Okay, plot lines. Plot lines! After reading her deceased mother's letter, a girl tries to reunite her father with his college friend. We just saw this. No. Yeah. We have seen it, but not just. Then it's not what I'm thinking it is. After reading her deceased mother's letter, a girl tries to reunite her father with his college friend. Oh, maybe it's not what I'm thinking of. I don't remember. I don't know. Coochie, coochie, hold the head. I was going to say that.   I'm Rick. And you can follow us on Instagram, Twitter. Him for more juicy content. It's so juicy, especially on Patreon. Am I right, peeps? Ooh, lick it. Nakedly. Thanks to the sort of supporters on Patreon. To all of you beautiful Patreon babies. Tons of exclusive content over there because we have probably thousands of things that have never hit the channel. True. Over the past almost four years. Yeah. It's probably in the thousands of things that will never be on the channel. Yep. But we have reacted to it. And so you're probably like, why have a driver? We probably have. Yep. All the good content is gone. No, it's not. But it's not true. But all of the juiciest of the juicy is on Patreon. Because Patreon spelled backwards is nortap, which means juicy in Swahili. Did you know that? I don't believe you. Okay. Okay. But today we're doing a movie of you. The newly released but not new film, Gali Kulian. Yeah. But apparently it also translates to In the Shadows on IMDb. Yes. Not to be confused with what we do in the Shadows. True. By Tekkawatidi, hilarious movie, if you haven't seen that one. Much more hilarious than this film. I know. Clearly a bad remake of that film. Truly. I mean, if they were going for a comedy here, they blew it. Oh, and Johnny, a daughter reads her deceased mother's letter and tries to reunite her father with her high school sweetheart. What's the movie? That's a plot line. After reading her, that was a no branch. She was like, oh, give me a hard one, Whitey. I thought that one was pretty easy. Anyways, this is a review of that film. It came out apparently in 2017, but I think it was just in the festival circuit for a long time. And I'm guessing they had trouble. Because I've been seeing Manoj post about it a lot and all that kind of stuff. But it was just released on Amazon to watch. So if you haven't watched it, go watch it there. So I just wouldn't do it like a normal review that we would. So it'd be 100% for review.  For all of the above. Directed by Depeche John, written by Depeche John, produced by Depeche John. And apparently he has heavily involved in this film. Slightly. And then starring our dostiest of dosts, multiple. Manoj Bajpahi, Niraj Kabi, both Dost and Ranvir Shori is the other one as well. And then I guess the other main lead would be. Yeah, Shahana Goswami, Goswami. So once again, if you haven't watched it, go watch it, come back. Yes. Make your initial thoughts, please. Yes, by the way, this is our 70th of the year. Our 277th all-time. Oh, okay. 40, no we won't, 41st Hindi of this year and 170th Hindi of all time. How many films did we watch last year? 80. Okay, we'll probably. I think we're gonna hit right around. Right, that same number. The 80, I think, that was the number. Yeah, so I almost wrote a paragraph, but to, it's been a while, but to synopsis. And usually if I write a paragraph, it means that I have so much good to say about the movie and that's the case with this. For me, this is cinema, this is the difference between, there's movies and then there's cinema, which enter the realms of the highest expressions of the purpose of the art form. Like Avatar, two through seven. Exactly, which, did you not like the original Avatar? I don't think it warranted seven spinoffs, that's for sure. I think it was. Did you like it? I think it was a fun visual film with a very basic plot. He's like a politician, he won't say yes or no. It was fine. Okay, all right. Yeah, because I liked it a lot. I didn't think it warranted an entire theme park and seven spinoffs. No, it did merit a sequel, not 14 year, or however long it was later, anyways, go. Anyway, I have a lot of good to say about this movie and virtually nothing bad. I loved it. Yeah. Really enjoyed this one as well. I have very little gripes about this film and I'll get into, once again, my gripes aren't really artistic gripes, per se. I don't have a gripe. I mean, I wouldn't call it a perfect film, but it doesn't have, if it was a really, if I'm looking at a diamond and I'm a jeweler, I'm looking at it and I'm saying yeah, this is a really well cut diamond that has very few flaws. This is a really, really nice diamond. But if you actually looked at a diamond, could you tell? Always.   I'm not sure, Konia. Yes, I thought this film was fantastic. I think everywhere through performances, writing direction, like cinematography-wise in terms of the feel of the film and the color correction of the film, I thought it was all really, really pretty. I'm betting they were trying to, they were shopping it through festivals for a long time because they probably really believed in the film. Absolutely, how could you not? Which is not shocking, if you've seen the film because it's a very well made film. Let's just start with the star of the film and our dostiest of dost, Manoj Bajpayee, dost. Is that you? Yeah, he's dost. No, is it dost, Manoj Bajpayee or Manoj Bajpayee's dost? And Johnny would know. Grammatically, is it Manoj Bajpayee dost or dost, Manoj Bajpayee? Dost, Manoj Bajpayee, yes. I've already said before, I think he's one of the best actors alive. Agreed. Not just in India, just alive in general. No, agreed. This is one of his finest performances. Agreed. And I would really love to talk to him again because I texted him after I was done finishing it and I said, did you sleep deprive yourself? Because it genuinely looked like he was... He's immersed. And he didn't go into specifics, he just, there was an entire process that, and I saw, I've seen him, and we didn't talk extensively, but I've seen him post that he said he was almost losing his mind while doing this. Not a surprise. I'd be really interested in how in depth he went in this film in terms to get the feel of the character, the look of the character, what was going on. He was immersed enough that I think there's a moment where he holds the phone up to his face to make a phone call. And his nails are long enough and unclean enough that it was either spectacular makeup, which I wanna say something about the makeup in a second. But my suspicion is I could smell him through the screen. I have a feeling...  I have a feeling he, like, as much as he could without disrupting the art of other people he's working with, did as much immersive work that was probably, well, I've read as well that for him, he nearly had a mental breakdown and it doesn't, if you're doing this kind of a role properly, it would take a giant toll on you. And then when you're done, you should probably be really grateful it's over, but also proud of the work. And he seems to be, as he should be. He really should be very proud of the work he did here. For this style of film, once again, it's 100% spoiler. Please go watch it. If you haven't watched it, it's worth your time. Really, please do. Please do not spoil the review. So please go watch it. It's on Amazon. I think you have to rent it, though. And before we spoil as well, real quick things that I'll elaborate on, when you watch it, if you have surround sound, that's great. If you don't and you're watching it alone, connect your AirPods so you can listen to the beautiful sound design that was done by the sound team. And when there's a moment, there's small makeup. I love small makeup. Like when the Revenant was nominated for makeup. Really small eyeliner? Yeah. I wanted the Revenant to win the Oscar because it was so, so the most of the makeup was so small and intricate and easily missed and Revenant won the Oscar. I took note of the beautiful makeup in this and it's not a surprise that the makeup artist is Kamalish Shindi. If I've mispronounced that name, forgive me. But if you look at Kamalish's resume, there's a reason the makeup is so good in this movie. So. What you done? Like Hollywood or just India? Everything. Everything. Ghost protocol and then stuff here, just like Life of Pi, it is an impressive resume that showed in some of the small detail. Especially in spoiler point. We're in spoilers. We're in spoilers. Like I was just saying, if you're still here and you haven't watched it, this is all spoils because I was gonna say something since, once again, go watch it if you haven't watched it. Since this film is all about in the mind and what's going on, Manoj had to really go to a certain place to kind of make you feel that he was, like something was going on, whether he was going insane, whether he was mental breakdown, all of the above, whatever it is, right? Sleep deprivation. You had to believe it. You couldn't just like not prepare and show up on set for this style of film. This role can't be portrayed by someone who's not an experienced and well-trained actor. It just, it can't be done. It's too difficult a role. Because there's so much going on behind in the entire film, because obviously the entire, and I will talk about the entirety of the film and what was happening. There's so much symbolism, yeah. But I thought he did such a stupendous job and he's had so many great performance. It's hard to really pick the best Manoj Bhojbhai performance. Satya, obviously, is a really high up there, phenomenal performance, his performance in Family Man, his performance in really anything. He's like, I think if, especially in Hindi cinema, if you take like the Mount Rushmore of the current actors, it's probably Manoj Bhojbhai and there was Pankaj, I mean, maybe you can have Nisheer Dinsha. Nisheer, of course. You can see in there. But it's those male actors that really just, if they're in it, you're like, this is... Yeah, well, and it's gonna be elevated. Same with Niraj, who's supporting in this. Niraj is that caliber of actor. Niraj, yeah. Once again, our dost, dostiest of dosts. But he was, he's always so good, right? And I think he has, have you read his book? I've not, I almost registered for one of his classes because he has a class that he teaches. And I know he has a really unique approach on acting. Very interesting. Which is so fun and interesting to listen to different actors in their approach because none of it's right, none of it's wrong. It's all whatever works for you. Exactly. If it's really there, it's the right process. Exactly. As long as it's not hurting other people. Exactly. Their performances as well. No, and a good acting coach will tell you that. A good acting coach will tell you, this may not be the place for you because this may not be the process that works well for you. And that's why I love talking to actors so much is because not everybody's process is the same. And it's not always the same necessarily with each role. I mean, there will be some foundations, but there'll be some alterations as well. Well, we've talked to our Dostaraj Kumar. He's like, sometimes there's lots of preparation. Sometimes I just show up on set. Exactly. The roles don't always require me to do that much. Exactly. Like extreme prep, this does. This is extraordinarily requiring. I would say this is probably my favorite of his, but that's hard to say in the same way. Yeah, because of how hurcly, this is a really difficult part. And what makes it so difficult is how subtle the difficulty is. Yeah, for sure. He makes it look easy and there'd probably be a lot of people who aren't familiar with acting process or technique and training who would see this and think he was doing a good job and look natural and that's what was so hard. But in the same way that, for example, when I think about my favorite Daniel Day-Lewis roles, not one of them is better than the other. Gandhi's better than all the other ones. But my personal favorite is Bill the Butcher. Same. I love him as Chrissy in My Left Foot. I love him as Lincoln. I love him as Daniel Plainview and the list goes on. Same thing with Meryl. I have a ton of Meryls. Sophie's Choice is kind of my favorite. And this may become over the time of watching his films, this may become my favorite. Yeah, I have no doubt it's easily up there for me. There's just so many that he is. It's like picking your favorite Noir's or Bob Goddard's performances. Yeah, that's just, you're getting gems. Yeah. And they're all so different and it's hard to pick. Which Rolex do you like? Yeah. They're all Rolexes. I like them all.  Apple watches, though. I don't have one. But Niroj as well. He's always so good, right? But he brings such a, he can be so intense and so intimidating like in this role at times.  Like he can transform himself and his body and his mind and how he's looking. But then, he can also be this intimidating. But another thing I loved about his performance in this was that he wasn't totally vilified. Obviously he was vilified. Which is a credit to the great writing in this movie. Obviously he was vilified and he was not saying he was a good guy.  But then like you'd show him like tenderly hugging his wife and like. Of course. Obviously I think he's a terrible person but then they don't just show the terrible sides and I think that's a testament to the writing. It's a great writing. Niroj's performance of these, fleshing out a full character as opposed to just making a villain. Exactly. Making it like. Real human beings. Enduring moments.  But he has his moments. He apologized to his son.  I don't know what's going on and you don't know where it's going but I like that they fleshed out his character. Me too.  Absolutely. The writing, they did such a great job. The writing in this and we'll talk more about the acting as well but since we brought up the writing you'll hear us talk about poor writing which is most exemplified with exposition in the first few minutes where they're telling you something versus showing you. And if you wanna see good writing that doesn't do just weak exposition, what? In the first 10 minutes of this thing you're immediately educated as to what's going on without being told. It's just, and throughout.  There's no fat on this script at all or this film. It's the exact right runtime. The pacing is a credit to the editing with along with I'm sure the director choices of when to edit along with sound design and scoring. I thought the score was fantastic. Just everything, the whole creative team feels like it was a unified whole and what they wanted to convey. And I thought all acting, I even put a note in here when he goes to the doctor to find out what's going on with him. Whoever played the doctor in that scene, that was a beautiful scene with those three. I believed every single moment between all three of those people that we were watching what was happening. And the makeup, the beauty of that cut on the hand and the stitching of it. And then when he took the bandage off a few days later and it had that raised white soft skin on it, I can't jump up and down enough for how good the makeup is on that hand. As was just a scratch on the head, just pretty consistent makeup wise throughout. Yeah, I 1000% agree that cut was, it was beautiful, it looks so real. And it's so symbolic. I mean, we can spend some time talking about the symbolism in a minute, but let's also talk about, the two other mains, the mom and the boy. Yeah, well, and also Renvier Shorrie. Yeah, I remember Shorrie, who we've seen in many things. Yes. I thought he did a really, really good job. I liked his chemistry, I liked his character. And they didn't really explain their relationship a whole lot. You kind of just assumed what it was, a testament to the writing again, of just, you know, they've known each other a long time, they didn't give us exposition of their backstory, but you kind of just, you're gonna kind of get what this relationship is. Exactly, you're gonna know what you need to know. And I liked their chemistry together a lot. But yeah, the kid, I thought, this director and casting director, Dilip Shankar. Yes. Fantastic job at finding this kid. But also, this director must be just really good at directing it. Finding good actors, one wanting to work with good actors. Yeah. Clearly. Knowing how to direct them. Clearly. I don't, I think he had an easy job in this one. Yeah, absolutely. And you're just copy, and Manoj Shabashbhai just kind of sit back and let them do their thing. But it's, once again, I think at least a testament to him and the casting of getting these good actors for this film. Yes. And just focusing on artists and good storytelling. Yes. And as opposed to, you know, I don't even know if this went into a theatrical release or anything like that. I don't know. If I wanted to make a film, he's like, if people see it, hopefully they do. Yeah. And with all of those people there, I'm sure there was a lot of encouragement from Manoj and Niraj. But that, he, a lot of the film depends on that young man. And he clearly either instinctively knows this or was trained or both to know acting's not about performing. It's not about presenting. It's not about indicating. It's about being real, being in the given moment, about creating the circumstances that lead into the senior portraying so that it doesn't feel like a scene begins, but that we've come into something really happening. I just, I felt he was engaged. And it says a lot in a film that has heavyweights like Niraj Khabi and Manoj Bajbhai for him to hold his own with as much screen time, if not more than them. And then Shahana Goswami. Phenomenal. I just, but I, the acting was fantastic. Yeah, she did an absolutely great job that this scene, oh, it was really heartbreaking when she was giving birth. Yep. And then she asked to bring that kid over because she had to take care of her other baby. Right. And my wife was watching, I was like, can you imagine? I know, but that would be instinctively what you're doing. Your thoughts would be on both babies. And then the fact that she basically couldn't even mourn right, her baby that she lost because of everything else that was going on. I thought she did a Phenomenal. Her performance was really great. Agreed. Loved her performance. My only gripe with the film, and it's not really that big of a gripe. I thought it was a little predictable. Really? Did you not pick up in the first 10 minutes that the boy was Manoj? Oh, I don't think that was predictable, but if the goal and only our director, Ryder, could tell you this, if the goal was for us to be clueless until he wiped the dust off, they failed. Yeah. Because I knew. I didn't know if that's what was intended or not. I don't know that it is. It felt like it's written that the audience, I think the audience could figure it out in the first five minutes. I think the audience could walk away and never know. That'd be difficult because of the end. It could still. Gotcha. Because I've seen what's the meaning of the ending. That's like, there's an article, what's the meaning of the ending of this film? And the meaning, exactly. The meaning of the ending of this film is pretty, there's only one thing about this that's left open to interpretation, but I think it's pretty heavily leaning in one direction and that is what happens to Minoja's character in the end. I have a very strong. I mean, he got out. You think he got out? Of the maze? Yeah, yeah. You do. That was, that's what I am, because obviously the Renvier, am I saying his name correct? Because it's not spelled the same as Renvier. And I don't, I wanna make sure I'm not saying his name. Is it Renvier? I think it would be. It's still Renvier. It's just an alternate spelling. It's just an alternate spelling. Obviously a couple of scenes before, he says, I don't believe you'll ever get out of here. Correct. So I think it was implied that he did that to me and they did that expanded away, which was great, obviously. I don't know if this is like a slum or what this is, right? See, I think, what do you think happened? Well, I think it's the opposite. You think he died? I think he killed himself. I think he killed himself. Well, where is he though? It's very possible he killed himself, but he still got out somehow, because he's not in that house. I think that's the out. What? You think he's floating? No, as if the spirit is ascending away. I think that's his, and here's why I think he's dead. I thought that was just showing the expansiveness of the maze. No, I think it was showing the unending, inevitable hopelessness of the maze in the midst of so many people wishing they could change the cycle, but that cycle is as permanent as the buildings. And the reason I feel that it's that way, that coin was his hope, his whole life. His mom had said, this coin was just worth pennies, but over years, it became very valuable, which means even something that was only worth pennies can be worth rupees someday. And he was holding on to that in the hope that his life could someday result in that, that all the things he was hoping for could result in that, that maybe he could get his mom out and their little penny life could become something worth rupees. But he puts the coin down under the picture of his family and leaves it. And for me, that represented, he's given up hope. I can see that, because I thought at that as well, I didn't know if it was going to be implied that he was, if he left, which was what I came away with it as, but I didn't pick up on the coin being left as well. So it's very possible that that's the interpretation that's correct. Did you also notice there was very, every once in a while, there were rustling of things very gently. There was like the rustling of the pages of the book. There was rustling of the edges of the sheets. Every once in a while, there was something particularly caught cinematically for just a moment that gave you a sense of wind blowing on something in the midst of this very stifling, which again, I think was that, even in the midst of all this, there's still, if you look for them, it's these mixed signals of there's hope, but even in the face of hope, you can feel hopeless because it's not enough hope. It's just the symbolism was out the wazoo. Even the cut on his hand is representative of the fact that he's hurt, which our character, Manoj, definitely is a hurt man. But he's hurt and doesn't even know how he got hurt or what hurt him. He just knows he's hurt and he's trying to self-care for himself. And I love the fact that it's such a wonderful psychological thing of he's really trying to save himself. He's trying to save that inner child that was wounded, but the wound is festering and he can't seem to get at it. So aside from just the physicality of being the poor guy and dealing with what he's doing in the reality of just the physical space, Manoj had to get into the psychology of a man truly imprisoned by his own hurts and wanting to save himself and not even knowing he's doing it on the edge of losing his mind. So I, monumentally hard roll. Oh, yeah. It's very few people that I can feel could have done this role as well of Justice as Manoj and a couple of them were probably in this movie with him. So overall, I thought it was an absolutely fantastic film. I think I gave it four out of five stars, I think. Yeah, it's one of my favorites of the year. We're getting close to the awards of what we'd say are the bests, but this has so many good, this has, the writing is exceptional. Again, make up and score, but the big one is for sure. And a phenomenal song number by Manoj. Yeah, which was really strange in the midst of all of that. To go into that, I'm not my son, which was just over and over, I'm not my son. And then the ones that the parents were cheating with come in and dance and sing with them, almost like they're having a foursome. It's really strange. If you miss that part, it's in, stay at the end of the credits. It's at the very end of the credits scene in this one. Fits really well. Anyways, let us know what you thought about this film. Did you like it? Did you love it? Did you hate it?  Yes, you are. And what should be our next Manoj Buzhpai film that we should watch? Let us know down below.


Happy National Indigenous Peoples Day to everyone here and everyone watching from coast to coast to coast. I have to thank First Nations, Inuit and Métis partners for working with us over the past few years to make meaningful progress on our shared journey towards reconciliation. The road is long, but rest assured we can and will walk it together. And on that note, today is a very big day for both reconciliation and environmental protection in Canada. Today we celebrate Canada's first ever reconciliation framework agreement for Bioregional Oceans Management and Protection, which involves 14 First Nations along the Pacific Coast. This is an important commitment to regional partnership in the task of ocean management. But before I get into the details, let's talk a bit about how we got here. It was in November 2016 that I announced a major government initiative that will serve the people of Canada for generations. A plan to safeguard the natural beauty of our great country. I'm talking, of course, about the Oceans Protection Plan, a $1.5 billion investment in ocean conservation, responsible shipping and marine safety. Providing jobs for people today while protecting our coasts and waterways for generations to come, while the Oceans Protection Plan is truly world-leading. And there are barely two weeks. The other leaders of the G7 and I are committed to ensuring the property, health, and safety of our oceans. Thanks to the Charlevoix Action Plan for the Health of Oceans and Seas and Resilient Coastal Communities, Canada has adopted planning processes for maritime space to provide more certainty to companies in the ocean sector while protecting the ecosystems of our oceans and communities. And we will continue to give our commitment, which consists of establishing protected marine waters in combination with the best scientific data of the planet, to the vast knowledge of the octaves. My friends, when we announced our Oceans Protection Plan a year and a half ago, we envisioned Indigenous peoples participating as trusted partners and guides in managing Canada's oceans. And as we implement the Oceans Protection Plan and the Ocean Blueprint commitments, I know we could not have better partners than the people gathered here today. The first nations of this region have long been leaders in defining what collaborative management looks like in protected areas and in working landscapes on land and at sea. Today is an important next step in our stewardship journey. After much hard work and excellent dialogue, I'm happy to be here to celebrate the Reconciliation Framework Agreement and Bioregional Oceans Management and Protection. With 14 First Nations, the Framework Agreement outlines how we'll engage on a nation-to-nation basis going forward as we develop the policies and practices that will guide us in managing these waters, which span about two-thirds of the BC coast. Together, we'll coordinate efforts on marine spatial planning and develop a network of marine protected areas. Our work will improve waterway management, increase emergency preparedness, and boost the response capacity of local First Nations. A blueprint for the future, this agreement will help us ensure that consultation, planning, and decision-making is collaborative in nature and clear in process. Together, we'll protect and preserve the northern coast of the Pacific, and in the course of time, we'll advance reconciliation. Our government takes environmental responsibility seriously. It is our duty to verify the position caused by carbon, to protect the population of marine mammals from disappearing, or to ensure the permanent protection of the largest coastal areas. We understand that Canada must lead the way. We also understand that the ocean represents not only an economic engine for First Nations, but indeed a way of life. The species that live here in one of the most ecologically significant regions in the world must be protected. The orca must be safeguarded. Salmon populations must be allowed to flourish. We have work to do here, work that cannot be ignored. These animals are integral to the culture and well-being of the families and workers who call this region home. The First Nations of the Pacific North, North Coast, have been protecting Canada's waters for millennia. And that's why our shared success in the fight to protect our ocean hinges on us all working together. The reconciliation framework agreement will help us deliver on the promise of collaborative ocean management for the benefit of all Canadians. And I'm especially pleased to note that we were able to come together to celebrate on National Indigenous Peoples' Day, a day that matters to each and every Canadian citizen as we walk the long path of reconciliation together. What we have demonstrated here today is that we understand that the best way to protect our coasts, our natural world for generations to come, is to do it hand in hand with people who have been protecting these coasts for millennia. That environmental protection, economic development and reconciliation need to be fundamentally linked. And today we take a massive step forward in doing just that, in true partnership with First Peoples.


So I am going to talk about what are the different components of the Hadoop system. But before that I will be talking about how does math reduce work and can talk about each of these components. It's fairly a lot of material but if I have time at hand I will also cover a little bit more about big high end interface. A little bit about me. I work for InMobi. It's one of the largest independent mobile ad networks out there. And we built this system alone because we are facing big data challenges as at other firms like Twitter, Facebook and Google. And I will actually illustrate some of the findings or some of the evaluations that we have done when we actually use some of the Hadoop software. And we started using Hadoop when it was fairly new which was about 2 to 3 years back. And we made some design decisions. Some of them do not work anymore. For example, we ran it at the time. It was not very mature but it has become very mature right now. So let's start it. But before that, how many of you have used Hadoop in some form? Can I just say a few? How many are aware of what math reduces? Still a few who don't know. Thank you. So we are really in a world where stuff that we do, everything kind of moves our way. Plus there are systems which actually keep on capturing what you're bringing. For example, central entities like social networks, Twitter, Facebook. Whenever you come in, whenever you like something, anywhere on the web, all of it kind of gets captured. And if you look at a number of interactions that we have online, it's a humongous amount of data. So there's a humongous amount of data. And there's a lot of system-contained data also. It has been around for quite a long time. For example, astronomical data, weather-related data, and so on and so forth. Also financial data. Whenever there's a trade on the stock market, there are various other parameters associated with that. It's a huge amount of data. Lately a trend we have seen more and more is that governments are opening up whatever data they collect. For example, sensors, data, economic indicators, and opening it up. I think there are a few talks on that as well in this conference. So it is possible to kind of take all these metrics and use and current data and coordinate them and find out things that were not possible. So this is actually bringing a lot of transparency. There are various apps and, for example, Samsung is a app that can use machine learning and whatever learning is there for processing different audio data and actual news data. And then we have file resolution videos. So all of these ads are going to huge amount of data. So we have truly in a world where the amount of data that we actually store, consume, see is just exploding. Harooq is one way of actually making sense of all of this data and using it for analyzing. It provides a framework where you can actually take all of this data and process it. So when we talk about the data stack of the big data, what are we talking about? Mainly we are talking about these four layers. Everything is in a fairly different state of maturity. Let me talk about each of these layers first starting from the bottom. So one of the big challenges is that we actually take all of this data and we capture it. How do we actually get all of these data to central entity? For example, analyzing a problem in case of Facebook. They have servers all around the world. So you capture the data but you probably have to get it for central entity where some of it can be processed. And then store it. So the challenges are how do you get all of the data and how do you store it? So whenever individual machines kind of play, how do you get around it and your system still works? Second layer on top is once you have got all of the data again, how do you predictably run jobs on it? How do you do different kinds of analysis that you have quoted on it? How do you kind of schedule jobs? How do you actually do some amount of validation as well? For example, we know that training codes at least in India have six layers. So whenever data comes in, you want to have some kind of validation seen so that whenever the uppers start using it, they do not connect to yours. So you can start using yours. So basically the role of this layer is to add predictability to the whole process of actually taking out this data and processing it in a predictable way so that a data analysis layer can take all of the different aspects of the data. So what is the difference between data analysis and data processing? Data processing is mainly in some sense a slightly dumber layer where it does not have context. The main focus is on the infrastructure whereas the data analysis layer you have a lot of context. The different aspect of data analysis layer is also you can take third party data. For example, you have data related to a geogram and then you want to take some economic related data. For example, in a neighborhood, you know the geographical location, say Bangor, in Guatemala or India. You know a certain set of people in certain localities. So when you want to overlay some of this data, there are disparate sources. So you take all of this data and then you can go over this and then kind of get insights which you would not otherwise could get. You could not otherwise get if you had these two data sources to separate it. So you have to kind of join them. There are challenges in kind of doing retrieval, doing validation and combining this data in ways that you can get insights. And finally, when we have taken all of this data and analyzed it, we have to visualize it. This is a family asian field right now. There is not much out there in terms of this. It is a very active research area because when you take all of the data, for example, let's take the case of in-mobile. Whenever we have magneto. So the three critical entities that are associated. There's a publisher, there's an advertiser and there's a user. So all of these people are active. For example, the user in the mobile world is represented by the handset. The handset has different attributes. For example, the size of the screen, whether it is video, what kind of video is in place. On the publisher side, what kind of site it is? Is it a sports site, is it an entertainment site or is it some other site? So when you take all of this data, it will be very difficult to take multidimensional data and plot it. So this is an active area of research where you can represent the relationship between different kinds of data. Because data in isolation is of context does not have meaning. So you want to visualize that after you have done all the processing, you should be able to figure out what is the relationship with different kinds of data and what is the correlation between different aspects of the data. So this is the data vision. So if you look at Hadoop, the Hadoop stack, it's actually very strong in the data storage and data processing. And to some extent, it's getting better at the data analysis part as well. But there's not much in Hadoop about data visualization. So people use tools like R and we use toolkits that are dedicated to JavaScript, for example, in Chrome. I talk a little bit more about those in the data part of the cloud. So on top of the aggregated data that comes through all of these processes. So let me talk about Hadoop. I just wanted to figure out what do people actually think. So what I did is this is a kind of visualization that is known as a world cloud. It's generated using a site for anybody can access and use it freely for the world. So I pointed to a friend's blog, that is, bottom words, you can see it there. And one of the kind of keywords I came up, the first one was Hadoop, obviously. The other was data. Hadoop is Apache project, right? The components are map-reduced. HTFs, people use it for learning. This also, just because of the times when this snapshot was taken, you can see, they had a lot of focus on education. So you can see some related to education. For example, there's education itself. There's students and there's students. So basically, this simple infographic can tell you what is the relationship between what are the words that are correlated and what are the components that are playing in the ecosystem. A little bit about the basics of HTFs. HTFs is a bi-system that is designed for storing large amount of data. So, stop the advance into terabytes across each amount of clusters, YAMU has clusters, more than 4,000 machines. It scales really well. It also allows for streaming data access. So what I mean by streaming data access pattern is that you can use any language and as long as you can read from standard input and write to standard output, you can actually stream the data of HTFs. So actually, it provides a generic framework where you can write methods to jobs in any way and then use all of these jobs to actually analyze data. It also assumes that if you learn a large amount of commodity, so that it actually has affected the design of HTFs for example, it has fairly good failure point models and fairly resilient. A little bit about HTFs architecture. HTFs, there are different kinds of nodes. When I mean a node, it's a machine. Then there's a name node. What the name node does is it stores all the data into a file. For example, when the file created, what is the name of the file, how was it actually stored after the cluster, so on and so forth. The data nodes are like the workhouses of HTFs. These are the ones that actually store data. So the data nodes themselves actually store what is on HTFs. So whenever you actually copy a file onto HTFs, what happens is broken into HTFs and depending on the likelihood factor, it's actually put in three different views. You can also make HTFs rat on that. What that means is that you have a large number of machines. You put those machines in different tracks and because of that, the amount of latency and point algorithms that you can add in a rack is dictated by the distance. For example, a complete rack page, the HTFs can still work if a whole rack goes down. So typically, if you thank your HTFs to get out of it, what it does is depending on the area of course stored a file from the local disk, copy to HTFs stores a chunk on that server, then it takes, suppose a replication factor of three. So that means that chunk is replicated by two more times. It takes that other chunk and puts it on a machine which is in the same rack and also takes a chunk and puts it in a machine which is on other rack. So what happens is that a rack which has two retina goes down. Then you have still have a copy and the main node knows that and what it does is it figures out that the chunks are under replicated and then it will actually copy those chunks down very nicely. So MapReduce is a paradigm that is the basis of program model for Hadoop. That is slowly taking because there is something new for DR which allows for different kind of models to be infected on MPI because MapReduce works well for a certain class of problems but if you go into social network analysis or if you want to figure out what is the relationship between different aspects of a group of people or a group of assets or a hierarchical data set, it doesn't work really well because of the way it is designed. We'll give a quick demo of how it works in Python. So I will define three variables, XY, ABC and now what I am going to do is I am going to define a function map. What it will do is take the length of these three and calculate the length. So I will define one function and run it over each of these. So that will be simple to look at. So I will say map. I have defined map. So what map will do is if you take this function, length of each of the variables that I have defined here. So now L contains the length of each of those.  What radius will do is to take all the values of L and it is just going to add it up. So what I will have is the total length of each of these things. I have nine here. So those who cannot read, I define three variables ABC, A equal to ABC, B equal to XYZ and C equal to TQR. Then define a map function. What a map function does is it takes, you define a function and then take that function and apply it to each of each part of the data set. And then the radius take what you do is now you have all these partial results to add them up. And this is exactly what the method is. So the way it works on Haroo face, you fire a job, the job gets translated and you define a method that is function. When you fire a job, what happens is it gets distributed across all of the data nodes. The data nodes are where actually all the data is stored. And I talked about chunks, right? So now each of the, for example, you have two, two gigabytes, right? And you have a chunk size of 128 MB, right? So you will have about 16 different chunks. And if you have, suppose you have maybe 20 data nodes and there's no 16 different machines. So you can take and split each of these and run the map function, whatever you have. Each of these. Now, so now you have as well, right? So it will take each of these and then it will map and it will run it locally. So you have a data local property. So now we have all the partial results. We will take all of these, shuffle them across, which is the shuffle case that is happening here from the different machines. Now, again, bringing to the point of point of this, any of these scales, the name node or rather the job tracker will actually know about that and they start the job on another though the different replicas that we have. So anyway, so once all the maps are completed or even before sometimes the map is completed depending on how your reducer is done, all of that data is taken up that state and then it is run on a regular node. So on the first step, I have run the map function and the second stage, I have taken and correlated all the output of the individual maps and in the third step, I'm taking and reducing it, right? So it's family intuitive and this kind of works for a large category of problems that let's talk about the data storage here. So I talked about HDHS but Hadoop can equally work well with other kinds of file system as well. You can use them as a store, you can access them. So you are not tied to HDHS as such. So it's a great file system to use. You can use something, if you are using stuff in the cloud I think there's a talk on, there was a workshop at least yesterday on how to use, map and use on the cloud. So you can actually use S3 both in a native mode and also in a block based mode and it works beautifully. I have used it myself and it's very easy to take your own, have your own Hadoop cluster process the data there and push it on S3 or use S3, pull data from S3, write it on a cluster and send it back. So it's actually works seamlessly from the Hadoop command line as well. SSFT is another way, HDT is a very popular protocol so HDHS works beautifully over HDT as well. There's something called a Cosmos file system that was company called Cosmix that actually at some part was competing with Google and they built a KFS system that was recently from what I've read on the internet I've not used it myself and there's another file system called CEK where it can work. So it actually supports a lot of data storage. They're not tied to any one entity. And because of the way it's structured you can actually pull data from disparate sources and use them as a, including maybe there are tools for actually pulling stuff from database. So when you actually take all of the data you want to kind of serialize it because it can be kind of complicated format or simple reason that it's more efficient to store that. So for data simulation you have something called as Aero. Aero is again a Hadoop project and it works relatively beautifully well. I have also used Protobox. There's a term tomorrow that one of my colleagues is doing on a system called Deewda which is built to do network analysis on a network and in that we actually used Protobox and it worked very nicely. Thrift is another library that Facebook has provided. What Thrift does is actually something very nice. Hadoop actually provides a Java API through which you can access the AWS file system. And you need not use it. You can use a drip service that can access the front end for over AWS and probably use it and use it from any language which can provide whole generation for any language. You can actually use Python or see any of the other languages and then link it with Thrift and use it really nicely. RC file. RC file is something very interesting. How many of you do analytics or looking through analytics in Hadoop? I would highly recommend that you should look at RC file. Concept of RC file is actually very simple. What it does is, whenever you have a book, typically Hadoop or any kind of hardware in your system stores it on the local system. But if you look at analytics systems, what typically you do is you are more interested in columns. For example, what is the average temperature of this city over a period of time? In the database, you can have the geolocation, the lat-long and the temperature and maybe rainfall and other things. But typically we run queries like what was the average temperature over a period of time or what was the max or the mean. So typically you run functions which are based on individual columns. So RC file actually stores data in a column or format. And the good thing is in terms of performance, it works really well because we are accessing data in a column or format. The second thing that storing data in a format does is that it provides extremely good compression. So take for example the numerical values. They are typically going to be integrated. For example, the amount of rain that a certain city has got over a period of time is going to be within a rain. Now when you know that rain, you can actually compress really, really well. And you can possibly get up to 30x or 40x compression otherwise if you just store in other format as compared to RC file. So I would suggest that you look at RC file. You can look at rain analytics. Hadoop RTI for HCR is another format which is used by the main node to kind of RTI and it can also be used by other people for RTI purposes. So Hadoop natively supports RTI through HCR. HCR is not kind of in data space RTI. So kind of a metastore. For example, we will not bring out our peak and height and each of these stuff. So each of them has their own semantics. Each of them has their own days of accessing data. What HCatablog does is it kind of provides an abstraction over that. So no matter whether you are using peak, whether you are using height or using raw, like map, your own custom Java map. It provides interface where all three of them can run simultaneously and it was really nice. It is a new project. I don't know how stable it is but something that even can look at, especially people who are looking at different kinds of tools for analytics. So one part is a story, but how it gets stuck from multiple machines or even multiple data centers. There is a project called SCOOP. The premise of SCOOP is actually very simple. What it does is it allows you to take a dump of the database. For example, if all of the metadata is typical, any kind of system, all the metadata that the data is actually stored in a database. So the database is not closed down. You can still use the data and store it in the database, use SCOOP and define the schema saying how do you retrieve what kind of data and what is the location. SCOOP specifies that and it will keep on getting the data and your jobs can keep on running the analysis that they want. It works really well. Now it is fairly stable. The other three in that category are Bloom, Scriven, Kafka. Bloom is actually an adult project. Scriven is actually a project that has been developed by Facebook. Scriven is an Indian project. Each of them provides different kinds of trade-offs. Although the documentation from Bloom is really good, I can tell you, if you are looking at data transfer, please look at Bloom as it is now. The reason for that is, though the architecture is really nice, in the sense that it provides sources, it defines the architecture of source and same, different output and it can apply to transformations all along the way. In addition to the transport that it provides, it just doesn't work well. We had a lot of errors with that. This place was black for a long time and Scriven came along. Scriven was actually really well and we used it as a network. When I came to network, just to give you an idea, we have four different data centers around the world and there are, and we keep on transferring data across data centers and it works really well. But the thing is, you can't use it just off the box. You have to do some customizations. We have written some customizations on top of Scriven to provide greater interfaces and so on and so forth. Probably with open source at some point of time. But Scriven, for data transfer, you are looking at something, look at Scriven. I have looked at it and read about it and used it a little bit but I don't have too much experience with it. But Kafka was really well, you want to kind of go back and forth. So what Kafka does is, in addition to providing the transport, it also, for example, machines keep on going now. So you are transferring data from one data center to another. And maybe connectivity is likely. It just went down even though it's dropping packets due to huge amount of data that you're transferring. What Kafka will do is it will kind of spool on to this. So Scriven also does that to some extent but Kafka has really good servers. So you can actually go back and forth and towards that kind of interface where it can actually use it in case of failures. It works beautifully in case of failures. My friends at LinkedIn have said that internally, whatever they have, it's also externally unlike Scriven which is some code that was done before. And it works beautifully in LinkedIn, at gas scale as well. So it says that a lot of people will look at this particular aspect as transport and also look at message queues. From my experience, I can tell you that we looked at actual NQ, zero NQ and radical. And it just doesn't work well because there's a large amount of data transfer there as especially across data centers because the amount of message queues you're going to send the amount of elasticity that you want is just not there. One more thing about Scriven Kafka is also it can provide a proficient subscribed kind of mechanism. And that allows you to kind of sort by confidence especially if you're taking social screens you can actually sort based on the data that is coming in future. Zebra is a columnar storage that I don't know how many it works about as we looked at about six months back and it wasn't a very good possible so you can kind of try it out and see if it works if you're looking at analytics space if there's another thing you can do but I'll still recommend RCPen and there's a key value store that is based on top of STS for the STS. If I have time, I can watch that talk and talk a little bit more about how STS works. So that's the data storage part getting data into the system storing it. So you move on to the next part which is how do you now that you have all the data in the system right? How do you actually run jobs predictably? What are the frameworks that are available so that you can use them? Azkaban is a good framework I think there's a typo I don't see it as an edge but you can search for it it's a product by link link it provides base where you can specify a process or a tag and then you can schedule it Uzi is somewhat similar but it's a little painful to use at least it was when we used it but I think over a period of time it has become stable so Uzi also provides scheduling and provides inquiry from videos so you can basically in a job case you can rerun it you can do some kind of validation and no trigger on jobs especially Azkaban and Uzi can extremely useful when you don't know when the data will come onto the cluster you can actually trigger a job and when all the consents are satisfied so these two frameworks are really good and in movie we have built something called as ivory what ivory does is it does a lot more than scheduling it provides a data-based framework and it looks at data coming in as fees so what I mean by that is suppose you have three or four different things in our case we have ads server, log data coming in ethnic log data coming in and once somebody has clicked it might go to a project so you have three different disparate teams and customer comes and touches on each of these aspects so you maybe want to combine all of these for example you want to figure out what is the dropout result how many people saw the ad how many people actually clicked on the ad and how many people actually bought the product so the only way to do it is to do a cross fees and do a drawing on those fees and when you are doing it at our scale it is extremely difficult because the amount of traffic that we are doing at this point of time we are showing three different ads per day and the amount of data that is coming into the network is about two bearable so the scale is very large so we had no option but to build an ivory because there is nothing of that kind of return this is an open source framework actually we have open source it you can actually take a look at it it is on Jethas so it is for ivory, moby and jethas you should be able to find out so you can do a lot of stuff it is threatening for example you can do cross fees validations you can do your own validations such as you know data should be in a certain format you have certain sub screens so on and so forth and actually you can write those rules when you are in the state and process in the system so it provides for that and under some area when you made a Hadoop cluster and we again speculated this for some time was Ganglia cluster monitoring and we used Ganglia for getting all of the data from our cluster like how many how many KDA instances are there how many, how much you see if you are getting those from the cluster what is the amount of for example when a shuffle this happens there is huge type of activity on the network so it is the network of the modern name because when you are being processing at last case anything could be a bottleneck this could be a bottleneck CPU could be a bottleneck memory could be a bottleneck or even the network could be a bottleneck and depending on the kind of jobs that you have each of these can be a bottleneck so it provides you have how the cluster is being at this point and it provides a huge value and you can actually patch it to even very very granular layer matrix for example how many what the number of baseballs are happening how many how many times a certain JDM was instantiated so you can get stuff related to JDM you can get stuff related to machine you can get stuff related to even so it is a fairly good framework to do cluster monitoring it is working beautifully in production for us and we use naturals for identity and that also has been working fairly fine there is also an upgrade for chakwa the last time about year back we looked at it was also that naturals did work for us and I thought for our needs also it was fairly heavy and so we didn't look at chakwa but chakwa is another option to the combination of cancer and cluster monitoring finally we have two people two people is a distributed coordination service so what that means is you have processes running across different machines so how do they figure out who would be for example the leader so you have algorithm for leader election or you want to find out how do you kind of see if a machine is up and running because you might want to have one or two critical service if you are running a script server and everybody is using the script server to access data on cluster the script service could not work out so you can actually do people and it provides it works really well out of the box to actually monitor and restart any of the services when they work out and of course highly sophisticated algorithm for that finally data analysis is not very strong here project that we have which is who provides good classification algorithms, it provides good clustering algorithms but probably it is the best at recommendations that is what most people use internally us and I have talked to other people in other companies such as LinkedIn or Twitter or Facebook they typically write their own map reduce jobs on top of HDFS and the the map it is being done because there is no generic library of that that works for everyone piggy bank is a set of udfs user defined functions that works really well if you want to need load store data and it provides a bunch of a bunch of user defined functions to talk about it then there is Hive what Hive does it provides SPL like interface on top of Hadoop again that is something that we will cover later Pegasus and Jira Jira works on top of Hadoop Pegasus, I am not too sure how well it is integrated with Hadoop but Jira works really well if you want to do graph analysis for example you want to do social research which are the people who have the most number of connections and who are the influencers in a certain kind of network based on certain kind of algorithm then you actually have to traverse and look at the connection between different kind of nodes so for that you promise Jira but most people again roll their out roll on their own stuff for this there is also a library called all reduce that works really well and someone suggested that manage is really good I need to speak Pegasus provides Hive.ly abstraction it is a data flow language where you can every time if you want to run in Java typically before Pegasus you have to write a map reduce to kind of go and write map reduce code for every piece of software out there and what happens is when you actually go and run for every software out there and just takes a huge amount of time to actually get everything processed so what if you have a want to do graphic prototypes Pegasus is kind of like you can use Hive which is very very close to SQL instance so if you know SQL instance you know SQL as well Pegasus is more of a data flow language somewhere between it is somewhere between programming language and Python and SQL so I will give an example right after this so SQL works really well what it does it takes whatever your reference it takes and translate it into map reduce jobs and gets the result and it works very very good abstraction pretty much everybody I know uses Pegasus we also use Pegasus for pretty much everything we do the only place where we actually use map reduce is when we know a job is going to run for a long period of time we can optimize so when we have figured everything out we know that it is going to run only at that point of time we can go to map reduce but the gains are in some cases not that much probably might get a 20% gain so it is getting better and better and actually at state where in some cases if you write a map reduce job and a normal program might actually be slower than people because people become very smart at figuring out what is the correlation between different types of data and how to do the data access so it is pretty smart right now so if you are looking at getting your feedback with this is Stardust with so it provides a very easy learning or something like high but high setup might take some time because again not much in the data inflation space Ambrose is a so well known actually it runs a job it does not run just one job it runs a series of jobs same about HPA so one job starts and then there are dependencies so how do you know which job is and when and how it works so Ambrose actually provides a good visualization for what is the correlation what is the dependency graph between how do they depend on each other there is a tab which is kind of open source there is gwp jpea is actually extremely good if you are doing social network analysis I don't know how many of you have looked at in-maps in-maps.linkedin so there is a in-maps.linkedin provides a lot of linkedin data and then clusters of the people that are going to prove may be some school friends who are the first, second, third company and does a very good job of that so if you want to look at a graph and turn connections then jpea is extremely good so this is not a new question but let me addition to the data analysis please so I come from, I come from where we do recommendations for early job seekers so we do recommendations so you explore a lot of tools in data analysis but most of the time I do because I sometimes don't know this is something other than that there is a new database it's a graph database called Python that's a distributed graph database which supports FBase and Casandra and you can use all your graph data with the power of graph database so if you know a basic traversal using Wendland or any of the Singapore sites you can know with Python and use the power of FBase and still be able to yeah I think there is a lot of time to talk and there is light about what next so today we are just using the MapReduce network but what I do, the next generation that is actually not 10% but testing in Yahoo and Facebook and a couple of other places is that it provides other ways of running jobs it's called YAAB, YAAB look it up, Python it provides retrieved values don't work really well with MapReduce graph cloud as you said does not work really well with MapReduce I want you to look at it I want you to look at it so this is how you can reach me you can I'll put up the slides there is a lot of supporting materials that I have for this but unfortunately there is not much time I'll put it up on GitHub maybe in a couple of places and this is how you can reach me as a responsible creator and give me an answer I'd love to look and give some other people about what they are using every time I talk to different people for example I haven't heard about Titan yesterday I found out about very awesome visualization interactive visualization so this statistical analysis at the visualization actually apparently it's changing really fast thank you


Let's check this version out here. Cool. Yeah, I was reading your mail here You're asking if that was too fast and there's too much. I can't agree. I think if you're going for you know Compression and deformation everything it's going to be Hard to see because there's so much going on the thing is I mean you can I think that's not bad You know that she's moving around and also I think that's okay But at the same time it could also be fun to just Be more focused where you have all that movement, right? All of this then she pulls her over there. Maybe just not as fast But then you have a nice view in the compression pushing up on the cheek kind of squishing that eye And by doing all that I'd have some minimal movement, right? We're at this line up here and maybe you know from here Sorry for me to hear just a little bit of movement and they played all the shapes in compression We could focus on her where she looks like this where she focuses on the TV and then almost looks at us She's almost kind of looking you know slightly past the camera Sure, that reads like what is she doing? Yeah, that could be a cool moment and it's all Showing the almost the escalation of that right where She's doing this and then does that and goes wait What is she doing and then looks almost at us and then by now imagine she doesn't go as far down, right? It's but more like maybe like this you could see a frown Starting to happen. What is she doing? And then when she brings her down just for a moment the eyebrows open up eyes wide And where she's getting really shocked like what is going on and then all of this happens That's kind of that's kind of what I would do just because She's pretty subtle at this point and this could be her moment. This could be her acting moment where you showcase her stuff But let's go back. Yeah, you got a bit of a drifty shoulder there starts to kind of drift up This is a bit fast That move from here to here is a bit fast and careful your as you go down like this. It's a bit of a an IK hand there because I Can buy this even though here. It's already a bit too angled You might as well bring that that wrist towards us from here, but once you do this You have a pretty broken wrist and every time you have an elbow arm moving with the orientation of a rotation of that Risping the same. It's gonna be IK flag It's gonna look weird This goes back in a straight line.  That's a bit of a linear Linear key and you're going pretty straight up. So watch your arcs Then as you do this probably rotate that chest So it doesn't feel so isolated on a mechanics wise That's cool. I like this. It's maybe a bit high frequency. Oh, that's all that's kind of funny I'll do a cracks girl Definitely as she moves over here, she's gonna put more pressure on this So a bit of sinking into the mattress and spreading of the fingers And then more involvement in the shoulder pushes up in a way That's a bit fast That's not bad You know, I think his her legs I can kind of buy this that being fast But then you got the same problem with the broken wrist having that angle there. So bring that fist down And then when you compress it can be around like this But when you go with this elbow to here, you still need to have some sort of change in the orientation So that will go over here And then this will help you to actually instead of like this. It would be Hangouts, you know that a bit more like that not that it's fully Straight in a line. You can have a little bit of a bend maybe like that But that way yeah, we have somewhere to go because again any type of elbow movement and That doesn't change your orientation of the wrist gives it an IK arm feel and you have the same thing on the other wrist So this is broken there You can help this by lowering the elbow We could also help you with the silhouette because right now the bend is right is away Straight away from the camera. So we don't really see the bend if you do a squint test or silhouette test It looks like a short arm So what if you bring this down? Bring this down. It would align a bit better with the wrist. I will bring that thumb a bit lower Nicer silhouette, so it's not so flat This is nice there. We can see some movement in the wrist there. That's cool I will make sure that this doesn't swim around There's all the comp all the pressures are on those wrists Is it those arms and on those wrists right so? There's so much weight on this that they wouldn't really move. They would go down with a squishy mattress And watch out if you track this it goes up up and then down.  It's just the head moving feels so isolated You could probably cheat the pupils at this point in so we don't lose them as much Also, probably Uh careful with this rig. This rig usually has those lids Rotated like that. I would flatten them out and be a bit more anatomically correct. This gives it a weird You know angry eyelid eyebrow shape And it can probably not go as low with the head only to reduce the distance. There's so much movement down here with the head That if you play this in real time That makes it very fast and the spacing is also a bit off where You can track this line right goes down goes down goes down and then whoa, it's kind of the same Same height here visually into the space and then suddenly it pops back down So that little pause over two frames gives it that little boop little hiccup there up there And it can probably just ease into this a bit more And I think that's okay, so you you get to this point maybe two three frames later And then she get when she goes That can overshoot almost I could get into this couple frames later and then there's less of a pause You can go straight down into this. It's like an overshoot and then back Into that the broken wrist here just watch out. I would just flatten that bring that a bit more aligned with the forearm and Then you can bring it over here as well. So it's not so broken through here Same thing put some weight on these guys so they don't move around You do mention those jiggles in the email.  They're not so cornered here But I think you can hold at least one or two more frames Before the head turn and then snap that over just a bit more If you think I would give it a shot and put those pupils cheated Bit more towards us but make sure the eye line is there same for her. She looks kind of up here Get some jiggly elbows as well. Watch out for that Then as you continue some Compression and more contour work mean like the fingers go around the cheeks Now they have to do the full compression and cheek deformation, but at least a bit more um clean contact Unless you're going for like ah geez. She doesn't really want to touch That but then I would probably take that pinkie bring it out as well You can still potentially Then I would do more like her richer in the fingers and not have that break of wrist finger like this It's a bit of a sharp angle there. Does it just feel like she's kind of touching her cheeks though? So maybe I would it's up to you if you want to round those fingers and make sure they contact and follow those cheeks It's a crax here watch out when you have all that um what's it called Knee my english escapes me the knee movement Uh, same thing with the wrist same thing goes for the uh feet You can see how there's all that knee movement, but then the foot's not moving. So it gives you that broken IK look Then as you continue on all that hip movement everything it's going to Influence then let the feet a little bit same thing here. You don't want those stuck IK feet She has that I guess She's moving it and it's in this hand Must be in this hand then, huh? Because this goes down here and then it kind of flutters over Cha I like that you're bending the arm. This was always a bit too straight, but you have a little bit of a Pop here. I would just start bending that sooner but then Uh High I would go a bit, you know as you bend Whereas it here like that's okay. You can have that And I wonder if you want to get away from this and it could almost I don't know if it's too much You can try where the fingers start having a bit more of a pains Um, you know a pains silhouette This is a bit more like farewell I'm out of here. Oh same thing here.   So it's blinding there. Yeah, watch out. So it's not so hiccupy And I think for her she could have almost a bigger boom little hit This is not such a soft collapse And that could be cute with if you hit this a bit harder, there's a bit more Uh head drag knows will be up here boom and then on that compression The heck would collapse Then she would land, you know like boom boom and on that second one she would then collapse into this pose We kind of cue it Just that drop but It might conflict with this So maybe just make this Let's hiccupy where you know if we follow this line again Head goes down pauses and then suddenly has a linear bam sudden move. Maybe if that smooth down With a bit more compression and impact in the head on this It might just be enough and that we don't Distract from this, but I don't think about it. Maybe could be a little bit of a bounce. See how it works Watch out. I can feet as you continue on They're not they're not so locked And when for her I like that. She has that head move up when she starts Uh, but I would just Exaggerate that push out in the chest and a bit more in the hips. It's such an A vocal outcry so much energy in the lungs That it feels a bit soft Uh in the body like the audio is not quite connected there And then that drop is a bit fast and that stuff moving over It's very fast and jittery and watch out. You got some Broken foot here nearly movement, but if you watch this in real time, it's a bit jittery fast Yeah, slow that down 20 percent Because there's some moments that are You know, they're not super cartoony and fast So I'm just looking for consistency And I kind of like how you have stuff going on here, especially through all of this So then to suddenly have that happening is a bit a bit of a style break there And then watch out how much those knees are turning But then this section is not really affected by that massive twist So I make sure that it doesn't feel so separated and broken through there All right That's kind of it. Thank you All right There's an email you can sign up you can start whenever you want you can submit whatever you want You get 16 submissions either way a like and subscribe would be awesome. All right. Thank you


Hey Bolo buddies. Thanks for watching. Okay. I have 29 bread and butter Bolo's coming your way. I'm Courtney I share with you my Bolo items my what-solds These are from eBay. I do cross post my items with list perfectly and I cross post to Mercari and Poshmark mostly every once in a while Etsy and grailed, but I'm a big slacker on those platforms three feels like Good enough for me. So eBay Poshmark and Mercari are the main platforms. I sell on I'm gonna tell you where I got it what I paid for it and what it sold for these are bread and butter items items that I sold for $35 or less and if you want to know more about list perfectly, I do have a how-to Demo video down in the description And if you use referral coupon code Bolo buddies all one word that will get you 30% off your first month I highly recommend it. I love it. I've told you guys before I cross posted over 3,000 items manually and it was It was awful. It was torture. So I am so thankful for list perfectly and I know I tell you guys about it all the time I'm really pretty due to do another Video on how it works probably because I haven't done one for a while and I need to cross post So I might as well do it with you guys, right? All right, let's get started. We got 29 Bolo items coming your way Okay, let's get this pulled up and the first item is this plush briar horse And I picked this up in a bulk buy at a garage. So I bought all of their Plush, so I probably got 50 cents maybe a quarter in this. This is Marshmallow She is rare and I sold her for $20 and the buyer paid shipping and she's super cute and I usually put a tape measure just to show length and Yeah, she sold pretty quick The next item is this vintage playmobil helicopter This one took a little longer to sell it came from a garage So I had about $2 in it and it sold for a best offer of $20 and the buyer paid shipping It is incomplete. So I just put that in the title The next item is this Barbie mermaid doll and The unicorn is missing the wings now I picked up a Facebook marketplace haul and they just Had a bunch of stuff lauded together This wasn't something that I would have typically sourced but it was in there. So I went ahead and listed it And that's why I like buying in bulk because I say this about when I buy stuff from YouTube all auctions It forces me to list things that I maybe wouldn't have picked up on my own And then I learned that way and I learned which items are Bolo's that I didn't even know about by doing that so this mermaid sold for $15 and The buyer paid shipping and I had about two dollars in it if we divided out everything that I got in that haul The next item came from a Dibble box speaking of YouTube auctions I bought this from Donatella bottlino and I Wasn't sure what was going on with this it is a Morgan dollar pendant, but I think that this is a mold and the reason I think that is My husband looked at it and he thought he saw like a Mold line or something and somebody messaged me about it And I think that they said that it wasn't legit and they learned it because they bought another one or something But look how cool it is. It's still super cool so the person that bought it knew what they were buying and Says cut out coin. Let me see I wrote no clue what it's made of sold as seen has some plateware Because I remember I had listed it and then I asked my husband to look at it under his loop And that's just what he thought he I mean, he's not an expert by any means on stuff like this, but Sold for $22 I took a best offer because I really didn't know what was going on and again, that came in a Dibble box, which is a reseller box and The next item that sold is this vintage gold tone ornate brooch pin and this came from my bulk buy 44 pounds of jewelry I bought it on eBay to sell on eBay and I took a best offer of $10 in the buyer page shipping the next item is this little pet shop jacket and I sold this for $16 and it sold very very quickly it came out of a mystery box and this is a rare hard to find item and It sold fast and I actually sold a pink one even faster. It was in another video So definitely be on the lookout The next item that sold is this 1982 Superstar my first Barbie dress This is for the dress only I do put it on a Barbie to show how it fits and to show the detail Better so it just display. It's a display and then I just put dress only and then I put Shipping and I got this at a thrift store in a lot of Barbie items The next item is this wood for Barbie sized all-house furniture. I almost just donated this and I was like, you know I think I'm gonna try it and it was in a Facebook haul where I was buying other things and they kind of threw it in So I took a best offer of $10 and it sold pretty quick actually and the buyer paid the shipping this lot of 15 biz Disney Pixar cars cars and I put these up for auction and I started it at $14.99 and that's what it sold for And you can see here it has two bids So if somebody would have bid against them this would have went higher because they had placed another like a backup bid sold for $14.99 and these just came in I think from a garage sale or they came out of a mystery box. I can't remember. I have so many cars to list Uh, let's see the next item Are these silver tone hoop earrings and they also came out of the bulk buy I bought it on ebay to sell on ebay and I sold these for a best offer of six dollars and the buyer paid shipping Nothing really special about them. I'm surprised. I listed them listed them actually. I just thought they were kind of interesting so um The next item are these giftums and I got these at Toys R Us. I want to say these were I don't remember 10 or 15 dollars a piece and I think I got them for 90 percent off So I probably have a buck or two in these. I ended up selling this for 10 dollars and 79 cents And the buyer paid shipping and I do have more available if anyone's interested My links to all of my stores are down in the description of the video If you do decide to come over and check out my store Feel free to make an offer and put bolo buddies in the comment section so that I know that you're from the channel The next item is this a Boyd's Jeremy frog figurine. This one is the ruby red color and you can see the marking on the bottom It is a b. It's kind of hard to see on this one But I sold this for a best offer of 10 dollars and the buyer paid shipping I picked up probably 30 of these at a garage sale a couple summers ago, but I I think I just sold my last one. So they have been great Um, I don't have much in them at all. I bought the whole table for one price I can't remember what the breakdown was But um, I sold this one for 10 plus shipping Then and some of the other colors went for 20 ish 15 ish. So um, it just really varied This is a disney pin. I think this one went internationally if I remember correctly It came from a garage sale and a lot of disney pins. I sold this for 1999 plus shipping Sunset stitchery. This is a cross stitch kit. I believe And this came from a thrift store for two dollars and I sold it for 15.99 plus shipping The next item is this little vintage tv if you look through it it um and push that button it changes the picture And I can't show you guys that but I did look through it. It was pretty cool. It's great for a doll house It's kind of like a souvenir type thing. I believe but I sold this for Best offer $15 and the buyer paid shipping The next item that sold is this veggie tails bus and this sold fast and I knew it would And I've got a video down in the description this If you want to know how to add a video, I do have a video that teaches you that Just search my youtube channel for add a video to your listing I think that's what my title is or something like that But I sold this for 30 and 40 cents and the buyer paid shipping and that one came From a thrift store and I got it for like 20 cents So that was definitely a bolo. This simpson has no figures and it's not working But I still sold the little play set. Maybe somebody can fix it. Maybe not I don't know. Maybe they just wanted it for their collection But I sold this for a best offer of $10 and the buyer paid shipping and I got these simpsons items at a thrift store And it was a big lot. So I have less than I might have 50 cents in that This awesome unicorn came from a thrift store. It was $1.50 and I sold it for $20 and I believe it's ceramic Let me get you the bottom here Here it is.  So I went ahead and bought it The next item that sold is this american girl horse. This took forever to sell I sold her for a best offer of $25 The problem was is that she's really big and her leg sticking out in her head makes the box really long So the shipping price for this was kind of high So I think that hurt the sale But 25 plus shipping on that one and that one came from a garage sale. I'm guessing I paid about five bucks for her this vintage Windup microwave is super super cute. It's got the turkey inside, but you wind it and it rotates I don't know. I don't think I did a video. No, I didn't do a video on that one I sold this for $16 and the buyer paid shipping The next item is another boyd's frog. This one sold for $17 and 59 cents plus shipping. It was a little more unique in color And um, I think this one was my last one. So I think I am sold out of the frogs I do still have some burcom bunnies left The next item this sold is this grandpa loving family figure and I picked these up Garage sales thrift stores you name it wherever I can get them and I sold this for $14 and the buyer paid shipping The next item is this vintage slide charm bracelet and this came from a garage sale for $1 And I took a best offer of $20 and the buyer paid shipping. It is vintage and it does slide against this um The actual bracelet part is also whatever that metal is The next item is this pep of the pig talking car And I have a video of it And I sold this for $14 and the buyer paid shipping and I got this at the goodwill bends Uh, the pig did not come with it. I added the pig The next item is this loving family crib. I sold this for $14 and 40 cents the buyer paid shipping and this was from facebook marketplace This little brown horse puppet came from the same place as the first horse It was a garage sale. So I got it really cheap and I sold it for a best offer of $10 Plus the buyer paid shipping This trundle bed came from an estate sale. I got a bunch of doll house furniture It's super super cute. This is what it looks like without the blankets. It's got this little teddy bear story Detail. It is super cute Um, I took a best offer of $20 and the buyer paid shipping. I had probably a dollar or less in this This item came out of a mystery box and I sold this for $14. It is a 1991 band-eyed power rangers megazoid yellow saber tooth um toy and it transforms and Yeah, pretty cool The next item is this monster morpher Truck blaze in the monster machine. So its legs go out. Let me see if I can show you here See they go to the side. He can lay down Super super cute. It's daring tin and I sold this for $30 and 40 cents. They didn't even make me an offer They just bought it for my sale price Um, I can't remember where I got this one It might have been a thrift store with some other cars Or it could have come in a mystery box or I could have gotten into the garage sale So I apologize that I cannot remember But I probably had a dollar or less in it. That's typical of how I normally Buy things like that. So, um, I didn't know this was a bolo when I looked it up So I was pretty excited about that So definitely be on the lookout for the lays in the monster machine Darington monster morpher All right, you guys those are my bread and butter bolos Let me know if you have any questions Again, the list perfectly video is down in the description of the video. Definitely check that out. Give it a shot Um, I think you'll love it. So and if you don't you can quit I always say if you hate it, you can quit but, um, everybody always tells me they wish they would have done it sooner. So, um Happy listing get those items listed. Thanks for being here. Thanks for watching. Be sure to like comment subscribe and share