versus two year migrations of the various data sources to the clubhouse. But ultimately, the result is the same on satisfy frustrated data users, data providers, um, you know, with lack of ability to innovate quickly on relevant data and have have have an experience that they deserve toe have have a delightful experience off discovering and exploring data that they trust. And all of that was still a missed so something something else more fundamentally needed to change than just the technology. >>So then the linchpin to your scenario is this notion of context and you you pointed out you made the other observation that look, we've made our operational systems context aware. But our data platforms are not on bond like CRM system sales guys very comfortable with what's in the CRM system. They own the data. So let's talk about the answer that you and your colleagues are proposing. You're essentially flipping the architecture whereby those domain knowledge workers, the builders, if you will, of data products or data services there now, first class citizens in the data flow and they're injecting by design domain knowledge into the system. So So I wanna put up another one of your charts. Guys, bring up the figure to their, um it talks about, you know, convergence. You showed data distributed domain, dream and architecture. Er this self serve platform design and this notion of product thinking. So maybe you could explain why this approach is is so desirable, in your view, >>sure. The motivation and inspiration for the approach came from studying what has happened over the last few decades in operational systems. We had a very similar problem prior to micro services with monolithic systems, monolithic systems where you know the bottleneck. Um, the changes we needed to make was always, you know, our fellow Noto, how the architecture was centralized and we found a nice nation. I'm not saying this is the perfect way of decoupling a monolith, but it's a way that currently where we are in our journey to become data driven, um is a nice place to be, um, which is distribution or decomposition off your system as well as organization. I think when we whenever we talk about systems, we've got to talk about people and teams that's responsible for managing those systems. So the decomposition off the systems and the teams on the data around domains because that's how today we are decoupling our business, right? We're decoupling our businesses around domains, and that's a that's a good thing and that What does that do really for us? What it does? Is it localizes change to the bounded context of fact business. It creates clear boundary and interfaces and contracts between the rest of the universe of the organization on that particular team, so removes the friction that often we have for both managing the change and both serving data or capability. So it's the first principle of data meshes. Let's decouple this world off analytical data the same to mirror the same way we have to couple their systems and teams and business why data is any different. And the moment you do that, So you, the moment you bring the ownership to people who understands the data best, then you get questions that well, how is that any different from silence that's connected databases that we have today and nobody can get to the data? So then the rest of the principles is really to address all of the challenges that comes with this first principle of decomposition around domain Context on the second principle is well, we have to expect a certain level off quality and accountability and responsibility for the teams that provide the data. So let's bring product thinking and treating data as a product to the data that these teams now, um share and let's put accountability around. And we need a new set of incentives and metrics for domain teams to share the data. We need to have a new set off kind of quality metrics that define what it means for the data to be a product. And we can go through that conversation perhaps later eso then the second principle is okay. The teams now that are responsible, the domain teams responsible for the analytical data need to provide that data with a certain level of quality and assurance. Let's call that a product and bring products thinking to that. And then the next question you get asked off by C. E. O s or city or the people who build the infrastructure and, you know, spend the money. They said, Well, it's actually quite complex to manage big data, and now we're We want everybody, every independent team to manage the full stack of, you know, storage and computation and pipelines and, you know, access, control and all of that. And that's well, we have solved that problem in operational world. And that requires really a new level of platform thinking toe provide infrastructure and tooling to the domain teams to now be able to manage and serve their big data. And that I think that requires reimagining the world of our tooling and technology. But for now, let's just assume that we need a new level of abstraction to hide away ton of complexity that unnecessarily people get exposed to and that that's the third principle of creating Selves of infrastructure, um, to allow autonomous teams to build their domains. But then the last pillar, the last you know, fundamental pillar is okay. Once you distributed problem into a smaller problems that you found yourself with another set of problems, which is how I'm gonna connect this data, how I'm gonna you know, that the insights happens and emerges from the interconnection of the data domains right? It does not necessarily locked into one domain. So the concerns around interoperability and standardization and getting value as a result of composition and interconnection of these domains requires a new approach to governance. And we have to think about governance very differently based on a Federated model and based on a computational model. Like once we have this powerful self serve platform, we can computational e automate a lot of governance decisions. Um, that security decisions and policy decisions that applies to you know, this fabric of mesh not just a single domain or not in a centralized. Also, really. As you mentioned that the most important component of the emissions distribution of ownership and distribution of architecture and data the rest of them is to solve all the problems that come with that. >>So very powerful guys. We actually have a picture of what Jamaat just described. Bring up, bring up figure three, if you would tell me it. Essentially, you're advocating for the pushing of the pipeline and all its various functions into the lines of business and abstracting that complexity of the underlying infrastructure, which you kind of show here in this figure, data infrastructure is a platform down below. And you know what I love about this Jama is it to me, it underscores the data is not the new oil because I could put oil in my car I can put in my house, but I can't put the same court in both places. But I think you call it polyglot data, which is really different forms, batch or whatever. But the same data data doesn't follow the laws of scarcity. I can use the same data for many, many uses, and that's what this sort of graphic shows. And then you brought in the really important, you know, sticking problem, which is that you know the governance which is now not a command and control. It's it's Federated governance. So maybe you could add some thoughts on that. >>Sure, absolutely. It's one of those I think I keep referring to data much as a paradigm shift. And it's not just to make it sound ground and, you know, like, kind of ground and exciting or in court. And it's really because I want to point out, we need to question every moment when we make a decision around how we're going to design security or governance or modeling off the data, we need to reflect and go back and say, um, I applying some of my cognitive biases around how I have worked for the last 40 years, I have seen it work. Or do I do I really need to question. And we do need to question the way we have applied governance. I think at the end of the day, the rule of the data governance and objective remains the same. I mean, we all want quality data accessible to a diverse set of users. And these users now have different personas, like David, Personal data, analyst data, scientists, data application, Um, you know, user, very diverse personal. So at the end of the day, we want quality data accessible to them, um, trustworthy in in an easy consumable way. Um, however, how we get there looks very different in as you mentioned that the governance model in the old world has been very commander control, very centralized. Um, you know, they were responsible for quality. They were responsible for certification off the data, you know, applying making sure the data complies. But also such regulations Make sure you know, data gets discovered and made available in the world of the data mesh. Really. The job of the data governance as a function becomes finding that equilibrium between what decisions need to be um, you know, made and enforced globally. And what decisions need to be made locally so that we can have an interoperable measure. If data sets that can move fast and can change fast like it's really about instead of hardest, you know, kind of putting the putting those systems in a straitjacket of being constant and don't change, embrace, change and continuous change of landscape because that's that's just the reality we can't escape. So the role of governance really the governance model called Federated and Computational. And by that I mean, um, every domain needs to have a representative in the governance team. So the role of the data or domain data product owner who really were understand the data that domain really well but also wears that hacks of a product owner. It is an important role that had has to have a representation in the governance. So it's a federation off domains coming together, plus the SMEs and people have, you know, subject matter. Experts who understands the regulations in that environmental understands the data security concerns, but instead off trying to enforce and do this as a central team. They make decisions as what need to be standardized, what need to be enforced. And let's push that into that computational E and in an automated fashion into the into the camp platform itself. For example, instead of trying to do that, you know, be part of the data quality pipeline and inject ourselves as people in that process, let's actually, as a group, define what constitutes quality, like, how do we measure quality? And then let's automate that and let Z codify that into the platform so that every native products will have a C I City pipeline on as part of that pipeline. Those quality metrics gets validated and every day to product needs to publish those SLOC or service level objectives. So you know, whatever we choose as a measure of quality, maybe it's the, you know, the integrity of the data, the delay in the data, the liveliness of it, whatever the are the decisions that you're making, let's codify that. So it's, um, it's really, um, the role of the governance. The objectives of the governance team tried to satisfies the same, but how they do it. It is very, very different. I wrote a new article recently trying to explain the logical architecture that would emerge from applying these principles. And I put a kind of light table to compare and contrast the roll off the You know how we do governance today versus how we will do it differently to just give people a flavor of what does it mean to embrace the centralization? And what does it mean to embrace change and continuous change? Eso hopefully that that that could be helpful. >>Yes, very so many questions I haven't but the point you make it to data quality. Sometimes I feel like quality is the end game. Where is the end game? Should be how fast you could go from idea to monetization with the data service. What happens again? You sort of address this, but what happens to the underlying infrastructure? I mean, spinning a PC to S and S three buckets and my pie torches and tensor flows. And where does that that lives in the business? And who's responsible for that? >>Yeah, that's I'm glad you're asking this question. Maybe because, um, I truly believe we need to re imagine that world. I think there are many pieces that we can use Aziz utilities on foundational pieces, but I but I can see for myself a 5 to 7 year roadmap of building this new tooling. I think, in terms of the ownership, the question around ownership, if that would remains with the platform team, but and perhaps the domain agnostic, technology focused team right that there are providing instead of products themselves. And but the products are the users off those products are data product developers, right? Data domain teams that now have really high expectations in terms of low friction in terms of lead time to create a new data product. Eso We need a new set off tooling, and I think with the language needs to shift from, You know, I need a storage buckets. So I need a storage account. So I need a cluster to run my, you know, spark jobs, too. Here's the declaration of my data products. This is where the data for it will come from. This is the data that I want to serve. These are the policies that I need toe apply in terms of perhaps encryption or access control. Um, go make it happen. Platform, go provision, Everything that I mean so that as a data product developer. All I can focus on is the data itself, representation of semantic and representation of the syntax. And make sure that data meets the quality that I have that I have to assure and it's available. The rest of provisioning of everything that sits underneath will have to get taken care of by the platform. And that's what I mean by requires a re imagination and in fact, Andi, there will be a data platform team, the data platform teams that we set up for our clients. In fact, themselves have a favorite of complexity. Internally, they divide into multiple teams multiple planes, eso there would be a plane, as in a group of capabilities that satisfied that data product developer experience, there would be a set of capabilities that deal with those need a greatly underlying utilities. I call it at this point, utilities, because to me that the level of abstraction of the platform is to go higher than where it is. So what we call platform today are a set of utilities will be continuing to using will be continuing to using object storage, will continue using relation of databases and so on so there will be a plane and a group of people responsible for that. There will be a group of people responsible for capabilities that you know enable the mesh level functionality, for example, be able to correlate and connects. And query data from multiple knows. That's a measure level capability to be able to discover and explore the measure data products as a measure of capability. So it would be set of teams as part of platforms with a strong again platform product thinking embedded and product ownership embedded into that. To satisfy the experience of this now business oriented domain data team teams s way have a lot of work to do. >>I could go on. Unfortunately, we're out of time. But I guess my first I want to tell people there's two pieces that you put out so far. One is, uh, how to move beyond a monolithic data lake to a distributed data mesh. You guys should read that in a data mesh principles and logical architectures kind of part two. I guess my last question in the very limited time we have is our organization is ready for this. >>E think the desire is there I've bean overwhelmed with number off large and medium and small and private and public governments and federal, you know, organizations that reached out to us globally. I mean, it's not This is this is a global movement and I'm humbled by the response of the industry. I think they're the desire is there. The pains are really people acknowledge that something needs to change. Here s so that's the first step. I think that awareness isa spreading organizations. They're more and more becoming aware. In fact, many technology providers are reach out to us asking what you know, what shall we do? Because our clients are asking us, You know, people are already asking We need the data vision. We need the tooling to support. It s oh, that awareness is there In terms of the first step of being ready, However, the ingredients of a successful transformation requires top down and bottom up support. So it requires, you know, support from Chief Data Analytics officers or above the most successful clients that we have with data. Make sure the ones that you know the CEOs have made a statement that, you know, we want to change the experience of every single customer using data and we're going to do, we're going to commit to this. So the investment and support, you know, exists from top to all layers. The engineers are excited that maybe perhaps the traditional data teams are open to change. So there are a lot of ingredients. Substance to transformation is to come together. Um, are we really ready for it? I think I think the pioneers, perhaps the innovators. If you think about that innovation, careful. My doctors, probably pioneers and innovators and leaders. Doctors are making making move towards it. And hopefully, as the technology becomes more available, organizations that are less or in, you know, engineering oriented, they don't have the capability in house today, but they can buy it. They would come next. Maybe those are not the ones who aren't quite ready for it because the technology is not readily available. Requires, you know, internal investment today. >>I think you're right on. I think the leaders are gonna lead in hard, and they're gonna show us the path over the next several years. And I think the the end of this decade is gonna be defined a lot differently than the beginning. Jammeh. Thanks so much for coming in. The Cuban. Participate in the >>program. Pleasure head. >>Alright, Keep it right. Everybody went back right after this short break.