From around the globe, it's theCUBE, presenting Cube on Cloud, brought to you by SiliconANGLE. In 2009, 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 Cloudera in New York City. Jeff Hammabacher famously declared to me and John Furrier in theCUBE that the best minds of his generation were trying to figure out how to get people to click on ads.  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 was only 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 more and more 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 more complexity. And as we've reported, we believe what's needed is a complete bit flip in how we approach data architectures. Our next guest is Jamak Dekhani who is the director of emerging technologies at ThoughtWorks. Jamak is a software engineer, architect, thought leader and advisor 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 as a primary criterion in how we organize so-called big data teams and platforms. Jamak, welcome to theCUBE. It's a pleasure to have you on the program. Hi, David, it's wonderful to be here. Well, okay, so you're pretty outspoken about the need for a paradigm shift and how we manage our data and our platforms at 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 a summary of what happened since 2010. But 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 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 of our industry in general, since the birth of internet, we are actually moving towards decentralization. If we think today, like if let's move data aside, if we said the only way web would work, the only way we get access to various applications on the web or pages is to centralize it, we would laugh at that idea. But for some reason, we don't question that when it comes to data. 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, 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, they're beyond the bounds of organization. And then look back and say, okay, if that's the trend of our industry in general, given the fabric of computation and data that we're putting 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 put a technology in place to look at it from a decentralized angle. Okay, so 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 want to bring up, I love your diagrams, you have a simple diagram guys, if you could bring up a 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've got to do the quality thing and then serve them up to the business. So what's wrong with that picture that we just described? And granted, it's a simplified form. Yeah, quite a few things. So, and I would flip the question maybe back to you or the audience. If we said that there are so many sources of the data and actually the data comes from systems and from teams that are very diverse in terms of domains, right? A domain, if you just think about, I don't know, retail, the e-commerce versus order management versus customer, these are very diverse domains. The data comes from many different diverse domains and then we expect to put them under the control of a centralized team, a centralized system. And I know that centralization probably if you zoom out, it's centralized. If you zoom in, it's compartmentalized based on functions that we can talk about that. And we assume that the centralized model will be getting that data, making sense of it, cleansing and transforming it then to satisfy a need of very diverse set of consumers without really understanding the domains because the teams responsible for it are not close to the source of the data. So there is a bit of a cognitive gap and domain understanding gap without really understanding at the how the data is going to be used. I've talked to numerous, when we came up with the idea, I talked to a lot of data teams globally just to see 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 lake, they didn't know how the data was being used. But yet they're responsible for making the data available for this diverse set of use cases. So a centralized system, a monolithic system, often is a bottleneck. So what you find is that a lot of the teams are struggling with satisfying the needs of the consumers. They're struggling with really understanding the data. The domain knowledge is lost. There is a loss of understanding and kind of in that transformation. Often, we end up training machine learning models on data that is not really representative of the reality of the business. And then we put them to production and they don't work because the semantic and the syntax of the data gets lost within that translation. So, and we are struggling with finding people to manage a centralized system because still the technology is fairly, in my opinion, fairly low level and exposes the users of those technologies such as lake, state warehouse, a lot of complexity. So in summary, I think it's a bottleneck is not going to satisfy the pace of change and pace of innovation and the pace of availability of sources. It's disconnected and fragmented, even though this centralized is disconnected and fragmented from where the data comes from and where the data gets used. And it's managed by a team of hyper-specialized people that are struggling to understand the actual value of the data, the actual format of the data. So it's not going to get us where our aspirations or ambitions need to be. Yeah, so the big data platform is essentially, I think you call it context agnostic. And so as data becomes more important in our lives, you got all these new data sources injected into the system. Experimentation, as we said, with the cloud becomes much, much easier. So one of the blockers that you've cited and you just mentioned it is you got these hyper-specialized roles, the data engineer, the quality engineer, data scientist. And it's illusory. I mean, it's like an illusion. These guys are, they seemingly they're independent and can scale independently. But I think you've made the point that in fact they can't, that a change in a data source has an effect across the entire data lifecycle, entire data pipeline. So 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 data mesh came from a series of requests that we received from both large scale and progressive clients and progressive in terms of their investment in data architecture. So these were clients that they were, they were larger scale. They had diverse and rich set of domains. Some of them were big technology, tech companies. Some of them were big retail companies, big healthcare companies. So they had that diversity of the data and the number of the sources of the domains. They had invested for quite a few years in generations if they had multi-generations of proprietary data warehouses on-prem that they were moving to cloud. They had moved to the various revisions of the Hadoop clusters and they were moving to that cloud. And the challenges that they were facing were simply, they were not like, if I wanna just like simplify it in one phrase they were not getting value from the data that they were collecting. They were continuously struggling to shift the culture because there was so much friction between all of these three phases of both consumption of the data, then 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 it's 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. It's gone through transformation but people that didn't understand really what the data was got delayed. And so there's 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 struggling. So we often, our solutions like we are technologies, we often point at the technology. So we go, okay, this version of some proprietary data warehouse we're using is not the right thing. We should go to the cloud and that certainly will solve our problem, right? Or warehouse wasn't the good one. Let's make a lake version. So instead of extracting and then transforming and loading into the database and that transformation is a heavy process because you fundamentally made an assumption using warehouse is that if I transform this data into this multi-dimensional, perfectly designed schema that then everybody can run whatever query they want that's gonna solve everybody's problem. But in reality, it doesn't because you are delayed and there is no universal model that serves everybody's need. Everybody needs a diverse data scientist necessarily don't like the perfectly modeled data. They're looking for both signals and the noise. So then, we've just gone from ETLs to let's say now to Lake, which is, okay, let's move the transformation to the last mile. Let's just get load the data into the object stores and into semi-structured files and get the data scientists use it but they're still struggling because the problems that we mentioned. So then with the solution, what is the solution? Well, next generation data platform is put it on the cloud and we saw clients that actually had gone through a year or multiple years of migration to the cloud but it was great, 18 months. So I've seen nine months migrations of the warehouse versus two year migrations of the various data sources to the cloud but ultimately the result is the same unsatisfied, frustrated data users, data providers with lack of ability to innovate quickly on relevant data and have an experience that they deserve to have have a delightful experience of discovering and exploring data that they trust and all of that was still amiss. So 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 pointed out, you made the other observation that look, we've made our operational systems context aware but our data platforms are not and like CRM systems, sales guys are 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, they're now first class citizens in the data flow and they're injecting by design domain knowledge into the system. So I want to put up another one of your charts guys bring up the figure two there. It talks about, you know, convergence. You show data distributed domain driven architecture, this self-serve platform design and this notion of product thinking. So maybe you could explain why this approach is so desirable in your view. Sure. The motivation and inspirations 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 microservices with monolithic systems, monolithic systems where, you know, the bottleneck, the changes we needed to make was always, you know, orthogonal to how the architecture was centralized. And we found a nice niche. And I'm not saying this is the perfect way of decoupling your monolith, but it's a way that currently where we are in our journey to become data driven is a nice place to be, which is distribution or decomposition of your system as well as organization. I think whenever we talk about systems, we've got to talk about people and teams that responsible for managing those systems. So the decomposition of the systems and the teams and 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 good thing. And what does that do really for us? What it does is it localizes change to the bounded context of that business. It creates clear boundary and interfaces and contracts between the rest of the universe of the organization and that particular team. So removes the friction that often we have for both managing the change and both serving data or capability. So the first principle of data mesh is let's decouple this world of analytical data the same to mirror the same way we have decoupled our systems and teams and business. Why data is any different? And the moment you do that, so the moment you bring the ownership to people who understands the data best, then you get questioned that, well, how is that any different from silos of disconnected 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. And the second principle as well, we have to expect a certain level of 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 share and let's put accountability around it. We need a new set of incentives and metrics for domain teams to share the data. We need to have a new set of 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. So then the second principle is okay, the teams now that are responsible, the domain teams responsible for their analytical data need to provide that data with a certain level of quality and assurance. Let's call that a product and bring product thinking to that. And then the next question you get asked by CIOs or CTOs the people who build the infrastructure and spend the money, they said, well, it's actually quite complex to manage big data. Now, we want everybody, every independent team to manage the full stack of storage and computation and pipelines and access control and all of that. And that's, well, we've solved that problem in operational world and that requires really a new level of platform thinking to 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 a ton of complexity that unnecessarily people get exposed to. And that's the third principle of creating self-serve infrastructure to allow autonomous teams to build their domains. But then the last pillar, the last fundamental pillar is, okay, once you distribute a problem into smaller problems then you found yourself with another set of problems which is how I'm gonna connect this data, how I'm gonna, that the insights happens and emerges from the interconnection of the data domains, right? It's just 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 computationally automate a lot of governance decisions that's and security decisions and policy decisions that applies to this fabric of mesh, not just a single domain or not in a centralized mode. So really, as you mentioned, the most important component of the data machine is 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 Jomak just described. Bring up figure three if you would. So I mean, 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 as a platform down below. And you know what I love about this, Jomak, is it to me, it underscores the data is not the new oil because I can put oil on my car, I can put it 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 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 mesh as a paradigm shift. And it's not just to make it sound ground like kind of ground and exciting or important. It's really because I want to point out, we need to question every moment when we make a decision around how we're gonna design security or governance or modeling of the data, we need to reflect and go back and say, am I applying some of my cognitive biases around how I have worked for the last 40 years or have seen it work? Or 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 role of the data governance and the objective remains the same. I mean, we all want quality data accessible to a diverse set of users.  So at the end of the day, we want quality data accessible to them trustworthy in an easy consumable way. However, how we get there looks very different in, as you mentioned that the governance model in the old world has been very command and control, very centralized. You know, they were responsible for quality, they were responsible for certification of the data, you know, applying, making sure the data complies with all sorts of 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 the equilibrium between what decisions need to be, you know, made and enforced globally and what decisions need to be made locally so that we can have an interoperable mesh of data sets that can move fast and can change fast. Like it's really about instead of hardest, you know, kind of putting the systems in a straight jacket of being constant and don't change, embrace change and continuous change of landscape because that's just the reality we can't escape. So the role of governance really, the governance model are called federated and computational and by that, I mean every domain needs to have a representative in the governance team. So the role of the data or domain data product owner who really understands the data of that domain really well but also wears a hat. So if a product owner is an important role that has to have a representation in the governance team. So it's a federation of domains coming together plus the SMEs and people have, you know, subject matter experts who understands the regulations in that environment, who understands the data security concerns but instead of 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 computationally and in an automated fashion into the path from itself. For example, instead of trying to 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's codify that into the platform so that every data product will have a CI CD pipeline. And as part of that pipeline, those quality metrics gets validated and every data product needs to publish those SLOs or service level objectives so, you know, whatever that 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 are the decisions that you're making, let's codify that. So it's really the role of the governance, the objectives of the governance team try to satisfy is the same but how they do it, it's 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 a light table to compare and contrast the role of the, you know, how we do governance today versus how we would do it differently to just give people a flavor of what does it mean to embrace decentralization and what does it mean to embrace change and continuous change? So hopefully that could be helpful. Yes, very, so many questions I have, but the point you make it to on data quality, sometimes I feel like quality is the end game whereas the end game should be how fast you can go from idea to monetization with a data service. What happens again, you sort of addressed this, but what happens to the underlying infrastructure? I mean, spinning up EC2s and S3 buckets and my pie torches and TensorFlows and where does that lives in the business and who's responsible for that? Yeah, that's, I'm glad you're asking this question, because I truly believe we need to reimagine that world. I think there are many pieces that we can use as utilities and foundational pieces, but I can see for myself a five to seven year roadmap of building this new tooling. I think in terms of the ownership, the question around ownership, that would remains with the platform team, but perhaps a domain agnostic technology-focused team, that they are providing a set of products themselves, but the users of 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 products. So we need a new set of tooling and I think the language needs to shift from, I need a storage bucket or I need a storage account or I need a cluster to run my Spark jobs to, 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 to apply in terms of perhaps encryption or access control. Go make it happen platform. Go provision everything that I need 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 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, and there will be a data platform team. The data platform teams that we set up for our clients, in fact, themselves have a fair bit of complexity internally. They divide into multiple teams, multiple planes. So there would be a plane as in a group of capabilities that satisfy that data product developer experience. There would be a set of capabilities that deal with those nitty-gritty underlying utilities. I call them at this point utilities because to me that the level of abstraction of the platform needs to go higher than where it is. So what we call platform today are a set of utilities we'll be continuing to using. We'll be continuing to using object storage. We'll continue using relational 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 enable the mesh level functionality, for example, be able to correlate and connect and query data from multiple nodes that's a mesh level capability to be able to discover and explore the mesh of data products as a mesh level capability. So it would be a set of teams as product platforms with a strong platform, product thinking embedded and product ownership embedded into that to satisfy the experience of these now business oriented domain data teams. So we have a lot of work to do. I could go on, unfortunately we're out of time but I guess my, first of all I want to tell people there's two pieces that you've put out so far. One is how to move beyond a monolithic data lake to a distributed data mesh. You guys should read that and the data mesh principles and logical architecture is kind of part two. I guess my last question in the very limited time we have is are organizations ready for this? We, I think the desire is there. I've been overwhelmed with the number of large and medium and small and private and public and governments and federal organizations that reach out to us globally. I mean, it's not, this is a global movement and I'm humbled by the response of the industry. I think the desire is there. The pains are real. People acknowledge that something needs to change here. So that's the first step. I think that awareness is spreading. Organizations are more and more becoming aware. In fact, many technology providers are reaching out to us asking, watch how we do because our clients are asking us. People are already asking, we need a data mesh and we need a tooling to support it. So the 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 support from chief data analytics officers or above the most successful clients that we have with data mesh are the ones that, the CEOs have made a statement that 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 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 that the transformation needs to come together. Are we really ready for it? I think the pioneers, perhaps the innovators, if you think about that innovation, careful adopters, probably pioneers and innovators and lead adopters are making move towards it and hopefully as the technology becomes more available, organizations that are less 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 are quite ready for it because the technology is not readily available and requires internal investment today. I think you're right on. I think the leaders are going to lean in hard and they're going to show us the path over the next several years. And I think the end of this decade is going to be defined a lot differently than the beginning. Jamak, thanks so much for coming on theCUBE and participating in the program. Thank you for having seen it. It's been wonderful. All right, keep it right there. We'll be back right after this short break.


The Doctrine of Agile Combat Employment presents one of the greatest challenges to today's Air Force. It demands of us to re-evaluate our operational norms that have been in place for decades to meet the threats of the future. According to General Brown and his action orders to accelerate change or lose, we need to identify systems and programs that are outdated and or unaffordable to make way for capabilities that will make us competitive in the future high-end fight. One such system has been how combat communication infrastructure has been employed to support operations around the globe. For years, the standard method has been to use a five-person team and several hundred pounds of equipment to set up a communication site wherever we went. Beyond just the weight, this equipment was time-consuming to set up and could barely support the needs of our airmen on the ground. It used hardware that has been in use since the days of the first iPhones and the result has been a communication network that has become infamously slow and has made the simple task of sending information a hair-pulling affair. Enter the GOCOM Kit, developed by Innovation Airmen from Dias Air Force Base. This kit provides an agile communication infrastructure using the fastest mesh network technology. Combined with emerging capabilities of companies like Starlink, this 20-pound kit the size of a carry-on bag is able to meet the demands of a 21st century Air Force. At less than $1,500, it is a fraction of the cost of other solutions and has already proven its ability to enable our airmen to take the fight to our adversaries no matter the location. Able to be set up in under 20 minutes by a single user, the kit is already in use overseas supporting B1 operations, providing speeds of up to 217 megabits per second to as many as 50 devices simultaneously. By using commercial off-the-shelf technology selected by our comm Airmen, we are able to easily and rapidly change out components of the kit as technology continues to advance. This kit is able to be easily shared with other bases and afford each unit the flexibility they need to meet their own unique demands. Flexibility, ease of use, and the fastest speeds available all in the size of a small backpack. The GO-COM kit establishes what the new normal of combat communications should be.


It's time for the Lone Gene Chronoscope, a television journal of the important issues of the hour brought to you every Monday, Wednesday, and Friday, a presentation of the Lone Gene Wittner Watch Company, maker of Lone Gene, the world's most honored watch, and Wittner, distinguished companion to the world-honored Lone Gene. Good evening. This is Frank Knight. May I introduce our co-editors for this edition of the Lone Gene Chronoscope? Edward P. Morgan and Larry Lusser, both of the CBS television news staff. Our distinguished guest for this evening is the Honorable Dr. Carlos Evia, well-known Cuban statesman. Senior Dr. Evia, I think it's only fair to warn you, sir, that Larry Lusser and I are going to feel free to ask you a variety of questions, not only about Cuba, but Latin America in general, because we discover not only have you held high office in the Cuban government from provisional president on down, but that you were a graduate of our Naval Academy at Annapolis, so that you have a sympathy for the and an experience with the American, the North American point of view. Harvard must be said, I suppose, that we North Americans can become very careless in our relationships and our attention to the problems and other matters of concern of our neighbors, and it's probably not an exaggeration to state that we often think of Cuba as the home of the Rumba, the Conga, and Ernest Hemingway, and perhaps, of course, sugar, and not much more. We learn, however, very very recently that the situation as regards politics and economics is intense, to say the least. Will you comment on that for us now, and give us a description of your version of the situation? I'd be very glad to. The situation in Cuba today is that of a dictatorship. And it follows the cycle when the evil that is produced when a democratic process is destroyed. The Cuban Constitution was suspended, Congress dissolved, and instead of the constitutional government, a dictatorship was established. The situation in Cuba today is that every newspaper and every radio has censored. A law has been passed that is like a public order and creates crimes under which even once the censors are taken from the newspaper that would be a mockery of the freedom of the press. And in defense of the Cuban newspaper men, I'm glad to say that they have not accepted this censorship without a protest, and that today most important papers do not publish editorials and columnists are not writing. They're just publishing wires and news as a protest on that situation in Cuba. There are more than 600 people in prison. There is a great movement to recover freedom of Cuba. That creates that the dictator has to increase the repressive measures. That also affects the economy of the country because it's impossible to establish and develop an economy unless you have a fundamental law. That's what is called the Constitution of a country. Well, now you're getting in the economy of the country in any country. You're getting down to the touchy, the sensitive situation, the pocketbook. Perhaps we can take that as our point of departure. Why should Americans be interested in Cuba from that point of view? Well, from that point of view, Cuba is the second largest dollar market for American exporters. Today, Cuba in 1951 bought about $550 million from the United States. In 1952, after this garrison revolt, it began to go down, even though the sugar was larger, the sugar crop. And in 1953, the first four months indicated a drop of 30% in the Cuban's ability to buy, mainly due to this strained economic situation produced by the lack of guarantees for everybody. Well, Dr. Evilla, if the dictatorship is actually hurting Cuba's economic situation, how would you say Cuba could get rid of this dictatorship? Would it take another coup d'etat to do it? Well, Cuba can only become a stable country again if we put back or establish again the Cuban Constitution. This is not a question of personalities, but a question of principles. And everybody, those who live in Cuba, the 6 million Cubans and the Americans who sell products in Cuba, or the Americans who do business in Cuba, have the same common interest in having a peaceful, democratic, stable country. Now, the only way to obtain that is to bring back into force the Cuban Constitution. Now, the overpowering population of Cuba, the whole will of the people, is to bring back that Constitution. Whether that will be by coup d'etat, by a revolt, by the dictator fleeing the country, by any other thing, I do not know, but I am quite sure that everybody in Cuba is doing their utmost to put in force the Cuban Constitution. Well, with the decline in trade in Cuba and from your vantage point in Miami as an exile, do you see any tendencies towards anti-Americanism coming out of the island? I don't think Cuba has always been very friendly to the United States, because there are many historical links. The United States and the Cubans have always fought together. We, as independents, have fought three wars, are a war of independence that you call that the Spanish-American War, that you have the link with all of the explosion of the main, General Garcia, the famous message to Garcia and every body studies here. Then we were your allies in the First World War and the Second World War. Cuba do not have a feeling against the United States and much less against the American people. Well, Dr. Evia, we should pin things down a little bit more specifically, perhaps. We've been talking about the present regime in Cuba and obviously we're referring you in terms of dictatorship to the regime of General Batista, who retook the government by coup d'etat and when was it, March of last year? March 1952. Now, rightly or wrongly, I suspect that many Americans think of Batista as the anonymous army clerk who, after the Machado dictatorship in 33, suddenly came to power and there may be a certain sentimental attachment to the man. You have your opinions on the other side. What would you say was his motive? What is he driving for? Well, I don't know at this moment what his motive could be. I think when he took the government in 1952 by a garrison revolt in the middle of the night, you see, we were 80 days from one election. Batista was a senator and a presidential candidate. There were three candidates in the field. I was one of them. I was just going to say you were a candidate. I was a candidate for the president. Another one, the second candidate was Dr. Agra Monti and Batista was the third candidate. He didn't have a chance to win at all and he was a senator. Now, the same week that the political convention nominated the candidates, this revolt took place. Now, in my opinion, he did it because he couldn't win in a democratic way. Now, today, his position is that he has everybody against him and he is forced to repress and to apply violence every day more. Now, that's the cycle of tyranny. It's a vicious cycle. Dr. Agra, your favorite trait of military dictators is to raise the threat of communism. Do you think there is a threat of communism in Cuba now? Well, I think the threat of communism is produced by the dictator himself because when you take the liberties of the people and create a bad economic situation, that's a breathing ground for communism. On the other hand, the communist party had dropped down between 1948 and 1951 from 150,000 members to 55,000 members. That shows that the atmosphere of democracy, of full liberty is asphyxiating to the communist. Now, that doesn't mean that all those people probably didn't want to be communist anymore. They probably went into other parties. As they did, they infiltrated greatly Batista's party and through him, they are in the government. Dr. Evia, in a contentious and possibly explosive situation of this kind, we must do our very best to be candid. Washington, from all the reports that we can get, doesn't betray any particular anxiety, outwardly at least the State Department or the Eisenhower administration about the Batista regime, although the New York Times recently made a very sharp editorial against General Batista. On the contrary, there is information that Washington officially is concerned about the possibility of Cubans against Batista trying to buy arms. Does that mean that getting back to Larry's question that there is likely to be an attempted insurrection or something of that kind? Well, there's no question about that. There will be attempts of insurrection in Cuba as long as the Cubans are not free. Quickly, a final question and broadening it out a bit. You've had a great deal of experience with Latin American-American relations. What would be your recipe for better relations between Washington and Latin America? I think as a Cuban, I'm going to speak is what I know. I think it is necessary to understand that a Latin America or a Cuban is a human being who wants to be free and to increase his standard of living. But both things have to be together, to understand the normal aspirations to live in a democratic and free way, and the normal aspirations to increase their well-being. Both things have to be taken together. Thank you very much, Dr. Avio. The opinions that you've heard our speakers express tonight have been entirely their own. The editorial board for this edition of the Long Jean Chronoscope was Edward P. Morgan and Larry LaServe, both of the CBS television news star. Our distinguished guest was the Honorable Dr. Carlos Evia, well-known Cuban statesman. This year again, as four years past, the official watch for timing the great American air classic, the Cleveland Air Races, is Long Jean, the world's most honored watch. Now, the Long Jean watch is employed for timing these air races conformed to the exacting standards of accuracy specified by the National Aeronautic Association for Timing Aviation World Records. Now, few watches in the world can meet these standards. And owners of Long Jean watches may take pride in the fact that Long Jean watches are official for timing world's records for sports and contest associations the world over, including the National Aeronautic Association, the American Power Bolt Association, the Contest Board of the American Automobile Association, and many others. Now, every Long Jean watch, whether for the official timing of world's records or for the daily service of the discriminating man or woman, is made to the self-imposed Long Jean standards of excellence, the standards which have won for Long Jean, 10 World Fair Grand Prizes, 28 Gold Medals, and highest official honors for accuracy from the great government observatories. So, when next you buy a watch, either for yourself or as an important gift, remember these facts. And remember, too, that although it is one of the very finest watches in all the world, you can buy a Long Jean watch for as little as 7150. 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we can come or come to this now like you know classification of these different subsystems that are normally there. So, we have seen this gross positioning that is happening there ok. So, this is a gross tracking that happens, but that is not sufficient ok. So, if you see the positioning accuracy that we had talked about was about 100 nanometer and you can imagine like you know no stepper motor drive can produce that kind of a high precision positioning ok. Whatever like you know fine micro stepping that you will think it can have simply because of this mechanical ok maybe you think about what prevents like know the positioning to happen so, finally. See like know even if you have very fine positioning sensors available would you be able to position like know by using this kind of a stepper motor the system the head or the laser spot on the surface of CD within the accuracy of 100 nanometers ok that is certainly not possible ok. So, so what we do ok so, then there must be some kind of a fine tracking or some kind of a fine motion system that is that will be there inside the CD. And as we open up then we will get more on to that we will observe that ok. So, we should look for now what is the fine like know fine tracking or fine positioning system there. Then there needs to be something needs to be getting like the laser see the CD surface may have some undulations ok small undulations in the range of few microns or you may put like know some kind of a small little scratch on the surface of CD and still like it scratches almost in the microns kind of a dimension, but still that will be harmful for the data to be read on this from the surface. So, the surface may have some few microns undulations and then like know you need to kind of tightly focus the laser on to the surface from where the data is to be picked up. There must be some there must be some kind of a focusing servo or it is like auto focusing that is happening inside inside the CD. So, there must be some kind of a small fine motion that is given for focusing direction. Then we have seen this tray handling system, tray operation system and things like that ok. Now and of course, you need to have a CD rotation system here. So, this motor should kind of drive the CD to make sure that it rotates in some control kind of a fashion. So, let us explore little more now. Now here again under thought that ok so, this all these pauses are where like know you need to really pause the video and think about yourself do not rush through just this lecture. Otherwise like know you will miss out on things see this thought thinking and keep preparing your mind to get to understand is very very important part of the process which I am giving you the cues, but I mean if you follow them only then things will happen ok. So, please do that. So, now let us think of these two subsystems only, this cross positioning system that you already seen some part of it and then fine positioning system ok fine tracking ok. How can with this be possible? So, suppose we know now we need that fine kind of a tracking there. How do you do? Suppose you are supposed to do that, how would you think based on your knowledge so far what kind of a sensors actuators that you will use in this system ok. So, do some thinking you may say ok oh look this is very fine positioning which is required I may use piezo actuators which will give you fine positioning ok fine like know that can be one possibility. How would you kind of set up such kind of a drive? Piezos typically need very high voltage to operate ok. We have such a high voltage operation possibility ok. Any other kind of a actuator comes to your mind which can do this job ok like that you need to kind of think about ok what kind of a way we sense that ok what it is really on the track or not on the track. So, you say for example, if you put a sensor on the system side here is that sufficient to kind of get us know that ok it is on the track or not on the track those kind of things. Or how do you kind of get a feedback from the from the CD surface which can tell us that. So, so many many different kind of questions can come here ok. So, we should kind of you know based on whatever our knowledge think about these different kind of questions and lease them down or like think about ok what comes to your mind as a thought to give a solution to these different questions that are coming up ok. So, let us move on I mean after your thinking is done you can move on ok to see further now what what we open up and actually check out ok. So, so these questions you know we can have say sensors for the sensors some encoder possibility or you know we can use make use of the track on the surface of CD itself to see if we can use that as a sensor. So, actuation what kind of actuation you can have ok. So, you can have some CD round drives have actually this servo motor possibility servo motors they use as actuators. Some some others they use stepper motor as actuators the things like that can be possible for gross positioning. So, again I am flashing the same picture here. So, you can see this is from some some view from some other CD round drive and this is from like know the the currently open CD round drive that we are talking about. So, this is the same picture as we saw. So, do you see any sensors up here or positioning sensing ok. So, you see that the stepper motor typically does not have any sensors that are that are needed for sensing the position ok. So, you observe that there is only like only the shaft coming out of the sensor and there is no sensor up here. See typically the motor shaft we should need to have a sensor if at all the sensor is there in the system ok. So, either motor shaft or like know the slider moving like know back and forth needs to have some some slight position sensing possibility, but if you observe carefully that is that is not there in the system. So, there is no sensor in this in this gross positioning kind of a system only stepper motor and we rely on the steps of the stepper motor to make sure the position desired position is achieved and then of course actuator is this stepper motor along with this leads screw and this mechanism this is a way is is useful for preventing the backlash you heard this term backlash ok. See if I have only like know say for example, only one tip going into the into these slots here then depending upon like know this is some kind of a manufacturing tolerances and errors the slot size and my teeth size may not match perfectly ok. There will be some kind of a within some kind of tolerance accuracy few microns kind of accuracy it will be not matching that means like know that much kind of a back and forth motion I will have around in the slot ok. So, imagine like know you have a hole in which there is a rod which is smaller in size is pushed and you know you can move the rod in the linear direction any radial linear direction back and forth in the in the hole that much kind of a you know you will see that kind of a thing is is a backlash in the system ok. So, these clearances that are given typically for like know smooth motion happening like we will we will result into some kind of a backlash in the system ok. So, this backlash is some problem which in mechanical systems you know you think of gears will have a backlash you think of any kind of like know this motion transmission systems they will have some backlash that is going to be there. And now what what we do here is like know we are using these two stubs here ok which are which are like know pushed against two opposite surfaces of the screw ok that way and then the spring is making sure that ok that is pushed and that force is maintained and that takes care of this backlash that will not allow the backlash to come into into play ok. So, that is how like know this backlash is handled by using this kind of a spring loaded stuff here ok. We will see in other cases like know when you have a gear transmission system instead of screw transmission how do how you take care of like backlash ok. So, this is just to focus on the on this you know you have this little kind of a you know shock absorbing element here ok. This is another system like this has this if you see this has only two wires coming out of this motor and then there is a gear system this is like a bevel gear system and then there is this gear think much what is missing in this location A ok. So, think about that ok what is missing I have removed something from there what it should have been to kind of like know can think about so that the motion from the bevel gear is transmitted to this rack here and if you observe carefully this area you see the small little spring sitting there and this rack is having like know two parts the top rack and the bottom rack ok. They are exactly same pitch and everything, but they are in the two parts like know the top part and the bottom parts are separate there ok. So, what are they for? So, think about that this spring there and then top part and bottom part and what is there in this location A ok. Think about these issues this is another kind of a mechanism in which like know you see the same the spring loaded stuff for the cross positioning system. Now on the guide rails ok so you see this guiding system. So, this is like know I would say one view and this is opposite side view ok. So, this is a view from the opposite side than this side ok.  So, this is a lens of a CD and you observe like know these locations ok. These are the locations where the head is touching these rods ok. This is one on this side only one kind of a touch is there on this side there are two touches are there ok two places it is touching. Now carefully if you observe ok then you will find that this touch is happening here on all the sides as you see here. The rod is covered on all the sides by this hole ok and this hole also ok. But on this side there is only one support here and then that too the rod is not completely covering ok. The rod the support is only like you know covering half side ok that you cannot see here actually the cross section view actually will show you that ok maybe where somewhere we have this separately open mechanism though this removed from the rod kind of a thing we will be able to see that ok. But here you you presume that know you take my words that this is only like know up to half of a distance. Now you think why this is done in this way ok. Again I am not going to kind of give you answer right now I want you to kind of like know think about this question why this construction is happening this way only ok. Like that you need to have now you can see from the pictures only but if you have actual drive you can kind of like know see this many of such kind of observations and then like you know think about and come up with ok why that people have done it in that way ok. So, that is where like know the our like nice concepts are going to get developed ok. So, think about this issue and you see here in detail how this again the same thing I am saying like know this is a spring here and then there is two racks are like one on the top of each other ok. So, lot of things to think about. So, how do you kind of like know get this spring loaded rack done what is the purpose here that you think about. So, this is like know little larger view of the same thing you can see the spring very clearly here and then there is a top and bottom rack and then this top rack is like you know moving with respect to bottom rack in the direction of like this laser pointer in this you know direction of motion of the rack it is they can relatively move with respect to each other. Now let us think about the fine tracking ok what are the requirements what are the sensors what are the actuators and we can like know open up and talk about that ok. So, so sensor that is used here is some kind of a photo detector here and actuator is some small kind of a little voice coil actuator that is sitting up here. So, so we will see this you know as you open up this will be evident, but now we need to think about how we can create this kind of a closed loop system here so that you can have a fine tracking and fine focusing that is happening here. What kind of a sensors should be needed what kind of actuators will be needed for that thing to happen ok. So, maybe I will leave you with with a small kind of you know actual observations and then so we can remove the head from the guide rod and like know start observing what is there inside the head means like know the optical pickup head we will start looking at. So, if you see the head you will find is there is this block which is held in the held by these wires and you see these are the wires up here ok this is a block ok the block has the lens inside it. So, the lens moves up and down direction means this is a focusing kind of a direction ok this block can move up and down can this block get stretched front and back in this direction think about that can this block move sideways in this side. So, there are three directions it can move or it can tilt ok can this block tilt also think about that. So, tilting is possible or not or like know tilting of this block is possible in any direction twisting is possible. So, so you think about the six degrees of freedom any rigid body has and what are the degrees of freedom this block will be able to move into ok given the constraint that you know you have these wires which are holding this block here ok these wires are simply like know some kind of a you know small compliant kind of a wires as you operate them you see like know how so, they may not be stretchable, but they certainly are bendable ok. So, you can see these wires operating actually ok.  What is there in this black portion we will see we will we will think about ok. It is very interesting construction done for some purpose ok what is there inside the why this. So, it is the wire is not fixed here there is some small space and then wire is fixed inside this black part somewhere towards the end. So, we will see why why that is done in that way and if you push the lens down you can see that ok this wire is you can see this wire like know it goes like from here it goes and like know comes here. So, there are these three wires that are used in in parallel and they are doing they are getting bend when this block is pushed down and up ok. Now this pushing down and up action will happen not by by somebody moving it up and down, but there there are some kind of a coils you can see here and then there is some magnet that is there ok. So, the coils are suspended in the magnetic field and when the current is passed they will kind of start doing this kind of a small up and down or some kind of a sidewise motions ok and that is what is kind of important the fine positioning.  So, I would leave you right now at this point and then like really discuss things more. So, so you can see this these are coils up here and then there is there are these two magnets ok. So, so this these two little dots that you see are actually a glue to kind of secure that magnet in position or thing like that ok. So, this is the do not worry about this these two black spots are actually glue and then like know you have these coils the magnets are fixed and the coil and this this lens assembly is actually moving ok. So, I will leave you with this right now and think about now how do you kind of like know make sense of all these components together to really think about ok system which will have this kind of a nano positioning possible. So, we will stop here for for this lecture.


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