Hello and welcome. My name is Mark Horstman, Data Evangelist with Dataversity. We would like to thank you for joining the latest installment of the monthly Dataversity webinar series, Advanced Analytics with William McKnight. Today, William will be discussing choosing your provider for implementing a data fabric. Just a couple of points to get us started. Due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them via the Q&A section. If you would like to chat with us or with each other, we certainly encourage you to do so and just to note the Zoom chat defaults to send to just the panelists. But you may absolutely switch that to network with everyone to open the Q&A or the chat panel. You may find the icons for those features in the bottom middle of your screen. To answer the most commonly asked question, as always, we will send a follow-up email to all registrants within a couple of business days containing the links to the slides. And yes, we are recording and will also send a link out to the recording of this session, as well as any additional information requested throughout the webinar. Now, let me introduce to you our speaker for this series, William McKnight. William has advised many of the world's best known organizations his strategies form the information management plan for leading companies in numerous industries. He is a prolific author and popular keynote speaker and trainer. He has performed dozens of benchmarks on leading database, data lake, streaming, and data integration products. William is a leading global influencer in data warehousing and master data management. He leads McKnight consulting group, which has twice placed on the Inc. 5000 list. And with that, I will give the floor to William to get today's webinar started. Hello and welcome. Hello and thank you, Mark. I trust that my slide is being shown appropriately here. It sure is. Screen arrangement. Alrighty. Well, thank you. And welcome everybody for an important topic, one that is increasingly dominating, I guess, the buzz in data management circles these days. And that's data fabric. And I saw that this was going somewhere last year, around mid-year, when Microsoft was introducing Microsoft Fabric. And we'll get to that. But I thought it was going to go somewhere. And we have definitely seen this happen. So a lot of our clients are asking for data fabric. But I can guarantee every one of them has a different opinion on what that is. But they know they need it. And I think you need it too. But I think we need to know what we're, what we're needing here, what we're asking for, and what we're possibly getting. Now, I definitely lean into the products that are out there and available for your data fabric. And that's why I've titled this presentation, choosing your provider for implementing a data fabric. But indeed, data fabric is an architectural construct. And it is something that you can put together yourself. And we'll get to that. We'll get to the DIY approach versus a product approach. But interest in building these is very high and mostly for good reason. It enables real-time trustworthy data across multiple clouds, which we all have. And you can expedite the migration of analytics to the cloud, guarantee security, get that one panel of access into all of your data, accelerate governance, and quickly generate business value. I think it's definitely providing a lot of value out there. It takes a while to get there. I can't say anybody has a fully mature data fabric in place yet. It's a work in progress. And definitely these products that are data fabric products are works in progress, for sure. And the method for building a data fabric has prior to, I guess, mid last year, has been assumed to be a messy do-it-yourself blend of everything, right? Data warehouses, data lakes, data integration tools, data governance tools, et cetera. But some of these advancements have made this move to more streamlined and integrated solutions simplify the process. So, now, one more thing. In my study of data fabric for this presentation, I thought I knew data fabrics. I started studying for this, and I realized there's so much more to know. And the more I learned, the more I learned, I need to learn and learn about this. So just know that we're all in that boat when it comes to data fabrics. There's still going to be some sloppiness and some messiness around what this definition is. But we'll get there. Something related here, I thought I would share with you, is the publication of our Enterprise Contribution Ranking Report for Enterprise Data Integration. It's now published and available. We went through all the criteria that you see there for all the products that you see there. It's the most technical, competitive product of its kind in the market. And you can get it at that memorable URL you see at the top there from one of the companies that purchased 3-print rights. Now, first, let's talk about the data mesh. Why are we talking about the data mesh when we're here to talk about the data fabric? Well, it's because there's a lot of confusion out there about the differences. And I think they work together really well. And so you can definitely pull ideas from both of these into your data architecture. And you will not go wrong. But what is a data mesh? So a data mesh is really, okay, you know, I give it to you straight. I give it to you from a working definition standpoint, not really something out of maybe kind of analyst speak. So I'm going to say that I feel like it's an acknowledgement of the decentralized nature of our data environments these days. And that means that we have multiple of these things. We have multiple data warehouses, data lakes, data integration environments as shown here. And so this brings a little bit of, shall I say, science into the picture to help make sure that when we're doing that, there's there's still working together. There's still common pieces in play here that allow us to work together and get a cohesive whole going. A lot of the same objectives of a data fabric, by the way. So this helps with concepts like a customer being different across different parts of the company helps get it get onto a single page in regards to things like that. It allows for more adaptive governance that allows governance to have more leverage in the organization because there is connection points between all the various sub data architectures within the organization. This concept of a data mesh is not attached to a vendor. It's consultant led and driven. The mesh aims to decentralize every component based upon what I call business domains and what has now come to be called a data product, which is confusing to me because I thought my clients were building data products when they were building products based on their data that they then sold to the market. So it's not that. So let's put that definition aside because this definition of data product has taken over. So it might be departmental, but more likely and the science says to make it along the lines of a business domain, something like customer product, et cetera, et cetera. So where each is managed in their own architecture with shared data channels, shared components, et cetera. So you don't have to, you cannot, I will say, build a great data mesh or a data fabric without being pretty good at data warehousing and data laking, if that's a word. So with a lot of these things, as you can see the data warehouse is there. It's a component of the overall architecture as it will be for the data fabric as well. But sort of the assumption here as you look at this is there's a great data warehouse there. And if there's not a great data warehouse there, you're doing all this stuff around it and you probably are best, it's best to put your energies into fortifying that data warehouse. So you want to bring the data warehouse to a certain level of maturity before trying to make a mesh out of it, before trying to make a fabric out of it. Hopefully that goes without saying, but I wanted to say it because I think we're moving forward, hard charging into the future sometimes with these architectural constructs and trying to gloss over our problems. So these are complementary approaches, the data mesh and the data fabric. Some of the principles and benefits of the data mesh is domain ownership, ownership where it belongs. So these domains offer a bounded context where our team will own a domain within the organization. It's data as a product. I love that concept where data sets are exposed via APIs. There's a catalog over the top. And with federated governance, you have global standards of service levels and quality. There's lots of cell service inherent within a data mesh and it's frequently compared to microservices. It has that kind of a field to it, that kind of an idea, breaking a bigger thing into smaller pieces. And some of the principles are data democratization, get this data out to everybody that needs it within the organization, cost efficiencies, because you're really getting good at a few products. They just happen to be across the organization and you're reducing technical debt with the data mesh. But okay, that's the data mesh. Hopefully, I'm not confusing you because I'm switching to the data fabric here. And again, they work together really well. So what is the data fabric? Well, I don't know how to best graphically represent this. And I looked around and nobody seems to do anything more than what I've done here, which is there's your architecture and I threw something over the top. A piece of fabric, if you will, it's really kind of that. It's a fabric over the top of your existing architecture. Now hopefully, or quite possibly, the architecture is a data mesh behind the fabric. All right. So the fabric is that unified and integrated architectural pattern that provides a consistent and scalable approach to managing and accessing data across multiple sources and formats. But there are some commonalities that organizations desperately want to have across all of their data, like security, like one panel of access to all the data, different types of components of data governance. Okay, the fabric provides that. And so that is the great thing about fabric plus it has a lot of connectivity to many, many data sources that you have in your organization. So legs, warehouses, all sorts of operational sources as well. So fabric provides these common shared services, connectivity, and application portability. The fabric is all about using metadata to automate the management and governance of data. So more operate automation is made possible by providing a unified centralized platform for data management integration and governance. And driving these data quality rules and different types of data analysis throughout the organization, no matter where the data may be, is really a great thing. Now a lot of us know what data virtualization is. You might think of the fabric as data virtualization on steroids. So they're with a lot more componentry involved in the process. So data virtualization, to me, it's like a logical data fabric. And still important in the mix too, by the way. Now there's another term debated as to whether I should bring this up here or not. But it is kind of thrown about in context of the data fabric and can be a confusing point as well, as well. So much going on. And that's the data cloud. And so what is the relationship of the fabric to the cloud? The fabric can operate on top of a data cloud. Now, so what's the difference? What's the data cloud? So just like I said five minutes ago about, yeah, the architecture for that data warehouse better be good. Well, what is around that data warehouse? What other components are there to that data warehouse? Now to me, the data clouds, they seem like a play by various vendors to try to make sure that everything you need, you get from them for your data architecture. So you need a catalog. Yeah, we got it. Data integration. Data warehouse, of course. Data access. We got it here. We're bundling it in a cloud. And I'm calling that the data cloud. And I'm not disparaging it by the way. But that is something that works in conjunction with a data fabric. A data fabric is platform agnostic. It can be implemented to integrate data residing on multiple data clouds, on-premise data centers even, or even edge devices. So this gives you great flexibility and adaptability to specific organizational needs. The data fabric is the architectural blueprint for connecting and managing data across the diverse environment. The data cloud is a specific platform or services that provide data storage, processing, and analyst capabilities, leveraging cloud technologies. So I hope that helps. Now, let's look at the factors driving the need for data fabric. I don't need to go through these. It's everything that's driving us towards this idea that data is important to the organization. And it's becoming increasingly important. And I have seen this time and again over the past few years of organizations readily acknowledging now that it's all about their data. And it's all about data getting ready for AI and that sort of thing. And these are the factors driving the need for data fabric. Because a fabric helps you organize the architecture that you have and brings some commonalities to it that again, the organizations out there desperately need to have on it. So big data cloud computing, edge computing, they've all contributed to this increasing complexity of data management. So this is, some of this is not new. We're just naming it now. Data architecture that connects an organization's data sources and processing within a unified layer, allowing universal access through a single interface. That's the data fabric. It standardizes data to overcome differences in formatting, schema and file types, making it valuable for end users in various use cases. So what are the principles of a data fabric? Hopefully it's coming together a little bit. Data fabric is intelligent and automated. So it doesn't just sit there and connect to sources. It uses metadata and semantic models to understand what's going on in there. It's a step towards overall automation. It's going to allow for more automation down the road because it unifies disparate data systems. It embeds governance, strength and security and privacy, and provides more data accessibility to workers, particularly to business users. A data fabric can allow decision makers to view all data more cohesively to better understand, for example, a customer lifecycle. It counteracts the problem of data gravity, which is the idea that data becomes more difficult to move as it grows in sizes. It operates around the idea of loosely coupled data in platforms with the applications that need it. And we'll get into some of the vendors behind the data fabric here shortly. But the vendors have definitely picked up on this. The benefits of the fabric are integrated intelligence. Like I said, more than just here's your data. We know where it is now. Come and get it. It's doing things. Data democratization.  It improves data security because security policy can now be enforced across the board. It gives you universal access to all data sources. It standardizes the data formats because the data that I've seen in organizations are in so many different formats. But this is going to standardize the data that you see, that you get back from these various sources. Reduce complexity for end users, improve data integration and standardization. And it enables access to data from various sources across the organization. So one thing that I'll hone in on here is the data security, because it's going to have, give you better data protection, which is something that let's just say I'm asked about all the time these days. It's a huge risk to organizations that the wrong data gets in the wrong hands. The broadening of data access also doesn't mean compromising on data security and privacy. In fact, it means that more data governance guardrails are put in place around access controls, ensuring specific data is only available to certain roles. Some of these data fabric initiatives really come from data security, because this is the benefit that some organizations are glomming onto and saying that is what I need, and that is why you're getting into a data fabric. Now, there's probably a good 10 reasons why you might want to get into a data fabric, and you may not care about the others. Now, I suggest that you probably do care about all of them at some level, and you will care about all of them in due time. But if the data security aspect of a data fabric, for example, if that cuts the mustard on your ROI for the project, go for it. Go for a data fabric.  I am advocating a data fabric now. So some of the challenges, some of the big challenges are if you're masking a poor data architecture. That's always a bad idea. Now, the data fabric, I must admit, I must admit that your data maturity might be allowed to be a little bit lower if you're going to implement a fabric, but not so much lower that it can't still must still be good. Hope that made sense. So still be sure you have a good underlying data architecture before you turn it into a fabric. Some of the key features of a fabric, and hopefully this is starting to gel and come out for you, the connectivity across all data sources and processing. To me, this is usually a number one consideration in picking a data fabric. You want access to all data sources and processing that you have today, that you may have in the future, that you can think about, and that you may have in the future that you can think about. It's a unified layer with a single interface, accessible regardless of location, again on premises, on the edge, and the standardizes data formats for you. It's a unifying layer that connects all of an organization's data sources and processing within a single interface. It eliminates the need to deal with the complexities of different locations and formats, providing seamless access to all data regardless of its origin. So what are the components of a data fabric? Hopefully this is starting to gel, right? Data integration. Yeah, because you're going to be moving data with your data fabric, data governance, putting those security policies, putting those data access policies and everything else into the entirety of your data organization, data catalog, data virtualization. So all data is at your disposal. Data analytics and data security. These are the components of a data fabric. Is this what all of them provide? All of the so-called data fabric vendors? Pretty much yes at this level, absolutely. I will get into what you can expect to get where the most important or the most, I'd say, beneficial components of a commercial data fabric are and where some of the lesser important ones are as we go along here. The use cases for a data fabric are, heck, I hate to even create a list, but these are some of the common things I've seen, but there are so many more. And you can think of them, I'm sure, for your own organization. It's really a data architecture. It's a great data architecture. By integrating across various data sources, data scientists can create a holistic view of the customers, data analysts can create a holistic view of products and sales. The scope and scale that it can handle is big as it eliminates the field of data silos. So how about artificial intelligence? Everybody's getting into artificial intelligence. And so most new applications, at least that I can see, have a component of artificial intelligence in them for sure if they're not full-on AI projects. Data fabrics can help AI applications to be more accurate. By providing AI applications with access to a wider variety of data, the fabrics can help to ensure that the models are trained on better data, more representative data. Data fabrics can help AI applications to be more efficient. Data fabrics can help AI applications to be more scalable. All right, these are some of the things you want to take off the table when you step into AI. As a matter of fact, what I've seen over and over again throughout the years, and I'm sure you have as well, if you think about it, there's a lot of great ideas that executives and others get for an organization. But then they think about, well, let's look at that data hurdle that we have to get over to get there. And I don't know if I'm up for that challenge right now. So since there's a big old data hurdle out there, I think let's walk away from this great project that I can think of.  And in my view, data fabrics can really help out with that. So you can get down to business quicker. You can get these projects done quicker because it makes your data much more pliable for various uses within the organization. We've got to get away from this idea of I'm building this application one and done. I have no consideration for anything else and that sort of thing. And we must do this with a great architecture of which I think a fabric provides a great component of that. Data fabric in action integrates data from various sources, like snowflake, Kafka, Salesforce. It enables data reuse for various different use cases. I just mentioned that. And it reduces time and cost for data re-engineering. Fabrics enable the integration and analysis of data from diverse sources for various use cases. This allows for data reuse, improve decision making, and reduce costs associated with data re-engineering. Solving these data distribution challenges addresses the scattered data landscape. It solves the challenge of scattered data across various internal and external systems that enables organizations to access all their data from any location as if it were stored in one place, as Microsoft would say in one lake, and presented in a clear business friendly format. This is going to simplify data analysis and decision making across the organization. Now there are components of physical data fabric and logical data fabric in all data fabrics, okay? They're not all 100%. They don't have to all be 100% one or the other. As a matter of fact, ultimately you're going to have some logical data fabric in the mix. But if you can give that logical data fabric and assist with some physical data fabric components to it, that is going to be great. Now that's a decision that you have to make as you step into your architecture and you look and decide where are we going to place our energy? There's so many ways we can go with it. Data cloud, data mesh, data fabric, artificial intelligence. Should we consolidate? Should we not? There's which projects make sense and our data quality and our data security. What about all that? There's no end. Now data fabric can be implemented in either one of these ways, physically moving the data or virtually accessing it. Physical fabrics centralize data in a common environment, kind of like the data warehouse or the data lake idea. While logical fabrics access data wherever it resides. And again, you'll probably end up with some combination of this. Everything's ultimately physical, right? But who's to say you don't need to do some more consolidation of your data before you put the logical data fabric on it? Well, that's a decision that you have to make by looking at your situation, looking at your maturity and just I would say definitely doing some proof of concept with data fabric to see what kind of performance you're going to get and grow it out from there as always, right? So implementation considerations, your time and money investment in this and what value do you intend to realize? Now, it's not good enough for me to sit here and say you need a data fabric. It's a great data architecture for you. It has to make sense from a business standpoint. It has to improve applications. As a matter of fact, I can't sit here and say, well, your application is going to be improved for the next 10 years and that's good. That should win the day and make a general statement like that. I've already said what about what if you could have your data as a service to the rest of the organization? And they don't have to think about that data mountain that they have to get over in order to do a good business idea. I've already said that, but what if it wasn't there? Where would the value come from? Now, would you get deployments up and running quicker and save money in the deployment cycle? Sure. Yes, you would. And would getting projects up and out the door quicker mean that you start realizing value quicker? Absolutely. That's a good thing. But there's so much more to it. There's so many more business capabilities that you might enable. What you ultimately need to do is look at your next several applications that will use the data fabric and look at how they're going to benefit from the data fabric. And they might benefit because you can build them quicker and cheaper. Great. They might also benefit from just being able to be better, like predictive maintenance, for example, be better at what they're trying to do for the business. And wouldn't that be great? Yes. How much better? Well, this all gets into return on investment, but ultimately, you end up having to do something of that nature to step into this. Now, I hope the guy in the blue shirt is okay. I'm not sure what he's doing there. All right. Now, this is a little bit about how it does the things it does and some of the things that you want to start to look at in these data fabric tool sets, or if you're doing it yourself, what you'll need to build. You want automatic, more or less automatic data integrations based on artificial intelligence, based on metadata. Advanced data fabrics leverage AI and metadata to automate data integrations, enabling efficient and scalable deployments. If there's any big area of data management out there that is having cycles pulled out of it left and right because they're not needed anymore due to artificial intelligence, it's data integration. Data integration has now become like merging libraries, but the books speak different languages, so you just got to slot them into the right place. Semantic inferencing is helping tremendously. It untangles the data meaning, matches up that data smartly, cleans and fixes all the data bumps that you're going to have out there and enriching the data otherwise, and improving schema translation. Schemas are different and can lead to a lot of confusion doing it oneself. And so the data fabric helps with all of this. All data fabrics on the market are semantic driven, and that's an absolute must, and fortunately they are. Fortunately, I will say that the market has not flooded us with an overuse of data fabric yet, the term data fabric yet. While a data fabric could have a much more shallow meaning than what I'm presenting here, it can do one or two things, I suppose, and you could call it a data fabric. So far the market has resisted calling those things data fabric, and that's only good for us. However, as you, the buyer of such things, start to insist on data fabric. I want a data fabric. I need a data fabric. As that term starts picking up, and I think it will, we're going to start seeing more watered down data fabric usage out there. So beware, now's a good time to look at these products as we'll do now. Key considerations for choosing a provider. Now, before I get into the considerations, let's just look at the do-it-yourself approach to a data fabric. And I'm not here to say you must get a tool or you can't get the fabric. You certainly can do it yourself. As a matter of fact, we did. We did a benchmark recently where we did a do-it-yourself approach. We stitched together several pieces, which I'll get to, and did our own. And then we did enterprise things with that environment. We projected that out, the cost of that out a good few years. Did the same thing with a data fabric provider. And I can tell you, it was SAP DataSphere. We've come to know that product pretty well. But there are other products out there. And I'll carry on with what we found as we go along here. But while I'm mentioning a product, let me mention some of the others you may have heard of, Appian, Cambridge Semantics, Cinchi, Denoto has something Informatica, something K2View, I recall, Microsoft with its fabric. Let me get to that. Solix, Stardog. Not all of the magnificent seven, I can say it, have jumped into this fray yet. It's all a work in progress. And again, I think this is something that is going to pick up and the rest of them are going to jump in and we're going to be a wash in fabric here pretty soon. Like the Microsoft fabric. Let me take a minute on that because that's kind of the 800-pound gorilla in the room. I get asked about it all the time. We've been on fabric, Microsoft fabric, that is since probably the first month that it was in pre-release. So we've been using it. Come to know a little bit about it. First question I get is, is it a data fabric? Because they're not calling it one. They're calling it the Microsoft fabric. Well, they can call it whatever they want. They are Microsoft, by the way, but I'm saying it is very much like a data fabric and the day may come when they call it that. It's an end-to-end cloud-based software as a service solution for data and analytics offered obviously by Microsoft. The focus is on data integration, data engineering, data science, and business intelligence. Now the pricing for Microsoft fabric warrants its own webinar. So I don't want to get into it too much here because it's a deep pit, but it's priced by Azure stock keeping units. And I think we're all trying to figure it out at this point. I feel like this enhancement of Synapse plus the Lakehouse concept, I think it's going to catch on fast. And the fabric is one data security, one governance, one compliance framework, and no more using separate analytics services from different vendors as Microsoft fabric offers and is a solution that really takes out any potential competition by having the power to unify data. Now I say Microsoft fabric, and I'm telling you about Microsoft fabric, but a lot of the products I could say this for as well. But one of the special things I guess about Microsoft fabric is its co-pilot capabilities. We've all heard about co-pilot, right? And some of these capabilities are soon to be launched, but you're going to be able to use large language models to help you with your coding directly in your workspace and other powerful capabilities that fabric helps you monitor your organization's data in real time, and you can trigger actions based on data insights. Now Databricks is a partner in fabric, but now Microsoft has more or less become a competitor to that with its data lakehouse approach. So watch this space, and there's going to be things happening here, I'm sure. So keep an eye on this. I think Microsoft fabric is a great evolution of Synapse. As I think about, I think this is about the other fabric products out there. So anyway, I've kind of taken an aside on that particular product, but it is one of the products that obviously it's not a do-it-yourself approach to a data fabric. Why would you want a provider approach like that? Well, again, we did a study, and here's some of the things that we showed. We compared the tasks of creating, integrating, distributing, and managing a data fabric with a common do-it-yourself set to a leading fabric provider, which I've already mentioned is the SAP Data Sphere one. Our do-it-yourself data fabric stack included a leading data governance tool, a leading data integration tool, an augmentation S3 for the data lake, and a leading tool for the data warehouse. And in our study, again, projecting to the enterprise a small three-year data fabric will cost about three years now, $716,000 with a product and $2.2 million without, large $2.8 million with and $8.7 million without. Now, anytime I mention cost, and some of these are mind-boggling costs, right, I will also mention that the return on investment is there in a well done data fabric. And that's what you want to focus on, not just for cost, not just measuring out various costs necessarily, but measuring out the output, the return. And it is there with a fabric. Now, with one of these commercial fabrics versus a do-it-yourself approach, there are differences. And here's where we found the differences were. There are large differences, a large meaning, large in a positive way for the commercial fabric system administration. Lot easier, data modeling, a lot easier, data security, a lot easier, somewhat easier, data acquisition, integration, and the consumption. Small, but still there, data preparation. And we didn't really find the catalogs that are part of the data fabric products to necessarily be that much different or better than a commercial data catalog. So it's to the point where I wouldn't necessarily want the catalog from the fabric provider. Yes, that'll add overhead to each's own on that, but something to look for. Don't expect huge catalog bumps when you go to a fabric. Now, choosing a provider, a lot of recommendations here. And I hate to use an overused term, but I'll say it because it applies one throat to choke. Yeah, one throat to choke with a data fabric purchaser. So far, again, the market has resisted promoting non-integrated tools as a data fabric. Oh, we have these three tools. Let's package them and call it a fabric. Hasn't quite happened yet. With one of these products, I'll mention organizations can streamline data management processes and achieve greater efficiency in handling complex data projects as we saw in our test. So hat tip to SAP DataSphere for what they provide with their data fabric. And that's the one we use for our great test. Okay, complete connectivity to data sources. Now we're getting into the, I think I have six things for you in terms of these are the things to look at. Universal data source connectivity. Let me define universal here, because that makes universal makes it sound like everything all time forever and ever, not necessarily. Of course, the sources you have in your organization, yes, what you are getting into and then that you can see that you're getting into, I would say, as far out as you can see, the next three to five years. And also be sure that you do a little think here in terms of what you're not planning on, but what may be a possibility. The good news is that most of these products have great, what we would call universal data source connectivity, the data fabrics connect to and ingest data from a wide variety of source systems, regardless of location, type, being hybrid, multi cloud or private or platform. And it abstracts the technical complexities data fabrics, hide the technical details, like connection type, JDBC, ODBC, REST APIs, what have you, to present a unified view of all ingested data. There's an equal, great treatment of diverse data sets. When I first heard this as a goal of data fabrics, equal treatment of all data sources, I thought, well, no, there are some more important than others. But if they're all brought to a nice high standard in terms of making them accessible to me as a developer, engineer, data scientist, what have you, then why not? Why not make them all equal? So equal treatment of diverse data sets. What you like as someone that's run these organizations a lot, what I like is things that are quantified. And we can say, we can list the data sources, one, two, three, four, etc. That is great when we can do that. When it's not a list, when things are maybe on the list, maybe not on the list, maybe you got half a source here, and then you got the other half there. The more confusing it is, the harder it is to get things done. So I like this, ultimately, equal treatment of diverse data sets. Centralized access, one panel. One panel access to your entire federated environment. Entire environment, including multiple clouds, including multiple of the so-called data products in a data mesh. Yeah, so you build the data mesh, you've got these multiple data products out there, how to bring them together and in something meaningful. Well, that would be your data fabric. Universal data access, centralized data understanding, actionable insights. And this is a step towards more action coming out of data more or less automatically. Okay, getting the data more, I know it's decentralized, but now kind of bringing it back into some sort of centralized understanding of the entire environment enables more automation and artificial intelligence. Single point of access, just in time access, single pane of glass view. Is the access the greatest looking right now necessarily? No, probably not. But just having access to that data is wonderful. And let's face it, most of the projects that we're doing out there right now, we're not building dashboards anymore. We're not building reports anymore. We're doing things with the data. We're moving that data right into automated processes. We're not bringing data to a point where it has to be interacted with as much. We're not doing it as much. That's usually a side light, I guess, of the projects that we're doing. And you too, problem. Preparation of data. I've separated this out from integration of data. We're talking here about getting predefined transformations working on data, versioning data, automatic schema drift, detection and resolution. It's always going to happen. Best be prepared for it. And workflow automation for repetitive tasks like preparing Salesforce data, something you'll do on a daily basis. Data preparation entails unifying schemas, rectifying differences in data models and cleansing data so it's easily serviceable for data integration. This is important because data preparation often kicks off the analytics or application loading lifecycle that's foundational to many of our data fabric use cases. Many offerings are broadening this paradigm with self-service constructs based upon low code methods with point-click GUIs. Now we talk about integration of data. Standardization is key. We need to see that data, the data models, the schemas, the semantics, they need to be unified to ensure compatibility of cross sources. Can you imagine trying to look at a date field and you have different formats of date in the field because they came from different places? Yeah, none of that. And that would be true for things that are wildly more complex than that simple example. Unified data for diverse applications. Multiple integration methods like ETL, ELT, replication, change data capture and streaming. So is data fabric a data integration tool? Yes, yes it is. And it's a unified data access tool. It's a unified data security tool, unified data governance tool. And it's built using semantic models. This is how it does what it does. I put it in here because you want to look at how well that is done. A data fabric provides a semantic layer that enables a map of your metadata. Unifying data based upon its meaning rather than just its location and preserving the business context and logic of that data across the fabric. I mean I've been trying to get organizations moving towards a data fabric well before it was called a data fabric, okay? And I faced that data mountain that I've been talking about. And with a data fabric in place, I won't face that data mount. We'll be able to get things done much more readily in the organizations that have data fabrics. Excuse me. And they all use semantic models. And I'm putting this in here partly to make sure that we remember a year from now, two years from now, that data fabrics should be based on semantic models. And if any vendor is pushing a data, a so-called data fabric out there and it's not based on a semantic model, you're not going to get the benefits that I'm talking about here. So let's just keep that in mind. Data management. Yeah, it manages data. Do they all have their own database? Yes and no. Yes, they mostly do. But yes, they also work with other databases as well. Data fabric must support data governance, including security and access control. Vendors of data fabrics must either provide mechanisms for data modeling, data profiling, data quality, life cycle management, and other conventional components of data governance and data management. Or they must integrate with them. And this is where it gets a little fuzzy in terms of wealth is just fully integrating with other tools. Is it truly a data fabric? Well, gone are the days. Gone are long gone are the days when there could be some sort of laminated data architecture that I could bring without knowing a company, right into that company and saying, hey, here's what we're going to do. Everybody's different. And everybody has their own thing going on, their own oftentimes spaghetti architectures going on. And you've got to pick up the pieces right where it is. There's no wishful thinking here. It is what it is to get the past, move forward, make a fabric of it, and get to the point where you can get the benefit of things like user behavior, monitoring, access control methods, and detailed data provenance. Where did the data come from becoming increasingly important in this regulatory environment that we're getting into. And finally, and again, sometimes this is the biggest benefit of a data fabric unified data security. Data fabrics implement various security measures to protect sensitive information like access control, data encryption, masking, anonymization, auditing, and logging, and so on. Now, what will you find when you step into the market for data fabric providers? You will find data virtualization, data movement, and data federations, strong capabilities there. Data federation is like when there's a single point of access to multiple data sources, it federates the data by creating a unified schema and translating queries to the specific language of each source. Low code, no code, they're mostly that. Tens to hundreds of connectors, I am sure that in the future it'll be thousands for some of these. There's data harmonization where these tools provide an evolving data model that harmonizes data of any variation that is structure, schema, source, and so on based on uniform standards and business terms. This semantic layer is an overlay for the underlying source data accessed in memory or through data virtualization that works together with those. Knowledge graphing, a data catalog, that's for storing data assets, metadata, and enriching those assets with business logic, taxonomies, and data models as needed. Data profiling, which we talked about, we didn't talk much about the data marketplace capabilities of some of these data fabric providers, and natural language query. Of course, any query engine that's being built today is with natural language, and so these definitely are as well. So you want to step into the market, evaluate these potential providers, here's some miscellaneous topics related to that. Like pricing, it's confusing. It can be, you are priced on in one example, and I think several examples actually, data warehouse capacity units, data lake compute terabytes, yeah, weird. Data integration jobs per hour per month, I see something like that, I'm going to write some big jobs. Optional data catalog, it's an optional add-on cost, and if you do get it, how much are you crawling, how many hours are you going to crawl that catalog per month? I've already said that catalogs have nominal benefit here, but they'll get better. And anyway, there's probably, I could probably put five more bullets on here right now for pricing, because it's kind of like this, it's based upon utilization. Now the future of data fabric, integration with artificial intelligence and machine learning, decentralized data management, focus on interoperability and open standards, enhanced security and privacy features, you have more of the same, more of what I've told you about data fabrics today. Self-service analytics and citizen data scientists, more things like Microsoft Copilot in the mix to make it all easier to get there. The utopia that a CEO would want for their data environments, not here yet, but this is a step in that direction where he or she can pretty much press a button and the data issue is, there's no data issues, it's just what you're doing with the data. It's just APIs and apps on top of a great data infrastructure supported by data fabric. Best practices, yeah, a lot of these obviously could apply to any project, but they're definitely worth mentioning because I see that a lot of shops are getting gung-ho about the data fabric, let's push headlong into it and you forget some of the basics. Define clear goals and objectives. Assess your data landscape and this is part of determining if you're building it oneself or if you're getting into a product. Consider user needs and adoption, standardized data formats, how do you want the data format standardized? You still need that governance forum. Yeah, implement a data, a robust data governance framework, you still need that for many, many data decisions that will happen throughout your data fabric journey, focus on data quality, always write, prioritize security, of course, and embrace a collaborative approach. Technical builders and business users and application developers in summary. And if you have any questions, by the way, I'm going to leave a few minutes here in just a minute for those questions, go ahead and get them in. A data fabric is a unifying integrated architecture that provides a consistent and scalable approach to managing and accessing data across multiple sources and formats. That's your top line right there. There's a bunch of benefits that I talked about and it reduces complexity for everyone involved and it includes integration, governance, cataloging, virtualization, analytics, and security. Those are the components of a great data fabric. Purchase considerations are complete connectivity to data sources, centralized entry, preparation of data, integration of data, support for semantic models, data management, and data security. These are some of the key things that you want to look at as you step into your fabric market. And in our analysis, we found that it's better to buy than it is to build. I'll put the caveat on there. You may be different. You may have a great mature data environment and great capabilities to do these things with componentry as opposed to a fabric kind of unified product. And that's great too. But just know that both are at your disposal. And that brings me to the end of the formal part of the presentation, considering your provider or choosing your provider for implementing a data fabric. And with that, I'll pass it back to Mark to see if we have any questions. Yeah, our community is so fun because we've got lots of fun questions and things that I think about a lot as well. So we'll get into a couple here. Is there any real world research or case study that confirms the benefits or a positive difference for data fabric? There's the one I did, which I talked about a little bit. But it was more assuming you do want a fabric and whether you should get into a product or build it yourself. So it did not start from the premise of is a fabric going to provide me return on investment? I would say to that question, no, I'm not aware of any such study. It's one of those things that's so new and people are dipping their toes in. But I think there's a lot of benefits out there that people can realize as you went over today, which was excellent. Your diagram depicts multiple data warehouses in the data masher fabric. Do they all need to be a certain level of maturity before becoming a component? Ah, great question. I love that question. Yes. Yes, I tried to stress that a little bit as well that the underlying lake here needs to be done well. The underlying warehouse needs to be done well. I can't tell you how many organizations that will call something a data warehouse that's really just a loose collection of different data. The data's not really integrated, not really looked at from a quality perspective, not modeled together or anything like that. But it's in one database now. So you did get somewhere. You did get somewhere. So that's a low mature data warehouse, if you will, not really fit yet for putting in a mesh, putting in a fabric. Please fortify those warehouses to some degree and get it up to standard.  You might look to that presentation because I had a section in there on data warehousing and what I meant by what I mean by level three maturity, which is where I would say that warehouse needs to be to be effective in a mesh or fabric. But there are several things you want to look at. Awesome. Yeah, I totally agree. What types of existing data environments and infrastructures might benefit best from a data fabric? Well, I've become an advocate for data fabric, so I'm almost wanting to say all, but I know I can't quite do that because that's not right. If you were at a super high level of maturity with your data environment and you're getting all your needs met, that's the bottom line, by the way. The bottom line is not, oh, I got to have a great fabric. I got to have a great dimensional data model. I have a great, this is that, that the analysts and consultants care about. It's what your users care about, what your application teams care about, in terms of are they getting what they need so that they can do their job, which is to turn business. And so that's the bottom line now. But I think, I think that to get there, I see many enterprises and what they've got. And so to get there, I think data fabric really helps you get moving in the right direction, getting all the data under control. You don't have to worry so much about security. You don't have to have some complicated data integration layer that you have to build that integrates all this data and harmonizes it. That's what the fabric's there for. So hopefully, I've shown that it does provide quite a few benefits. Yeah, I love that answer. I'm smiling in agreement over here. Well, thank you, William, for this great presentation in Q&A. I'm afraid that's all we have time for today. Just to remind everyone, we will be posting the recorded webinar and slides to Dativersity.net within a couple of business days. And we will send out a follow-up email to let you know the links and other requested information throughout the event today. Thank you again for attending today's webinar. And I hope everyone has a wonderful day.


Welcome to digital asset news to get top stories and crypto currency and digital assets and bring out a bite-sized pieces Let me tell you what's going on the crypto currency market in 60 seconds or less First up money has been recovered from the hackers from the colonial pipeline ransomware Extortion where we had originally thought that Bitcoin had been compromised or hacked in the network But in reality the Bitcoin was actually returned because the hackers used a rented cloud server And the FBI went in there and got Bitcoin that way on top of that We take a look at how President Trump comes out and says that Bitcoin is a scam and it's a competing currency with a dollar Which is good news for us I believe because he just acknowledged that Bitcoin is actually a currency and we're gonna take a look at some past performance as far as the former Bull run that we just had in 2017 and how it lines up with what's going on today with the recent dip to find those stories and everything else Just click on the link below


Mae'r ddau yn cyfant yw'r ffordd o'r holluc. Mae'n beth o'r amser i wych, ac nolod dwi'r ddim yn y tuflau'r yma. Mae'r dyfodol yn ysgrifennu'n newydd o ddweud o'r ffordd, gyda nhw'n olw'r ffordd o'r fan o'r moyeb. Felly mae'n rhaid i wedi'n dod i gael feme ac ydych chi'n wneud yw pobl eogel. Mae'n gael i, mae hi'n ei hollwch i'r wneud, bobl mae'n gyhoedd yn y Lleig. Fy digwydd eogel ei wneud y rhaid. You know, I was really being patient I suppose I received extremely early days in the season We perhaps haven't found our top, starting eleven, as of yet. Yeah, definitely, obviously. The Gaff has been working hard, also went to get the right players obviously not signing players for the sake of it he took his time and a few of the boys have not really done much during pre-season so its going to take time for them to build up the fitness but i'n sure looking at the people it's brought and you know they're all really good at signings and we could all add some different to the club.


Thank you Mr. Speaker. I have the honor today to talk about the worry of people in Meshkekouin-May-James. The residents in us, who are frontline workers, and they belong to the health family team beginning of the pandemic, but there was no child care for their children due to the orders from the Ministry of Health. And so the other child care cannot serve, is not open this summer and the other one has its own clientele. We cannot ensure the security of children workers, but they face the number of children by room when they open and buying personal protection equipment. Without clear directors from the government, Mr. Speaker, the residents of Meshkekouin-May-James, James Bay, are very recognized the work. It's now the time of the Ministry of Education to do its part and to invest directly in our child care, to ensure that workers such as Madame Haudenry can continue helping our people. Thank you Mr. Speaker. Thank you very much. Member Stevens, the Member for Sarnia-Lampton. Thank you Speaker. I'm pleased to rise in the legislature today to share an important milestone from my community of Sarnia-Lampton. After three very challenging months, as of 9.30 a.m. yesterday, Blue Water Health and Sarnia-Lampton has zero cases of confirmed COVID-19 patients in acute care. This is the first time this has happened since the onset of a community's first hospitalization due to the virus on March 21, 2020. Of course we must recognize that this virus is still in our community and we must remain vigilant in our efforts to follow public health guidelines, but this does mark a milestone for Sarnia-Lampton. I want to take this opportunity to thank all the dedicated frontline workers across Sarnia-Lampton County for their commitment to our community. The collective efforts to control the spread of the virus by the people of Sarnia-Lampton are starting to pay off, Mr. Speaker. On Friday, our community will proceed to phase two of the provincial reopening plan. I'm excited for those businesses in our community that will be reopening after so many weeks of sacrifice. And I'm encouraging everyone in Sarnia-Lampton to once again join together and support the many local businesses that have struggled through these challenging times by committing to shop local. Together, Mr. Speaker, we can control the spread of this deadly virus and quickly help to build back our important small business community. Stay safe. Thank you very much. Member Statements, the member for Windsor to come see. Speaker, Ontario welcomes about 20,000 temporary foreign workers each and every year. That's more or less the entire population of Niagara on the lake, or Amherstburg or Collingwood or Essex, Huntsville or Tecumseh. Generally speaking, they live in open concept bunk houses or too many of them are squeezed into a crowded home with little privacy. This pandemic has hit these workers hard. The Globe and Mail claims 600 migrant workers have already tested positive for COVID-19 in Ontario. Speaker in my area, two young men from Mexico have died. They were 24 and 31 years old. Canada is failing our temporary foreign workers and Ontario is failing our migrant workers as well. They come here because Canadians don't want to do the work. They put in 60 hours a week or more to support their families back home. If they call in sick, they don't get paid. And that's a problem. Speaker, these are essential workers. But because of their circumstances, they are also treated as expendable and exploitable. Their wages should be protected. If they catch COVID-19, we need a wage subsidy for our migrant workers. Good things grow in Ontario when we need migrant workers to get local farm-fresh fruits and vegetables to our kitchens and tables. We can do better. We must do better for these essential workers. Member Stevens, Member for Carlton. Thank you, Mr. Speaker. Mr. Speaker, Bursala is a small village in rural Somalia. It's home to the Somali Hope Academy Primary and Secondary Education School. You might not know this, but the Somali Hope Academy in rural Somalia has a very unique and special relationship with Canada. The school was built and funded by the Somali Hope Foundation, a Canadian-led initiative. Somali Hope Academy's development was a dream of Sergeant Mohamed Elmi, an Ottawa resident and sergeant of the Ottawa Police Service, who in his youth fled the Civil War in Somalia. As a Somali Canadian, Sergeant Elmi works tirelessly to build relationships and make a difference in the Ottawa community. However, he always wanted to make a difference in the country of his birth, a country that has continually suffered immeasurably over the past 30 years. His dream was to contribute to a brighter future for the children of Somalia, and education was a key part of his plan. He envisioned a world in which all Somali children, boys and girls, can pursue free, quality education. This is a very special year for us as we celebrate our 10-year anniversary. Fundraising began in 2008, supported by Human Concern International, the Somali community, the Ottawa community, the Ottawa Police Service, and the Lerner family. Since the Somali Hope Academy is run entirely by volunteers, 100% of all money raised goes directly to the school. In 2012, Sergeant Mohamed Elmi's dream of providing free education to underprivileged youth in rural Somalia finally became a reality. This year, we had to cancel our gala because of COVID-19, but we will be doing a virtual gala, and I encourage everyone to go to somalihopeacademy.ca to find out more. Thank you. Member Statements. The Member for St. Catherine's. Thank you, Speaker. My office has logged 112 different times that nurses have reached out to me during this pandemic. These are the same frontline workers that the Premier calls heroes. In fact, under normal circumstances, I would have invited them here, and they would have come, and they would have sat right over there. Everyone would have clapped and given them a hero's welcome to this assembly, except there is a big difference to how I see them as heroes and how this government does. They are heroes during your press conferences and on your social media posts, but when they need something, they are met with silence. You know, not a single nurse in the NHS has received the pandemic pay yet, and when one of our Niagara regional hospitals had an outbreak for a few weeks back, one nurse called in to say that the province loosey goosey guidelines and PPE left them in total chaos. Last week, the government had to answer a question on their legislation capping nurses pay. In February, in Richmond Hill, this meant that this government took back 19 cents an hour from the nurses that worked there. 19 cents. Nurses are heroes, for sure, but so long as you don't ask when you are getting paid or for more protective equipment. So long as you come cheap, these men and women and their response to the pandemic brought our province back, and I am tired of seeing them being called heroes, then being treated in a way that does not be fit a hero. It is not good enough that nurses are heroes only when they are quiet and come cheap. Nurses and all frontline heroes deserve better from this government. Member Statements, the member for Ottawa, Van Yen. Thank you, Mr. Speaker. I have a great pleasure to speak today in the House. It's a coincidence. It's the day of my birthday. It's my birthday and my first Member Statements, and I want to share some good news. Mr. Speaker, the well-being of seniors is a major worry everywhere in Ontario. I visited many of my constituents before my election, and I checked with them during the pandemic. There is Montfort Renaissance and Organization, which is a bilingual centre that offers services to help seniors in their home and also in the residence and day services. It's exceptional with Montfort Renaissance. It seems that that residents that was not taken by surprise by the pandemic. In fact, Montfort Renaissance had already in place a pandemic place. Employees had been trained on best practices and the crisis plan that was put in place right away. COVID-19, the number of cases there is zero, none amongst residents. This organization supports directly the healthcare system and takes care of our loved ones with dignity and respect, and so I have a great pleasure to congratulate them publicly for their success. We know that we have to think about services giving to our relatives that need services, so we have to have enough funding and actually exchange to follow the leadership that Ontarians should get from the government. Thank you, Mr. Speaker. Thank you very much. Member for Willowdale. Thank you very much, Mr. Speaker. June is Filipino Heritage Month, and this morning I want to recognize the over 280,000 Filipinos in Ontario, many of whom call my riding of Willowdale home. Filipinos began immigrating to Canada in the 1930s, and over the past nearly 100 years have made a great contribution to our country and our province. Over the last few years, I've gotten to know many in the Filipino community. They are some of the warmest, most welcoming, hardest-working, kindest people I have ever met, and they are fiercely proud of their heritage and culture and have shared their wonderfully colorful language, mouthwatering cuisine, and the values of serving their community and the importance of family in our province. In 2017, I was honored to join the ranks of the Order of the Knights of Rizal, an international order created to honor and uphold the ideals of Philippines national hero, Dr. Jose Rizal. As Sir Stan, I have enjoyed many special occasions celebrating with my friends in the Filipino community who never hesitate to invite me into their homes to enjoy great food and to learn about their spirited culture. Unfortunately, Mr. Speaker, this year, celebrating Philippines Independence Day on June 12th, and all that the month usually has to offer wasn't possible. But I wish all my friends in the Filipino community and across Canada happy Filipino Heritage Month. I look forward to seeing you again in person and enjoying my favorite summer treat. Hello, hello. Salamat po. Member Statements, the member for Sudbury. Thank you, Speaker. Melody Hughes, for my writing of Sudbury, has been raising alarms about issues in long-term care for years. After her family had a devastating experience in 2015, Melody tells me that her mother, Caroline Hughes, was sexually assaulted. She was a Mackenzie-placed long-term care home at Rivera Living Managed Facility in Newmarket. And Melody says the investigation into her mother's sexual assault left her with zero closure. She still has no answers, feels that the home was never held accountable. In 2018, Melody spoke with the Premier about the urgent need to fix long-term care. The Premier told her Speaker that he was committed to improving care for seniors in this province, but after reading the Kitty Armed Forces report on long-term care, Melody believes that Ontario is moving backwards. Reading the CAF report sent Melody right back to the moment when she learned her mother had been assaulted. Speaker, we need a long-term care judicial, fine-and-fixed inquiry that will compel this government to fix the problems in care that everyone knows has existed for far too long. No one should have to go through a Carolan experience. No senior should have to experience the pawing conditions in her long-term care homes. Melody is calling on us to take action.  Thank you. Member for Oakville. Thank you very much, Mr. Speaker. And today, I'm honored to stand here in the legislature to acknowledge recipients in my Oakville writing of important financial grants from the Trillium Foundation and sincerely appreciate their recognition from the Minister of Heritage, Sport, Tourism and Culture Industries. There are four recipients of the grants. The first is Acclaim Health, which is a registered charity that has been operating for 85 years with programs that support physical and mental well-being through independent living, reduce social isolation, dementia care, and end-of-life care. Their grant will help reduce social isolation for caregivers of people living with dementia in the Halton region. The next organization is the YMCA of Oakville, which has been operating in the community since 1956. The YMCA of Oakville has played a role in improving the quality of life for thousands of people in Oakville. Their seed grant is provided for them to pilot a free 12-week group fitness and peer support program for young people aged 13 to 18 who are struggling with their mental health. Another recipient is Homesweet Hope, whose aim is to break the cycle of poverty for single parents and their children. Their seed grant helps them scale up the homeward bound program to help young single mothers find careers. And finally, the Halton Environmental Network, who has been working to support and enhancing the capacity for local climate action and environment sustainability across our community. Congratulations to all these great organizations for their grant, and thank you for making a positive difference in our community. Thank you. Thank you. Member Statements, the member for Milton. Thank you. Thank you, Mr. Speaker. I rise today in recognition of a former liberal member of Provincial Parliament, Walt Elliott. Mr. Elliott peacefully passed away on June 4th at the age of 87.  Walt Elliott faithfully represented the former riding of Halton North from 1987 to 1990. Before entering into politics, Mr. Elliott worked in his community as a schoolteacher and later as a principal. He was actively involved in fundraising for many community organizations, including but not limited to United Way Children's Aid Society, Ontario Agriculture Museum, Halton Region Museum, and McMaster University. During his time in this house, under Premier Peterson, he served as the parliamentary assistant to the Minister of Housing. On behalf of all of my colleagues in this house, I want to pass our most sincere condolences to the Elliott family and the way we can all strive to serve our community as honorably as Mr. Elliott. Mr. Speaker, thank you. Thank you. That concludes our Member Statements for this morning.


My wife told me that you are building it.  Please, change my life right now. This is your unit now, so you can finish it to your liking. Yeah, you know, get it to... You look like I asked you to get it. Yeah. Maybe by its own unit now. Your unit now, you... You really are for rental. Yeah, this is not, that's for rental. You own this now. This is a gift for you coming to Kenya as an in-law from this community. No. Yeah. You have bedrooms? Yeah, these are two bedrooms. Two bedrooms. Yeah, these are two bedrooms. For me? Yeah, yeah.  Yes. I travel from one country to another. Yeah, yeah. Yeah, so maybe I can furnish this and give it up for him. Yes, it's up to you now. It's no longer mine.


Hello again, and welcome back to Retro Tech. Today, we'll be covering a brand new monitor that I've never had the chance to review before. And it's not one that often comes into my shops, obviously. So I thought I would give you the opportunity to get a behind-the-scenes look at the monitor itself. I'll talk a little bit about the job I did on it. But first, I'd like to just introduce you to a little bit of marketing material from today's featured company. And that is Ikagame. Ikagame, I think is how it's spelled, or said. So anyway, the one we're looking at today is this one on the right. And that is the TM20-20R. And it is an analog-only monitor from the early to mid-1990s. So you're only going to get 240p and 480i support out of this. But the cool thing is it is a shadow mask tube. So you'll be able to tell slightly a difference between this and the normal Sonys that I cover a lot more often on this channel. One of the things I will bring up is that this has a unique pullout board that you can use to adjust your geometry and things like color and stuff. So that's really cool for an old analog, mostly analog monitor. So let's go ahead now and get into the behind-the-scenes footage of this repair. All right, so here is the actual Ikagame. This belonged to my friend Adrian. And you saw some of me repair some of his other monitors in the past, including a 14-inch Ikagame. He had two of these that he brought into me. So I thought it would be a good opportunity to show you these. Now these didn't actually need any component or circuit board work, really. Just a cleaning, calibration, tune up, and overall check out. So that's what we did for him. I will let you see how metallic I want to say this monitor is. It's extremely well built. It's got cast iron in it and a lot of steel in it. So it's very heavy duty and very solid. So you're definitely going to feel that. For example, this 20-inch monitor weighed almost 90 pounds. So very heavy, very thick, heavy duty. Here is the deflection board. Now you'll see a lot of these little potentiometers on here. This one's for horizontal hold, side pin cushion adjustments. So not only do you get to make some adjustments with the pull-out board I'll show you later, you also get to make adjustments directly to this board through these potentiometers if you need to. And if you have any issues with your deflection, you can go in and recap this board. Now this one had, I did test some caps. They looked to be in great shape. So we're not really going to go in and add the extra expense of recapping something like this. At least not the time being, the current condition is pretty darn good for something from the early 90s. Now behind that was the power supply. See how that's bolted into that large heat sink and onto that large plate metal. That's right behind that deflection board. So they're going with more of the modular card design similar to what you're going to see in a Sony BVM. This is the other side of the monitor. There were five slots here for cards. Four of the five slots, actually it's more than five, but there's a couple that are double numbered so they're only specific things. There were four cards in it. And so if you get one of these monitors, it doesn't turn on for some reason, go back and reseat those cards. And that's a pretty common issue. If those cards come dislodged, they can prevent the monitor from turning on.  So just showing you a little bit of the behind the monitor here, just so you could see how the flybacks separated on its own and it's built onto the back of the PVM, or I'm sorry, the monitor, not PVM, the Ikigami. And then the flyback has controls. There are three different focus knobs, which is a lot for a flyback. Most of the time they have one, sometimes they'll have two, but I've not seen one before three focus knobs. So that's kind of interesting. There are some additional build-outs. You can probably add to this monitor if you wanted to. But this is the pretty standard setup, which is just three composite video inputs with relays out, and then you've got an RGB input, which surprisingly can be switched back and forth to component input if you want to. Here's a look at the menu or the button bars on here. And I told you about that pull-out tray. You just push in and the tray pops right out and you've got all these really cool controls. I'll show you briefly here in a second what you can do with these controls, but you can do everything from color setup and other things. There is, you see this probe in thing right back here. Let me show you. So there is a probe for this. You could plug it in there and it's a light probe. Adrian did have the light probe, but I'll be honest with you, using those light probes at this point is a completely risky gamble because the light probes themselves actually need to be calibrated. And if they're not calibrated, then you could go in and have it set up and hook it up to your monitor. And this goes for the same things for the ones for the Sonys. If that thing's not calibrated correctly, it'll adjust your colors and it could adjust them incorrectly. So you're incorrectly calibrating your monitor for color. So it's not really good to use those really old ones unless you for sure have them calibrated. So just note that, please, on there that it does have the ability, but you need to make sure that your probes are calibrated if you're gonna actually use them. So here we have the monitor turned on with a picture in it. And so I can show you a little bit of how this tube looks. You will notice that it does have that rounded class edge on the front, as well as the sides. So that side horizontal curve is always present on CRTs, unless it's a flat screen CRT. But on these older ones, even the Trinitrons, the good ones will have that curve. It's just, this one has a vertical curve as well. That's the Shadow Mask Tube technology right there. So here's this button pattern. You just press it to activate it and it'll turn all the buttons on like that. And you could pick your adjustment you wanna make, like horizontal center here, for example. And then you just spin the wheel and dial in your exact geometry setting you want. That is really nice. It goes, I feel like you get a lot more control with that instead of just making like one digit and it jumps. This one, you're actually turning a wheel and it's going at very small increments so you can really move that screen around to a perfect spot. Now, it will, you're gonna have to calibrate these a little bit differently, because again, you're trying to put a flat screen. It gets the entirely curved surface. So keep that in mind when you're doing a calibration on something like this that you're gonna have to try to make it almost match that curve a little bit around the corners. And that way it doesn't look so, the linearity and things won't get so messed up. It'll look kind of nice on there. But that's the way you can function through this monitor. You pull out that tray and you can use a lot of those. And that's a nice addition, considering that you don't have to really get into that deflection board, which is easily accessible to change those settings on those potentiometers. If you just need to do a quick tune-up, you could do that in that little bar.  This does have a 700 line tube so it is slightly higher line count than the Sony, this would be comparable to like a 1953 MD or something like that, similar to like a medical grade on there, or even if it was a broadcast grade, like a 1954 Q, that's what I consider this comparable to. Maybe even the series before that's a 43s and 44 Qs. But still, this one had a very sharp picture and it will show good scan lines. 700 TV lines is plenty of resolution for any kind of 240p resolution game. It obviously doesn't have any lag and it's just an all-around great option. If you do come across one, okay, these ones do tend to sell for slightly less than the Sony monitor so that's a good, you could usually get a better deal on these. And here I have, now I'm gonna show you finally both of these hooked up in tandem because you can easily hook them up together just by crossing, just by feeding the in and outs on those inputs on the back of the monitor. And again, this was a cool little thing for Adrian to have now, it's two of these side by side. They were pretty darn close to the same after I got done making the calibrations and color adjustments and things. Everything looked pretty much as close as possible. I'd say they were in about the same quality condition, both of them, pretty similar. But again, probably looking at somewhere in the neighborhood of $600 for this monitor. Today's market price, if you find one on like eBay, if you buy one through other channels, you can maybe even find this one for really cheap. I feel like this is probably a monitor that a lot of people just don't even know what it is. Ikegami and I've covered them before and most of the people watching here, you guys know what they are, but a lot of people do not and probably have never heard of this brand if they're newer to the scene. So you can still get these for a decent price and they are very cool. Again, with that separate setup, shadow mask tube, the pullout tray, different kind of monitor. I will tell you that this monitor did have a built-in overload feature. So if you pumped the brightness or the contrast too high and something bright came on the screen, I did notice a little overload tally lamp down here would come on and give you a brief warning saying, hey, you're risking overload. That's a very big deal. That was like something that Sony didn't have till later on like the D-Series overload. Well, there are overloads on the older monitors, but not as responsive I'd say as this one. I've experienced other monitors that their overloads didn't seem to notify you when you're up for an overload as much as this one. This would be on comparison with like the D-Series where it will immediately start notifying you on there if you're getting too bright of a screen, if that protection circuit's still functioning properly. So that's pretty much it. A pretty easy job again, no need for a board repair. If you run across one of these, it's definitely worth checking them out and possibly picking up if the price is right or if you're looking for a CRT. That's pretty much it for today's repair video. I thank you for joining me. If you're new to the channel, please make sure you hit the subscribe button and if you enjoyed the video, do me a favor and hit that like button too. And if you have any questions about this repair, please leave them in the comments below and I'll get to them. Or somebody from the community may be able to help you out too. Thanks again, everybody. I'll see you guys next time with some more retro content.