This is covering the spread part of the Fandall podcast network Big college football playoff implications on the line in week number 12 We're gonna break down some of those key games with Brett McMurphy of the action ever getting his read on those games and talking about college Football betting in general and what should be a fun week across the nation This is covering the spread right here on the Fandall podcast network and number fire calm My name is Jim saw this I am a senior writer and analyst for number fire calm joined you once again and by dr Ed fang a check out add on Twitter at the power rank check out his work at the power rank calm and Ed Follow we could college football coming up. How you doing? I'm doing pretty well. I'm still stunned at how much Oregon's offense just ground to a stop when bow nicks got hurt for a series but Washington with a huge win and Oregon's loss clearly helps some of the big 10 teams out here in my area Yeah, definitely does you call you had a Washington plus 13 and a half for that game, too That doesn't hurt either as well, but we're joined as mentioned by Brett McMurphy You can find Brett on Twitter at Brett underscore McMurphy if you somehow do not already follow him there His work is over at the action network where he is a college football insider and Brett's got the new CFP rankings Last night got a fun slate on tap week 12. How are you doing today? Great good to be with you guys Ed I've been an admirer of your work from afar good good to meet you in person and good to be with you Jim It's always fun every college football guest we have on immediately like has to like talk about Ed stuff Which I appreciate always makes me very happy inside Ed is the best So I echo those sentiments as well We're gonna break down all of college football week 12 and get Brett's thoughts Not just on those games But also on the unique perspective that he has with betting college football games in just second But first a reminder to make sure you are subscribed to covering the spread wherever you get your podcasts Of course Apple podcast Spotify Stitcher Google podcast Amazon podcast We are there search for covering the spread hit subscribe And if you like what you hear leave us a rating interview as well twisted tea and a fan dual have joined forces to bring you a One-of-a-kind contest series that gives you a chance to compete for your share of thousands of dollars in psych credit Introducing twisted tees college football picks a sports betting focused contest series That's entirely free to play the contest is simple each college football game will be assigned a money line spread and total market With assigned points to each market all you have to do Is make six selections based on those markets and earn points for each correct selection you made if at the end of The day your score ranks among the best in the contest You'll be eligible for your share outside credit head to faddle comm slash twisted tea picks and make your picks and remember Please drink responsibly now Brett It's fun to have you on the show for today because Ed and I both come at this from a a numbers perspective That's where our background is but you've got a super unique Perspect not just relative to us but also relative to most of the betting industry as a journalist So how does that background help inform the way you view betting on college football? Probably the biggest thing is never believe anything a coach says in a press conference. I Know I've talked to you know Stucky and Colin that work with me at the action network when we're having when we're on a Call or doing a podcast and they'll say Coach coach XYZ said this this week and I'm like guys He's lying. Don't listen to him. It's not specifically one coach It's basically every coach college coaches specifically. I'm not talking NFL college coaches specifically are the most paranoid coaches on This planet and they are not going to give an ounce of information Out that they think may help their team and one great example is and and look I'm an Oklahoma State alum So I'm not dissing anybody but my own alma mater Oklahoma State hosted ESPN College game day a few years ago Usually the way you do it is when the Broadcast team comes in the day before the game you meet with them You go over injuries and the agreement is look here's the skinny But you can't announce it until right before kickoff pretty much everybody will do that well Oklahoma State had a court starting quarterback with a broken leg Oklahoma State did not reveal that to any of the pre Pre-broadcast meetings college game day was there. No one at ESPN knew about it ESPN televised the game kickoff Here comes a starting lineup the quarterbacks not in the lineup. They you know reach out to the PR guys Hey, what's going on? Why isn't he in there? Oh, it's senior day. We're starting the senior. Okay, that's fine Now it's in the second quarter and the guys still not in there.   So I guess a long-winded answer is when you hear a coach on a Tuesday press conference talking I Do the opposite because he's not telling you the truth That's fascinating Jim and I were talking before the show about Katie Jefferson and how he didn't play for Arkansas Yeah, perfect example. Yeah, when did that come out? We were we didn't know exactly when that news came out. I Had heard about it early in the week, but I couldn't confirm it and then I think about 20 minutes before kickoff I can confirmed it and tweeted that he was definitely out You know, there's there's been a lot of quarterbacks this year, especially in college football where yeah, you know The uncertainty Spencer Sanders is a perfect example. You know, I reported the past two weeks. He wouldn't play They've they've alternated with the freshman quarterbacks Sanders wasn't supposed to play last week But basically they needed him and he said, you know, look, I'm not a hundred percent I'll come in there and play the idea was to rest him for this week's bedlam game against Oklahoma but yeah, there's been tons and tons of quarterback intrigue and mystery and Basically the college coaches don't want to show their hand They want the opposing team to prepare for both quarterbacks Whether that works or not if you want to run the numbers on that and give back to me I doubt it really makes a big impact, but the college coaches feel like it does I mean the quarterback is certainly one player that's gonna move the market Jim and I were talking about whether, you know, LSU going from three to three and a half was the result of Announcing the Jefferson injury. It seems like not because I think it closed at three and a half So there was no extra bump. Well close at six. Oh, okay I think that was probably after Brett's tweet to be fully honest. I think it got to six eventually Yeah, it moved and also that was a weird game because LSU was in this as you guys know They were in the flattest spot, you know known to mankind. So that's probably why it didn't move more But yeah, it did move there at the end. Yeah, Jefferson. I think like warmed up I think Colin was tweeting about I thought I saw a tweet from him on saturday Yeah, and that's the that's the other thing I've noticed This happened with Michael Pratt quarterback of Tulane that happened with KJ Jefferson Now when you have quarterbacks that are injured you see them come out and take first team reps in pregame Yeah, and then they go back into the locker room and come out and they weren't street clothes You know, I mean you can't install a game plan in 90 minutes. So like yeah, I get it I get it but it seems a little bit on the paranoid side of things Now that could be one thing, you know that you notice is people putting stock into what coaches say in press conferences But are there other things you observe when you're consuming betting content you're talking with bettors Are you think are there things that you think we as bettors either Over or under value when we're trying to bet these games based on what you've seen and based on your knowledge of the sport I mean, obviously it's a case by case basis I think Maybe a lot of people that are into analytics. I know Colin is real big numbers guys I think Sometimes they don't take into consideration The motivational aspects especially at the college level um The nfl I think is a whole different animal, but for the college level it's just it's impossible to have Great games back to back weeks. Um, it just is it's you don't see that for 18 to 22 year olds And everybody can say all the right things You know, we're not looking past this game yada yada yada, but I think that's the biggest thing is is to look at some of these perceived flat spots and see if you know If the team looks like they can take advantage of it Um, that's what I would look at. I mean, I tend to look at that more than Uh than a numbers guy. I'm not a numbers guy.  I know which coaches don't like other coaches So if there's a scenario where this game could get out of hand, is this guy potentially gonna Run it up or the flip side Do is there a great deal of respect between these guys? Will they this guy not run it up on him?   You have a chance to win it I thought the motivation was there. Was your take different? Yeah, I love dark and saw I did I did I love dark and saw um and again Here's another I don't again if you want to run the numbers I'd love to see what the the number is for teams After they tear down the goalpost You can say you can say they're playing for the sec west and they are Um, perfect example is kansas, you know, look at kansas and what they've done this year incredible job by lance leipold They get to six wins They get bold eligibility for the first time they rush the field they tear down the goalpost It looks like there's they've won another national title in basketball And then what happens they go play at texas tech texas tech beats them, you know that games played earlier in the year Um, probably goes down to the wire texas tech went when pretty handedly Uh, I get a you know the the pace of the game turnovers different things But that's kind of the things I look at like that, you know, kansas hasn't been bold eligible in 15 16 years whatever the number is, you know, they've just won this game. They're gonna celebrate. They're gonna celebrate It's hard to get that same emotion and everything going into that next game now. It's obviously not a hundred percent Um, but it's just something to you know, kind of keep your eye on when you're when you're breaking down these games each week Got a couple of homework assignments read. I like this. This is good. Yeah. No, I like it too Uh, just just real is there an example of coaches that either have mutual respect or a pair of coaches that perhaps don't have mutual respect that uh that we can Maybe put it into our handicapping maybe just one example Uh, it would be for the future seasons.  Um, You know jimbo and lane kiffin Um, you know, they're calling each other a clown or joker, you know, depending on what week it is Um, I think a misnomer out there with kiffin and saven is I think people Feel that there's this disrespect between them that that saven doesn't like kiffin. That's that's it's totally the opposite There's a great deal of respect there between those two kiffin You know, he likes to do his thing on twitter and and have fun like that, but he respects Saving more than anything. So, you know future seasons who knows where kiffin, you know, we'll be coaching in the future Ole miss or somewhere else. Um, but I don't think kiffin would Would purposely run it up on saven if it was jimbo fisher He would run it up and then kick an onside kick and try to score again This one is not coach related, but I know that pappas jerald has a decapitated monkey in his office That stems from his playing days when they beat iowa. He does not like iowa's fans Stepping from like words they said to his kids That's one that's legitimate if they had the chance to run it up if they were good enough to run it up on iowa They would that's not going to happen anytime soon. But like I think that that aspect to it is uh Definitely exists. So if you know, there's a decapitated to like stuffed animal in the office Run the run the numbers on that one too. Ed. We'll we'll assign you that. All right. There we go Love it. Let's dive into some games here for week number 12 and talk about TCU at Baylor TCU two and a half point favorite here. A total is 57 and a half and brett TCU keeps getting tested. They keep on passing those tests. Can they do it again here on the road against another? Very good team As I said, I'm an oakland mistake. I'm a big 12 guy I went to big 12 to get into the playoff the only way that happens is if tcu runs the table 13 and 0 I just I just feel at this point. They're you know, we've talked about Motivation emotion. How do you get back? I just feel I don't see how they have anything left in the tank and maybe we've been saying that for a couple weeks now But this is their ninth consecutive game You know, they've won at kansas in an incredible atmosphere They they beat oakland stayed in a couple of over times They've they've won six straight games by 10 points or less And now they just beat the biggest baddest bully on the block in texas And don't tell me that didn't have added motivation when garret former tcu coach gary paterson Was a special assistant or analyst whatever you want to call it With the longhorns Now you have to go at baler baler's coming off a loss everyone. Oh baler's not any good They're only playing their sixth game of the year six consecutive game So I don't think they're going to be as beat up. It's their final home game I I would take the bears and the points. I just think it's a better spot for them And at some point, you know, sonny dykes has done an incredible job incredible and by the way I was fortunate slash lucky enough to to have tcu is a long shot to win the big 12 So that would turn out great, but I just I just think it's a it's another tough spot Um at this point it sounds weird. I don't think tcu is playing to win I think they're playing not to lose and I think baler will come out with with nothing to lose And uh, you know pull off the upset Brett while we're talking about the college football playoff. Let's assume tcu goes 12 and 1 wins the big 12 And then michigan loses by seven at ohio state tennessee runs it out there 11 and 1 Who to who does who does and let's assume ohio state and in georgia take care of business Well, what what do you think happens with the the third and fourth spot? asking for a friend here in an arbor what happens with that The pack 12 is you see is usc 12 and 1 or did they lose again?  So it's georgia ohio state michigan winner That's one spot. That's two spot Uh, tennessee would be the third spot I don't think 12 and 1 big 12 champ uh, pcu gets in so then yeah, that would open it up for uh the michigan It missed If ohio state loses they I think they have a better shot because you just look at the non-conference schedule You know michigan has played They have one win against the top 25 team right now, right? Yeah, you know Uh So you think one win tennessee is ahead of one win michigan. Sorry one lost tennessee is ahead of one lost michigan Oh without a doubt without a doubt because right now they're so close they're they're They're two and one against top not top 25 teams against top 10 teams. Nobody else has played that schedule They're not going to go anywhere. They're going to score, you know half a hundred their final two games um And again, michigan a close loss against ohio state and then you look at again This is how the i've done the mock committees Where you go in and do the selection committee stuff and it kind of you look at it a lot differently I'm not saying it's the right way, but you look at things a lot differently. You look at the things they're looking at And it's almost you break it down in the simplest terms I think they would they would ding michigan for their non-conference schedule because they didn't play anybody That's a decision michigan may They chose to play Yukon, you know, they chose to play, you know, colorado state That's that was their choice um Yeah, I Man that would be tough I don't see i've never thought of the scenario if it's between 12 and 1 tc That means tcu loses to baler and then beat somebody in a rematch I they tend to weigh the conference championship so a lot more heavily So that I would probably give a slight lean to tcu. I think of the three Tennessee's fine, but I I agree with you. I think what would be between tcu and michigan fascinating What's uh, talk about that pack 12 and talk about usc a ucoa usc right now A one and a half point favorite total is 75 and a half that has actually gone up It was 74 and a half So that's pretty delightful usc on the outside looking in right now But still a shot to get to that college football playoff ucla looking for a bounce back here Who you got coming out on top usc at ucla?  I still I still love ucla this week Um, you know you guys have seen it usc's defense uh, I would call it a matador defense, but I want to You know immediately apologize to any matadors out there They can't stop anybody and You know, you're not supposed to you know, I talked about don't listen to the coaches Yeah, I think you can listen to the players and Dtr came out monday You know don't I guess he didn't read the the rule book where it says don't trash talk He's saying we want to score. We're gonna hang 60 on usc again. That's what I want to do in the roast point I just think ucla's offense is so good right now. And yeah, I know they lost to arizona Um, that I think they'll they'll handle usc. I don't see any stops in this game I yeah the The total is crazy mid 70. I think it opened 80 81 or 82 and it's dropped down I I don't see any punts So you just have to wonder on these fourth downs are they gonna convert and score or they gonna not make it And then you've got the link of the field to come back Um, but I like ucla in this game. I just think they're better overall And I think uh, if I am correct then the pack 12 will officially be out of the running for the college football playoff Yeah, bro. I mean, I actually have ucla is a slight favorite in this game Any thoughts on the total if you don't think there's gonna be any punts or stops I I know it's funny. I took I took over in usc arizona and got there. I had over in UCLA arizona and didn't did not get there So Man when the totals are this high like you cannot you literally have to score every possession Right. Like if if there's a turnover deep in a territory if there's a Miss fourth down conversion, you know, um Man, I think though the emotions and everything nobody's gonna play slow if they're, you know They're both to the quicker pace teams in the country I would If you made me pick a side I would absolutely take the over because who wants to root in under In usc ucla when you know, there's no defense gonna be played Right and you also have a probably tight game that could end up in overtime, which Absolutely Certainly doesn't hurt But yeah, I agree with you. I think I think ucla is the better team than usc until they lost to organ Excuse me arizona and on my ap top 25 ballot I had ucla ranked above usc probably for the last three or four weeks until the lost to arizona Awesome. Yeah, we got another fun one. Uh, utah at organ Oregon is a three-point favorite got a total of 61 point uh five Man organ. I don't I don't even I don't even know what to say about like that game And how they lost that to washington. You knew I kind of thought it was a possibility But then they actually went out and executed Like I mentioned before bonix gets hurt and organ runs the ball for three times Uh with a lack of a first down. So, uh, I presume next is playing and what what do you see in this game? I guess a good utah team Yeah, I mean, I I really like utah They were my when when that they were my choice to win the pack 12 before the season I guess they're still alive for that Um, but I actually like organ in this spot as long as nix is is a hundred percent And I don't know Ed you guys could maybe tell me if if organ beats washington And what's what's the line in this game? Is it? five six four five six Yeah, maybe Yeah, I'm guessing it's more than a field goal. So I don't think Washington beat didn't beat organ organ beat itself some very aggressive, um Play calling by dan landing backfired You know if that games played a hundred times. I think organ wins more times than uh than it loses And I just think you know organ's still in a great spot To win this to get To and win a pack 12 championship that the only concern is You know, I talked earlier about motivation organ had their site set on the college football playoff Even with that loss to georgia. They were they were running Through everybody and knew if they won out they could get there now that the college football playoff is no longer a possibility Is this a big letdown for them? And I know you're gonna say how is this a letdown? You're playing for a chance to win the pack 12 championship. Well, I'm not 18 anymore. So I'm just Remembering how my 18 year old self would have thought of that. So I don't love it a lot But I I do like organ because I think the line's too low as long as organ's not flat after the the washington lost Brett what what happened with bow next like what what does your kind of take?  What what what is your take on it? Yeah, nobody did. I mean we do on our podcast for the action network We have a you know a bit where the home bow nicks and the road bow nicks and the bow nicks effect and Yeah, and I didn't I didn't see it either and a lot of people did and then sometimes it's just a change of scenery You get you get somewhere else. You hear you hear things differently Somebody may be telling you the same thing, but you hear it differently. It and everything just clicks. I mean You know look michael pennix was incredible at indiana, but my god what he's doing at washington Like what you know, right? Where was that? Is that because he's in an offense that suits him better? Is that the same thing with with bow? um You know, I think maybe there was just so much pressure on him Right in the sec that to get away and get up to oregon. Maybe it just it freed him up and he's able to You know, no, I think that contrast is is the perfect one because i'm not surprised at all that pennix is thriving at washington Oh, he's looks incredible. Yeah, yeah, I like the first time he threw a ball for indiana. I was like This guy this guy can ball straight ball and obviously he wasn't successful in all his seasons at indiana, but like That potential was there and and nix just struggled right and and um, yeah, I mean it's nice to see him thrive and It's just yeah I just want to get your opinion on that because you know if we can put our crystal ball for next season And figure out who the next bow nix is going to be but that obviously helps Yeah, I mean He's obviously just so comfortable in that offense and I think probably part of it, you know, indiana obviously isn't Their offensive philosophy is never going to be confused with with what washington does And obviously the the pack 12 plays more up tempo more offensive geared type games And that probably played in into it too. But yeah, he's He is uh, he's just been incredible in a in a year with a lot of great quarterbacks pennix is probably You know, unfortunately getting overlooked by by a few other guys Yeah, bow nix went from being fun to watch for one reason to being fun to watch for a very different reason this year All right, brad any of the bets you're seeing in week 12 that you really like for this week I was trying to think if there's any, um Any motivational spots, I think there's a a small one. I don't really love it love it, but uh, navy's catching double figures at ucf obviously navy and the Um academies are always dangerous getting double digit points And it's not necessarily a play on navy because they're coming off Notre Dame. So well, they're going to be flat Well, they lost they've lost a lot this year It's more play against ucf ucf just went to new orleans beat tulane Which was the quasi american conference championship or at least locking up a bid in the championship game Now they come home and play a navy team with seven or eight losses and next week They've got rival usf, which maybe it doesn't matter this year because usf has been horrid Um, but I just I think navy will keep this close Um, ucf has some injury problems at quarterback. Do they do they play conservatively? And just think okay We need to win two games and then get to the american championship game win that we're in the we're in the cotton bowl um And look We talk about, you know teams that don't like teams or coaches that don't like coaches Other than army, there's not a team out there that's going to try to run it up on service academy So I would I would uh, I would take navy plus, uh, if you're getting more than two touchdowns against uh against ucf Yeah, that one is 16 and a half right now at faddle sportsbook, uh navy plus 16 and a half on that one, uh That's definitely a spot where you don't want to Can't disrespect the troops too much. That is for sure. All right. That is brett mc murphy checking out on twitter at brett underscore mc murphy Find his work over at the action network. He talked about the podcast over there Uh linked the action network is in the show notes over on number fire dot com brett We appreciate the time it was great to have you on and good luck to you in week 12 and also next week, uh, too You got it guys go bruins All right and ed what's going on for you this week at the power rake A lot of football and a lot of football, uh, a lot of one cup stuff coming in the newsletter A lot of nfl and college football stuff As usual during the heart of football season. Check it all out at the power rank dot com And I am on twitter at jim sonnis. You can find my work over at number fire dot com Want to thank you all for tuning in back once again tomorrow to break down nfl week number 11 This has been covering the spread right here on the fan dual podcast network


A video on its own is less important than a video with the story of the person who took it. Video and photography on its own is kind of like wallpaper if you don't know anything about the context in which it was shot.


We couldn't get out of there with a vanish, which I thought we could really did don't back dash on me I read that like a book Don't back dash. Yo, I got the reeds today. I'm on fire How's it going everybody? Dato do it here with another dragon ball fighters ranked match session video and this time around we're actually going up against Ironically enough the exact same team. I was playing last week or not last week yesterday. Wow, that was a big gap in time But yeah, I'm expecting to get absolutely destroyed. This is going to be a great follow-up video But we're playing Krillin and the main team Simply because I haven't played him for a long time and they just recently showed off what the holiday skins look like and they gave Krillin a reindeer costume a little strange considering he doesn't have a nose, but Yeah, they just went for it dude. Oh Where are you? Why are you trying to bait out my reflex? Have you not seen purple Krillin in action?  Don't try to reflect again, you know, I was gonna make the same read twice.  Let's go But yeah, the all the holiday costumes look really good They they did like have some odd choices though like Santa TN and not like Santa boo or something But yeah, I was pretty happy with most of them and I'm super excited. We're just getting new stuff for fighters.  I think I'm going to go ahead and like go into an instant level three here Krillin was looking a little with too much blue health, you know in my opinion Is he gonna reflect again? You see that kind of guy? I Think he went for a key blast Genuinely don't know though. We've been getting pretty lucky so far. Oh, wow. He just mashed All right. Well now that we got a mashing that just means, you know The wake-up game is pretty much in our favor right now He expects me to bait out the reflect every time is basically what I'm saying. Oh, I Have the defense of a toddler and he's exploiting me Switch out. Yeah Okay, right back into another level three like I said, we want to keep playing that wake-up game now that we've you know We've got his number. Oh my god This this is not it guys the the bass five seconds have not been some of my best work This has not been my greatest Goku the fight was this way. I will forgive you. He's playing the riskiest game. Okay, sparked up That was bad. That was a bad reflect got caught by the overhead started to panic a little he's gonna have to spend three bars Even then is that gonna kill that's pretty scaled Okay, give me a beam. Hit me. Hit me with a beam. Where'd the beam go? Oh My god betrayal top-ten anime betrayals Krillin my lunch dude share a little oh God, I Didn't know what side I was on either If I'm being Frank I dropped that combo please hit please hit. Oh god. All right. We're gonna have to after image He deflected good choice good choice Okay, we got him.  We got him. Okay. Not sure what he went for there But mission accomplished we take game one. Oh, that was a good game.  That was that was a lot of fun ah Kind of went quiet at the end that Goku you are not wearing the purple outfit. I thought we discussed but that is fine All right, let's see if his Napa. Oh, he tried Napa clap. He tried clapping me at the very beginning Luckily for me. I missed Look at those big meaty claws do oh no This ain't it. Oh god. I was just mashing there. We got lucky so many times What are you doing? All right, should we go out to yeah, yeah, yeah, yeah, we got blue health We might as well save up we might as well save up. I was mashing there completely That was the definition of a scramble We both knew that it was kind of just gonna go in whoever's a oh Jab check this man Over at Yamcha we haven't done that yet We have not busted out that option yet and Krillin comes right back in oh No, sir. We know super dashes here All right, but to be honest you there's probably There's probably some openings. All right. Let's down tech. Okay Don't on ray. Oh Yeah, okay, never mind. We're fine He teched he teched I drew that means I messed that combo up something fierce got him Can we kill though? That was an awful choice by me. Oh I got opened up.  Maybe I can rush it, but then I just realized go Yamcha wasn't on point. I mean Yamcha was on point. So You know, there was really nothing to do there Krillin's destructor. That's getting exactly the most versatile Level one super in the game.  Okay. Wow. We got a focus. Where is my focus at? Oh, we also got to get Krillin back in Big drop from me couldn't convert. Oh, please. I just hit my computer with my leg. Oh, and I got teeth I got chow suit hard. Oh, that was not good All right, we got to get Krillin back in but we can't just do it unsafely Like he just did kind of right there All right unsafe time unsafe time Wow, that was cheap. Oh My god, but that wasn't exactly the cornerstone of honor of honor right there either No bar spins No bar spin. You're not gonna respect the Krillin and kill him Big mistake All right. Now we get a good oaky here with spirit ball one of the best level ones in the game. No questions asked Well, I told you told me it was the best level one. I gotta prove. It's the best level one Yeah God spear ball is so good. Yamcha is so good Yamcha's top tier he has to be I Don't know what your definition of top tier is but Yamcha is my definition Yes, sir. E All right, we in there. We got to hit those lights. We got a stag room a bit Big mistake. Oh Krillin time. It's too late though. You're already dead. Oh Didn't even get to didn't even get to clock this man Oh, yeah, we're two zero by the way, I forgot to mention that All right, we couldn't get out of there with a vanish, which I thought we could really did don't back dash on me I read that like a book. Don't back dash. Yo, I got the reeds today. I'm on fire. Oh Come on. Yo, your boy is ultra instic dato doya. It's been achieved with these eyes Oh, I could have gotten a little could have squeezed out a little bit more damage.  I'll give it up. I'll give it up. That was great work great work You can't doubt his work ethic Cross up. Oh bad. Literally the worst I'm getting opened up like We're out. We're out. We're out.  We in there Uh, uh, can we extend this to the maximum? It's looking like we can even more Okay, same side same side focus focus your chi boys Same side. Is that your back? I see same side What's the mix up though? You're trying to see up.  That was flames That was a flames three. That was a great set Should we give him a bonus round? I'ma give him a bonus round But just one though because I do have to go so thankfully this match went pretty quick Over at yamcha Yamachaa All right, let's go Oh What's the mix up? Oh, he clapped us both Please nappa have have mercy I do have to go after this so if he beats me, this is going to be this is going to look like the biggest This is going to look like the biggest. Oh no All right.   I think he thinks he's a little regular old baby genius. Let's go Big miss.  That's what the mittens were for Also great extension. I should have mentioned that earlier when I first saw it, but Oh Oh my I went to clap and knock the pop filter off my mic And this is why don't back dash on me boy Guys, if I make any harsh pee noises and you hear it like a pop Please don't judge me. It literally fell off. I'm sorry. I'm not the most I've only been doing youtube for like a little over a couple months. No, that's that's disingenuous a little under a year What you can't expect professionalism out of me All right, but I will try to keep that in mind and not talk with too many Forget like pterodactyl, but You know the you know the letter that starts pterodactyl unless you don't Can we talk about dragon ball fighters? Oh god, I'm gonna beat down pretty bad.  I've just I've literally I'm I've literally a loser Please live. Oh wait. No, uh, please live. Oh Okay, okay focus. We still have sparking blast. That's uh, something we do have Okay, imagine if that hit I would have been pretty happy with that. Oh my god. I'm getting cheese like nobody's He hit me with a low.  Come on. Come on focus focus.  All right. Let's go. Come on Is that gonna connect that doesn't connect because I accidentally went for the medium version. No No, no, no leave live live live. We're gonna live. We're gonna be fine. We're gonna be fine We're not fine. We're not fine. How did he extend that? How long did he spend in train? What juice does he drink? Well my word I'm sorry. I have to go. I have to go. It's not a joke. It's not a running joke. I know what it looks like But god, okay. Okay. Thank you so much for watching. Thank you to my opponent. Sorry about that I'm gonna try to reattach. No, you know what? I'll just hold it. Thank you guys so much for watching It's for a I was a really fun one. Sorry to my opponent for having to to leave But if you have a favorite moment from this rank session, let me know down below in the comments While you're down there if you like these videos and want to see more of them Make sure to hit the like button and subscribe speaking of videos I have a few on your screen right now that you can feel free to click Uh and just give them a watch if you're interested other than that though. I'm dato doya. I'll see you in the next video


Welcome to Big Things Conference 2021. Isn't it great to be back? And this year with a fantastic hybrid version. So really looking forward to today. Welcome to Track 2. We've got some really exciting content lined up for you later on today. Kai Verna will be here making some predictions, which should be fun. We're going to be talking quantum computing with Ismael Faro. Álvaro Barbero will be trying to make sense of what lawyers say, which is always something of a mystery. And then towards the end of the day, Juan Jormilla from Jeff will be here to tell us how they are democratizing entrepreneurship using AI. But I know you are keen to meet our first guest. Before we do that, I just would like to remind you of two things. First of all, don't forget our hashtag on Twitter, which is BigTH21, get tweeting. And second of all, this is really important. Please make the most of this opportunity to get your questions to our speakers. But don't leave it to the last minute. Get your questions in early, otherwise possibly we won't have time. Now, most of us, I think even the non-techie amongst us, are getting used to having lots and lots of data at our fingertips. I enter the fitness app, for example, on my smartphone and I can check all manner of information, time series data about my activities during the day, my heart rate, my specific moments during exercise during the day, even details about what type of stroke I was using when I was swimming in the pool this morning. It's just incredible how much data we have at the individual level. But then we start increasing this data over a large user base. That's when performance issues can occur. And that's why AWS have invented TimeStream. Our first guest here today is here to talk about these issues and tell us a little bit more about how TimeStream resolves them. Our guest is Javier Ramirez. Javier, great pleasure to have you. Thank you. Thank you very much for being here, Nicholas. Javier, welcome. It's a great pleasure to have you here. I hope you had your coffee this morning. Yeah, I had actually two, so this will be quick. Okay, but not too fast then. I'll try my best. I cannot promise.  Yeah, sure. Great, so let's line it up for you. Hello and thank you very much for choosing Track 2, which as we all know is much better than a Track 1. So my name is Javier Ramirez. I'm a developer advocate at AWS and I specialize on everything data and analytics. As Nicholas has said, we are going to have some time, not much for questions, but if you have any question, any suggestion at all, feel free to contact me on LinkedIn or Twitter. You have the details on the screen, Ramirez for LinkedIn, Superoconign for Twitter. But I'm here to speak about Time Series and why do I want to be speaking today about Time Series and Time Series databases? And even before that, what is Time Series? So Time Series data is basically any kind of data sequence that it has any metric that you want to monitor and track and analyze and possibly delete over time. So pretty much anything with a time stamp is a Time Series data. Of course, you've heard the term before, but very likely if you are thinking of business use cases, you have always associated Time Series with things like meteorological or maybe environmental scientific measurements or about financial information in which you have things like, I don't know, stock trading or cryptocurrency or recent analytics, things like that. But truth is, in the past few years, we've seen more and more use cases for Time Series. And Nicholas was saying at the beginning, if you are into health and you have a risk band or into a sport, you can know exactly what you are doing. I bet if you are a runner, you have one of these applications that tells you where you were at any specific time, if you are faster than yourself from the past, if you are faster than your friends, all kinds of things. Of course, industrial IoT, where you have a lot of sensors. You have a factory, you want to make sure your machines are working fine. So you want to control humidity, vibration, noise, speed, whatnot. And that's been a very important use case historically for a Time Series in power and utility industries. But other than that, there are many use cases today, for example, small cities. If you want to have a city in which you coordinate all means of transport and traffic lights and garbage collection and lighting on the streets, you need to have a huge amount of data flowing and you need to be able to forecast with that data and plan accordingly. Or if we are thinking about DevOps, you have a large fleet of servers and applications to monitor, you want to make sure the health of the applications and servers is okay, but also you want to do capacity planning. For that, you probably have heard also the Time Series term before. And if we are thinking about new ways of working like, I don't know, like the sero economy and all those things, if you want to have a variable amount of workforce or if you want to plan any kind of demand, you also need to be storing and analyzing Time Series data. Telemetry for any smart vehicle, from Formula One to your family car to any plane. More and more, we are tracking what's happening over time to make sure everything is working fine, to do our recommendations, to plan when you have to do the next revision at the workshop, whatever you need. But it's not only for things around hardware, because right now I've been speaking about use cases that are tied to sensors. But if you have any kind of shop and e-commerce, you want to know how much and when and what you are selling. You want to know also how you can plan on your stock and inventory, so you have the right amount of things, exactly when you need them, not before and not after. And it's not only about e-commerce, any kind of application, you want to analyze the clickstream to know what your users are doing, if they are happy, where they are spending more time, what could be better. And if we are thinking about this kind of analytics, of course, an important use case is in-game analytics. You want to know exactly where players are in the game, when they are stopping, if you have any in-game purchases, you want to know exactly how some actions are affecting these purchases, and for that, having data with a timestamp, with the sequence of events that led to that conversion is super important. Of course, a special case also is media streaming, you want to see how people are engaged with your contents, and so on and so forth. So as you can see, time series data is not only for banking, it's not only for science, it's not only for sensors and manufacturing, it covers a huge amount of use cases, and it's present pretty much in any company. And all the use cases have some things in common. For example, in most cases, you need very, very, very fast ingestion. You're going to have data coming from many different places all at the same time. That's going to mean, in many cases, you're going to be getting duplicates. You're going to be getting out-of-order data. If you are solving this with some kind of append-only storage, that might be a problem. And the other thing is this data is not going to be constant. The flow is going to be all over the place. At some point, you're going to have a burst of data. Then maybe at night or of office hours, things are going to be much quieter. And then there was hour, and everything is up again. How do you plan for that? It's really not easy to plan at a scale, a system that can adapt constantly to the flow of data you are getting. Since many of this data is going to be from physical devices, you're going to experience every kind of connectivity or hardware issues. It might be the case for a while, a device goes silent, and you're going to have a gap in the data. How are you going to represent that? Are you going to interpolate data? What are you doing with the missing data? And what if one of the sensors goes back to connectivity, is sending you later data, sorry, which is late, that already happened. How are you going to go with that? Again, it's not easy to work about those things. And when you are working about data about time series, the recent data, the hot data, is very important. But you also want to have the historical data, at least for a while. And it would be nice if you could work together with both the call and the hot data in the same query. In summary, you need to have some system that allows you to work with time series semantics. You cannot just use the same databases you've been using in the past to work with this kind of tolerance in which you need to do sophisticated period over period comparison, trends, patterns, and so on, and so forth. As the Amazon CTO says, the modern applications need to collect, store, and work with a huge amount of data at a scale that cannot really be supported efficiently by a traditional relational databases. And don't get it wrong, I really love relational databases. They are great. They are just not designed for the specific use case of time series data. So we are lucky, because at AWS we have millions of active customers, and many of them have been working with time series for a long time. So I can tell you how they have historically worked with time series data. And the first approach is very nice. Just use and abuse your traditional relational database. I know they are not designed for time series, but that's what. You can make a relational database do anything you want. So it's not ideal. They are going to be missing some things, but you can use a relational database for time series. And actually, modern databases like Postgres, like Aurora, are pretty good for some time series work. We even have some articles in our blog about how you can use Postgres or Aurora to deal with time series. Many customers historically, when they were dealing with the turn of ingesting data, which was very fast, moved away from relational databases and went to SQL databases. Things like MongoDB, elastic search, they can ingest huge amounts of data very, very, very quickly. But they are not great for doing powerful queries, for doing powerful analytics. Other customers started using the Hadoop ecosystem, specifically H base as the database for storing time series. And as you know, Hadoop is not really easy to work with. Many moving pieces, many different things. So it's not ideal. So in summary, customers got creative how to use the tools they knew for this new type of processing for this time series. It was the case that even at AWS, long time ago, you can see there, we are still using the old logo. We haven't been using the old logo for the past five years. So a long time ago, we even defined reference architecture on AWS to working with time series data. And in this solution, we were suggesting customers. For ingesting data, use DynamoDB, fully managed SQL database. Then for actually processing the data, use EMR, a managed Hadoop cluster. And then for doing the analytics, use Redshift, a powerful cloud data warehouse. And this work, it was great because customers could actually do time series analytics without having to manage infrastructure, but it was not really ideal. It was not really fast. It was not ideal for the kind of analytics we want to do today. So of course, it was not only AWS that noticed this trend. If you go to dbanji.com, it's a wonderful site with a lot of information about different trends and patterns and usage for databases. You will notice how time series databases are actually getting quite popular. Out of the more than 300 databases they are tracking, they are 38. You can see there the blue slides on the chart. So they are 38, which are classified as time series databases. Actually, if you take a look at the trend for the past eight years, you see time series databases are getting the more attention, the most attention, only behind graph databases. But if you take a look at the past two years, you can see time series databases are actually trending more than any other kind of database. So basically, it's kind of a new thing until a few years ago, we didn't really have specific databases for time series, but it's something that is growing quite fastly because, as we saw earlier, it's very interesting for many, many different use cases. So with this, of course, there were a lot of customers when they saw new databases, both commercial and open source databases like InfluxDV, or like QtvPlus, or like Men's SQL, or like OpenTSDV, or like even Prometheus. So when they saw there was like these new databases that were specifically designed for time series, they started to run it on top of AWS because in the end, since we offer virtual machines and containers, you can run pretty much anything on top of AWS. And they were quite happy. The first customers we saw adopting these were mostly coming from the financial sector, and they were quite happy. They could work with time series data without having to manage the infrastructure. And they were reporting some interesting savings, and that was okay. And we even started defining some reference architectures to help them with these, you know, how to build with time series on AWS. So it was something like, okay, maybe you need to have a message queue in front, something like manage Apache Kafka to deal with the variable amount of data, then your time series database, commercial or open source, I don't care about that. And then maybe you want to integrate with the data lake to do more complex analytics. And it is already looking more sophisticated. This is looking more modern than the previous picture. But if you notice right there at point three on the chart, you can see time series DB, which is a black box. It looks blue here, but it's actually a black box. It's something that is not managed by AWS. In something you need to maintain. So if your time series are small, if you are not getting a lot of data or if it's not too fast, maybe you can get away with a single server. But the moment you want to scale, the moment you want to have reliability, speed, the moment you want to distribute your system, it's not going to be easy to maintain those databases. And that's why we started thinking about doing something about that. We started thinking how we could help customers that want to deal with time series with databases that were actually designed for cloud that could take advantage of all the things that you take for granted when you are talking about cloud security, scalability, reliability, flexibility, paying only for what you use, those kind of things. And we did this because, as we saw, building with time series is not easy. You can use a relational database. It's not going to adapt well. You can use time series database. It's not really designed for cloud. So it's not really going to be flexible enough. It's not going to be easy to work with them. There is a specific kind of time series database. There is this open source project called Prometheus. And Prometheus is a very interesting database. Apart from giving you time series storage analytics, it gives you monitoring. So Prometheus is both a monitoring tool and a time series database. We saw a lot of customers, specifically customers running containers, that were using Prometheus to monitor their workloads. So something we did was starting offering managed Prometheus. So you get exactly the same Prometheus that you have open source just managed by AWS. So you don't have to worry much about scaling. You don't have to worry much about integrating without the real services. So if your use case is about DevOps time series, if you are already using Prometheus, just for you to know that we offer Prometheus. But I'm not here to speak today about Prometheus in itself. I want to speak about a more generic type of time series analytics, not only for DevOps. And of course, if we are talking about time series, it's not only about analyzing the data, it's very important to visualize the data, to have some way of seeing those trends, configure alerts, see what's the status of the system. And there is an open source tool. It's called Grafana. You're probably familiar with that. And with Grafana, you can build dashboards like this. So what they have on screen, it's a dashboard which is getting time series data from two different tables. One is just raw data from sensors, getting temperature. And the other table is doing some aggregations to the test some complex events and putting their some interesting points on a screen. So of course, this kind of dashboard, when you have it live, you can see it's working, it's getting a lot of events per second. You can change the range, the scope of what you are searching. But I want to show you how you configure one of these individual charts, one of these visualization. And if you see here, to the right, you have the useful Grafana configuration, colors, legend and so on. But at the bottom is the important thing. Let me zoom in. Here you have a query, a SQL query, in which we are configuring what is going to appear on that chart. And I'm doing a very simple select. I'm doing something like give me the sensor ID, and then I'm aggregating time in intervals of 10 seconds. And for those 10 seconds, and for that particular sensor, give me the maximum temperature. That's about it. You can see a couple of fancy things. We have an ago function and we have a been function for working with time. But other than that, this looks like regular SQL. If you are familiar with any time series database, you probably know they don't use SQL. They use their own query language that you need to learn if you want to go with them. So what is this Grafana dashboard? Which database is speaking to that can allow SQL and still works as a time series database? So that's what I want to cover in the second part of this talk. Now that I already told you what is time series, why they are interesting, what's the trolling of dealing with time series today, which solutions you could use? I'm going to tell you about the new kit on the block, about Amazon time stream, a purpose built time series database, which is designed for the cloud. That means it's fully serverless. You don't have to worry about any servers. So you pay for three things, basically. How much data you are ingesting, the more data you are sending in, the more you pay. How much data you are storing? If you are storing data for a year, you pay more than if you are storing data only for two weeks. And how much data you are querying? If you are running a thousand queries per minute, you pay more than if you are running couple of queries every hour. But other than that, you don't have to pay for any servers. If you are not sending or storing or querying data, you pay zero. There is no minimum, there is no maximum, a scale as much as you want. Of course, this runs on the cloud, so security, you can not really take it for granted because security is always a responsibility, but it's integrated with the same security mechanisms you have on AWS for the other services. You can encrypt the data. You can use your own customer managed security keys if you want for encrypting your data. And it's specifically designed for time series. So apart from the standard SQL that you can use, and I'll talk more about SQL later, but apart from the standard SQL, we have extensions for working specifically with time series and for getting time series analytics. Sounds good? I hope so. So what is looked like behind the scenes? How we are building this database? So we have, we announced time stream about two years ago. For the first year, we were under private preview. We were testing with a lot of reference customers to make sure we had that right. To make sure the use case, it was what most customers were demanding. And then last year, we actually opened to the public. And guess what? In all this time, customers that were using time stream didn't have to change anything. We've been adding new capacities. We've been improving the service, but you don't really have to worry about that. Since it's a serverless product, you don't have to worry about updates, about upgrades, about patching, about anything like that. So the way you work with data at time stream, it's first you have to design, define some tables as you would in any other database. And those tables can leave both in memory or on magnetic storage. Actually, we're working now on a third layer, which is SSD. So you can choose to have the data in memory, on SSD, or in magnetic storage. So every time you ingest data, data getting ingested in memory. And you choose for how long it's going to be there. You can set a minimum of one hour, a maximum of one year. So after that period, data is going to be only on the magnetic storage or in the SSD layer. And again, on magnetic storage, you can choose to store for only one day or for store for up to 200 years. And after that maximum time you have defined for your table goes on, then the data gets deleted forever. That's basically it. So you have full power to choose how much your data is going to be in memory, which is more expensive to store, but faster to query, how much of your data is on magnetic storage, which is way cheaper, but also slower. And you can always query both of them seamlessly. You don't have to do anything special. You can include hot data and cold data in the same query. It works. It's just the response time might be different, but that's about it. You don't have to worry about anything else. So this is what we built. We have a multi-layer architecture. At the top, we have the ingestion layer. The ingestion layer scales up and down with the data you are sending. You don't have to worry about that. You don't have to pre-warm. You don't have to do anything. You can go from zero to millions of events per second without any warning. So it will ingest all the data. And when we ingest the data, we replicate the data across multiple availability zones. That means different data centers in different locations and in different hard drives. So the data is always within the region, but it's automatically replicated in multiple data centers in that region. So you can make sure you never lose any data due to a hardware malfunction. So once the data is there, it's storing memory. It also goes to the magnetic store. After a while, it will be deleted from memory, and you define that time. And then we have the query layer, which is at the bottom. And the query layer, it's independent from the rest. So it grows as much as you run queries, and you don't have to do anything about that. So you have, you can't forget about managing servers, adding more capacity, removing capacity when you don't use it. This is elastic. It's totally independent one layer from the other, and you don't have to worry about any of this. If you actually look behind the scenes, it gets a bit more complex, because this is not a big installation. We have a big cluster. Actually what we have is what we call a cellular architecture. So basically what we have here is we have multiple copies of this architecture. And when your application is sending data, we are automatically redirecting to one or to the other to minimize the impact of any failure that could be in the system. But again, that's not your problem. That's just how we manage behind the scenes, because for you it's a CC, ascending data and retrieving data. If you were talking about a conventional database, or even about a wide-column database like Apache Cassandra, you probably would be thinking that when you are storing time series, you store already different metrics on a single row. Imagine you have a machine, and that machine has different metrics. In this example, I have a server, and the server has three metrics, CPU utilization, the memory utilization, and the number of bytes is sending over the network. On a conventional database, on a conventional architecture, you will store that as a single row. On time stream, we do this a bit differently to have more flexibility. A different metric is stored as a different record. So in one record, we have the metric itself. Here you can see one for the CPU utilization, one for memory, and one for the networks. And then we have the dimensions. The dimensions are the things that are common to the whole row. In this case, since all the metrics are for the same server, the things that describe the server are the dimensions. In this case, it's the region, and the ASET, and the VPC, and the hostname. So those things are dimensions. The other things are measures. So you can have over 100 dimensions. You can have up to 8,000 different measures for each record that you are sending. So it's not too bad for each, you know, 8 different measures in each table, more than per record, actually. And when we have these individual records, we can combine them to create what we call a time series. And historically, you can see that better in this example. Here I have nine different rows. So even if there are nine different rows, you can see only three different time stamps. Second zero, second one, and second two. And each time stamp appears three times. One for each metric, one for the CPU, one for the memory, one for the network. So basically, when I have this individual record and I want to convert to a time series, what I do is I tell the stream. I want to aggregate by one or two or three, or whichever dimensions I want. For example, if I aggregate these metrics by Rihyon and ACID and VPC and measure name, sorry about that. I didn't tell you about that. So yeah, I was just trying my superpower with you. So if I aggregate Rihyon and ACID and VPC and host name, what I get, basically, is the time series. So that's how it works when you are working with Amazon Timestream. So when you ingest in data, there are many different ways in which you can ingest data. Of course, you can use the AWS probably SDK for your favorite language. But we also have adapters for working with IoT, for working with third-party tools like telegraph or Apache Flink, and for working with many different solutions. This is just simple Python example in which I'm composing a JSON record and writing individually. If I were to run this example, imagine I have 100 different metrics. So I'm writing 100 individual times, and imagine I have a metric every five seconds. So eventually, I'm going to have about 50, almost 52 million writes a month.  In particular, you pay 0.50 for each write of up to one kilobyte. So that means you will be paying $25 for ingesting 52 million writes a month, which is not too bad, but you can do better. If instead of doing individual writes, I batch the writes and I put multiple metrics for the same dimensions in one go, I can actually write much less. Rather than writing 100 times, I can go out to only 11 times. So my cost is way cheaper, it's not even $3. But I can do even better. If I do this in groups of different writes, so I can be within the maximum kilobyte, I can do better writes, I can actually put all these metrics into only three writes. Which means for the same workload, I'm going to be paying less than $1. So I guess for you to know, it's not really important the numbers here, it's just for you to know that in cloud, it's always interesting to understand how you are going to be billed. Because for the same workload, if you just go with the naive approach of writing individually, you are paying $26. If you are batching, you are paying $2.85. And if you are batching and grouping by the common attributes of the records, you can go with just below $1. So always interesting to see how you can save money. But before I finish, I know I have about five minutes. So that will be plenty of time. So before I finish, I want to speak about a couple of things. First, I already told you, you can choose between memory or magnetic storage depending how much recent data you want to have to query faster. But I want to speak about how you actually query the data. So you just SQL with extensions. But if you know SQL, you only have to learn a few functions to start using Timestream. That's it. And actually, we have a GDBC driver if you want to play with that. And you pay for how much data you are scanning on your queries. If you go to the web interface, that's, you can actually run interactive queries directly from the web interface. But of course, you can also use your Toramil language of choice to run those queries automatically. So when it comes to SQL, the SQL we accept is a standard, no more and no less. Select from where, group by, union, blah, blah, blah, whatever you want. But we are adding specific things for time series. The SQL you see here is basically the thing you could run anywhere else. But we have things like the percentiles, which are interesting for this kind of metrics. You have things like the being in the time in intervals of 15 seconds. So you have metrics at different time points. You can normalize at 15 seconds interval. You have functions like a go, for example. So small things that are interesting. But the most important bit is you can actually convert individual records into time series. So this is what it looks like. If I have this table here, which is the same I was showing you before, in which we have multiple dimensions and time stamps and some metrics, I can do this. What I'm doing this query is saying, for the dimensions of the region, ASET, BPC, and instance, I want to have a time series of the CPU utilization metric. And this is going to unfold this table. So it's going to look like this. It's going to give me, for those particular dimensions, I have the time series data. And now that I have in a single structure all that data over time, I can apply time series functions like interpolate, fill the missing gaps with the full data or with average data or with whatever I want. Derivative, if I want to do which is the change rate between one metric and another in time, integrals, correlations, all kind of interesting things you want to do with time series. And of course, time stream doesn't live in a vacuum. We have integrations with Apache Flink. If you want to do some preprocessing before exporting the time stream with telegraph, if you want to collect operational metrics from servers with SageMaker from machine learning on AWS, GDBC driver integrates with different services. But there are two I want to show you very quickly before I finish. One is QuickSight, a business dashboard. I showed you before a monitoring dashboard with Grafana. We also have a business dashboard on AWS. It's called Amazon QuickSight. So if you want to present business metrics on a pretty dashboard, you can integrate directly time stream with QuickSight. And okay, this is what it looks like. So this chart feel a bit less geeky and a bit more business friendly. Okay. And other than that, if you want to automate at any point the data workflow, you truly want to do some kind of orchestration. And what's better for orchestrating than airflow. So you can integrate Apache airflow both with time stream, both with the open source version or with the version managed by AWS. So I told you at the beginning, there are many different use cases for time series. I want to highlight just a couple customers. One is Amazon.com itself, the Amazon group. They are using time stream to help improve the production of renewable energies for supporting the whole business. The other is a German company, Sieggeek. They sell tickets for events and they wanted to analyze which was the average time the users are waiting in the queue before actually getting to buy the tickets, all those things. And for that, they are also using time stream. So as you can see, different use cases in which you can be using time stream. The last thing that I have is just to tell you that other than doing analytics, there are many use cases for time series when it comes to doing machine learning. And if you are using the AWS with or without time stream, you have multiple solutions to forecast what machine learning ready size from your data. That's all I have. Thank you very much for having me here. If you want to learn a bit more about time stream, you have some resources on the screen. Javier, thank you so much. That was a fascinating talk. I think the two Espresso's work wonders. We don't have much time, unfortunately, but we do have a couple of questions. One technical, one not so technical. Let's start with the technical, shall we? Yeah. So Miguel Angel Monjas asks, isn't there an ingestion plugin for MSK Kafka? So, hi, Miguel Angel. Thank you very much. So, I'm not aware we have a data plugin for Kafka, but what you can do, if you have Kafka and you have already an application written in Java with Kafka, you can just use the SDK to send the data. Another thing you can do is just use KafkaConnet. So we have MSK. We have now a managed connect service for Kafka on AWS. You could write the connector to just using the SDK right into Kafka. So there is not the direct connector, but there is the SDK that you can use for writing your data. So thank you for the question. Okay. Javier, finally, not such a technical question. Okay. But coming back to what we were talking about at the beginning. Yeah. Becoming more and more used to time series data, is this a trend that you think is going to continue, or are we perhaps going to get tired of this obsession with time series data? I mean, I'm talking as a typical user. And perhaps you could answer, how is this changing us as human beings? Are we becoming our own little data scientists? It's not. I mean, it's a very philosophical question. Of course, I don't have the answer here, but I can tell you, with technology, we are able to track things now that we couldn't in the past. So we can predict some things. We can prepare for things. For example, in the past, you will go to the workshop only when the car was already broken. And that was too bad, because then it's like, oh, I don't know how long I'm going to be without the car. But with time series, you can get there before our tooling gets bigger. So maybe it's a quick fix. So I hope that trend of using time series for good, it is there. We have not the technology to apply that to more and more scenarios. So I see the trend is actually getting bigger. I don't see it as something that is conditioning as a human, but it's like, no, it's like a kind of superpower. Now you can use those things to be more efficient. Where we put the limit, that's not for me. That's for a philosopher. Maybe we'll leave that for next year then. Maybe, yes. Javier, thank you so much for your time.


Now, as gunshots echo across the windswept snow-covered reaches of the wild northwest, Quaker Puff Wheat and Quaker Puff Rice, the breakfast cereal shot from guns, present the challenge of the Yukon. It's Yukon King, swift as the strongest lead dog of the northwest, blazing the trail for Sergeant Preston of the northwest mounted police in his relentless pursuit of lawbreakers. Un-King! Un-King! Gold, gold discovered in the Yukon, a stampede to the Klondike in the wild race for riches. Back to the days of the Gold Rush, with Quaker Puff Wheat and Quaker Puff Rice bringing you the adventures of Sergeant Preston and his wonder dog Yukon King as they meet the challenge of the Yukon. Here's a taste treat you want to repeat. It's Quaker Puff Rice and Quaker Puff Wheat. These famous ready-to-serve breakfast cereals are actually shot from guns to make them bigger and better tasting. They have a comeback for more nut-like flavor, a come-again tender crispness, and they're good for you. Yes, a delicious nourishing treat and so easy to serve. Make sure the big red and blue packages of Quaker Puff Wheat and Quaker Puff Rice are on your breakfast table every morning. The moon was full and the northern lights streaked across the sky, but the trail ran along the base of a thickly-wooded slope and the tall pines cast black shadows across it. Cass Evans urged his team to greater speed. What's that? What's that? Suddenly a shot rang out from far up the slope. Cass ran forward, jumped on the sled, and lay flat. Caster boys, run! Another shot kicked up the snow and back to the flying sled. Then another. The trail swung away from the hill and out of the shadows. Cass looked back. He could see nothing but a dark cabin huddled among the trees. No sign of a human being. He drove on toward Dawson. Reached town, he unharnessed his dogs and back to the palace hotel, turned them into the run, went directly to northwest-mounted headquarters.  That's right. I'm Cass Evans. I own the Aurora mine on Empress Creek. I know. What can we do for you? Well, let me tell you the truth. I think someone's trying to murder me. What? Well, let me tell you about it from the beginning. Sit down. Thank you. In case you're not familiar with the Aurora, it isn't a placer digging. It's really a mine. We've driven our shaft straight into the hill. I remember when you started to operate. About a year ago, wasn't it? A little more than a year. We've been very successful. But three weeks ago, there was an accident. A cabin. Some of the timbers gave away. I was in the mine at the time. In fact, I was the only one in there. Work had finished for the day. You knew the cabin? I was close enough. I could have been killed, but I wasn't. And actually, there was no great damage done. But it wasn't an accident, Sergeant. No? Someone had sawed almost completely through the timbers. I see. I believe I know what you're thinking. That it would have been hard to call it an attempt of my life, an attempt to kill me personally, unless, of course, the timbers were weakened just before work ended that day or just after it. You see, I make a habit of inspecting the mine just before I go to bed, and I do it alone. I test the timbers. You get what I mean, Sergeant. I can see the possibility. Well, that was the first time. But since then, I've been shot at twice while driving into Dawson. Once a week ago and once tonight. Well, that's something else again. Why didn't you report it a week ago? It was a single shot that time with the country. I heard the bullet go by my ear, but it could have been a stray shot from some hunter's gun. Is there anyone? No, but there were three shots tonight. And it was at the same spot on the trail, a good spot for an ambush. You see the trails at the bottom of a wooden slope? There's only one cabin along that stretch. Did the shots come from the cabin? No, no. Go on. Well, there isn't much more to tell, except that I found out who lives in that cabin. What? His name is Elliot. Tom Elliot, a prospector, young, new up here. And here's the funny thing. This Tom Elliot was at the mine on the day of the kevin. He asked my foreman for a job. Mike was going to take him on. He had dinner at the mess hall with a man. And then he told Mike he'd changed his mind. Do you know this Tom Elliot? I... I don't think so. And I didn't meet him at the mine. And one's dealing with murder, Cass. One tries to find a motive. I understand what you're driving at. And all I can tell you is that I have no recollection of knowing Tom Elliot during the past 11 years. The past 11 years? Hey, don't remember anything. It's a strange thing, Sergeant. Certainly, yes. 11 years ago, a fisherman saw me thrown off a wharf in San Francisco. He pulled me out of the water. I went to my temple, in one of my chests, and no memory of how I got them. And no memory of anything before that? A complete blanket. What about your name? The initial C.E. on a handkerchief. I made up Cass Evans to fit them. Then you don't know who you are or where you came from. No, sir. During the past 11 years, you've never seen a familiar face you couldn't place that might be someone you knew before? No, sir, do you? Of course, I spent most of that time at C.E. or in Shannon. Well, C.E., you must have had enemies. Yes, someone who wanted to kill me at any rate. I've... I've often wondered why. This Tom Elliot. Well, Mike says he's only about 25. 11 years ago, he had only been 14. Elliot. Evans. Could your name really be Elliot? Could he be a relative? Mike says he doesn't look anything like me. Well, I think we ought to meet him and have a talk with him. How long are you going to be in town? Until late tomorrow afternoon, but I don't think you'll find Elliot. It is Kevin. It seems to be deserted. Who told you he lived there? It takes him on a car door. I... I inquired when it was shot out a week ago.  See, that's the place. The cabin set back among the trees about two, I should say, 100 feet up a slope.  Drop around here in the morning. I may have Tom Elliot waiting for you. Oh, you understand, Sergeant. I'm not making any accusations. I understand. Still isn't there a circumstantial evidence against Elliot to justify our asking questions? Uh, leave it in your hands. Good night, guys. Good night, Sergeant. Oh, uh, just one more thing. When you asked tax who lived in the cabin on Lookout Hill, did you tell him you'd been shot at? I haven't mentioned it to anyone but you, Sergeant. Good. Good night. The Sergeant called in one of the constables to take charge of the office. Then he and King headed for the Monte Carlo. Tex was behind the bar. Howdy, Sergeant. Howdy, Tex. Well, what's on your mind, Sergeant? A prospect to name Tom Elliot. What do you know about him? Well... Start with a description.  Well, it's good business to remember your customers. Tom is young, Sergeant. I'd say about 24 or 5. About 5 foot 10. About 180 pounds, clean shaven. He's got dark curly hair, dark eyes, almost black. I think his nose must've been broken once. It's just a little crooked. But he'd be good-looking if it weren't for the lines around his eyes and the way his mouth turns down like this. Oh, tough. He tries to act tough. But when he laughs, he can't help but liken him. He don't laugh often. He lives in Old Trappers Cabin on Lookout Hill. Say, why are you interested in Tom, Sergeant? Does it have anything to do with Cass Evans? Oh, that's an interesting question. What made you ask it? The way the kid talked about him when we heard about the cave in at the Aurora. What did he say? There'd be good riddance if Cass had been killed. Hmm. Do you have any reason for not liking Cass? Uh, shut up like a clam when I ask him. How long has it been since you've seen him? It's over a week since he's been in. What would you say if I told you that Cass Evans had been shot? Tom did it? Well, how'd it happen? Has Cass hurt bad? No, he hasn't been shot. But I take it you'd suspect Tom if anything happened to Cass. Well, from way talk and way looked.  Did Tom talk about Cass to anyone but you? Well, the place was crowded. There always is. Lots of people must've heard him. No, Sergeant, I'd find it awfully hard to believe that Tom could shoot Cass in cold blood, no matter how much he hates him. Just isn't that kind of an arm. You're a good judge of character, Tex. There's only one thing for me to do and ask to find out what's behind Tom's hate. That means we're hitting the trail for Lookout Hill, King. It was long after midnight when the Sergeant reached the cabin on Lookout Hill. Lookout Hill! The Sergeant of King started up the hill. Cass was wrong about the place being deserted, boy. Light in the window and dogs out and back. Of course, there may not have been anyone in the cabin when he drove by since the shots didn't come from the cabin. That's understandable, eh, King? Well, we'll see. Do you talk, Elliot? Yes. Sergeant Preston, Northwest Mountain Police. What do you want, Sergeant? I'll have a talk with you. What about? About Cass Evans. I don't know any Cass Evans. He owns the Aurora Mine on Empress Creek. Oh, I guess maybe I've heard of him. And had a lot to say about him in the Monte Carlo. Oh, dad, I must have been drunk. I don't know Cass Evans. Well, that's going to be your attitude.  Right. What for? Someone fired at Cass Evans from the woods near this cabin tonight. I didn't do it. You'll have a chance to prove it. Put on your paga. You're coming back to Dawson with me. You're out of money. Up your head, you're coming back.  Back, King. Now, are you, Sergeant? Step inside. Have a dog to keep back. Stay outside, King.  I'll take your gun, Sergeant. He had a gun in my back, Sergeant. I couldn't do anything. I couldn't warn you. Too bad you couldn't get rid of him, Tom.  What was your objection to my arresting him? Why, Sergeant, what would be the use of Tom going to jail for Cass Evans' murder until Cass is dead? I guess maybe we'll have to blame him for your murder, too. We'll continue our adventure in just a moment. Say, fellas and girls, it won't be long now. I mean until the new year 1950 is here. Well, I am here. Great Scott, where did you come from? Well, I guess I shouldn't really be here. Well, yes.  Gosh, you haven't got any clothes on. That is hardly.  See what it says on it? Sure do. It says 1950. That's right. That's who I am. Oh, so you're the new baby New Year. Right. Well, you're a good day in some hours early, aren't you? Well, I was just checking up. Oh? I knew your resolution. Made any? Me? Oh, you bet. It's not to tell all the fellas and girls about a resolution everyone ought to make. Well, what's that? It's to eat a good nourishing breakfast every morning the year round. Well, that's a swell idea. And so easy to keep too, especially if you include big heaping bowlfuls of swell-tasting quaker-popped wheat or quaker-popped rice with milk or cream and topped with fruit. Oh, sounds good. Oh, it is. Quaker-popped wheat and quaker-popped rice are shot from guns to make them crisp and tender. Yes, these king-size premium grains are actually exploded up to eight times normal size to make them bigger and better tasting. And what's more important too, both delicious kinds furnish added food values of restored natural grain amounts of vitamin B1, niacin and iron. You mean they're good for you? You bet. And for variety, you can eat quaker-popped wheat one day, quaker-popped rice the next. Boy, I can hardly wait. 1960 is going to be me trying them. And fellas and girls, don't you be missing out. In 1950, eat quaker-popped rice and quaker-popped wheat, the ready-to-serve breakfast cereals shot from guns. Ask mom to start the new year off with a breakfast treat that can't be beat. Get next to those big red and blue packages of quaker-popped rice and quaker-popped wheat. Now to continue. The hard-featured black-bearded man who held a gun on the sergeant ordered him and Tom Elliott to sit down on the cot in the corner of the one-room cabin. Then he settled himself in a chair near the stone. I'm not going to take a chance and tie you two up until Brant gets back. But if either of you make a move, I'll shoot. I've seen you around Dawson.  Some folks do. Are you the one who fired the shots at Cass Evans tonight? No. Who was the other one? The one called Brant, sergeant.  What can you do to make me? I can pull this trigger. Well, sooner or later, anyway. I've been a prisoner here for more than a week, sergeant. Twice they've tried to shoot Evans. They'd succeeded either time. They'd have put a bullet through me and left me lying near the trail. They'd have looked like a gunfight with two casualties. Great scheme. Only Brant can't shoot straight.   Who is their boss, Tom? I don't know. Somebody who tried to kill Evans a long time ago and was afraid that Evans will recognize him and send him to jail. You talk too much, junior. You've been doing the talking all week.  Don't try it, Laramie. My hands have been perfectly free, remember? Go on, Tom. I plan to pin the blame for Evans' murder on you because of what you said at the Monte Carlo, right? That's right. I don't take any of that back. I have no sympathy for him. Why not? Because he's my father. Your father? And you find that a good reason? He deserted my mother and me when I was only 13. I left the two of us to run the ranch, went to Frisco with a load of cattle, and never came back. That's a sad story. Save your laughs. It gets sadder. We lost the ranch and Mom died. She was always sure that something had happened to Power. He'd come back to us. I'm glad she never found out the truth. What truth? No, he just ran away and left us, changed his name so we couldn't find him. When I think of it, him, the owner of the Aurora, with all the money in the world, and just a few dollars would have meant him up. You've got it all wrong, Tom. Yeah? Yes. Now that I finally have it straight... King! In response to his master's voice, the great dog King would have been lying just outside the door, threw himself against it. The fraction of a second, Laramy turned his head toward the door, and the sergeant was on top of him. As they rolled on the floor, the sergeant held Laramy's gun and ran away from him. One shot went into the ceiling, and then, with relentless pressure, the sergeant twisted Laramy's wrist. The gun skidded across the floor. Get it, Tom. I got it.  Good thing you didn't look through my pocket to find these handcuffs. They looked better on you than they would on me, Laramy. What a bike, John. You mean what have you accomplished? Nothing, I'm glad to say. But you've shown enough criminal intent to put you in jail for a long time. All right, King, I'll let you in, boy. All right, fella, it's all right. How are you doing? Good work, King. Sergeant, you said I had it all wrong. What did you mean?  I didn't have anything to do with that. I never met Dexter until he hit Dawson two months ago. So it's Dexter at the motor. Thanks very much, Dexter and Brent. But about Pa... He was shot, Tom. Moldy was robbery, I suppose. He was thrown into the harbor for death, but he was rescued. Well, then why didn't he come home? Because he lost his memory and never regained it. He changed his name because he didn't know his right one. His only clue is the initials on a hand-feature, C.E.  Yes. His name is Charles Elliott. Wouldn't you like to tell him that, Tom? You know I would, Sergeant. Lost his memory? Then he couldn't have sent Dexter to jail. Dexter had nothing to be afraid of. He has now, and so have we, until Dexter's behind bars. Sergeant, there's Brent, too. He's coming back here. He's still in Dawson. We'll pick him up there. And if we meet him on the trail, he'll join our party. Let's go. As they started out with Laramie riding the sergeant's sled and Tom driving his own, Ben Dexter was pacing the floor of his room in the palace hotel. Brent was sitting on the bed. All the stupid, blundering fools. I told you how it was. I thought I hit him with my first shot. He dropped on his sled. Never mind, never mind.  You got away. It won't happen again. Now, because the next time I'll see the job is done right. What's more, it'll be done tonight. Here? In the hotel? Now listen, do you know his dogs? I ought to. I saw him often enough when I was working up at the mine. Can you pick him out of the run in back of the hotel? Sure. Do you know his sled? Yeah. Then go downstairs. The back way. Get his team harnessed. Right away. And wait, that isn't all. After you finish harnessing the team, walk around to the front of the hotel, and into the lobby. Yeah? Now ask the clerk what room he's in. Say that you have an important message for him that must be delivered in person. I got you. When you got the number of the room, climb the stairs to the second floor. Wait there for a few minutes. Then go back to the clerk. Tell him that he must be how he didn't answer your knock. Yeah, but the key of the room, if the clerk doesn't have his key... A man doesn't always turn his key into the desk. Now look, the clerk won't pay too much attention if he be half asleep at this hour. Now do you understand? Yeah. I say he didn't answer my knock. So you leave the hotel by the front way. Go round in back. Use the back stairs and come up here. Oh, I got you. Then you and me... That's all you have to do. Come back here after you're finished, and I'll take charge. Right, boss. Grant followed Dexter's instructions, but it was nearly half an hour later when he returned to the room. Well? All set. Been going long enough. Oh, I had a little trouble finding this leadin' and the dog started to bark.  Anyone see him? No. All right. What's the number of his room? 210. Just below this one. Good. Now when he opens the door, I want you to hit him once with a buddy your gun behind the ear.  Okay. I'll put this lamp out. Two men walk swiftly down the corridor to the back stairs, then down the back stairs to the second floor. Now how they walk slowly, very softly. Then struck a match. Message, Mr. Evans. It's important. I wanted to make sure. Come on, inside the room. Now, we tie him, dress him, and gag him and take him out to look out here. Or you can try to put a bullet in him. This time at close range. When the sergeant, Tom and Laramie, reached Dawson, their first stop was at the northwest-mouted headquarters. Laramie was locked up. Then the sergeant and Tom drove on to the palace hotel. Okay. I'll be with you. Hold on. Oh, it's late, Sergeant. After 11 years, I don't think your father will mind being wicked. Come on. Come on, King. As usual, Johnny, sound asleep. Wake up, Johnny. Doers door for a little. Just a little. What room's Cass Evans in? 210. Five. I don't know what to say. He isn't in. What? Well, it's practically morning, isn't it? Want to go out? Oh, I didn't see him go out. You probably didn't see him come in. No, I mean, this was only about 15 minutes ago. A fella came in and asked for him. I told him 210 and he went up. In a few minutes, he came down again. Said he'd knocked on the door and no one answered. Someone asked for him this time of night. I think he works at the Aurora. Forgotten his name or a big guy with a red beard. Sergeant, that's Branch. Johnny, how long was he upstairs? Just a few minutes. Are you sure? It only seemed to be a few minutes. Come on, Tom. The sergeant, Tom and King, ran up the stairs. There was a hanging oil lamp at either end of the corridor. Halfway down it, the sergeant could see the dim forms of two men. They were carrying another man. Sergeant, there's Branch now.  He and Branch dropped Cass ran for the end of the corridor. The sergeant ran out. A leaf over Evans still form and caught Branch just as he reached the stairway. He grabbed the shoulder of his pocket and whirled him around. His right crashed into the red bearded jaw and Branch staggered back against the wall. His hand went for his gun. The left knocked him down. The sergeant jerked Branch's gun from its holster. Watching King. The sergeant ran to the end of the corridor and opened the window. The second he climbed through and dropped to the ground near the rear door of the hotel. As he scrambled to his feet, Ben Dexter came through the door holding a gun pressed in charge from the side. Ben turned but too late to swing the gun around. The hard impact of President's charge jolted the gun from his hand. Ben tried to turn back into the hotel but the money grabbed his left wrist and twisted his arm behind his back. You've gone far enough Dexter. You're under arrest in the name of the Queen. Dexter and Branch joined Laramie in jail at night and Tom's father was taken to the hospital. He had a slight concussion and it was morning before he opened his eyes. He saw the sergeant, King and Tom standing beside his bed. Sir, I impressed him. Hello, Charlie. That is my name, isn't it? You remember? Yes. Something's happened to my head. You were hit and hit hard. But it seems to have brought back your memory. I can't say that it was him. Charlie Elliot. Do you remember your son? My son. Hello, Pa. Rulia Boy. Now you've come up here to... No, Charlie, you're wrong. Tom had nothing to do with those attempts on your life. There was a man named Dexter who was trying to kill you. Do you remember him? Remember Dexter? Dexter. Yeah, today he sold the cattle. The man who robbed me. The man who shot me. Easy, Pa. Yes, Charlie. There's no need to get excited. We know all about it. But Dexter's in jail and so are the two men who were working with him. Now stay there for the rest of their life. All you have to do is rest easy. You can wait. You'll have a long time to get acquainted with your son. I'll be right here, Pa. Come on, King. The case is closed. In just a moment, Sergeant Preston will give you a preview of Monday's adventure. Well, sir, fellas and girls, next Sunday is the first day of a new year. And right here is someone who wants to say something to each and every one of you. Here is Sergeant Preston himself. And naturally, I don't have to tell you that King is here to right beside you. Fellas and girls, King and I just want to wish you a happy new year. May the coming year be the finest, the happiest ever for you and your family. How about it, King? That's King's way of saying for the both of us, happy new year. And Sergeant Preston, that goes for me and all of us here. And for the Quaker Oats Company, makers of Quaker Puffed Wheat and Quaker Puff Rice. Yes, from all of us, a happy new year to one and all. Listen Monday when Sergeant Preston and Yukon King meet the challenge of the Yukon in the case of Rainbows End. When Andy Miller was released from prison and paroled to me, I found him to be a likable young man determined to make good. His conviction had been based on circumstantial evidence and I decided to reopen his case. This decision took King and me on a strange trail that was marked by murder. Be sure to hear this exciting adventure Monday. These radio dramas, a feature of the challenge of the Yukon Incorporated, are created and produced by George W. Trendle, directed by Fred Flower Day and edited by Fran Stryker. The part of Sergeant Preston is played by Paul Sutton. They are brought to you on Monday, Wednesday and Friday at the same time by Quaker Puffed Wheat and Quaker Puffed Rice. The breakfast cereals shot from guns for a delicious hot breakfast, eat Quaker Oats. The giant of the cereals is Quaker Oats. Delicious, nutritious, makes you feel ambitious. The giant of the cereals is Quaker Oats. Say boys and girls, do you want to be a star some day in sports and activities? Then start on Good Quaker Oats breakfast tomorrow because nourishing oatmeal gives you more growth and endurance than any other whole grain cereal. Remember, Quaker and Mother's Oats are the same. This is J. Michael, wishing you good bye, good luck and good health from Quaker Puffed Wheat and Quaker Puffed Rice.