table football and you have to pay for that, just $0.50, we put like that because nobody gets stuck there, but the money we collect there, we go for charity, we have maybe the residential go to a hospital or we will use it for next year, or we will see what we will use, for what we will use that money. The plan was to use the money for financial aid or other initiatives in the Python world, all the games and table footballing and the games you see there after the conference we will donate to a hospital nearby, so we wanted to do something for the community from people needing, and $0.50 is not much, you can have other people. Few reminders? So there is not allowed food and drinks in the rooms, but you can take your bottles with water. Don't get dry, it's summer. And the tram service is on a strike, so there are some hours that you won't have the tram, and in the other time, normal time is correct, there are less trams than usually, so maybe you have to be waiting 20 minutes for the tram, so maybe it's better to go walking to the hotel. Yes. We are sorry about that, we can't do anything. Okay, we are finishing just a few words about this year, this year was very hard for everyone involved in the conference. We are in a status of change in the Euro-Python ecosystem. We started late, we called late, it was really, really hard getting teams, setting up work groups, working with the local team to set up and to work with each other. I can say for both sides, I think, that we are really happy with all the work we did together. You will judge the outcome, and we really hope that you enjoy the conference, we tried to do our best. The bigger motivation around all the changes is to have Euro-Python and the whole Python ecosystem and community in Europe more community-centered. We would really like the community to be on the small stage, to be helping, to be part of it, and we really wish that in the future, we can, we don't have a small list of people to think, but we have such a big list that we don't need to say names. So we really need your help during the entire year. We need your feedback. Critics are very important. Critics help us to improve next year. Critics are more important if you tell how to others or how would you like to others, and they are even more important if you step ahead and help us doing that. So if you think our community should be more open, you should step and say, we need to do this, and you help us changing the things. If you think our, the website is not good, help us. If you think we don't address technical aspects of the Python world, education, whatever, help us.  This year, building a conference is around many, many important things. Those are hard work, diversity, which comes with respect. Diversity is not just about gender, it's about being different from each other and at the same time on the same thing respecting each other and have a good time. You can be different by gender, you can be different by race, you can be different by religion, by different levels of Python knowledge. Maybe there are a lot of our people here just because they want to learn about Python or other languages here because they want to learn. Everybody have been there, everybody have been in the situation of not knowing something or being not with the same knowledge of others or the same kind of, on a different from a bigger group, so please welcome people in and help everybody to build a better community. One note for me is that we, everybody know that almost all tech communities or broad communities have diversity problems and problems to manage people together and I would really like to pass the message that if we care about community, we should work on education first because it's a long term problem and we are not, we cannot force people to do something. We just can help everyone and if we do a mistake, we just need to say sorry and start again. So the most important thing, if we want to change our culture, we want to improve our culture is to teach from younger generations to value those values and help each other and get involved in the community. So if you are involved in education, if you would like, if you see people that are learning, be kind, go talk with them and welcome them to the community. Again, as I said, final note, sorry for the delay. As I said, Europe is a community conference. Nobody here gets paid for their work. There are thousands of hours of work dedicated to this conference. I'm biased on this, but I would dare to say that it's a fairly good quality conference. The venue is amazing. The food is good. And if you have ever been to even commercial conferences, there are not so many people that really want to please their users. So at least enjoy the conference, appreciate the effort of people and if you don't like something, tell us and help us to improve it. I want to give a personal thing to the people that were really involved in this year. So I want to thank Oyer, Borja, Alejandra, Daria, Christian Barra, Alexandra Head of Raul, Marc Andre, Lemberg. So thanks to Fabio. There are other people that help a little bit less, but also help a lot later. So Harvey, with the dinner's day, and Luis and Patrick that helped a lot with some television that you will see all the tools there. We hope so. And also the EPS members that they helped to do all the European Python community and help a lot with that. And this ACF-ISS as well. So as you can see, it's not a big number of people. And the time we dedicate is we take from our spare time, our family, our friends. So thank you for everyone for coming, and I hope you enjoy the conference. Sorry for the delay. Thank you. Thank you very much, really. So everybody knows we are late, terribly late. So we are going to shift a little bit as time goes. You can come in the meanwhile. So as I already mentioned to the keynoters, their time is their show, so we will eat until our time to be finished. And don't worry, you get your food. We can manage the shifts, and we'll try to shorten the delay we have. Thank you again.


Hello everyone this is financial author Amit Dan of amitdan.com. On this channel you'll find financial travel and more videos just like the one you're watching right now. If you like what you see please give it a thumbs up and consider subscribing. The market will be buying foods and groceries so this is called Mirpur 2 raw market or grocery market so I'll be showing you various products and fish and vegetables and we'll be buying from the stores right here and we may need to bargain for some of the items so let's start and we'll start doing our groceries. Okay so we're going to be crossing the road and you have to watch both sides so we have our car and driver over there and he's going to be waiting for us while we do the groceries. So we have some green vegetables and red vegetables and looks like there's some tomatoes and green chili and beans so these looks like local produced vegetables I do not know the name but I haven't seen them different types of husk is this this could be spinach local version of a spinach so we bought this one it's very green and then let's move on so we have some garlic potatoes so there's some rice and onion garlic and other stuff in this store so here there's some potatoes so how much is potato? so potatoes 20 taka per kilo which would be about 25 cents 25 cents us per kilo so how much are the eggs? okay so extra one one dozen is 80 80 taka so one dozen is approximately one dollar so that seems to be a very good price are these olives no potatoes okay so we have different version of potatoes and how much are these? okay so these potatoes are 40 taka per kg so it's about half dollar per kg and looks like we have some olives here and how much are these? okay so these are 40 taka per kg so about 50 cents 40 cents per kg and how much are the limes? okay so the limes are four for 20 taka so it comes to approximately 10 cents each so you got some coriander here and some mint and green chili and carrots and we have some so 300 taka per kilo so it seems to be expensive comparing other products in here so right now we are looking for cucumber and 40 taka per kilo so it's a cucumber is 40 taka per kilo so which is about 50 cents per kilo so we're gonna pick some up too what is this one? so this looks like a very healthy types of beans very green so we're moving to a different section and let's take a look okay so we are kind of entering a little bit inside of our market and I can see some cabbage here and some eggplant and banana green bananas and some looks like bitter melon right there so how much are these? so each one is 30 taka so it's about 30 40 cents each so which seems to be a bargain so he's gonna chop it up and then we'll just get the top part of the cat okay the one is taking a still picture which will be for Instagram so we have lots of variety of fishes and this is a raw fish market so they will cut it to pieces okay so I'm going to fold my pants because this looks like wet and muddy so we have more fishes and then some small fishes so here's some small fishes one kg is about 80 taka one kg so per kg is almost one dollar I made a mistake so these are 200 taka kg not 80 so it's about 225 dollar per kg so I don't know the English names of these fishes but they are local looks like shrimp those looks like shrimp but the bigger version of shrimp and it's a 400 taka per kg so that comes to about eight eight four dollar four five dollars per kg so we're moving towards another section and possibly we'll be buying more fishes so these are all live fishes here so these are all local live fishes so how much are these how much are these so these are 500 500 taka per kg so it's about six seven dollars per kg but these are live fishes okay so we've got some more live fishes here and they seem to be very big and healthy fishes so these are in a water tank and then so how much are these per kg okay so depending on what you're buying it's 300 taka per kg so you're looking at three four four dollars per kg so that seems to be a big fish so what is this fan for okay so the fan is cooling down the water so the fish stays longer okay so this is to for the fish to get more oxygen all right so we are moving so you have some more fishes here in water tank so it's still another picture it's still picture I think it's better here I think a little bit far yeah okay so we are taking another live fish picture for Instagram all right so are we done or we have more so so it's another session of the market looks like they have some sandals stores and other sorts of stores even they're selling bags so we're going to the live chicken session right now and we have some dry fish so it's lots of different types of dry fish we have here and I can see lots of flies too and what are these the spice different types of okay so we have different types of spices I can recognize some of them for example looks like black pepper this is cloves right and cardamom and then it looks like cumin it could be cumin or this black seed and we've got mustard here and this looks like mushroom okay so these are dried lemon and then we got dried fish over there all right let's move on okay so we are entering the chicken market so we're not going to stay here longer we will pass it fast so more markets okay so we're going to just quickly browse through the beef market I can see some papayas here so here you'll find different types of beef beef and lamb and all together okay so you're gonna get out of the market now so I think we are done okay so we are done now we'll be heading home so that'll be all for the fish market and grocery market which is the street market and you're gonna be heading to our some thank you for watching if you like what you see please give it a thumbs up and consider subscribing and there will be more videos coming up actually let me just show you looks like there's a machine there which is grinding the sugarcane sugarcane and then it's making juices here so this is another way of the street drink so this machine makes the sugarcane juice it's supposed to be very sweet and healthy all right let's get going


You want to introduce yourself? Yes, I would like to introduce myself. Anika? Yes. And what is your name? Ashree. She is from a particularly wonderful country. She is from a particularly wonderful country. And she made with her daughters a national law university. And she is doing her first year right now. And she is doing her first year right now. She is a senior in our school. She is a senior? She is a senior in our school. She is studying the same school. They are all the same studies. And she is a role model to everyone. Because she is the one who started it. She is from a child class partner. We have a particular window. She is a child who stretches a lot. And she is the first law graduate. And a merger of child. But in Zanitha, I am the team leader. For idea is central chapter. Idea is the organization that supports. We have a partnership with Kela Hyko. And I am glad to talk to you all. And I am in service of the idea Kela that we can distribute to the city. Please start eating. Yes. How do you say 3 words? Abhagash. Abhagash. Right. That is a father. I am a university and virtual client. If you have something in college yet. So that's our team. This statue of course does not just represent a person. This statue represents the action of a person. So when we look at this statue. We do not just remember Mahatma Gandhi ji. We also remember his action. And the way he lived his life. He had the Gita, the Quran and the Bible. The Guru Granth Sahib So I hope when people look at this statue when they drive past this road they remember the action of Mahatma Gandhi ji. What's your name? My name is Shivapriya. Shivapriya. Shivapriya. Shivapriya. Shivapriya. No. Shivapriya. Shivapriya. Shivapriya. Shivapriya. Shivapriya. Shivapriya. Shivapriya. Shivapriya. Shivapriya. Shivapriya. Shivapriya. Shivapriya. Shivapriya. Shivapriya. Shivapriya. So what happens here? It is dark. It is very dark. I think it is a tent. It is a tent. What is it? It is a tent. It is also ok. It is outside. Sir, please add it. Where is the house? Which district? It is a tent. It is a tent.


It's your girl Coco. So I had a vlog ready for you guys me going to the outlets But for whatever reason the video is super bright and you guys won't be able to see me in it and I decided not to add those clips in so what you're going to see is just me finally getting back home and Me showing you guys the luggage. So I hope you enjoyed Hey guys, so I am home now and the luggage is I purchased the Samson night Let's get the exact name It's the Samson night Outline Pro medium spinner and I have the carry-on one And let me just pull the tags off I guess I can pull the tag off It's kind of any of them. All right, so I'm gonna actually pull it over here and read some of this stuff off to you. I need to pull this off So basically Listen out Let's sit down I'm gonna see if I can read it off to you guys so They this is just like the tumble test in The hand test and the drop test and all of that They have to they have the free form and then they have the Outline Pro spinner, I think it's called something else, so I'll double check So I got them from the outlet They do have a cell online anyway. I had to buy these pieces separate, which I'm totally fine with I like them. I went with black They have something else called Blackout, which is weird because this one is Oh, this one has like a little I guess a little silver writing in the middle and the other one was like all black, but I like this one All right, so it has a 10-year warranty You guys don't make sure you always do that with your your stuff and And What else is here nothing really Yeah, nothing really, but I think it's really good for me I did go to to me to me was For data cell like 460 But I don't know. I just that ain't like the style. It was just like too busy for me I wanted something a little bit more sleek and just like no real color As far as trying to recognize my luggage when I travel I'm gonna just put something on it that I don't know It's mine Otherwise, that's it So I've never had a carry-on because I hate carrying bags on the airplane But if I take a quick little trip and I have my you know proper size Things like these three ounce bottles, then I'll take the small one. I'm excited. I like them a lot The one that I currently have is a Material one and that thing is so beat up like it's so beat up. So this is good good storage I'm gonna stick them inside my closet downstairs, and that's it. I wanted to end the vlog properly Show you guys everything so I'll pop in some videos and kind of talk through what's inside of the bag And then I will catch you guys in the next one guys. So let's go into what the Inside of the bag looks like. All right See, I'm some sexy torso If I lift this up That's that one So I can roll it Right And then And this is the carry-on Right They are not sets. Oh well They're not um, they didn't come together. This one has a little scratch on it So the locks are here I'll show you this one so there's locks here and Let me come a little closer So here's the locks Right here, and then it's set to zero and then you Push it over See this one's a little different than the other one This one has like a little little thing Wait, but how do you do this one? But this one don't have no Here's both suitcases. I have a medium and I have a small and They each have Handle on the side. So if I wanted to carry it like this I could and They also have Like this and like this so they both tall These are not the strongest. They don't feel heavy. They do have locks on the front So The small one has locks here everything is set to zero right now and then You just move that open and it opens here, and I'll show you guys the inside So the small one has a different lock opposed to the big one Right here you can plug in Your Phone I Don't know if you guys can see that it's like a little piece and this is on the carry on the carry on it has separate piece here you can zip it up Put your clothes in here you have two pockets Pocket here and a pocket here You're gonna see that and then this side I hope you guys can see that It actually has Let me move this on It has this Here you can hook it in this piece for your Toiletries, and then you have the piece under it


We're four months into the launch of NBA 2K20, which means most things at this point are final, which means it is a fantastic time to reflect on all the things that were said week one. One of my favorite past times, guys, is to go back to the launch of any game and see what IGN said about the game. It's funny, because they're almost always wrong. It's scary how wrong they can be. They're so consistently wrong, it's almost a skill. Hehehehe. I haven't seen the review, because obviously I don't trust them with any gaming information. But today, we're reviewing it and some other companies as well. And we're gonna see just how correct they truly were. Ain't no bias here, guys. There's no bias. I think, hopefully, I don't know. So the video dropped September 9th, which I believe was the exact day it released, which means they got the game early, a review copy. NBA 2K20 review by IGN pulled 400,000 views. Oh my God, it was very disliked. I wonder what they said. All right, let's watch. I'm very excited. On the court, NBA 2K20 is a dazzling experience that matches the energy and presentation of its... Why are we playing? Excuse me, sir. Are we playing on broadcast? My God. If you're trying to demonstrate that you at least have played a game of 2K in your lifetime, at least play on the 2K cam. I'm gonna try not to nitpick this video, but it's gonna be hard, guys. It's gonna be very hard. It matches the energy and presentation of its real-life counterpart. Enhanced footwork, dribbling, and player spacing help improve an already sturdy foundation. However, outside of the surprisingly well-written story mode, NBA 2K20 loses its focus off the court. It excels at recreating the pro game, but intrusive opportunities to purchase microtransactions exist in nearly every corner, and complex modes like My League are given far too little attention. Mm, okay. Am I agreeing with IGN right now? They hit the nail on the head.  Give me your editing tutorial or something, dude. I want my audio to sound like yours. What mic do you use? NBA 2K20 upholds the recent franchise tradition of having top-notch presentation. Apart from the occasional lifeless gaze, players look just like their real-life selves and have the apparel to match. But they don't just look the part, either, as NBA 2K20 pulls out all the stops to make sure they feel like their real-world counterparts, too. Well, you know, before we started the game, the coach came in and told us that we knew what we had to do. A lot of this- Now, I'm gonna say this. I feel like all of that stuff he just mentioned, I wouldn't talk about it in a review. I feel like it's pointless. Yeah, it doesn't add to the immersion. Yeah, you're playing a basketball simulation game, sure. But it doesn't make the game any more enjoyable for me. I don't listen to any of that stuff. I skip past it all. I think we all do. So I feel like if you only have six minutes to give a review, man, why are we talking about things that don't matter right now? There's a lot of game modes to get to. This is amplified by the badge system, which attaches unique abilities to players beyond their raw stats. To take the Pogo Stick badge, for instance, this allows for players like Anthony Davis to quickly regroup after a blocked shot, making it easier to block several shots in a row. Defenders are better equipped to deal with speedy players, especially in transition, where tactics like walling up are employed against drive-heavy players like LeBron James. Learning went to pull up for a jumper and went to drive it to the basket provides a welcome new layer of frantic strategy. The transition is Russell Westbrook exploding. Hey, I'm impressed. Not gonna lie to you. First of all, they're playing on the 2K cam shot at you. The gameplay doesn't feel horrible.  Who does that? He's not good by any stretch because I saw him do a couple horrible takes. I don't even know if this is his gameplay. This might not be his gameplay. I'm not sure. Per and went to drive it to the basket provides a what the ball in your hands feels earned. The newly added dribbling animations help the game look different too. Watching Yannis barrel down the court with long strides is just as majestic as you would expect as you can see here. We're safe with that dunk. No question. Ah. A lot of the gameplay improvements are magnified in the new. Man, I feel like that's one of the negatives. How did you just mention that it's something neutral? The dribbling in this year's game, yes, is more realistic but it's way more boring. It seems like in the name of balance, they just stripped the game of all the fun that was dribbling. Now remember in 2K 15 and 16, you used to be able to do all kind of crazy. I'll be afraid to hop on the park in the stage just because you never know when you can run into a playmaker that knows what he's doing. There's no fear like that anymore. Nobody can strike fear in your heart. It's the same behind the back. You ain't talk about the behind the back just yet but I'm gonna give you some time, bro because there's minutes left to this review. But I'm glad we're talking about the point in the game that nobody ever plays. A lot of the gameplay improvements are magnified in the new WNBA mode. Spacing feels particularly good which leads to better off-ball movement and smoother play in general as you can see here. Brown, defended by Cambage. I like the ball movement. And Williams weighs it up a hand. Plus there is a novelty to getting to play with a whole new set of stars like Candace Parker or Brianna Stewart. Still many of the franchise's legacy issues persist and a few new ones crop up. The CPU rarely plays with the urgency it should ignoring easy two-for-one scenarios and milking the clock even when they are losing in the final moments of the game.   Kevin, what are you doing, man?  God, you think you're Stephen Curry who played with the guy for a few years? Infrequently, the CPU will also dribble out of bounds for seemingly no reason at all. Perhaps worse of all, is a far too frequent double team that happens on any player who scores a handful of points in succession like this one. That's a nice throw-down.  Gordon with no luck. Hey, hey, that's a good look, man. That's a good look. You know why they did the double team stuff? It's to stop people from badge grinding on my career. So did they make the doubles and triples and quadruples? Let's throw the whole squad. They might even throw the coach at you once you score two points. It's unbelievable. I'm glad he pointed that out. Unfortunately, NBA 2K's best mode, My League, received disgracefully few enhancements in 2K20. My League is so close to being feature complete that it would be easier to ignore the lack of improvement if some of the major problems, like the inability to sign in and trade players, were seen to. Instead, it's mostly ignored in favor of a casino like My Team, which causes a host of complicated problems for NBA 2K20. I rate this guy. Yo, I rate this guy. The casino like My Team. That's how you know they weren't paid by 2K, because they won't let them say something like that. Hey, first of all, we can't be running around, calling My League the best mode. People gonna think you're crazy, bro, relax. Second of all, you're right. It's scary how little improvements My League, My GM gets.  I'm telling you right now, it's a tragedy that that game mode has been pretty much the same for the past few years.  It just needs more and needs more attention.  It's like, yo, I don't play My Career, so I don't see the benefits of that. But it's like, My GM, My League got nothing, though. Right? We got the same perks on 2K20. Damn, it's like, yo, could we hire more people or just a little bit of focus in some of these directions? It seems clear that this mode's central goal is to extract as much money through purchasable microtransaction packs of player cards as it can. If you don't want to pay, you're forced to grind out various Monday-in-challenges to slowly earn them instead.  Its only redeeming quality is the satisfying card-pack ripping sound effects, which just serves to remind you of how preferable real-life card collecting is to the joylessness of my team. Woo! Shot! Better available game modes aren't universally a disappointment, however. The My Career story mode, when the lights are brightest, is the best entry for NBA 2K yet. This year features some witty writing and better-than-usual performances from a star-studded cast that includes Idris Elba, Rosario Dawson, and Thomas Middleditch. I know. I know, it ain't easy watching your brother fall like that, but hey, listen. Don't let moment of darkness take away from what y'all did tonight. God! That was inspiring. Hey, I do agree though that the story this year for My Career is pretty good.  Unless it's like The Last of Us, or Metal Gear Solid 4, or actually any story mode that's not as sports-titled as any decent. But for what it's worth, 2K20 was on the better end. Story navigates some interesting issues well, like the manipulation of student-athletes. Mix that with a few fun gameplay variants like participating in a series of mini-games for the NBA Draft Combine. That wasn't fun! Sets the new standard for story modes in sports video games. It's also worth noting that while microtransactions still plague My Career, progression is much faster than in prior years, and even lets you test out various builds before committing to your player archetype. I don't know, buddy. I mean, have you tried grinding badges? I feel like it's not faster, man. Also, the way you're upgrading your player is hurting my IQ. Online play is pretty good outside of the odd exclusion of being able to play as WNBA teams. Any latency was only noticeable due to the precision of the controls, but it never outweighs the fun and competitive play provides. Oh, he's capping! He just looked for you! What game did you play? It's not the same one as me! I could tell you that! Did he just up and say that the latency didn't impact the gameplay net? What game did they give you to play? I wanna play that one. 2K, how do I get that game? Give me that game. I wanna play that game. It's more enjoyable than ever to beat down online opponents. It's worth mentioning that while we only experience small inconveniences with online ourselves, other players are reporting widespread issues with random disconnections, crashes, and other online bugs. There we go. 2K says it's already working to address these issues, but they seem to be persisting as of the time of this review. That's a good call, reviewer! Good call up, that one! With the button! Here we go, the verdict, guys! Don't give him a 10! And the 2K continues its tradition of upping already-tremendous gameplay and emulating real-life basketball in ways that didn't seem possible just years ago. The inclusion of the WNBA may be this year's biggest step forward, especially because it does an excellent job showcasing the many gameplay improvements from last year. My career also continues to improve in small and noticeable ways, surpassing the standards set by previous iterations. Still, NBA 2K20 would have been better off by focusing on its complex modes, like My League, and cleaning up my team to be less dependent on microtransactions. How about parts? In spite of that, NBA 2K20 remains the best at what it does. It might just be time to ask for better. I know, it's definitely time. What are you talking about right now, man? You were doing so good a moment ago. It's obviously time to ask for better.  Hey, you didn't mention that they had the same part back-to-back years. You didn't do that, why didn't you do that? In fact, I didn't see no playground gameplay. Did you even include that in this review? I don't think you could have because you played this before the game long. So the review of NBA 2K20 didn't include the most popular mode. Why did you do that, IGN? Because on the modes that he did cover, I feel like he hit the parts he needed to hit, man. But why would you release a review if you know that you didn't play the whole game yet? Or the most important part? Of course you think my league is the best part of the game. You haven't played Playground. Come on, man. Do your due diligence, IGN. I'm surprised they gave it a 7.8. Usually like these companies, they must be getting paid or something because I don't know why they'd be giving them 9s and 9.5s every year. But for some reason, 7.8. As a professional and authenticated raider here on the internet, I'm gonna say that's not a bad rating. Now, I will say this, IGN. Nobody gave you the authority to rate anything online, right? So don't. I wouldn't say 7.8. I'd say like 6.8 if you asked me. So I don't know if you guys know how this usually works. Like before the game launches, like 2K will have a couple launch events. And so they'll invite a whole bunch of YouTubers but mainly it's like journalists in the industry.  And so you read these articles trying to get a good idea of how amazing or horrible the game is. And sometimes it just doesn't give you a good impression of the overall picture. I mean, IGN's review didn't even include the most popular mode. I don't know why they would do that. At launch, the game has had problems such as my career player is not being awarded earned progress and my team's single player triple threat challenge is not being accessible. These kinds of hiccups are usual for visual concepts and partly prove that the NBA 2K series as strong as it is in some areas needs to keep working.  Have you ever rated something that you described as needs to keep working as an 8.5? Like you hand in a paper and your teacher was like, I don't know, man, you need to keep working on this. I'm giving you a 85%, congratulations. It's not adding up to me, but that's just because I have logic. The gameplay heart of the series is well-intact, although some choices to develop for visual concepts makes parts of the modes aren't the best. Keep delivering on the gameplay front and the overall strength of its league structure while inadvertently reinforcing how my part needs significant changes. That's true. I agree with you on that for show. Hey, shout out to you, Matthew. Didn't read your whole article because I don't like to read, but you seem like you know what you're talking about. Okay, now Bleach reports. I mean, it kind of goes like, there are dozens and dozens of dozens of people that go to these events and they'll play the game and then 2K will send these companies all of these like review copies so that they can play it early so that when the game drops, they can come out with their review. But that's kind of backwards because if you drop your review before the game releases, then you didn't play park. Unless maybe they had like a private server, but I don't even think 2K has the technology to do that without f**king up. The addition of the WNBA is the exclamation point as far as new additions goes. So of all the things that were added, because it was plenty of fantastic things 2K added, like I feel like the pie chart had a potential to be amazing, but it ended up being more limiting than it was amazing, whatever. I think the badge system this year works better than it did in previous years. Although I don't like how they did like quick draw. That shouldn't be a badge. So like, yeah, it needs fine tuning, but there's plenty of fantastic features we can point at. Are we sure that WNBA is the best one? Now maybe if you had some sort of micro integration or like maybe then we could begin talking about something like that. But you just play on the play now with them. And you can't even play WNBA versus NBA, which I think would have been hilarious. You could only do WNBA versus WNBA. As an entry point for new players is simply the latest addition for returning players, 2K20 is an expected good time with enough upgrades to warrant a look. And they didn't rate it, which I like. I like that they didn't rate it. They know their role. They don't have that authority. So usually when 2K comes out, a lot of people will ask me like, yo, agent, what do you think about the game? And I'll reserve judgment because if I'm uploading my take on September 7th or September 9th, then I don't really know what I'm talking about. I had to play the game. I have to grind my players. I have to try out the different modes. And then you get a good sense of how that game is feeling. On top of that, we all know that 2K never comes out perfect, polished. So you usually had to wait a couple months just to see which game breaking bugs are going to be patched. For example, if you practice dribbling in the prelude in the beta, the dribbling in the final product when the game released was completely different. You wouldn't have known that if you just played the earlier version of the game that wasn't the retail version. So let me give you my review here. 2K20 was gonna be a bad game. Which sounds like, does that much of a review? I don't know. 2K19 was really good. And I think for the first time in my history being a part of this community, 2K19 was the first year where we all recognized while it was going on that it was going to be a top 2K. There was meta issues. There was like seven foot stretch big shooting over shot contest issues for sure. But it was fun. And it was different. And a lot of people enjoyed it even with his flaws. When 2K20 came out, I was unsure about the dribbling. Every dribble had I talked to wasn't feeling it at all. Yeah, sure. If you are a schizo or one of the top 0.001% dribblers, you can figure out a way to make creative dribbles and make the game fun for you. But for most average or good players, they're never even gonna come across those dribble moves. They're just gonna keep doing the same abusive behind the back over and over again. They've repeated the same neighborhood after lying about that. So there's that. Ronnie just capped about so many things. The expectations were so high off of good 2K19 that we just wanted the world. We wanted to close out the PS4 in good fashion. And I don't think they did that. And I like for them to prove me wrong and drop some updates that drastically changes the way the game plays or makes it more fun for sake. Isn't that the point of playing games? But yeah, I'd get a game like a 6.8 if you asked me. And that's without even talking about the microtransactions. Cause all of these companies for what it's worth, they all mentioned that that's something that's been plaguing the game. It's so common at this point that they just kind of prefaced the video like, guys, we know there's microtransactions, but let's just ignore that because we can talk about that and give it a two out of 10 every year. I came into this video thinking all of these guys are gonna miss the mark. I mean, aside from a couple mishaps here and there, they kind of hit the nail on the head. Yeah, 2K20 is not perfect. I wouldn't even say it's great. It's high. But the part that sucks about it is they just came after a really good 2K19. So I can't help but think we just whiffed the whole year. And that's been mentioned in all these reviews. I'm actually surprised.  Cause I've done videos like this in the past where I look at some of these reviews and you know, they're always very, very wrong. Always. I'm still not gonna go to IGN for my reviews. Hey, if you guys enjoyed the video, drop a like, man, subscribe to the channel. Hey, this video's on the screen.  You can click on anyone and then whenever you want to. I'm gonna head out though cause I need to get my 10,000 steps today on this weight loss grind.


All right, let's go ahead and start the session, how to avoid overcomplicating your data architecture. So first thing a little bit about me. So I'm Christina Lin, hi everyone. So I just wanna quickly walk you through some of my previous experience, so you know where I'm coming from and I started playing video games for a very long time but that's way too long. So I started working in the industry a couple of decades ago, I don't know, but yes, I started working and then when I started working there's a lot of adoptions in AS 400s. So I was doing a lot of data work on site-based. So that was actually the first time I was working in an insurance company and we were working with, I think it was called Tata at the time, I think it was called Tata today and they were working with us on creating this huge insurance system where everything was built in with COBOL on the mainframe computers and we're extracting everything out of COBOL and put that into our Oracle and site-based database. And that was the first time I saw a store procedure that was 200 lines long, maybe longer, I'm not sure, but it's a lot longer. This is like the first time I saw how people can deal with data in such a precise way of moving data and there's a lot of temp tables going on in and out of that table thing, right? So that's what's going on there. We're using messaging queue, IBM MQ, if you still remembers that thing to get data out of AS 400s and we're communicating it through putting it into Oracle or DB2 at the time and providing information through that. And then we slowly move on. So once I started my career there and then I moved on to working more in the integration space, I was kind of head of the integration there and we were kind of designing things around the SOA, I don't know, service-oriented architecture, if you still remember that, but we were doing a lot of like SOA work and we were adopting a lot of like the old IBM SOA framework and I found it's really difficult, not difficult in a way that is very over complicated again. So that's when I started looking into other things like integration technologies like Camel. So I was really into Camel. So I done a lot of project with integrating data with Camels and stuff like that. And because Camel talks to a lot of data source, so I was working with a lot of like traditional databases, MySQL, Postgres, and then also doing some of the construction I think when it came in and at the time there was a lot of enterprise integration thing that we need to do. So we also have, I was also doing a lot of like asynchronous communication through either IBM Q or ActiveMQ or RabbitMQ at the time was all messaging queue. And then everything kind of like moved into like the air warehouse. So I did a lot of like things on Hadoop and they slowly moving on to Spark. And then I decided, okay, I wanna, and then we, I was kind of working on, moving on my career with Red Hat. So I was doing a lot of Kubernetes stuff. But now I am with Red Panda today because I feel like streaming is going to be a big, it's going to be a very huge part of what data is going to be, especially with the data architecture. And I think for today, what I want to do is, is to share with you on, you know, what I see my experiences is when working in the industry for such a long time, I wanna share with you with some of my findings, some of my observations and my recommendations. I'm not saying I'm 100%, right? I really wanna hear your feedback on my thoughts of how data architecture should look like, at least for the next couple of years, right? So I would love to hear your feedback, anything, any comments, I would love to hear them. So let me know. But I wanna talk about what I see and what I've been working with from the beginning, working with a lot of financial institutions mostly because I do a lot of consulting for them and I do a lot of work with them. So this is the reality I see like in those companies where they would have a really legacy system that's doing a lot of the core work. They're trying to move out from this like really core system but it's really difficult. Sometimes they say, don't, if it doesn't break, don't fix it, that kind of mentality. A lot of people still has that. So they would have a mainframe running somewhere in their environments. And then after a couple of years, they slow moving on to the monolithic application development where they have large application servers running a huge package of software that is providing services and they're slowly moving their things out of the mainframe or adding more services on top of what they're providing. And those monolithic applications will be accessing a lot of like the traditional RDVMS relational databases, right? Like all of those in Mexico and all that. So they would have that. And the main way of communication would be using SOAP or REST depending on the era they were implemented. And also after that, there's a huge jump into the big data era after the SOAP services was adopted because people finding now they have a lot of data they wanted to kind of reuse them. They want to kind of mining the gold because they have gold, of course to get it out of the system and trying to find meanings for them, trying to make better, precise business decisions on top of it. So people were using Hadoop, but just because of the sheer amount of data they were putting into that data store, they need to find a way to kind of make it easy for people to run and calculate the databases. So that's when they would probably introduce things like MapReduce and all that kind of stuff to make it faster to calculate things. And because the way that to set it up is kind of difficult. So we kind of figure out different things on top of it to run like Hive. Maybe later on that have Spark to do it. And then once they have that running, so this will be mostly isolated in one single department where they're doing a lot of like business analysis. This information probably didn't probably send out somewhere. They were just mainly regenerating reports. They provide that to those kind of like business, more business decisions and stuff like that to help them to make, to kind of better their workflows and all that kind of stuff. And then they switch on to the next era which is Event-Driven Architecture. So that was really, Event-Driven Architecture was there for a long time. I think even so I was a little bit of an Event-Driven, but I saw people when I was in Red Hat, I was seeing a lot of people adopting the microservices thing because of the new Kubernetes platform where people are starting to say, hey, I have all this different type of like applications. I want to handle them independently. I don't want them to be coupled. I want them to be easier to deploy all that kind of stuff. So people starting to use a lot of like microservices and then also because of that, they're much more free. There's a lot more freedom of using the data stores, right? The data stores can be a little bit free and then it's easier and they want it. And because of the nature of the platform itself, it's easier to apply, it seems natural to adopt a more horizontal scalable data store. So that's when the NoSQL database or the key value stores what becomes really popular for these kind of type of services. So that's when we see them coming around and also because of the adoptions of the Azure devices, you know, maybe five or six years ago about the IoT craze that everybody's starting to collect all this data coming into the company. Data warehouse is no longer where they want to store the data because it needs to be structured and some of the data that they want to keep from these devices weren't that structured and some of them were like string data and stuff like that. And that wasn't the things that they want to put into their warehouse and they don't have the capacity to process that and put that into their data warehouse. So they were looking into like things like data lakes to kind of quickly store things on the cloud. Also another move that I saw was kind of going into the cloud because just because of how difficult and how complicated to maintain the cluster of data warehouse internally because there's a lot of layers of technologies on top. So that's why people were using a lot of cloud services and that. And then a couple of years later, I see people starting to like, LinkedIn started the Kafka thing where everybody loved the way that it can now handle a lot more capacity for the traffic, especially for the ingestion part. So it enables the data to flow inside your companies and stuff to a lot faster. And then the crazy AI thing everybody talks about today, like if I don't talk about AI today, I feel like I'm left out, you know? And then that happens to a lot of the companies out there as well, they want to reuse the data they're collecting and they want to put that into machine learnings and then eventually adopt into the AI space. So that's kind of where, and then you see this progression still exists in the company, a lot of the company today, they're still there and they're kind of just slowly building things up, a little bit of difference between these are the different communication protocols that they're using and the capacities that they can handle, right? And if you want to move the data in and out from these systems that you built throughout the era, you gotta have to kind of like figure out different ways to move the data. And then like a lot of them realize on a lot of the batch services underneath the hood. And for real-time access, you have HTTPs, like an HTTP is a good protocol, but the problem with HTTP is that it's a request and response nature making it slow. So probably not ideal for a lot of like one way or fast-feeding data communication for that. So Kafka seems to be knowing a lot more load today. And that is kind of what I see in the reality kind of just and then like you can see that all of these are adopting different places still there. And just a quick note on the top, like telling people, hey, I've seen this through a lot of, especially like in the insurance industry where I used to work a lot in. So like you can see that they have the core, they have different services, like loan claim and all that working in their legacy services and they're adopting VIs and adopting like new app-related functionalities throughout the days. And that's kind of like, just give you a quick reference of what they are because I know it's really hard to see what they are because I like to give a reference for that. So, okay, so what are the challenges when we have that kind of facilities and things like that running in the company? Well, the first thing is data silos, right? Because just because of when they are created, when the data's are created, where they reside in the different departments, people just find they are inside a particular space and it's really hard to get them out. So there's a lot of time we have to duplicate way of collecting data and store them, duplicate in different spaces just because we need them somewhere, right? So that's a lot of data silos that people see and especially with the legacy systems that's really easy. It's very hard to face them out. So they're still there. You still need to connect to them and stuff like that. And also another challenge is because of the regulations and compliances. And I think that's mostly off the thought just because of the booming data infrastructure, the booming data systems that we have today, regulations are catching up. So I found a lot of the clients that I work with or some of the people that I work with are struggling to kind of fit that into their system. Sometimes they have to do that after they have implemented a particular data structure or data architecture and to have that as the last piece. So you gotta find a way to kind of work around it. So like that is causing a lot of issues and problems and that's a big challenge for them. And another one that I saw that was kind of talked about a lot for the last two or three years is the open data initiatives that API started all that. So people wanted to wait to quickly collect data but also sharing the data out. And what is the best way? What's the fastest way? What's the safest way with the regulations to provide the right data to the external vendors out there? So that's kind of another thing that people were struggling with that have a challenges with finding other things. And another one is the data overload. Because we're now having a lot more data coming in and out of our system. And we have trouble kind of finding where to put them. Should we delete them? Should we store them or where should we put them? Should we process them? All kind of stuff like, so there's a lot of overwhelming data. There are like, especially we're catching a lot more time-bound data, right? Like transaction histories, customer interactions, even if we wanna get the behavior of where they are there's a lot of just tracking data that we're collecting. So how do we make it useful? And how do we store them? That's a lot of things that they have. And lastly, but last but not least the data pipeline chaos, right? And it's because we have all these needs and challenges, right? So we're creating a lot of data pipelines to kind of make sure that the data flows and to get them to one point to the other. And a lot of the data pipelines were just built on top of somebody's laptop and then if that person is gone, that's gone, right? So I've seen that doing a lot especially with the marketing team where they have like one single Google Sheet to kind of maintain as a data store. And I see that happens a lot internally as well. So I just wanna take a step back, right? Cause I do a lot of integration and data thing like, so let's take a step back of like what they are. I think you already seen this but I just wanna re-emphasize this, right? So what types of data do we have, right? So we either have a batch pipeline. So they're typically dealing with like larger sizes and their nature, the nature of this type of batch is used for more of a legacy protocols or legacy systems like file transfers, right? Running inside a database to get a lot of things, relational database to get things, right? So they are more vertical scaling. It's really, some of them does horizontal scaling but most of them were kind of vertical bound. So in order to kind of increase the capacity, you need to kind of increase the capacity of your hardware. So that's what they are. And they tend to have a longer latency, right? And then like where you see them, you kind of see them in the data warehouse database. And that's when they, that's kind of where they resize. And the things that they're running on it's either a batch, it's either batch services like you just write batch and just move everything. Or you can do like that reduce on top of your Hadoop data stores and all that, right? So that's kind of what batch is about. And then we also have micro batch. I think that's kind of a middle ground when we don't have anything that we can process quicker to something that we can do for most of the BI or business intelligence reports. This is where I see them were mostly used. So they are smaller in size. Because we want a much more recent report, right? They can't be like two or three days late. That's not going to be useful. So we have a defined chunk of data sizes. And just because of the scalabilities and all that, so we want to make sure that we're isolating a particular chunk of data and run them frequently. And then having some kind of like temp storages to kind of store historical stuff and to run that and then tend to scale better with the horizontal scales. So you can generate reports, generate data sets for this type of, for getting your data out there as well. But I think what I've been seeing more recently is the use of real-time data, right? I think people are using a lot more real-time, they want real-time decisions, they want a real-time feedback. That's why people are using a lot of the streaming platform, we use the stream platform to provide them like real quick, direct feedback on those pipelines. So working with pipelines. The problem, that's good because we have all this pipeline, right? It's all here. But the problem that I, when I ask people, like what is your biggest problem when working with pipelines? I think the biggest one that I ask, when I ask around is the difficulty of accessing data because the data that we're getting today are not as structured as we would like it to be. It could be image, it could be a video, it could be a different structure of data, it could be a JSON, it could be just a CSV file or whatever that is coming from, right? It's just different data structures, right? So the lack of metadata making it really hard for us to kind of locate our desire, like the desired data that we wanna get from, right? From the repository. And a lot of the analytics tools or platforms are pretty strict regarding to the data format. So it's really hard for them to actually reuse the data that was provided into the required format. So therefore you gotta create more complex data pipelines in order to kind of transform the data into something that they like. So it's not like an easy single step of, okay, I need to get that. Or sometimes it's more like, okay, so I know what the data is and I can get it out, but the problem is the security measures or regulations, compliances that can introduce a lot of middle ground that people can wear and how they can access the data. So that's one thing that people find difficult when they were trying to access data. Another one is the noisy data. I think nobody can get away from that, especially with the Data Lake, or should I call it Data Swamp? Like people were kind of finding there's a lot of duplication of data because people just keep throwing data into Data Lake. This is a great store, it's cheap. You can just put everything in there, but they're not very well organized. So it kind of just becomes this huge glue of things where we're trying to pick things up. It's really hard to find it. It's really hard to use them and it takes time. So, and also the data, the relevance of data. So some of the data are outdated and why is it still in there? Why is it still in my transactional database? Why is it still in there? And why is it, should I be cleaning them out or should I kind of, should I, if they're outdated, should I clean them or should I just kind of archive them? Or another one that is really common with the noisy data is data mismatch.  So garbage goes in there and that was not relevant. It's really hard to map who they are. Sometimes, you know, some persons working from this company and they have the same name, but the company changed names. So there's a lot of things that's causing the data to be very noisy. So you gotta find a way to kind of make sure that it's clean, that regular things to do. And also because of that, the performance and we're getting, because we're getting a lot more data nowadays, comparing to where we are before, just the sheer volume of data it makes processing a lot, a lot, processing a lot difficult, right? There's a lot of performance problems. Do I have enough timeframe to run my batch? Do I have enough CPU powers to calculate to process what I want, right? And the method of retrieving data is also the problem, right? Because if I'm trying to retrieve data through API endpoint, that means I have to make multiple API calls, sometimes depending on how they design the interface. It takes a lot of time to actually get what you want. So you kind of, and also where the data is stored, location of it is making the performance. It's gonna sacrifice your performances. Another one is data visibilities. They have no visibilities into where the data's are. They don't know the metadata. They don't know, even if it exists, just because of the data silo, right? They don't know, hey, this data exists like, maybe 10 years ago when we designed the system, right? They don't know that. And there's no well documentations or there's no metadata shared across different places. And that's why people find it's really hard. It's just blind to blind, you can find things. Another one that people face, I see is troubleshooting, right? Like, it's really hard to kind of find out what's going on when you're facing with noisy data, when you're having performances problem, where you don't have visibility into things. I think it's all contributed to, it's really hard to fix things because you just don't have good data qualities. So these are the things that I see that works today, right? So, and one of the most asked questions for me recently because of the AI craze, everybody was asking like, oh, so how do we prep ourself into this AI world? Well, like, if you take a look at AI, it's not like magic, right? AI is something that you need your machine to learn. In order to get your machine learning, the logic that is supposed to apply, you need to actually create data sets. And that's the machine learning part of the work, right? And I think that's mostly placed with the data architecture. So how do we get to the point where we're providing data sets, whoops, providing data sets to kind of start generating models, to do model trainings, to do model predictions, and then it will generate the model registries. And then we'll use the same data, similar data or different data to kind of become the reference data. And when there's actual events coming into the system, it will use that data model that we have used to, we just generate that, the model that we just generate using the data set that we provide to kind of apply that into the system, which is the inference part of the AI structure where you can use the reference data plus the data model to make real-time decisions or artificial intelligence decisions for users, right? So that's kind of where things are. So if you look at it, what we need to do from the data architecture part is probably just getting the data sets, right? So that's why I was thinking like using the, kind of the things I've seen, like different methodologies that people has been adopting. I was, I really liked the way that, you know, they started talking about like data mesh and stuff like that. I think it's a really good way of doing it. The problem with that for me is it's really, there's a lot of methodologies and there's a lot of concepts and there's a lot of things you need to, you need to take in and like have that concerns and to get it adopted. So I would say, you know, it's good to be there. It's good to kind of become that mesh, right? But how do we get to that mesh? I wanna focus this on more on the more practical side, like more on the infrastructure side or like how do we do that for your data? And I think this is a way to get you to that. So the first way is to build the infrastructure highway for your data, right? And like what every good book would say about, you know, building your, redesigning your data architecture is make sure that you are working with a smaller set of domains. It is never be cool and it will take forever for you to get everybody coordinated inside your company to start working on that project. So it's always good to have a isolated, you know, domain so you can start working with that and slowly spread your work across and expand that to the entire company but always start with a single domain. And then you start your data modeling work. I won't dive into the data modeling today because, you know, to be honest with you, I have yet to find the best way to model my data. I've tried Kimball, I've tried Iman but I can never, with those two methods I can never satisfy anybody, everybody. There's always people complaining. I just like, I have to always change models and stuff like that. So this is a call for you.  We can see like what's the best way so I can do another talk next time about data modeling. But for me, I think a good data modeling it will be a good foundation. But once you get that first initial, but I'm not saying you shouldn't do it, you should still do it, try to do it. You're not gonna make everybody happy but you should still do it. But provide a data access endpoint for people to start retrieving your data. They need to know what they're getting, right? So data model access points and then also federated governance, right?  So we'll be focusing more on the self-services data and streaming infrastructure, right? So that's where I think we should focus on and how we should do our things, right? So this is my way of thinking, right? So in order to get the data flowing we need to kind of create a streaming highway basically. So instead of asking for data, everything should be push-based. Well, not everything should be push-based. Everything would ideally be push-based. If something happens, it will be pushed through the networks of your data and that will get processed into its ideal state. For instance, if you have like three different systems that require three different formats then you will push that data into that and then quickly convert the data into their ideal format and then push that into the system they're trying to ingest, right? There's always gonna be a request coming in for pulling the data, but that's another story. That's the endpoint that you're providing. That is the endpoint that you're providing here, right? Right here on the, it's really hard to use this that you're using here on the right where you can use it to access your data but mostly I think data inside your system should be like human blood where it's just going to traverse through your entire system flowing through when things come in and then things get triggered and they get processed. So data will be flowing in and out through this streaming pipelines or streaming process that's set between different connection points or different services and they get turned or different data ingestion points and then they get received and then they process it and then put it somewhere or trigger another one which is going to make things a lot better in terms of the data pipelines. I'll show you a little bit later on why I say that. Of course, I'm not going to deny that there's going to be always a need for batch pipelines. I'm not telling you not to do them even when you have streaming data because there's always a need for different types of data like streaming, like video content. I don't think you can do that through streaming very well, right? So I will still do those in pipelines. Some of the longer BI business reports that these historical data, I think some of them still requires micro batching. So there's just different types of different pipelines they deserve to in different places but most of the data inside your company, inside everything should be pre-prepared. Even for the BI ones, ideally we want them to be somewhere that is in the most ready place. So when you're trying to query them, when you're trying to pull them, when you're trying to aggregate them, they're all in a much better form in order to get it out as well. So what we see is we'll have data warehouse in the middle, at the end words, it's trying to store more structured data and data like it's kind of just where everything was temporary stored and real-time engine for you to pull in data or transactional database for quick transactional application where they don't really like to integrate with other things. But like any transaction that happens in the transactional database should then be signal into your data, data highway that letting people know that, hey, your data is being changed and here's the signal who wants it and then people will pick it up and they'll put it in there as well. So that's kind of like my view of what the data infrastructure of how you should set it up. So basically having a interconnect point for everything.  And each one of the logical representation we call them topics, they can be set into different model names. So if you ever want transaction data you can get it from as particular topics where you can kind of receive from different topics. If you ever want to hear a difference between the latest transaction interest rates or whatever that is, you can receive it from there. So just a quick shambles plug on the streaming platform. So we rep Panda is a great streaming platform to kind of do that. I think we are a direct replacement for Kafka. So basically you can use us to stream data in and out of your system to build that highway, of course. Our biggest difference between us and Kafka is that we actually optimize your hardware, right? So instead of relying on JVMs and then PageCache and then disks, we just avoid all that management all together. Also, we use our Threeper Core architecture where we can quickly write our streamer data in and out of the broker as quick as possible. And we also have a lot of add-on stuff like automatic partition balancers as well. So we can automatically balance your brokers because everything, if your brokers are in balance it could take a while for it to kind of, it could jeopardize your streaming highway so everything can be like really jammed up or there can be problem shooting issues as well. And then we also do a lot of like RPK toolings. Like my favorite, my personal favorite is the RPK tooling because it makes me easy to kind of to create the applications. So that's that. Do think about us when you wanna build your highway but what is that little dots, right? In like on top of the highway. So I call them the, I think I got these ideas from a lot of times when I was doing integration as well is that these joint points were a little pipelines or data pipelines that you can create in order to process the streaming data comes in and out of that. Then they do a lot of things. And I heard people doing a lot of like patterns. There's a lot of, there's millions of talks that I go to that talks about different patterns but we've been doing that for a very long time in the, in our Camel community. It's called enterprise integration patterns. If you haven't heard of that I would strongly recommend you to kind of read that book because we've been doing those kinds of things for a long time. The idea doesn't change but the way that things can implement can be a little bit different but basically what you're doing is you're either doing transformations or you're doing enrichment or you're doing some kind of rerouting off your data from one point to the other. So that's kind of where the dots are. And so when I was saying there's the chaotic pipeline thing that was going on I think what we can do is to organize them a little bit better. And the way that I categorize them with like batch, micro batch and then real time we can also kind of split them up and we're having them more in the detail of what each of them do, right? So we can start with the most used one. So this is what I see that people will use more often is the stateless streaming pipelines, right? So these are the pipelines that is going to handle a lot of the traffic when there's data coming in there's initial data coming in when there's things you gotta do some kind of reformatting, normalizing transformations, filtering, validations. Validation is one of the things that you probably want to do in order to make sure your data is clean, right? All of this type of things that you can do that you do like mostly and these type of pipelines does not need to remember any of the historical things that has happened. Anything comes in it just look at it and quickly process it and just let it go or store it or send it somewhere else, right? So these type of pipelines shouldn't take you that long to kind of create and it should be easy to scale out because just because of the nature of they don't remember anything. So these should be very easy to horizontally scale out. And then you get state for pipeline this gets a little bit tricky because this we call we used to use them as like complex and processing or time window based processing where it will need to remember some of the states and that causes complexity because of if something goes wrong then I still need to remember the state or where it kind of last went off. I still need to pick it up, right? So these type of pipeline is a little bit they're a little bit less though you don't see them that often but you still see them then you still have to kind of take care of them. So these are the second level of pipeline that you see you'll see more of them. And then on top of that you see you're starting to see more of the large volume data processing where you see like, you know micro batching where you want to do a lot of analytics based on a huge set of data as well. So these type of things, you know some there's there's smart way of doing it like storing it in a time like time series database or things like that to make it easier for people to kind of process depending on what they are. And also you have the batch pipeline where you expect it's the most traditional ways of like sometimes they're just still in files or you need to get them into the data sets all that kind of stuff. And you still need that batch pipelines. So these are the hierarchies of pipelines that you see in the entire system, right? And this is just a story about the drawing why it's like squiggle but I'm just trying to kind of showcase, you know where the state of where the pipeline starts, right? So when the ingestion comes in it will goes into your highway and then that's when your student pipeline kicks in. So first of all, the stateless pipeline comes in and they will start dispatching they'll start transforming they'll do like quick stuff and because there's a lot of them because it's easy to scale them they'll quickly process it into the right place and then the stateful pipeline kicks in, right? Because once they satisfy their requirement of processing they will start processing it depending on their time window base or if a certain requirement that was met that triggers that pipeline to start or to start processing all that kind of stuff this is the stateful thing and they will start receiving that and putting that back into the highway. And once everything gets into the highway there are some micro batch processing that you probably wanna do that first still get the continuous feed of the new data plus the historical data that you already have in your data stores no matter where they are and then the batch pipelines that get stored or that gets processed at nine that just needs to be done like in a large file sets, right? So, and I think I'm going with that because that kind of determines where RIP HANA is going in the future and how I see things are going with all this data pipelines and how do we make it easier for data engineers to get their things out, right? So what we're seeing is there is a lot of there's going to be a lot of stateless streaming pipelines going in and out and the way that we currently do that is to kind of store them somewhere or receiving that from a streaming service streaming places and then just receiving that consuming that from a streaming platform loaded into my computer system loaded into CPUs, loaded into memories process it and then put it back into the streaming services there's a lot of like data ping pong going in and out in and out the traffic, right? So you buy increased egress traffic you've got memory loadings and you got CPU processing and sending it back. So there's a lot of waste of energies. So what we think what we think will be happening a lot later on in the data engineering world is that people will start thinking about, you know is it a better, more efficient way of, you know processing the stateless pipelines? If everything just happens in the streaming broker level with the streaming platform level can we just inject a wasm or, you know, not wasm we choose wasm because we like it to be more at language agnostic so you can choose whatever languages that you wanna use but everything can be processed, transformed and filtered, validated inside your system. So that's what we're trying to do like in the rep into space. So I think in the future, what you can do is you can start putting your, applying your stateless transform logic or transform pipeline logics into the broker. So the broker will directly do that internally when the data hits the broker CPUs or hits the broker memories or where we are and we'll start to transform it and then it will do a quick move over or replication of the same thing plus the transformations into the same broker and that could be quite quickly accessed. So there's no extra work or extra flow that needs to be done for these type of transactions. Another one that we think that we, I've seen at least like at least you can see all the new investment that was going into the industry lately that everybody was going to a standardized tool of accessing data and that was more like a SQL base. So you can see a lot of other things were happening. Like Flink was all over the place, right? If I open up, if we start a Flink course today we're going to have to have a lot of people because people want to learn about Flink use SQL to retrieve data. So I think SQL is not going away and there's going to be like a standardized people or it's going to be much more standardized and there's a lot of technology out there that's going to allow you to use that tool to access data and data can come from different places and they need only to be enabled and being accessed by this interface. But another things that we also see is the efficient use of cloud storage. So you know now that we have built this infrastructure highway, right? So with this infrastructure or highway how do we most often with this thing will probably offload the use data into a data store say a S3 object stores, right? These all on the cloud. But the problem is every single time when we're trying to offload this data or process this data, you need to do a lot of transformations or transfers and normalizations in order to get the data access. What happens if there's a way that way we can reuse the stream data so when the data comes in and out of your streaming highway everything gets stored somewhere if we can reuse that cheap object data store using your SQL interface to access that there's no need for you to kind of export it put it somewhere else. If we can reuse that as the center of your data store wouldn't that be better, right? So another things that in Red Panda we're trying to do is we're trying to be we're trying to work with Apache iceberg provide you with that experiences. So once you have the data highway created what we're gonna do is everything that hits into your disc if it's if it's if you need to move it to outside store we currently do it with tier storage where we can move the data over to the S3 buckets and then you can have or any other object stores on the net and then you can kind of use the iceberg on top of it to do the to do the metadata and to do access for your historical data. And that's kind of where we're going. And I think with this it's gonna solve you a lot of problems. First of all, I forgot to mention that when you're creating the highways when you're creating highways you wanna make sure that you also set up your schemas schema registry is one of the things that that we all do. Hopefully you don't forget about it, right? Schema registry so people know where they are so it dissolves the data not transparent problems. And then you also have because all the data's are flowing in and out in and out of your system and it's easier to access them through the highway so we're breaking that silo, right? Hopefully we'll be breaking that silos. And it's because we're injecting the data validations with those stateless quick data transformation data pipelines either internally in your broker or externally we're hoping that this will then clean up your data so it's a lot cleaner. And because of the way that we handle horizontal with streaming traffic it's easier to scale them out so we can certainly adopt a lot more traffic so that it's gonna solve our performance's problem. So that's kind of why I was preaching this idea of like building this data highway for your data pipelines and also adding that differentiate different data pipeline natures to make them easier to access and it's easier to scale them out it's easier to manage them and all that. So that's kind of my talk for today. So thank you very much. And here, if you wanna learn more about RIP and what we think there's a lot of documentation and learning resource and I'm always there in the Slack community if you wanna hear more about me or if you wanna give me feedback especially the data modeling part, let me know. Let me know, ping me on Slack, I'm always there. And I think we can go ahead and start the Q and A. Let me, right, I don't think there's any Q and A so is there anything else that we need to do? I guess we can probably end the session early, right? If that's the case, thank you very much and I would like to hear more of your feedback. Oh, okay, there's a question coming in. Okay, this is from Derek. Thanks, Christina, that was very informative. I'm still in the beginning of our project. So learning towards, yeah, this is a bit long. Derek, can I, maybe we can talk about this offline. This is a bit long, I still need to read it. The interface is not that easy to read. Can I meet you over there in the Slack community if it's okay with you? Or can you email me? I have an email, Christina at rependa.com. We can kind of do that there. So thank you very much, I think that's it. Thank you all. Hi, Christina, it looks like we got one more question and I don't know if you wanna take a look at that. Oh, okay, sorry, this interface is not. Yeah, it's a little, it's a little tough to see sometimes. I'm from Thomas. I can read it out too, if you like. It says, can you share your thought about data affinity? Moving data to processing is expensive compared to moving processing to data? I think that's a good question actually. That's why I preach for, that's why I kind of preach for moving data. Sorry, this thing is moving. That's why we preach for the wasm, kind of the wasm approach of doing things, right? Because like what you said, moving data to processing is expensive, right? So when you have unstructured data, moving it over to processing, instead we should always do our data to kind of create or process our data right away, like inside the broker or as quickly as we can and then to have them ready and broadcast them out. So I think I do think that's what we're trying to do for Red Panda as well. So I don't know how that answers your question, but that's kind of my way of thinking about it. Maybe I need to give you the better thought about this. But yeah, we can probably do this a little bit later. But yes, I think you're right. My thought is, you should always pre-process your data before it gets to the destination as much as you want, as much as you can. Hope that answers your question, Thomas. All right. Awesome, that was the last one. So thank you, Christina, for your time today and thank you everyone for joining us. As a reminder, this recording will be on the Linux Foundation's YouTube page later today. We hope you join us for future webinars. Have a wonderful day.


Good day fellow investors! It's time for your comments again and we have two very interesting comments today. First is about how to handle a 50 bagger and there is a great comment about that and then what's the value of my research and should the price be lower? Let's start! Before we start let me just thank you for all the support and the positive feedback I got on the book Modern Value Investing. If you look at the 100 top paid Kindle books under the topic stocks we are at 23rd place which is an amazing thing for me and we are just ahead of you can become a stock market genius from Joel Greenblatt. So thank you, thank you, thank you, thank you for your support and for the great reviews there if you have read the book please leave a review perhaps we can enter the top 20 or even the top 10. Thank you, thank you, thank you. Let's go back to the comments. So Van1718 says I purchased Amazon at 40 and sold at 45. Yes, yes I know. I'm thinking however that this could be a chance for all of us who missed Amazon like me this is the comment on Mercado Libre. Long term I'm bullish of webcomers and I'm bullish most of South America and Mexico. I believe it's worth taking a small position now. I do remember thinking later on with Amazon that it never pulled back enough to create a good entry point. This may well be as good as it gets for those who wish to go long Mercado Libre. Now this is what happened with Amazon from when the stock price was 40 till 45 now it is 1837. So if you invested let's say 2% of your portfolio in Amazon back in 2006 now that 2% would be 50 times higher so practically 50% of your portfolio but the problem is here okay how can I hold something like this for what was it 12 years and just look it go up go up especially since like ever and this is a big big problem we all see now Amazon from here till here it's an amazing return but there are really few except for Jeff Bezos who is now the richest person in the world that held onto those stocks for such a long term. So the question is how can we project ourselves to hold something like that practically forever and that's the key Buffett says my favorite holding time is forever. So you might approach investing like I'm going to take 10, 20, 10% on my returns you will be happy you will get a boost from that but the key is positioning yourself in creating a portfolio for the long term rebalancing carefully the positions and then you start building buying businesses buying wealth buying businesses to generate growth that are well positioned and then you start looking at things a little bit differently than chasing the 10, 15% it's easy to get into that game of chasing the returns a little bit I'm a little bit up I'm a little bit down and that but when if you can shift that mindset to the long term to okay these are the businesses I own I like them and we'll be talking more about portfolio positioning and creation now as I'm preparing a video a series of videos on how am I looking to prepare to fit to make my model portfolio that I'm building for my research platform so this is a big part okay are we holding forever and then when you're holding forever you have different assets you're well diversified over your portfolio you try to buy cheap but cheap doesn't mean below book value or low P ratio cheap means cheap in comparison to what can happen like it happened with Amazon so when you do that when you think okay I'm going to hold it forever then you think differently about stocks so on one hand we have short term gains chasing a lot of energy this spending and then you have okay I'm really looking at great businesses perhaps I buy a few of them per year and I include them in my portfolio over the next 20 years and then I reach great gains and this is exactly what I do in my research platform and other has a comment about that so he finds that my research platform fee of 250 49 dollars is too much for those who have just 5000 dollars to invest he's in his 20s and then he calculates that just to break even on that money he would need a 10% return to make more money is 20% just to make a 6% profit so that's no way the risk reward of that price is good now I know that 249 dollars on 5000 is a lot but a I think that the time that I invest the education that I provide the analysis that I provide that you will continue to invest 5000 dollars per year so after 10 years you will be 50 000 dollars 100 000 dollars 200 000 dollars and over the 10 years you will pay me let's say two two and a half grand let's say because you get the price now fixed and I will be raising the price but that two and a half grand will give you so much data so much knowledge so much diversification so much insight into what's going on in the world of investing that I think I will make you at least 100 000 on that two and a 5000 so that's a huge return think long term think well we where will your life go think where will your investments go and think on education also if you have a smaller portfolio and we'll make our best to increase that portfolio so that the fee becomes smaller smaller and smaller another very important thing perhaps on 5000 if you're unexperienced if you don't know a lot in the next crash you might lose 50 percent if you are hedged if you're protected if you know what's going on if you have great assets in the next crash you might not not lose anything so that's 10 years of my fee so that's also something to think about in the very long term again apply the long long term perspective on whatever you do with investing in life and your life will be great and that's really something that we have to again put into perspective we have the long term how we're going to hold one stock like amazon or we're going to think about those few percentages here and there that simply make your mind go crazy so the message for today is think long term educate yourself try to find what really leads to great investment returns and that's holding a business finding those businesses that you can hold forever or at least until they become crazy crazy expensive just another note here from heidi he is for index fund investing and he says that the best way of investing is nothing but index investing over the long run and he is against my ideas of having hedges my ideas of being protected and everything because of the opportunity cost of everything now why am i so much against index funds because i think that index funds are okay if you buy them cheap index funds are very risky if you buy them expensive and that's my message buy index funds if the price earnings ratio is five ten and they give you ten percent earnings return not don't buy index funds if the price earnings ratio is 25 and they give you four five percent return over the long term i just think that investors that dedicate themselves to investing can do much better that's my message and you can do better by owning hedges and being long other things that give you a better return than four five percent that's it hedges is always a cost to a hedge if it doesn't work of course if it works then the return is amazing and that's something each of one each of us should balance how to approach that and in the long term mindless index investing will do good as it did good in the last 45 years but might also do very bad as it did from 1929 where everybody was crazy when everybody was crazy about stocks till 1960 something when index funds came back to the previous levels something like that might happen again and that's what i'm always discussing risk reward risk reward it's all about risk reward when investing thank you for watching looking forward to your comments and i'll see you in the next video


Everybody forced here ripping 20-22 flawless football hobby 2 box case number 32 random teams random right now We're gonna get to the break in the same recording best of luck everybody. Thank you very much for filling this one up Last break of our Friday night. We've got personals after and they are full six and four ten times names first one two three four five six seven eight nine and ten Michael S down to Christian G And the same amount of times on our teams here one two three four five six seven eight nine and ten Bills down to bears Well Michael S with the bills Ryan M Titans a shams Packers and me a Sean M with dolphins Neil B. Taxens Chris M Commanders Andrew W. Niners Rob C. Chiefs Robert G Cowboys Jonathan L. Colts Brad K Panthers light M Eagles Dale C. Jaguars Andrew W. Rams Kyle Z. Bengals Frank L. Broncos Andrew W. Vikings Ryan V. Lions Roy M. Giants Felix N. Saints Trish V with the Patriots James P. Jets Felix and Buccaneers Sean O. Raiders Neil B. Browns DeSean J with the Steelers Jeremy Y. Seahawks Andrew W. Ravens Matt M. Cardinals Peter M. Falcons Frank L. Chargers Christian G Bears Results are getting posted right now I add Debacks lost it away at the end there taken All right, there is the results link in chat for you Let's rip Let's rip Fit you with this thing later for sure. All right. Good luck y'all Jalen Hurch gem shield no Eagles are a good team again, I would say we're not having like a big rookie in this Hurts Hurts is that dude recently, so why is this briefcase dusty? Everything is here everything in here is that's true different camera. I'll try to make sure you guys don't see in there So first up is going to be a dolphins double patch card Jalen waddle and Tyreek Hill 11 of 20 They're gonna get shut down this weekend by the Patriots Waddle and hail to 20 dual patch nice car dolphins Are the dual patch here this time single player George Kittle to a 15 Kittle dual patch to 15 Player worn that is a Buccaneers RPA Rashad white 17 of 20. That's a solid one RPA Rashad white 17 of 20. That's a pretty one right there All pro ink for the Chargers 5 of 15 ladanian Tomlinson Awesome LT all pro ink to 15 Rookie frame signatures three of three Aiden Hutchinson Three or three. I've never seen that numbering in that color on the foil before Rookie frame signatures of hutch for Detroit Great hit on the hutch.  It looks like Maybe not be. Oh, I thought they were that they had one 21 not one 11 never mind disregard Jerome Betis on the auto number two eight six of eight the bus auto for the Steelers All this champions auto here One-time champions auto and then another awesome running back hit here You've got flawless flyers of Josh Jacobs on the auto 19 of 20. It's good running backs in this one. You had LT Jacobs bus Jacobs, okay, let's see. We've got a Darrell Green Ruby gem three of 20 Diamond Guarantee Washington on the Dale Green Ruby Jen three of 20 Mac Jones Mac Jones 13 of 20 Ruby for the Patriots and Then Eagles dual patch autograph Jalen Hurts to 25 They are indeed a good team to get in the random Two four color patches on the Hertz auto to 25 Lamook sweet Jalen sweet Jalen It's a solid box Solid solid solid break hit spaced around as good. You sure this your last false break of the year. Are you sure? You're three for three now be ashamed to Keep all that luck just Know where to use it, huh? It's a work of case Probably not It is not for any specific game or events Well, then all right first card's pretty nice Jesus guys get a glimpse of the dual Nike swoosh laundry tag of Daniel Jones and Eli Manning Two of two for two swooshes for two QBs. Good Lord deuces That's pretty sick man Eli and Danny Sleepy Manning two of two dual swoosh laundry tag here for the Giants It's a great Giants card player Warren relics on both True loses the true Whoa, how do I want to head one of these in the first box? You know to a 15 early I get 15 a 15 dual patch kettle for the Niners. It's four kettle patches now. It's a lot count both cards Flawless Flyers autograph five of eight is Donald driver for Green Bay Donald driver. Who's he driving? patch auto three of ten Bob Lily Cowboys RPA 16 of 20 Matt Corral Panthers Good hit for Carolina there Rookie autograph of Malik Willis 17 of 20 for Tennessee Malik Willis auto number 17 of 20 Titans and a juice Landry autograph six of 20 for Cleveland 2021 flawless Jarvis Landry auto for Cleveland Steve Young Diamond Gem 8 of 10 San Francisco Ruby Gem for the Raiders to 20 Derek Carr the Diamond Guarantee here and a rookie patch autograph of Jamar Chase 18 of 25 Bangles the big hit on the back three color RPA that's so sick Bangles Kyle Z Kyle Z 18 of 25 RPA Chase very nice solid case there a couple of really nice patch autos on the in case some other nice cards That will do it. Thank you all very much moving on to personals