And I'm going to cover what is data microservices, right? So the thing is knowing that you have had an understanding around what is the whole microservices thing and how is customers actually adapting to this new culture of moving from monolith to microservices. So what we thought was it would be good to actually present to you guys. It's not just about the applications. You also need to look at applications and data as one thing rather than suppressing them. How many of you here have at least worked a bit on databases, at least on nosy per databases or adoops or not bad? So most of you are more into development, is that? Don't really interact, I mean with database stuff as such. Okay, so we have very few people. We will try and make it much more easy for you guys to understand for those who are not coming from the database background. But again, the concept is kind of very interesting. Like I said, if you look into all these big internet giants like Google, Facebook, Yahoo, the way how they design things is not that they only look at applications. They look at applications and data together as applications start generating more data and collect all this data and then you try to find insights in it and make your applications even better. So that's what the talk is all gonna be. Again, we'll try to keep it short and sweet so at least you're not bored. So before we start myself, I'm Vish Fanindra. I actually run the data engineering here at Pivotal for the South Asian market. So I handle both the pre and post sales organization here at Pivotal. Been in this company for about 10 years. All right, so Carlos, you wanna just introduce yourself? Yeah, I'm with Pivotal as well. I'm a solutions architect. So I'm more in the PCF side, I'm deploying PCF and show people how to play apps, screen-based apps, screen quality of apps on PCF on using micro-sale systems. Okay, that's my intuitive handle. So before we start, I just wanna take a step back and just wanna demystify a bit around what exactly, why are we doing this now? What exactly is this microservices? What is this data microservices? The idea here is if you look into the whole, you know, new enterprise world, right, especially some of these companies that are moving into agile or adapting DevOps or looking at all these big monolithic applications where they're trying to break them down to, you know, monolithic, sorry, microservices architecture. So one thing is very clear, right? The thing is, the time it takes, you know, everyone has ideas, right? The time it takes for you to be able to make this a reality, make this idea into a reality and then being able to make money out of it is the whole thing, right? You can have n number of ideas, but then if you really can't make revenue out of it, then there's no point. So this is the concept, what we call as a concept of cash, right, where you really have this idea and I wanna do something materialize it and make it real and at the end of the day, start taking that, innovating on the idea that is out there. So it's kind of iterative process. So historically, you know, we all started with waterfall methodology and then we moved into drill practices and lean practices. Then as we started getting mature, we started looking at CI, CD, ultimately, you know, talk about continuous deployment. Who's doing CI, CD, A, conscious deployment? Very few. So again, going back to all these consumer-grade companies like Amazon, like Netflix, these guys, they kind of push release code to production every few minutes, several times in a day, right? So this is kind of iterative process. And if you look into this, right, there is one, the evolution of this has one common characteristic, which is the shorter the release cycles are, the software quality is improved because you're basically taking a bigger problem, deconstructing that problem into smaller problems and you're attacking at each one of those rather than looking at a bigger picture, right? Whereas on the other side, where the traditional way of building applications, waterfall model, where your release cycles are at least like nine months or a year. After one year, things would have changed, the market has changed, you really have no control and it's like building bridges, right? There's this talk by Anshi from Pivotal. So just recently we conducted the Spring One conference in Vegas. He talked about help developers do what they love, right? This is an amazing talk. Everyone should watch it, it's up on YouTube. Just look for Anshi on the side. So he had, it's a 15, 20 minute talk. I kind of really loved it. My background is more into data, really talking about data, analyzing the data. So I'm more into the analytic space, not really into the development space, but I kind of really loved what he said. It's like building bridges, right? You take years to do this, right? From a software perspective, you can't really think that long. You've got to really be looking at doing things very iteratively and very fast. So that's the idea here. So one thing is very clear. If you're going a gel, generally in a traditional monolithic architecture, you have hundreds of developers. All these developers tend to work on one big thing and the challenges, all these developers tend to work in a dependent mode. They can't work independently. Whereas on the other side, what you're doing is you're deconstructing it, you're making it into a microservice architecture where each one is like a service and you have smaller agile teams really focusing and building that one thing. It's more or less like, let us talk about this SOA and now we talk about microservices. There is some kind of convergence between SOA and the microservices. But the point is, the smaller the teams are, they're more focused, they're kind of independently trying to build things. And the beauty here is that you're not really going with what we call as one big giant database. From a database world, your applications are not just trying to hold the data, the complex data models that you build and the way how you store data from these applications is not just in one location. Because the moment you try to deconstruct this into a microservices-based architecture, the idea is you're going to pick a right tool for the right job. So which means one service would probably use no SQL database, one service would probably use a simple SQL like MySQL or Postgres or whatever it may be. So the idea is with this new architecture, we're talking about polyglot persistence where you have a lot of tools that are out there or backings that are out there that you could actually possess the data in and you're not sticking with one big giant database. Now, the moment you deconstruct this into a microservices architecture, then if you're running this in a platform like structured platform like Cloud Foundry, how many of you have heard of Cloud Foundry? Today Josh covered about Cloud Foundry. So again, Cloud Foundry being a structured platform, being able to deploy these services become much easier. So you see a lot of things like updates and upgrades and scaling your applications and your persistent layers horizontally and being able to migrate them to a new environment or being able to redeploy them and redistribute them. All of this becomes much easier when Cloud Foundry. And the beauty again here is this Cloud Foundry becomes like an abstraction between your infrastructure and your microservices which means you could easily change your infrastructure. So in the past, it was all about your applications are tightly coupled with your infrastructure. Any change that version upgrade, anything that has to happen is a lot of panic between the developers and the operations team because everyone has their own KPIs. With this approach, you're really talking about automating all the activities. Now, again, from an enterprise architecture point of view, if you look into it traditionally, you have all these big monolithic systems as you start growing all these systems, then you would end up introducing things like integration bus or enterprise service bus. So the integration bus or enterprise service bus really helps in moving data from one system to the other. As they start getting bigger and bigger, then your enterprise bus becomes the key for all the integration points between these systems. And the challenge then is if you change one system, which means all the other systems will have impact, which means you need to have a team of people who is managing all these integration bus or enterprise service bus and also to make sure that all these impact analysis is done for every change that is being done on any of these systems. And if you look into the left-hand side of it, we kind of, we scope that to microservices based architecture, but if you look into the other systems, you have all these big giant databases trying to generate a lot of data and all this data then has to go into a platform like Data Warehouse, which is where all the data gets collected and analyzed and used for management reporting. So that's where you introduce another set of tools like ETL. If you've heard of ETL, if you haven't heard of ETL, it's an extract, transform and load. So you introduce another piece of software, which means you have this huge problem where your Data Warehouse holds D minus one, which means yesterday's data, which is what management is using for doing the decision making. And on top of it, this system has to be able to help business to make decisions and this system has to give feedback to the applications, which is not really a right approach to start with. Because like I said earlier, the idea is applications generate data. You color data, you try to find insights in the data, make your applications better, which in turn will generate more data. That's what all these commercial, I mean, Kajima grade companies like Google, Facebook, yeah, all these guys are doing the same thing, right? So with this, you have a problem where you don't have data available in real time, which means your analysis is not kind of accurate. Now, if we zoom into this and really look at the data pipelines, so whether it's an integration bus or whether it's an ETL tool, you have data coming from one system and you're moving it to another system. In between, you end up doing the last processing steps. What I mean by that is you could probably do data filtering. You could probably do some kind of data transformation. You could probably do multiple things before it lands into the destination. So the challenge here is if we kind of deploy this into an architecture where it, which is kind of traditional monolithic, if I have to change one processing step, which is what I was mentioning earlier, any change that has to happen requires a lot of impact analysis because it's not straightforward. It requires you to go look at how is this data going to be used by other systems, which is a huge mammoth task to start with. So, which means it's the updates and upgrades and scalability, all of these becomes a problem. The one most important thing, I'm sure you all know this, no one cares about day zero thing. It's about day zero or day one, whatever you call it. Day two is what is important. So you build a cool application. You deploy into production. That's not where the real work starts about how do I manage this? How do I really make this better? So it's all about day two operations with this kind of architecture, all the day two operations, the upgrades and scalabilities and redeploying it, all this becomes really a complex task. So what we are seeing is, to basically break this into a bunch of spring boot maps, where you have each processing step, an app with a proper contract. So if you look at this spring boot, so you have this abstraction layer. I was talking earlier about Pivotal Cloud Foundry. So you have different kinds of messaging queues or in-memory cache layers that you would actually use with this spring boot, all these contracts of the microservice architecture. So the data pipeline of the abstraction layer, everything is kind of automized. Like it's automatic. Once you bind the service to a specific app, it kind of starts moving the data from one app to the other. Yeah, you wanna add something there? Here, I don't know if you heard about, this is what they call spring cloud stream. This is one of the spring products that you have, spring libraries. And what make it cool here is that those guys are completely independent, right? So they don't need to know each other. So they only need to know how to send the data over or how to receive the data. So they're completely independent, they're not completely independent. So you can scale this one, you can scale down that one and so on. And the layer here, they bind the abstract layer that can be message queues like Kafka, or Raptome Queue, or Geode, or Redis, and it moves the data between them. And then you can scale these layers. So you're keeping writing spring boot apps, but now rather than you are doing business logic, you are doing processing the data. You can receive data in a stream flow or can be a batch, but this guy here that's a spring boot application is gonna process that data for you. And he can send the data over using these abstracts layer. He'll make you very easy to program this. And if you are developing microservice, one very common way of moving data between microservices via REST APIs, right? Here, you don't have to rely on REST APIs. If you need a synchronous communication in that make those microservices completely independent, you can use this. And the message layer here, you wanna move the data for you, right? This is making you very powerful because you, as a developer, doesn't have to change there that you develop software. You still use the spring boot as application. And spring, you wanna inject the code for you that makes the data move between the services. So it's the same concept as before, but now, if I'm all part of the technology that makes various to move data around microservice. That's why you call this data microservice because you are specifically moving data between them and processing them. And then you can layer them as here, the data is going from this guy, for example, that one, for that one, so you create your data pipeline. And your data pipeline, you can transform your data as the data come. And then you can sync your data in a database or you can send the data to someone else and you can trigger another process. You can put things on as a Lego and create your data plan as you wish. Making very powerful because, for example, if you want to, this test test here wants to consume some data, doesn't have to ask this one. You can just type this guy straight to the bind and start to consume the data from there. You don't have to give this, and this guy doesn't have to know that. So make very easy, flexible inject process that process that particular data for you and that doesn't pack the flow of your data pipeline. Your data pipeline is still there processing the data and you can inject different apps to process different things. And your data pipeline doesn't have to change. The next one, it's the same. Yes, and here, that's the... Sorry. This is how you do this in Spring, right? So as you use, if you are a Spring user, pretty much put annotations in your code. So there, on the top, you have enabled binding, what that means, processor annotation, that it means that this is a processor. This is receive data from someone, process the data and send it to someone else. Who you're receiving from, you don't know.  The time that you do this, that you create your pipeline, then the data is gonna start to flow. So this basically talks about the previous slide. So again, this kind of talks about deploying these Spring boot apps into a platform like Cloud Foundry, which not only allows you to make it transport transparency, but it also allows you to make your infrastructure to be transparent. It kind of has all the integrated metrics and it has all the integration monitoring. And it comes with a dedicated login auto healing. So you have containers, you kill one and it will automatically start the other container. So that it comes with a lot of goodness, especially from a Cloud Foundry perspective, which kind of automates all your applications and kind of binds your applications with your data services. One detail here is that, yes, you can deploy this in Cloud Foundry, but you can test this today in your laptop if not Cloud Foundry. Why you want to deploy this in Cloud Foundry? Because you want to aggregate logs. You want to have high availability and so on. That the platform can give to you. Then you, as a developer, doesn't have to worry about this. The platform can give you the message layer that you're gonna bind your applications to it. So you don't have to worry about this. But of course, if you want to test those things running, you can test in your laptop.  That's the powerful of Spring. The same data that's here that you test in your laptop is the same code that you're gonna go live. No change. You don't need to change anything, right? And you can run it in your laptop today or in a platform like Cloud Foundry. No change in your code. This is the powerful that comes from that. From the annotation of Spring, from the injection code for you, depending on which platform you are. So this is the Spring, the Cloud Data Pipeline. Carlos, you want to touch on this? So you saw a bit of Spring Cloud Stream, right? So that's the, it's a library from Spring Cloud that you use to bind applications together or if you want to call data microshaps together to move data between applications. On top of Spring Cloud, Spring Cloud Stream, you have what you call Spring Cloud Dataflow. When you saw Spring Cloud Stream, you still have to write some code and bind this to the message layer and so on. Spring Cloud Stream, abstract this for you, give you a higher level of creating your pipeline. You now can create your pipeline using a DSL language that's made very easy to say. I want to connect HTTP server output with a processor that gets the output from the HTTP server, process this and sends to a database. You can mix and match and create different pipelines using a console, a common line, using the DSL provided by Spring Cloud Dataflow, making it very, very easy to create pipelines. I'm just gonna just give you a very, very quick example how this can work. So let's, yeah, yeah, yeah. Is Carlos audible to everyone? You hear him? So let me, yes, I'm trying to just one second. So here is the console of Dataflow. They call the Dataflow shell. This is how we interact with that layer that I show you. Here you can create pipelines. You can say, I want to receive, put a server, HTTP server that receive requests. Whatever someone sent that request to me, I want to capture that request. I want to do something of that request, transform from CSV to JSON to XML, whatever you want to do there. And then the output of this processing, I want to send to a database, or I want to send to a file, or I want to send to a log into a file, or I want to send to my have doops and clusters and so on. You can do this, this orchestration, how you data move around just using this console. That's the power of Spring Cloud Dataflow. It abstracts to you a lot of code that you have to write to bind those things. Of course, when you bind things here, that software, that application has a rep to be there, right? But once that you have a collection of them, that's what Spring Cloud Dataflow gives you, you can use them to orchestrate your data pipeline. So Dataflow, here you have a server that just manage your applications. It doesn't run your applications for you. The ASB, you're gonna be there to move your applications around. This guy here is just an orchestrator. He just connects things. He doesn't move any data around. He just say, hey, A goes to B, then goes to C. That's it. How the data goes through A from B is not his job. It's the main search layer job. So the ASB does this, does the security, does everything, and that's why it become a big monolith because he wants to do everything. These guys here only does one thing, orchestrate how the applications connect to each other. That's the bind, but doesn't do any more than this. Doesn't move the data around. How do this is the main search layer that's provided by someone else that if you are in Cloud Foundry, you can use the main search layers that they provide like Kafka, like Raptor and Q, and so on. Do you know what these types of things are? Yeah, you have each steps doing what they do best, right? So you need to move that around. So you have Kafka that move that around very fast. Can persist the data, it's durable, so you can replay and so on. Nice feature that they give that for you. You can use that to move your data around. When you develop your application, you don't have to know about this. You just say, I want that my application received from A and send to B. How the data is gonna move if you're gonna use Redis to move the data, or Kafka, or you're gonna use Raptor and Q. It's not your job.  You can say, yes, I want to use Kafka for this job, or for this pipeline, but for the other pipeline, I want to use Raptor and Q and so on. So the developer, if you are developing your application that received data from a third party, you don't have to worry about, ah, I'm gonna receive from a Kafka endpoint. I have to send this to a file. You just say, I have an input source, I have an output source. When you bind things together, using this guy here, they wanna connect to you to the message layer that you want to use for that particular pipeline. That's why the platform does this for you, right? So those things, the message layer, the binding, the high availability of the applications, when you run this in Cloud Foundry, they're gonna provide this for you, right? So all these services run as part of Cloud Foundry, right? The moment you push all these microservices, basically you're binding them together. At the time of execution, you're not really writing your stuff based on one ESB or anything. When you write for ESB, you write for that particular ESB, right? Most of the time. Even though they put standards together, but never work because, yeah, someone, in my case, you do this, in my case, you do that. Here, the only thing that you say here, I'm writing a Spring Boot application. How they're gonna run, if it's in Cloud Foundry, if it's in your laptop, if it's in Kubernetes, is how you decide, you as the manager of the guy that managed the platform, inside this platform you want to use, and how you bind them. If you like, wrap the MQ to move that around, you use wrap the MQ. If you like Kafka, use Kafka. If you don't like them, use hesitate party, you can implement that and bring that over as well. So, you as a developer, don't have to worry about this. You as a manager, or as a dev ops team, or part of the dev ops team, define where those guys are gonna, if you're gonna use Cloud Foundry, or now, it'll be fiscal servers. I wanna put the things there. I have process, I have a puppet chef, these scripts that put those things up, put those things down and monitor that for me. No changing the codes required. The same way that you interface with this guy, you're gonna keep interfacing that. You are not binding to something. Only you run time and say, I want to bind. I want to run this in Cloud Foundry. I want to bind this to Kafka. Then you are telling that. That's the main difference, right? It's about the flexibility that the platform unfortunately, to be able to, to be able to, I think that's the value that is. So, here, if I do a stream list, I see the pipelines that I have created so far. So, I have here one very simple pipeline called full, that pretty much every second gets the time and sends over, and the log gets that time and sends to the log. Very simple pipeline. Here I have another one that's called tag count, another one called tweet length, and another one called tweets. Those are my complex pipelines, because what I'm doing now is, I'm getting the other from Twitter, and for the data that I get from Twitter, I'm processing that data, for example, to aggregate by hash tags, to aggregate by language. If people are speaking in English, people are speaking Portuguese in Spanish and so on. So, all those pipelines here was aggregated by me, but if you see here what I have used, there's a time application there, there's a log application there, there's a tweet stream application, there's applications that are not developed by anyone, might be developed by someone of you. To develop your application, I spring both application, put available for me there that I can use and match how I like it. Right? So, someone developed the tweet stream that connects to the Twitter firewalls, get the tweets from there, that's it. Only does this, get the tweets. How you're gonna process the tweets is up to you. So, I'm saying here, get those tweets, count them and count them by hash tags. So, now I'm putting something else. I'm using that to count my tweets and aggregate by hash tags. That the guy that developed the application Twitter, doesn't even see that. You only say, the application only does one thing, connect to Twitter, get the tweets, that's all. Here, you are using these and all the applications putting together to do something that you want, to do some part of a data analysis. That's here, the data analysis, analyze the tweets. That you, when you deploy your application, you know about this, but someone that developed the tweet stream, only did one thing. Okay, let's connect to Twitter, pass your credentials, start to get the firewalls there and start to feed the tweeters in. How you want to do the tweet, I could say here, get all the tweets sent to a database. Get all the tweets sent straight to Hadoop or process this and also send to Hadoop. Because I want to process this later as well. I'm processing this in real time. As the tweet comes, I'm processing this. But also I want to process this in real time. I want to process the hash tags for the last month, for the last year. My data is going to Hadoop in HGFS, you'll be there in HGFS, and also I'm processing online here. So you define what you want to do. You can have multiple streams coming, aggregate them, and you can have multiple outputs. Going to a database, relational database, going to a HGFS and so on, file system and so on. That's the power that you have here. You keep changing things and mixing things around. So if I, let me create a very simple application here. Just, I'm going to deploy this full application here. I'm talking to you there. Yeah, but say that. Let me not put these tweets to run. I'm deploying tweets, that's the only I'm talking about. So what I'm doing now is, there's no connection there. Oh internet is not there? Probably not. So keep talking like this, I'll come back.  Yeah, so while Carlos is fixing that, the other thing we have to look at is, we talked about Cloud Foundry aspects where it helps you to be able to scale your applications automatically and it allows you to be able to quickly be deployed and migrate from one platform to another. All these things are kind of quite possible. And moving on, right? So if I were to, this is something that's just to finish this up. So when I talk that, so here I create a pipeline, right? So JSON filter, a transformation in that JSON filter, send to Spark, process this in Spark, get the data back and now I'm sending. Aggregate the data and send to Jyot, do something else and send to a relation database, right? This is my pipeline.  JSON filter is a Spring Boot application. Transformer is a Spring Boot application. Each of those things in your pipeline is a Spring Boot application. So essentially what you create is a catalog of application that you can use, you can connect them together and create a pipeline. So Dataflow catalog has this list of catalog of applications for you that you can use to connect them to process your data. That's the main idea. In those apps, they run as Spring Boot apps. It means they can run in containers, they have high availability. If you are running this in Cloud Foundry, if someone crashed, they can restart. If they can restart, they can be deployed again and so on. So the platform can take care of the high availability that you don't have to. And the data keep moving around, right? That's the main thing. And for some reason, for example, if that one of this transformers that stop to process, let's say, transformer here crashes, the data is not moving on. If you are using some message layer like Kafka, what's gonna happen? Kafka gonna persist this, then what you can do? Replay. Then when replay, and send the data again, and now you can process, right? So you didn't have to develop none of this, right? You are using the tools that are available to you to connect them together and create a pipeline. That's the powerful thing there. Clashes, crashes, then we can go through this again. Yes, if you are using a message layer, a message box like a message broker like Kafka, you can replay your message because they persist the message and allow you to replay that. So the powerful here, because they can persist on data there, you can replay, you don't lose the data if you have a product that you are out for like five seconds, 10 seconds. And that's why you can do that, right? If you have a product that you are out for like five seconds, 10 seconds. If you are processing feed, financial market that you don't want to lose for some reason, you could use some message, persist the layer like Kafka that allows to have that persistence for you. If you don't care about losing data, you can lose a faster message layer that can lose data but process data fast. Then you decide how you want to connect this, right? You don't have to be by eye. I bought that in ESB, I have to use the message that the transfer message that they give me, I cannot change anything because I have to use as it is. Here you can connect things, right? And you can, the best tool for the job. If persistence don't lose data is important for you, so you have to lose a message layer that passes the data, you can replay that. If it's not important for you, you can lose something that everything's in memory. If crash, if you lose, that's fine.  Depending on what you want to do, you choose the tools that you want to work. And the same principle applies, the same way that you do this, how is the same? You match them using the data flow shell that I showed you. And then after that, they're going to deploy as Springboard apps and going to run independently. If they want to talk to, this guy doesn't know about this, they don't need to know about this, the message layer. You want to show the UI to them? So let's see if the connection is... Do you have my point to the internet? Supposedly. So this is the data flow UI. Before I show this, let me show just here one thing that is... Just to give you a context or give you a high level architecture of those things, right? The data flow that I show you. Yeah. So this is the main architecture of Spring Cloud Dataflow, right? What I show you was a shell that connects to this guy that is also a Springboard application that can run locally in your machine or in a platform as cloud-funded. In this case here, I'm running in PWS. So this guy is running the IPWS. And then I have a shell in my machine, that's what you saw, that send requests, REST requests to talk to the other media. And by doing requests, I can ask what you have deployed, I want to create a new stream to deploy and so on using the shell. If you don't want to use the shell, you might use the Flow UI. There's a graph interface that you can create your pipeline graphically, just drag and drop. The guy, you're gonna also gonna talk to the server.  This is running somewhere, can be on a laptop, can be in Cloud Foundry or Kubernetes, whatever you want. Then this is how you interact with this guy. And this guy here now knows how from receiving the requests, start the applications. If you say, I want to send time to log, meaning I have two applications, an application that generates time, application that saves the log. So there are two applications that this guy wanna say, hey, run two applications, whatever you are, inside Cloud Foundry. If inside Cloud Foundry, you're gonna run these as containers that will be managed by Cloud Foundry. And those applications, this guy wanna say, hey, you have to talk to each other. So bind to the same message broker, then also depend of you, you can say, the message broker that I want to use, but you'll be Kafka or you'll be robbed and so on. By doing this, now you have two applications running inside Cloud Foundry that are talking to each other via message broker. Right? And then this guy here can monitor those applications, find out if the one crashes. Oh, let's say that you have update. I did a new version of my application, now I have to upgrade. Then you can have cannot upgrade C as well. So meaning that you don't have to put all the service down before upgrade. So this is what the high level architecture of Spring Cloud Dataflow. This is a Spring Boot Apps. It's not something special. This is Spring Boot Apps running on the Java JVM. Very, very, very simple, right? So the same technology that you write, that you use to write your Microsoft, your web applications is the one that's here. So far, are you guys on track with us? Okay, awesome. So, okay. Back, so you wanna show the UI now? No, if they request I cut me some. Okay, let me get the other one. Okay, this is the Graph interface. That's how, the other way that you can interact with Spring Cloud Dataflow. If you check here what you have, this is a list of our application that I can use to create my pipelines. I have FTP source, what that means, that I can now have FTP server or FTP client that connects to FTP server, copy the file over and bring to my pipeline. The output of that FTP can be a JDBC. It means you're gonna open a JDBC connection to a database and save the content of that file inside the database. You created the pipeline as you wish, right? So, if I go here on the streams that I have, so those streams here show you what you have, the same interface that you have in the common line there, you have here, from here you can deploy, you can deploy, you can destroy. Remove the stream from here. It's kinda the same information, but in a graph cut way. So, this is what they call about, and here, let's say that I develop a new application, right? So, if I develop a new application, let's say I create a source meaning, I create an application that can consume data from Salesforce. So, that application connects to the Salesforce using the Salesforce API, it brings the data from my pipeline. So, now I can create pipelines that has data from Salesforce. If you want to register that application to Cloud Dataflow, it means what? Now, someone that use Dataflow can architect pipelines that has data from Salesforce, that has data from FTP server, that has data from Hadoop, and they can process the data together to give you something meaningful. I can register my application like this. So, there are three types of applications in Spring Cloud Dataflow. So, the task you're gonna talk later about this, but here we'll talk about streams, moving the Dataflow on point to Delta. So, you can have a source, it means that you consume your initial point of data inside the pipeline. This is the data that bring the data, is the guy that bring the data into the pipeline. It's a source, FTP can be a source, HTTP server can be a source, and so on. A file can be a source, and so on. These are the name of the application. A processor is something that receive data from one side and send the data to the other side. You can use a processor for what? For transform data. Receive XML, give JSP. Or you can aggregate data. You receive data here, then you aggregate by name, you aggregate by city, or as the tweets, you aggregate by hashtags, or you aggregate by language, and so on. And then you have a sync. A sync is receive data and stop the pipeline there, because now the data will be persisted, or you're gonna be going out the pipeline.  These are the three types of application that you can develop, and then you can create your data stream pipelines. That's why we call data microcells. Finally, there's a task there. This task is for what? Batch. Let's say that you want to do a batch job. Every 10 tweets that I receive do aggregation using all my tweets from the last year. So it's a batch job that you're gonna take longer, you're gonna have to go to some place once the data is there, and gonna run. That's what they call a task. It's a batch job. And you can combine a batch job with your stream. And by doing this also, you can have trigger-powerful things, right? Let's say that you are processing logs. You receive a log that has a failure, an error in the log. That error can trigger a batch job that will send an email or process something else, or open a ticket, do something else that outside the pipeline. You can combine those things together. So that's the powerful things there. And that's how you raise your applications using the web interface. You can do this the same thing from the common line as well. Okay. So you want me to just wrap it up? Okay, keep going. So I don't see if I want this one here. So probably three things that I want to summarize. Converging data in the cloud, in the applications, and the data, they can't be separated. Building really data-driven apps, which means smarter the apps are, more the data gets generated, the better you can make again the smart apps. And then being able to use Open, I guess most of you, since if you're using Spring, you are kind of aware of the open source community and all of that. So Pivotal is really a big advocate of open source. We, whatever products that we carry or offer to customers, all open source. And what we provide is just the support and the top value added services on top of the products. But so we believe truly that all the enterprises, because now if you look into some of these consumer-grade companies, which I was referring to, Google, Facebook, they're able to wrap it in a way because they're all using open source. There's a huge community backing. Moment you are into commercial products, which means you're locking yourself. Your innovation is totally dependent on how fast can that vendor, that you have the software product vendor that is innovating, right? So the speed is all up to the vendor. But now with open source, you have a community. Agile is something that we also believe in. They're shorter innovation cycles. It reduces the TCO as well as the time to market gets improved. And lastly, now it's all about cloud-ready. So you have to really look at all your applications to be running independent of your infrastructure. It should, whether you move your application to Amazon or you'll move your application to private cloud, whatever be the case, it has to really be in a cloud-ready and you should really focus on solving more business problems rather than creating, you know, working on things that are already available in the market. So that's pretty much it. I have a few more slides, but I think this is really good enough. Probably Carlos will kind of- If you have questions, can, I'm gonna try to run the demo here just, but if you have questions, why- Yeah, if you have any questions, probably I'll take the questions. If not, probably Carlos will try to show the demo. The demo is actually quite cool. It will actually show, it will color all the tweets by hashtag and it starts showing the graph in real time, how are the tweets, what are the top, you know, hashtags within real time. So just to show you some things here, for example, this is, I'm in my laptop here, so let me just get this. There's a question there, yeah. Yeah, so Spring Cloud Dataflow is the, the next version of XT. XT is gonna be End of Life next year. So what is- Can they migrate to the cloud? Yes, you can. What you did is, from Spring XD to Spring Cloud Dataflow, you removed the runtime. And the runtime now is a cloud platform, like Cloud Foundry or Kubernetes and so on. So that runtime business availability and binding message brokers and so on, you left this to the platform. And now you'll focus only in the apps in the orchestrator of them. So if you check here, if you see this here, what I'm doing there is, I'm starting the Spring Cloud Admin on my machine, right? This is a Spring Buddha. So now I can bind my shell to this guy here and run my pipelines locally in my machine. The experience is the same as here or can be on the cloud wherever you are. Same experience, always Spring Buddha application. Now, what I'm gonna do here is I'm gonna run the, I'm gonna bind this to my local machine now. So now I'm binding to my local machine. If you check here, see that you received the request. So now I can, if I do a stream list here, have nothing here. Let's say that I want to create a very, very simple application, very simple, see I have something in the pipeline here. Very simple. I said that I want to create this tweeted thing, right? Now I'm gonna run locally in my machine. So I'm gonna run this. I have to, so one thing that you have to do is when you start Spring Cloud at a flow, you have to rest your applications, right? So you need a catalog of applications to run. So when you start that a flow, that a flow doesn't know anything about your apps. So you have to say that a flow, this is the my catalog of applications. This is the application that I'd be able to use. So what I'm doing here is I import, I'm saying that a flow, this is not the apps that I want to import. Now I can use in my pipeline. I'm using this from the, and another thing here that you check here is that's, I have a rabbit here, what that means. Those applications, they can be connected via a random queue. Then if you want to bring the ones that for Kafka, you're gonna have one for Kafka and so on. The point here is that now, after this, I'm gonna have a catalog of apps that I can use to create my pipeline. If I do this here, so now you see that all those apps are registered. So now I can have services for, I have Sync, JDBC, I have such TCP, I have and so on, right? So one thing now is if you check that, what I did was I'm using RobTemQ. So I have to start RobTemQ. You have to go to college. What? Yeah, yeah, I just saying that now I have to start RobTemQ. Because what I said was that I want to use the RobTemQ. Yeah, now this, let me start Kafka because for some reason my broke is not, let me bind this to the other one because let's try to run this on the cloud because now my RobTemQ is not starting up for some reason, so you don't have much time to figure this out. So I'm gonna bind back what I did now. I bind this back for the data flow in the cloud that's this address here. And now if I do a stream list here, I have what I had before, right? So let's say if I deploy full, so it's very basic, just time log, then after that I'm gonna deploy it to it. So full is deployed. So now if I check here, it's failing for some reason. Let's wait a bit. The demons of demos are no, so keep answering the questions. Any other questions? Probably give it another two, three minutes if not probably we'll do it some other time. Any other questions? Here. Learning so far is that not Kafka is like a Kafka. It's a AWS. Yeah, think of it like an abstraction layer or a noise on top of your cloud. How does it compare with something else like CloudForms? How does the CloudForms, is it? We have CloudForms. Okay, Red Hat has this thing called OpenShift which is comparable to the CloudForms tree. Have you heard of CloudForms before? CloudForms, yeah, but I never use it so I don't know much about it. So what it does, what's there? So you create orchestration, you orchestrate apps as well. It's like Docker Compose, something like this. Okay, so the application running containers, so you have orchestrate of containers. How CloudForms works? Is it a platform as a service or is it? So we follow the platform as a service. So CloudForms is compared, it's like platform as a service and basically things, you know, there are vendors like OpenShift who offers, they're running on Kubernetes and dockers, whereas you have other vendors like IBM Bluemix, IBM has their own CloudForms, so. So, not so sure, I think CloudForms. Hold on, hold on, hold on. Is it done? Yeah, so now I'm gonna start the stream deploy. So we are running everything on CloudForms so probably takes a few seconds. Okay, so it deployed both. The first two streams has been deployed, so now I've deployed the final one, there's the tweets, one that connects and receive the tweets. It's deployed, so now if I go here, I'm running this locally so to avoid any, I'm showing, I'm still not showing here. So the streams, these are the streams. Deploy all the three streams and now we should wait a bit to see the data comes in and once that is ready, you're gonna show here in the dashboard so then I can see the visualization. So if I want to check what's going on, so I can come here for my logs. So this is the Kafka, I'm using Kafka locally in my machine, this is the message block that's moving the message around and here you see that something is crashing here so I can check the logs. For some reason, the tweets cannot process. So the data is not flowing to the other parts of the, I don't know what's going on. Okay, thanks a lot for coming and they probably will be conducting these sessions quite often, so probably next time we'll be able to show the same. I know, I know what it is. It's ready, it's fixed. It's ready, it's out, so now the data is gonna show. The data goes to there. So this is the demo that, this is what I want to show, so red zone was out so this comes from the cloud, this comes from the tweets and based on the size, it means how many words you have, how many tweets you have with that particular word, right? So this is the hashtags and now I can have another one here that also is in real time. This one's for language, so you can see English, Spanish, Korean, and so on. So all of this changes in real time? Those aggregations are be done by the tweets that I, by the things that I show you here, right? So tag counts, receive the tweets, you see that tweet stream? I get the tweets from there. Now I'm putting a counter and I'm putting a counter aggregate by hashtag text, right? So it's counting. Have a time that's had real one, two, three and keeps counting that inside reds, reds is the database they are using to do these analytics. The second one is by language, so I'm using here field name lang, name language. And now if the language is English, put in the bucket lang, English. If it's Spanish, put in the bucket Spanish, and so on. And finally, this is the guy that gets the tweets so he doesn't do anything else, gets the tweets and now those two here process the tweets, right? I have three streams, one just to collect, another in two orders to analyze. And based on these analysis here, if I go to the interface, I can see them running in here. So I put three pipelines together in order to get this analysis here. I didn't have to write one line of code, right? Because the tweet application that conserves the tweet is already being read. The aggregation, I just use the DSL from, tweet itself from Spring Cloud that flow itself to say, count, use the field value counter that's a application that count things based in the hashtag text. Use the field value counter based in the language. So I use the SL itself to do this very basic aggregation of data, but I didn't have to write any code for this. And now I can see this here. So the point is that you can use Spring Cloud to process the data that you have. The data doesn't have to come from the cloud, doesn't have to come from the internet, can come from a file, can come from a database. Anything that you want to process on stream you can do here. And if you want to process in batch as well, you can trigger the start of the batch from here. Questions? Thanks for staying up. Wait. Thank you. So, thanks.


All right, in this video, I'm going to go over my May stock photography report. And May was actually a really good month for me. It was my best month of the year. Not only was it my best month of the year, but it was actually also almost double my second best month for the year. So got a lot of good news to go over today. I'm going to jump right into it and kind of get into the report. Now my best site for May was Fine Art America. I made like $128 on two sales of my prints on Fine Art America. And that's really good for me.  You know, I don't get them very often. And it's normally like every other month. So getting two in one month is really good. Getting two very large print sales in one month is amazing. So that's $128. Let me show you quickly, you know, exactly how much I'm making and how have my Fine Art America set up since it was a good month. So I'm just going to go through here and show you my sales. So the way that you set up Fine Art America is you can go through and basically choose how much money you make on top of whatever the costs are for the sale. So somebody can go on there by my photos and Fine Art America hails everything. They print it to them, they ship it to them. And I basically just set my own commission. So I've got my commission set for the largest size prints, which are the ones that are 60 inches. So this one here is 60 by 40. I've got it set to $70. So you can see for this particular photo, I have the normal price with $70, but there was a 20% discount for that month. So basically they took $14 off as part of the discount. Everything gets discounted. You can't really control that. All you can do is, you know, raise your prices when they do discount. But I don't think it's really worthwhile. They don't get sales during discounts that often for it to be worthwhile going in and updating it. So that was one metal print. It was a 60 by 40, which is amazing. You know, it's amazing to me that somebody prints off a 60 by 40 of one of my photos and puts them on their wall. That's a reward in itself. So this extra money is just more on top of that. And I also had this photo here, which was, again, another 60 by 40. But this was like a gallery wrap. And this one wasn't with the discount. So those are two $70 sales. So this is how I go through and kind of show you that because those are the types of things that I am selling on Fine Art America. You can kind of see here, I'll scroll through. So I often get questions in the comments about, you know, what sales, you know, what types of things sell. And you can kind of see here, for the most part, that the landscape photos that I take. So landscape and travel photos I find do fairly well on Fine Art America. So next on the list is Shutterstock. I actually had a pretty good month for Shutterstock last month with $87. Again, that's my best month for the year for Shutterstock as well. So again, May was a pretty good month for me. The top two sites did quite well. Now for Shutterstock, most of that was one photo sale. I had one photo sale that was more than $60. So somebody must have bought like an extended license for that particular photo. And that was the majority of my sales. So I don't expect to see Shutterstock this high every month. It was pretty much just an average month for Shutterstock with one $60 sale. But that $60 sale pushed me out from like $30 or $40 that I've been making regularly to $87. So that's how that happened. And you can see here, it also brought the revenue per download up, right? So with Shutterstock, you know, you get a lot of 10 cent sales, a lot of $2 sales. But then when you have a large sale like $60, you know, we're only getting 53 sales a month. It really pulls up your revenue per download for that. So that, you know, pulled up to about $65. Next on the list was deposit photos, pretty average month for deposit photos, 46 sales for, you know, about $32. Next was CanStock Photo. Again, another site that typically, you know, I only see, you know, $2 to $5 a month on CanStock Photo. But again, one large sale there, I think it was like $18 of that, came from one sale. Or actually, no, that was only, sorry, looking at this again. $23, that was all one sale. So one sale must have been an extended license on CanStock Photo. And that was $23. So again, something that doesn't happen every month, but again, came through in May. Next is Adobe Stock. So Adobe Stock, $21, which is about average for Adobe Stock for me this year. So nothing big there. However, something interesting did happen with Adobe Stock that I want to talk about in a bit more detail. So if you go to your Adobe Stock, if you do use this, I'm gonna go and take a look here. Adobe Stock has decided to set up a free section. Well, actually, not that they decided to set up a free section. They think they've had a free section on their website for a while where they have photos for free. But what they've decided to do is start paying their existing contributors to add some of their photos to the free section. And so what they've done is they've gone through and nominated photos that can be added to the free section. Now, this I'm sure is in response to the success of sites like Pexels and Upsplash that I talk about in this channel a lot where basically there's these sites that give photos away for free. And I'm sure that Adobe has been monitoring that closely. Getty recently bought Upsplash, one of Adobe's main competitors. And I think Adobe has looked at it and said, this is something that we need to do if we want to compete in the stock industry. So we need to have a much better free site set up. However, Adobe is one of the few sites that is actually paying you to add your photos to the free sites, okay? There are other sites that are paying you but they're much smaller sites. It's the first really big main site that's doing this. And what they're doing is, essentially the way that they're setting up, they're gonna pay you $5 for adding a photo to the free site for 12 months. And that's important. If you go through and look at the Learn More, take a look at it, is that you're not putting it on the free site indefinitely. You're putting on the free site for 12 months and you're getting paid $5 for that. Now, not anybody can upload to the free site either. You can't actually upload directly there. You have to upload your photos to Adobe Stock and then Adobe Stock goes through and nominates those photos. So it says you can put this in the free collection. So if you look here, 89 of my photos were nominated. So if all of these get accepted, then I get $450, okay? So I'm gonna go through and kind of show you how they're nominated and what I plan to do with this because I know a lot of you probably have photos on Adobe Stock and you're probably getting similar messages here. So the question is like, is it worth it, right? Like would I be better off just to keep my photos on Adobe Stock and sell them or put them on the free section? You know what I mean? And it really comes down to you. Do you think you're gonna make more than $5 off each of those photos over the next month, right? And it also gotta think about is putting them on the free section potentially gonna cannibalize your sales on the other sites like Shutterstock, meaning that if you move a photo over to the free section on Adobe for the $5, will you get lower sales on Shutterstock? So to answer that second question, I think the answer is no. I don't think you'll see a significantly lower sales on Adobe or on Shutterstock if you put them on Adobe. The reason is is I don't think stock buyers shop around that much. There may be a little bit going on, but I think most of these sales come from subscriptions and when they're sold on subscription, if somebody has a subscription to Shutterstock, even if they see it on Adobe, they may still buy it through their subscription just because then they know they're gonna get from Shutterstock, keeps it all in one place, okay? So I don't think that'll be the case. Now, obviously, if you add it to the Adobe free section, you're taking it out of the Adobe paid section. So you definitely will see less sales on Adobe or no sales on Adobe while it's in the free section for the next 12 months. So it's really kind of what you expect for it to be for those $5. So if you take a look at my photos here, what you can do is, I'm just gonna uncheck this. They've given you a new checkbox here for the Elable for the free section. So for that, you can easily go through and you can see all my photos here. So if you look at my most popular photos, none of those are Elable for the free section. The first photo that's Elable for the free section is this one here, this landscape photo where I've got 72 downloads. And this is a fairly popular photo for me on Adobe stock, right? And often these photo sales are larger, it's a larger file. So I do notice this photo gets sold a fair amount on Adobe stock. Interestingly enough, it doesn't get sold very much on Shutterstock at all, but Adobe stock, it seems to be a good photo for me. So in 72, you can kind of see here, where they're not letting you move all of them, right? So you can scroll down and most of the popular ones, they're not, right? The next one that I'm allowed to upload is this one with this 39, 27. And you can see here, so if you have that little checkbox, the little nominate box, that means you're allowed to nominate it. So as you can scroll through, I don't know how they came up for this. It looks pretty random to me. I suspect they have some sort of algorithm they're using some way here to test. Do you know what I mean? They wanna test out from their point of view too, how much they're going to make, right? Because obviously taking these photos off here, putting onto the free site, they're losing money on those sales as well too. So they've probably got an algorithm behind here that they're gonna test out how this is gonna work and then probably optimize that algorithm for future years, right? So you can scroll down and see, down here, seven of these photos, the ones that are available, okay?  What I've decided to do is my most popular ones, the ones in the top row here, I think that I will get more than $5 in sales on these five photos on Adobe Stock over the next year. So I'm not gonna add those ones to the free section, all right? However, I am gonna add the rest of them. Some of these, this photo here, I've got 15 downloads on it, but this photo has been up here for five or six years, okay? So more than likely, I'm getting average three a year, I was actually getting a lot more than I am now. So I think I might only get $2 sales from this photo over the next year, so I am gonna add it as long as these ones. And I kind of, when it gets down here, some of these two photos, they're kind of a no-brainer, right? I haven't made $5 on Adobe with them yet. So I might as well add them down, the one downloads. And as soon as they've even chosen some that have zero downloads, this one I understand here because it's a photo that was taken recently, so maybe it's a good photo that just hasn't been in the search results so they wanna put it in the free site. But this photo and these photos, these have been up here for quite a while and not gotten any downloads, but they wanna add them to the free section, they're willing to give me $5 for these three photos added into the free section. So those ones that in my opinion are no-brainer, I doubt I would have made a sale on Adobe over the next year anyways, with those, so I'm gonna add them in, okay? So that's my plan to go through here. If you look at this, obviously this looks pretty good. Like in my Adobe earnings for this year, just under $100, okay? So if it's almost halfway through the year, I'm doing this up until May, so maybe, you know what I mean? I would have planned I'm making $200, $250 from Adobe this year and now Adobe is coming to me insane. I'll give them $550 if you add all these photos, okay? So I'm gonna go in, I'm gonna do this. I know a lot of people won't, I know a lot of people are gonna disagree with this thing and I'm happy to have that discussion down in the comments. But for me, an extra $400 from these photos, assuming they do get accepted, they don't commit to these being accepted. All they say is that you're nominating them and if they get accepted, they go through. I'll obviously post here what they get, but let's say I do get $400 for those. I'm looking to buy a new lens for the summer. I'm going out, gonna be doing a lot more photography. I wanna get a 50 millimeter, one for lens or a 40 millimeter, haven't decided which one yet, for the L-MEL camera system, right? I used to have a 50 for when I had Nikon, I moved over to L-MEL, haven't bought it yet and this is gonna pay for almost half of the lens, right? So from my point of view, this makes sense. I'm gonna go through it, but again, a lot of other people wouldn't, depends on the number of sales that you have. So that's my plan. I'm going to do that and then I'll update here how they get accepted and you'll obviously see these in my monthly revenue reports as well. All right, next on the list was Pexels. Again, not a great month for Pexels, but I haven't been uploading that recently. So I've kind of fallen down the ranks in Pexels. I'm still like in the top 100 all time, but if you look at like my last month, monthly performance, it's not great. So that's why, and the reason is I just haven't been out great taking many photos recently, just really not a lot of time more than anything else, but I have actually been planning a lot of photography trips over the summer is when I do a lot of my photography and I will be, you know, so I'll be, you know, getting back into taking a lot more photos, processing a lot more photos. And when I do that, I'll upload it to Pexels and I would expect my numbers to go up then once I do that. One, two, three, I off again, a pretty average month for them, Dreamstime, big stop photo, which leaves me with $200, $321. So before this, my biggest month was January, where I think I was like at around 170. So this was a really good month for me, but that being said, someone expected, I've said in past videos, if you follow along, a lot of the photography that I do, I do it in the summer. And so they're like summer landscapes and a lot of it is done during my vacations, right? I basically go on family vacations, we take photos and I come and I put them on the stock sites. So a lot of those stock photos are really useful to travel companies in the summer, right? So people that are, you know, planning their advertising campaigns for the summer are doing that in the spring. So you think about May, June is when I get a lot more sales. So this is pretty typical for this time of year. You know, maybe the fine art America isn't as typical because I got two photos up there, but the rest of them, you know, seeing a bit of a bump up in the other sites is pretty typical just because of the type of photography I do as well as the type of year it is. Anyways, hopefully you found that useful. Make sure if you did, you go and give the video a like. And best of luck selling your photos online.


A significant body of evidence indicates that oxidized cholesterol may be one of the main triggers of Alzheimer's disease. But that's not all. Clustral oxidation products are associated with the initiation and progression of multiple major chronic diseases, including heart disease, diabetes, and kidney failure. And they're produced when animal products are heated. All forms of cooking can do it, and since you can get maximum cholesterol oxidation at only about 300 degrees Fahrenheit. But is there some type of cooking that's worse than others? Well, if you look at the full meat, which is like baby horse meat, higher levels of oxidation in general were found in microwaved meat. And indeed, microwaving chicken or beef appears to produce about twice as much cholesterol oxidation than frying. Whereas if you look at bacon, raw bacon wasn't found to have any oxidized cholesterol. It has cholesterol, like all animal products, but it's not oxidized until you cook it. Grilling seems to be the safest the first time around, but then when you put it back in the fridge and reheat it later using the same method, they all shoot up. It's not just heat, though. Although levels in raw meats are usually low, concentrations tend to increase dramatically after exposure to pro-oxidation agents such as light. What are you supposed to do?  No, you could wrap the meat in red plastic wrap. Clear plastic wrap doesn't seem to work, but the red blocks some of the light waves and can delay cholesterol oxidation. This was for horse meat slices. The problem is worse with sliced meat products because more of the meat is exposed to air and light. Same problem with ground meat, it's just so much more exposed. Unless you keep meat in some kind of vacuum pack, even in a dark refrigerator, the oxygen exposure alone can shoot up oxidation levels or in the freezer. Yeah, cooking raw fish can boost levels from 8 to 18, but after a few months frozen fish, even raw, starts out about 10 times higher and just goes up from there. And in terms of which meat is the worst, microwaved or fried, chicken was twice as bad as beef. The reason it seems has to do with the polyunsaturated fat content of the muscle, which goes fish, then poultry, then pork, then beef, then lamb. So white meat is more susceptible to cholesterol oxidation. Yes, red meat has more saturated fat, but fish and chicken tend to build up more oxidized cholesterol. So chicken and roasted salmon have been shown to generate greater amounts of cholesterol oxidation products than other types of meat. Surprisingly though, the highest increase of oxidized cholesterol in salmon was found through steaming, mainly just because it's exposed to heat longer. Cholesterol oxidation increased after each cooking procedure, but steaming increased the total amount by more than a thousand percent. There are two ways chicken meat may pull ahead, though. One is if you feed the chickens rancid fat in the first place, and unfortunately all sorts of substandard stuff ends up at the rendering plant to be turned into animal feed, and also irradiation when chicken meat is radiated to improve food safety from an infectious disease standpoint. It may diminish food safety from a chronic disease standpoint, but hey, I said better than dime from salmonella. In terms of dairy, in my last video I talked about the potential dangers of ghee, which has made me wonder about UHT milk, which stands for ultra-high-temperature processing, to make like those little half-and-half-no-refrigeration-needed coffee creamers. That does seem to boost oxidized cholesterol levels by about 50% worse than just regular pasteurization, though interestingly, if you can find goat milk half-and-half, that would be safer. Same problem with eggs. Egg powder in processed foods is good for shelf life, and may not be so good for human life. So that's like packaged food with eggs in it, like pasta, many baked goods, mayonnaise. So even people who stay away from egg eggs may still be unwittingly exposed through processed foods if they don't read the label. If it's all about oxidation, why not just add synthetic or natural anti-oxidants to the animal products themselves? They've certainly tried. But what about adding lemon balm tea to hamburger patties? It didn't work, but that's likely because they couldn't add enough without affecting the taste. What about adding cherries? Their red would blend right in, and it worked. Two different types of tart cherry significantly reduce the cholesterol oxidation, but meat with a cherry on top seems a little out of place. How about just good old garlic and onions? Here's the amount of oxidized cholesterol in a plain pork chop, significantly reduced by adding onion and garlic. Though interestingly, in chicken, cholesterol oxidation was helped by sage, but not garlic. In fact, garlic may even accelerate fat oxidation. So there are several measures that can be taken to reduce cholesterol oxidation in foods, reducing the total cholesterol content in food by not cooking food with cholesterol containing fat like butter or lard. Maybe we can feed animals antioxidants before or add them afterwards, use as low a temperature to cook as possible, maybe some kind of opaque vacuum packing or something. But if you take a step back, only foods that start out with cholesterol can end up with oxidized cholesterol. So the primary method in terms of reducing cholesterol oxidation in foods may be to reduce the total cholesterol content of the food, not just by like adding extra with butter, but instead centering one's diet around whole plant foods, which don't have any cholesterol to get oxidized in the first place.


Hello in this lecture, we're gonna finish up the master budget We're finally down to the balance sheet where we can tie everything together So I won't go through the whole thing again. We're on the we're on the balance sheet We're on the last piece. This is the last piece that we need to complete and within the balance sheet, of course We have our assets which we will start off with as normal Assets and we're gonna start off with our cash cash being the first asset now Where's the cash gonna come from? It's gonna come from our cash flow budget So we're gonna scroll up here and say equals I'm gonna find my cash flow and we ended off here at this 40,000 we got this 40,000 there. That's our Indian cash from the cash flow and The next thing we have is the accounts receivable now for the accounts receivable. We're gonna have to do some Calculation over here. We're gonna go over here. I'm gonna do another little side calculation we're gonna have to have the beginning receivables plus the sales on account and Then we're gonna see how much we collected on those sales on account to get the ending Accounts receivable. So the beginning receivables if we scroll over here, we're gonna say where do we start off with that? We go all the way up here and we say that on the balance sheet. We started off with 342 248 342 248, that's where we began with then we had sales total Sorry about that. We got over here total sales if we scroll through our sales budget all the way up top sales budget We had one million four forty seven two hundred in sales And then they told us that 70% of those sales. I'm just gonna say 70% It's already formatted as a percentage home tab Numbers group percent over here. I'm gonna go ahead and make all this bold here Just so you can see it a bit better and make it all bold. So that's a 70% are on credit The rest are cash sales. So they don't affect our receivables. We got cash at the time of sale So we're gonna say of the total sales one million four forty seven two Times 70% of those were on credit meaning they increased our receivable We didn't get cash for them then we're gonna have the cash collected from credit sales the stuff that we collected on the sale So I'm gonna say this equals and I'm gonna have to sum this up I'm gonna hit some and we're gonna scroll over to where we have this budgeted I'm gonna scroll up here. We have the budgeted for three months out. So up here. We have Collections of receivables. So that's on July August and September if we sum of those up then we get the one million one seven 17608 so that means that the receivables at the end of the day This is what we started with then we had sales plus this on account Those are the portion sales on account minus the fact that we collected on these these are the collections that we had on Account means that we'll have an Indian receivable of 337 680 3 3 7 680 that is what's going to be in the receivable over here. So we have accounts receivable And I'm gonna say this number equals this 3 3 7 6 80 Next we're gonna have the raw materials so raw materials in this so we'll do a quick calculation over here as well for the raw materials Inventor we're gonna start off with the Indian raw materials what we have and it's gonna be in units We know what we had in units. So I'm gonna say this equals I'm gonna scroll up to our raw materials budget up here. So we're gonna scroll up to The raw materials budgets and we see this Indian budgeted inventory. We had this 4,000 right there We're gonna take that 4,000 and we're gonna multiply times 21 because it's cost 21 per unit 21 dollars per unit So there's the units 21 dollars per unit Therefore we equal the units of 4,000 that we had at the end times 21 dollars per unit that gives us the 84,000 so this number here the raw materials inventory in dollars is of course the 84,000 alright, so next thing that we are going to have is the finished goods inventory So the finished goods inventory is gonna equal We're just gonna scroll up to the cost of goods sold calculation. We have it right here We calculated right there. So it's a 321 360 if you want to look how to calculate that again You could go back to that video and check out the calculation to get to that number now. We're gonna go to the total Current assets these are all of our current assets we're just gonna add these up of course like we could underline it home tab font group underline and then scroll over here and have this equals the sum of The 40 down to the 321 660 and that'll give us our total current assets and then of course the next piece that we will have is going to be Equipment, so we have long-term assets. We've got equipment Property plant equipment includes equipment and the equipment's gonna include what we started with So if we scroll back over here and we take a look at where we were at at the beginning We had 600,000 in equipment so we had 600,000 and you'll recall that that we purchased more So I'm gonna say this equals the 600,000 plus and I'm gonna say That this is going to be increased if we go to the cash budget We can see it there that we purchased equipment for three 130 cash now of course again, this is assuming we paid all cash for it in this problem We did we paid 130 cash therefore we increased it to the 730 then we have the accumulated depreciation. So we've got accumulated depreciation and The accumulated depreciation once again, it's gonna be what we had before up here plus the the new change that happened So the accumulated depreciation we was before 150,000 so we're gonna say we're gonna go back down here. I'm gonna say this equals 150,000 Plus I'm gonna say the sum of and the accumulated depreciation was that was under the overhead and It was the fixed portion. So this fixed portion of overhead this 21 per month that that is the Depreciation so we're gonna say I'm gonna sum this up. We could have just picked up the 63 over here But it's the 150,000 plus the 63,000 or the 21 plus the 21 plus the 21 and There we have that then If we have the equipment net Equipment and we take the net value of that meaning the equipment plus the accumulated depreciation To get the book value. We can also call the book value of the equipment. That's gonna equal the 730,000 minus the accumulated depreciation remember the cumulative depreciation is a contra asset book value then of the equipment being 517,000 now we can have our total assets Total assets. We're just gonna sum up the outer column


Hello, welcome to Entrepreneur India, today we have Hero Enterprise Chairman and Serenipati Art Foundation Founder Patron, Mr. Sunil Khan Munjal with us. Welcome Mr. Munjal. Thank you. So Mr. Munjal, I would like to start with, how and why did you start Serenipati Art Foundation? You know, we are quite lucky that as a nation, we have one of the richest cultural heritage in the world, but what's sad is a lot of it is actually going away, it's dying or declining and does not appear to have enough patronage. For us to not be doing more was something that I felt was not fair, it was not right. So actually I didn't start from here, we started this 20 years ago, we set up a similar organization in Lujiana, called the Lujiana Sanskritic Samadam, which was only for the performing arts, and that is still continuing, it has done over 100 performances of every kind of performance art form. When we moved here in Delhi, many of our friends used to say, you know, why you do all this in Lujiana, why not here in Delhi, so my own feeling was there's plenty of opportunities in Delhi, but when we looked around we found it was wonderful initiatives going on individually in each art form, but there was not one which looked at the collective arts together, so that was one of the reasons of setting up the Serendipity Arts Foundation, is to figure out a way to encourage the arts as the art forms used to be in India, the pre-British time, and that we felt would be a useful thing, alongside encouraging and propagating the arts. The festival is obviously a very public event, so it gets talked about, it gets advertised, it gets publicized, because the football runs into lakhs every year, the number of artists which come in are very large, even just in the one performing arts, we have over 2000 artists coming in and 10% of them come from overseas, so and this festival has grown both in size, scale, and quality year on year, every year. If I think back from where we started on 2016 and where we are today, it's been a long distance in a short time. We have become one of the major supporters and promoters of performance arts, it's a relatively new art form, or a relatively new art form getting attention worldwide, first in the US then it will be in Japan and Korea and the Serendipity Arts Foundation is now considered one of the big champions of performance arts. We have built a festival where not only is everybody welcome, but everybody is also included, so we have for example wheelchair access to all venues of the festival, we have sign language experts for deaf and mute visitors and even for visually challenged and blind visitors, we have braille catalogs and tactile markets of important pieces which you can touch and feel and for all of them we have curated walks every day of the festival. This year, last year because it was in December, in December this time we also organized special programs for people with learning disabilities and mental challenges, so the idea is to continue to expand the scope of people who can come in. We encourage the young, we work in many many schools, we invite school children every day, we also invite street children from street, we also go to some of the orphanages to invite children from there and we also invite the elderly, so that everybody becomes a part of this. It is yes. So Goa is a unique place, it has a culture which is very inclusive exactly like the festival that we're talking about. Goa is also one of the few cities in the country which has the ability to house a large event. Any large event needs lots of place for people to travel, to stay, facilities to run an event like this. And also Goa has the last 10 or 15 years has become the home to many artists, creative people, authors, artists and you know, poets, many of them have gone and settled there. The other places where you could possibly do it is the big cities like Delhi, Mumbai, Bangalore, Hanuman etc. while they're all equipped but we felt that we should first try and set up in a place where when people go they either go to relax or on a holiday or will specially come and stay at the festival and we were fortunate that's how it worked out and Goa has now embraced this festival as its own. So we get many messages from people from Goa asking you know when is the next one, what are you going to do because the quality of the festival is completely world-class. It's like an international festival taking place because we do use some of the top minds in India as curators. We have 14 curators who work on this festival. In addition to that, we have always had between five and ten special projects by other high-quality artists. So this is an interesting question you ask because we don't often think about the long-term impact of something like this. So we had a study done, an impact study done a couple of years ago and the result was actually quite profound as to the impact it's having on people and economic activity in and around that place. So many people said my kid who had no interest now wants to do arts or crafts or music in his or her school and we know that many of the artists who came there got encouraged set up other training schools. We know that the economic activity in and around Goa at that time when the festival takes place is dramatically heightened. Obviously because of the kind of football that we have which runs into lags, many visitors come, so the hotels do well, the flights do well, the taxis do well, the restaurants do well, the shops do well. So it benefits everybody. So it's like a win-win for everybody. So we have been wonderfully supported by the government and the government machinery of the state and I think that is why we are also getting these messages and calls from other states to bring this to them and the long-term impact I guess will only be known in the long term because we're only four years old but the signs are very clear that it will have an impact on people's personality, their ethos, their interests and their ability to adopt the arts as very much a part of their lives and or also their professions. So this was one of the reasons actually when we were starting up, setting up the foundation, we felt that many of the rich traditions and cultures which have been around in India and South Asia for centuries, they're not declining and just going away because exactly as you said it's not liberative. People cannot make a living only on that. So we actually tried some experiments to see if we could help some of them contemporize their skills. Using the same skill, make products with design, colors, materials which are usable by people around us today which people would buy and use in their homes and their offices to place a work and we thought that would be both innovative, creative and a practical thing to do and in the very first version of the festival in 2016 we had Manju Nirola who's the Secretary of the World Crafts Council and she's very actively involved of course in the Crafts Council of India and Dr. Jotinder Jain who's probably the finest mind in crafts and creativity that India has produced. Both of them went around, met with such artisans including Varanasi since you mentioned Varanasi and also went to Adivasis and the other rural areas where a lot of these skills lie and helped them create new words which when they were set up at the festival people just couldn't believe what amazing things that Indian artisans can actually produce. In fact people first objected they said oh this is not handmade or they said oh this is not Indian and so fortunately we had both the curators and in many cases the actual artisans who had done the work so we said here's the artisan we talked to him or to her and here's the curator who designed this and it was a sense of joy it gives you when you see an objective being made of this nature which is both creative and socially useful but not enough is being done in this area it's a very important question you're raising. I think India needs to focus much much much much more than we have done and I'm saying it many times only for this reason because not a lot of people realize that the number of people engaged in this profession runs anywhere from 100 to 200 million which is by the way the larger than any other industry that we have in this country and the awareness and focus of this of policy makers of governments of regulators seems to be very low it's seen as something esoteric something you know nice to talk about but I'm not sure we've done enough to encourage more of these to grow so that instead of leaving these professions not only people stay on but more get engaged because this is the true strength of India and India's soft power.


Hello everybody hooded Cobra Commander 788 here, and this is a special review of two exclusive figures from Joe Fest 2019 the only way to get these figures without resorting to eBay was to be at Joe Fest in Augusta, Georgia I was there and this figure was given to me with the express purpose of Reviewing it and that's what I'm going to do I have another exclusive figure to show you as well that one is a loner And I will be returning it to its rightful owner as soon as I'm done recording this review This is the unmasked commando codename snake eyes according to the file card This is by scorched earth creations with level 7 and black major This is a custom of version 2 of snake eyes with his mask removed so you can see his scarred face This figure comes with some appropriate accessories. It includes snake eyes is Uzi in black It looks like a black casting of the version 1 snake eyes Uzi It has the back pack from version 2 of snake eyes with an additional paint application That just makes it pop a little bit more and it has snake eyes is sword Which will fit on the backpack. So that's really nice I'm assuming they are using black major figures to customize this And I have to say the accessories actually fit on a little better Than other black major figures that I have seen so The accessories look pretty good and they actually fit on the figure fairly well the figure in addition to those accessories includes a Timber wolf instead of the classic light gray with the white paint wash This one is black with a silver stripe on the back very striking this exclusive includes a second head This is the classic snake eyes version 2 head all in black It does not have the gray paint application on the visor I will not be swapping out the heads on this figure just because I want to be extremely cautious with it and Avoid breaking it what really makes this figure special is the head And I want to spend some time on it and get some close-ups so you can see it as we know from The gi joe comic book snake eyes his face was badly burned in a helicopter accident His face is scarred. That's why he wears a mask.  What's left of it But snake eyes his face is pretty mangled and this really captures that well I can't stop looking at this head sculpt a lot of effort went into it and it paid off This is how I imagine snake eyes to look without his mask looking at the rest of the figure We have the classic version two snake eyes uniform in black It looks like there's a paint wash over it Maybe to make some of the details pop a little bit more black bandolier instead of the classic gray one on the vintage Figure it does have the silver dart gun on the wrist just like the vintage figure And moving on down the figure We have the same details that we had on version two of snake eyes You know the pistol and the knife and the belt and the boots But of course all in black and that's appropriate for snake eyes You don't really need to add a lot of paint detail on snake eyes He really needs to be mostly black and that's what we have here But it's really the head that sets this apart if you really just wanted a custom figure of snake eyes version Two you could pop the second head on and that's exactly what you would have But I don't think I would do that I'm too much in love with this new head sculpt like I said It looks like they're using a black major figure to customize and I have dealt with black major figures before I don't collect them, but over the years I have managed to acquire a few of them and some of them are of varying quality Some of them can be kind of loose But this one doesn't seem to have that problem The accessories seem to fit fine and the joints seem to be fine not too loose Credit may be due to the customizer for that But this figure doesn't seem to have some of the problems that other black major figures have The figure did come with a file card and the file card recounts some of the G.I. Joe comic book continuity Very nicely written. We have some new artwork by Jason Mark Sowell So nice artwork on that. Here's where it says 2019 scorched earth creations with level 7 and black major Made and printed exclusively for Joe Fest 19 and looks like we have eight flag points Wouldn't be nice if we could still send flag points in for things This file card is blank on the back. This figure came polybag With a bag that had a nice sticker with the Joe Fest logo on it if I hadn't promised to review it I honestly probably would have left it in the poly bag because this is such a nice set As I said earlier this figure sold out It sold out very quickly is my understanding a lot of people snatch this figure up and I can understand why So this I believe is the kind of exclusive you can expect to see at Joe Fest down the years Since it is not an official Hasbro convention. You don't get official Hasbro exclusives But that gives you a chance to get these beautiful custom exclusives This is another exclusive Joe Fest figure and I believe this one sold out as well This is the traitor by Broca Customs This figure was loaned to me by Stuart so I could do this review Thank you Stuart as soon as I'm done with this I will box it up and send it to you this figure is based on a couple episodes of the animated series called the Traitor in which Dusty temporarily defect to Cobra. This is basically Dusty in a Cobra uniform But that is not a standard Dusty head nor is this a standard Cobra uniform as with the other figures This is an updated and very nicely sculpted head a great detail on the hair great paint application on the hair Just very sharply sculpted and it looks great. Yeah, I can imagine this is Dusty I think this it does look like Dusty I suppose the easy thing to do would be to just put this new Dusty head on a standard Cobra trooper body But that's not what they did. They have some other parts at play here We have the arms from some swivel arm 1983 figures With some silver pockets on the upper arms and some black gloves The chest is the same chest that was used for zap And short fuse but of course in Cobra colors with a nice Cobra emblem right on the chest He has a blue waist piece with a black belt Moving on down. He has blue legs and these are not standard Cobra trooper legs Looks like they used airborne's legs for this and that's works that actually works really well It has some additional detail that you would not get if you had the standard Cobra trooper legs He's got a silver pocket on the left leg Silver pistol on the right leg He does still have the knee pads because airborne had knee pads and then he has some black boots This figure did come with some accessories and on this one I did have a little trouble with the accessories and since this figure does not belong to me I'm just not going to force the accessories into the hands or force the backpack onto the back I'm just not going to take any risk of breaking this figure the figure came with a black figure stand And this figure stand does have a Hasbro stamp on the bottom I could probably use this figure stand with the figure, but again, I'm not taking any risks with this figure I'm not going to break the heels off on that figure. I won't even risk it But it does have a figure stand And as with the other figure it has an alternate head so you can put traitor Dusty In his full Cobra uniform or if you did not want this figure to be Dusty You could put this head on it and just have it as a Cobra army builder I do like this figure. They could have been lazy about it This could have been a very simple and easy custom job, but they didn't do it the easy way They went the extra mile to make it special and that really comes through and this figure could be of special interest To fans of Dusty or fans of the animated series who liked those episodes That was my review of two exclusive Joe Fest 2019 figures and to those who gave and loaned these figures to me Thank you for the opportunity for those who did not make it to Joe Fest in 2019 The expectation is they will be doing it again next year So make your plans early come out and see what kind of amazing custom exclusive figures they have next year Thank you for watching. I am hard at work making vintage GI Joe toy reviews. I have some coming up for you soon I'll see you then and until then remember only GI Joe is GI Joe


The Feather NR-52 Blue Fruit LE is powered by the Nordic NR-52-832 chip, which comes with integrated BLE radio, supporting circuitry, and antenna. In order to have a look inside, we'll have to remove that shiny protective shield. A hot air gun should do the trick. There we go, nice and easy. And inside we find some capacitors surrounding the NR-52-832 itself, timing crystal with supporting caps, and some passive components along the antenna's feedline, and of course the antenna down there at the bottom.