In today's video, we're opening the new Ruxen 2.0 Mystery Pack. What's up guys, we're back with another Mystery Pack opening and this one is the Ruxen 34 slash Sassy Auto collab for the Mystery Packs. We did this once before, it's like late last year and they did really well. You guys really liked them for the most part and this time we've upgraded them a little bit, added a few new things, changed the price a little bit and we're going to open up a few in this video. These packs are available while you're watching this video. Just click the link down below. You can order them off of Sassy Auto's eBay store. They are available right now. So go check out the link if you guys want to pick some up and let's get into the preview of what you might get. But before that, our giveaway will be everything we pull out of these three Mystery Packs. I have the examples of the purple, silver and red that will be opening up today. No gold because there's only a few of those out there and they really just have like a really awesome single in them. So I don't want to like open that up and just you get what's in there. So before we actually get into the details of the Mystery Pack 2.0, we need to talk about Mystery Packs. Mystery Packs are not always guaranteed value. So these are going to be $49.99. So you're not always going to get $49.99. Most of the time, probably not anyway. And then a lot of people are going to get like double or triple, you know, they're going to get the red. They're going to get like pharaoh servant pack or something like that. That's going to be worth way more than the pack where you're going to get purple and you're going to have like, you know, not a great Mystery Pack or whatever and you're going to lose a little bit of money. I want you guys to keep that in mind because you can't have a Mystery Pack where the floor is the same price as the value. It just doesn't work out for the people making them. But we want to make them as valuable as possible. So we added a bunch of really cool stuff to hopefully add some value to you as a fan of me. And hopefully it works out so you're happy with what you purchased.  I pulled 75 every single time because that's not how it works. I want to remind you guys of that only by this if you're really excited about it and you just want to, you know, potentially pull something awesome, but maybe not keep that in mind. So we have the 2.0. We're going to talk about what's inside. So here on the back, we have a little graphic here. So the Meguri Mystery Pack 2.0 So we have both me and Sassiato include six modern era booster packs. So here's some of the modern era you can get. It goes from the past. Battles of Legend Crystal Revenge. A lot of awesome starlights in there. So that's pretty cool. Darkwing Blast. We know that's a great set at the moment. Amazing defenders. We were calling that atrocious defenders, but it's getting better because the pearly stuff is getting really expensive at this point. So it's actually not that bad anymore. And then Photon Hypernova. So really a pretty good variety here. Like the worst ones, amazing defenders and that one's not even as bad as it used to be. So those are some of the packs you can get in there. I don't know really all the options. There's obviously going to be more than that because it includes six modern era packs, but there's probably some other ones in there. But those are some of the good ones right there. Then on the purple. So we have a couple of errors on like the packs. So we have Code of the Duelist on here. That is actually not in here, but for some reason it's on the graphic. So that's not in here. Some other things you can get. So the minimum guarantee like if you buy and you get the worst Mystery Pack, which is the purple color, then you're actually going to get one of these as your extra or there's probably a couple other ones. But no Code of the Duelist. Eternity Code is pretty good. We've got the Battles of Legend Arm again. So you can pull the 10K Dragon. We've got what is this? Flames of Destruction. You can get the original Imperme. You can get the Ghost Bill and Haunted Mansion. So that's some stuff you can get in the purple. This is like if you buy, you don't get a silver, red or gold. You're going to get a purple and you'll get one of those packs as your bonus pack, which are all pretty solid packs. The next one, we have another error. So we have Circuit Break. This one is actually supposed to be here. So I guess this Circuit Break should be where the Code of the Duelist is. It's not in Silver. So you're not going to get a one in five Silver Pack and pull Circuit Break. That would be a little weird. So we fixed that. So that's supposed to be over here. Duelist Alliance is in here. We've got Extreme Victory and then we've got a Photon Shockwave. So pretty cool variety there. You get like tour guide out of here. This one has ghosts and oldies. It's very classic pack. Really strong for like the original Meadow when it came out. Then of course, the Photon Shockwave with the Ghost Shirt Galaxies is pretty amazing. So that's one in five. Like a 2010 to 2015 booster pack. There could be others in there, but those are the ones we've displayed. Then we get to the red where all the really high end, you know, you got like First Ed Flaming Eternity which has like the original Sacred Phoenix Ultimate Rare. Cyber Inn Dragon could be pulled out of a cybernetic revolution. That's a hobby pack, by the way. So that's pretty insane. If you pull a Cyber Inn Dragon out of one of these and you're, I mean, you're making like 20 times your money or something, maybe more than that. Then a Ferris Servant, as we mentioned, and then Raging Battle First Edition you could get that Ghost Rare. So pretty crazy stuff in the red pack. It's only one in 10 too. So it's not like crazy hard to pull, but you know, pretty cool stuff. So if you do pull one of these, you're definitely making your 50 bucks back. And then finally, one in a hundred. So these are really rare. Like these are a lot harder to pull than the red. Vintage cards inside. Some of the vintage cards include a 10,000 Dragon. So it's like a $1,400 card. There's also a DDS Exodia. So there's stuff like that inside. There's some really, really quality stuff out of those. There's not a lot of these out there because we didn't make a ton of packs. So one out of a hundred means there's not that many of them. So if you do get that, you are extremely lucky. So that's the mystery pack, but there's also a few other things included. So last time we did random new booster boxes. So you might get a redemption code for the newest booster box from a core set. Those are still included. So you may or may not get a booster box worth your entire value. So if you get a booster box, these are 50 bucks. If you get a booster box, that's worth at least 50 bucks. So you just made your money back.    Also, I'm doing a signed card with every single order. So that means if you order one of these, you will get a signed card. If you order one or if you order five, you're going to get one signed card. So it's not like you get like five. If you get five, you just get one.  So hopefully you enjoy that. That's a cool little thing. If you're a fan of me and just a little added value to this that we were able to do without like, you know, putting extra packs in there and stuff. Then we've added two new things. Well, that was, I guess, was new too, but we have two other new things. First of all, I have a dual redemption code. So if you want to duel me on my stream at twitch.tv slash ruxin34, you could pull one in here and then you just get that and you can say, Hey, ruxin, I want to duel you. I got one of these cards in the mystery pack. When can we do it? We can schedule it. I can also do it. It says on there like on my stream, but I'm also if we meet up at a YCS and you have that card on you, I'll duel you. We'll do something live. We can even do that if you want to do something like that. So we have thrown those in there. There's a bunch of those. I think it's like 25 or 50 of them. I don't remember how it is. And one more thing. One lucky winner out of here will get to compete on guess that price. So there is a guess that price entry. So you get to be the person with me guessing the prices. If you guys get this out of the mystery pack. So we've added a few new things that you could pull. Guess that price being one of them. And if you get it and you want to do it, hit me up, email me or whatever and we'll schedule a time that we can both make it happen. And it'll be really cool because we'll have a viewer doing the actual game. All right, we've talked long enough. Let's look into these things and let's see what we can get. This, I believe is a purple version of a mystery pack. So in here, the purple stuff, we can get the purple pack. He sent me the example so we can just kind of see what everything looks like. So as you can see, purple, pretty obvious. And inside we have some packs like maze of memories. We have maze of memories at the front. We have Dawn of Majesty first edition. We have legendary duelist duels from the deep dark wing from Blast, ghost from the past too. So there's one and two in here and then maximum gold L Dorado. So out of here, we've got a pretty decent variety. So like these are really great. These are kind of like middle. These are not that awesome. So that's what that's what I would put right here. This one's middle. This one, Dawn of Majesty has a stardust chance. So I would maybe like put it more towards the middle. Duelist from the deep is definitely the least interesting. So we'll start with that, but it still could include a ghost rare. It also has forbidden droplets. So it's not like the worst set ever. So like, even if it's, you know, not awesome, it at least has the potential to pull some cool cards. So that one did not have a foil inside. We go, let's go Dawn of Majesty. Let's see what we can get out of this purple pack. Not really purple pack, but the pack with purple in it, I should say. What if we got a stardust and gave it away? How cool would that be branded bond? Gusto, Gunkin, Jarved generosity, two toads, slower swallow. We got the tail end of Gusto and a stall turn. Very, very cool. All right, maze of memories. Let's go. We have branded Utopia. Oh, whoa, wait, what? Is that a Euro pack or something? The super was second to the front. That was, that was weird. I don't know what happened there. All right, maximum gold Eldorado. Here we go. Let's pull something epic. Gold rare. Let's see if we can get that access code. That'd be pretty sick. Danger, Nessie. That's not bad. We have a Phantom Knights of Break Sword, Trap Trick and number C1000. Numeronius. Okay. Kotaka inverted. So yeah, Numeronius. He's at it again. Darkwing Blast, first edition. Will we get that starlight rare? Here we go. One, two, three, four. Pretty excited for these mystery packs. Sasiata and his team, as well as me, have put in a lot of work and it has been pretty cool. So we're excited and hopefully you guys really like these. That's a huge super rare. That's a great card. All right, that used to be 20 bucks. I think it's more like nine now, but still really, really good and goes from the past two. Let me know in the comments how you're feeling about them. Are you excited? Will you be picking some up? And oh, yeah, I guess I should have said this at the beginning. I saw, I'll just put it towards the beginning. Okay, no go-sure there. All right, purple pack. Let's see. This is the lowest impact. So not like pharaoh's servant, but there's like some newer cool stuff as we saw, like eternity code and stuff like that. We got a battle's legend arm again. That's very cool. So these are very, very nice. Like these are probably the best ones you can get out of the purple pack. The other stuff is not this good, of course, but this one could have the 10k dragon, which is also being given away in this, you know, in the gold pack. So let's see if we can get something. If we get a 10k dragon, that would be absurd obedience. We got Goyo and glacial beast, a blizzard wolf. So nothing there. I think I remember, didn't somebody get one out of the last pack? I think somebody got a 10k dragon. All right, here's our silver pack. So this is an example of what a silver pack will look like. So inside you're going to have silver and then you're going to have your bonus pack. So the silver could have, you know, a little bit better of a pack than a purple because it is one in five to get that one. Let's open this up. First we'll see what our, what in the world our packs are. So we have lightning overdrive at the front. Maybe we can get that black rose.  Okay, that's pretty good. Rage of raw. We got crystal revenge. We got amazing defenders, mega pack. That was actually a really good one because mega pack is actually a higher impact in terms of value. You get the pot of prosperity. Ghost ride. This is pretty good because let's see. Amazing defenders isn't like amazing, but it's not bad. So I put these like kind of in the middle because this has black rose potential and this one has like the pearly stuff and these are good. So there's really not anything bad in this one. That's pretty solid. Lightning overdrive. It's always, it's usually good. If you don't get anything bad, you know, we're trying not to have like mini with like bad packs inside. So we've like completely removed. Like we had code of the duelist originally. We decided to take those out. Sun Avalon. We got dark eye. We got Boozion. We got zeksel to try to get very nice. So we're trying to keep like, at least if it's bad, like maybe something like a, I would call this rising a bad pack, but at least it has like a higher in pull like a ghost or there's also like the forbidden droplet. So if you open it blaze cannon, there's an ultra rare. If you open it, you can at least pull something like exciting. So that's like kind of the goal and then lightning overdrive. The same thing with that because it's got to start like black rose. So it's like, even though the set's not really, really good. I'm going to pull the black rose. I don't even know what's happening there. I'm just going to do this. We've got the tune harpy lady evil twin keys to kill. Okay. All right. So yeah, we key skills not bad. It's pretty decent. Funny enough, a lot of the sign cards that I sent out our battles watching crystal revenge cause I had a bunch of those secretaries laying around and I tried to pick some cool ones that you guys would like arm protector. We have E. Pearly happiness starting it off with a super rare. So I signed like, well, which one was it?  So you may or may not, you'll probably get a doodle beast of your order. We got a lot of doodle beast. We got a lot of a, I'll work with the other ones. I can't remember now. I tried to pick some cool monsters that you guys would like. Beech over landing. We have a flip frozen. I ruck some special bad. Just kept going double rucks and special. Wow. Binary blader. We've got a flow under ease. Oh gross. Flow under ease. Don't love those. We've got dark honest and always. Yes. Ascended sage. Pretty good card. Actually about to keep them off to you. Okay. All right. Ghosts from the past to will we be able to pull a ghost share guys? Here we go. Will we be able to pull a ghost share? We have what wrote? Wookie warrior lady. He's a wookie. Are we? We should have done two from the back. It's like Star Wars. Let's go. We have wookies. We have Yazi go strict and okay. The sacred waters. All right. Nothing crazy there out of ghost in the past. Unfortunately, but that's okay. That means the packs that you guys get will be better. All right. Silver pack. What will we get earlier than the purple packs? We got some earlier packs. Let's see what we will get. Will it be epic? It is an extra victory pack. First edition. So we could get the tour guide out of here. Maxi is also a secret rare. Keep that in mind. So there's a couple of huge cards in here. All right. What? I think it's three probably. It's what I'm going to do. We have a car. Curry resident destruction. Mech Lord Emperor of scale. Red dragon vase. Lancer linworm. Mech Lord army of was L the resolute. Mech Lord army chaos and vanity and a mech time blast. So we're starting it off. Well, not really starting off. We're just not pulling very well. That's honestly pretty good. We don't want to pull too crazy. You know, take out of the packs that you guys have. We'd rather than be in your packs. All right. The final one. This is a red, you know, pack, an example of a red pack. So inside you would get, if you were to pull a red pack, you'd open it up and you'd be like, oh, wait. Where's my pack? Where's my pack? Oh, it's red. We got the one in 10. Let's go. You know, that's what it would happen. Okay. All right. So inside this one, we got battles of chaos, which is a fantastic collector pack. Kingscourts pretty good as well. Brothers of legend. Get that Dragoon. This one iffy. We've got a ghost of the past one and then ancient 2021. So not as good, but 2022 is really good. 2021 is more iffy, but it does have a lot of nice reprints inside. Let's start off with the synchro storm, which does still have the ghostry. It does still have the Baron. So there's a couple big things in here that you can get. So that's what we're really looking for. Speedroid. You got a super rare, very nice. And then a synchro. Okay. 2021 mega pack. Let's see if we can get what is it? Noblemen of Crossout, triple tactics, talents. Megatins, no matter how like not great they are for Megatins are always still pretty good for the like meta reprints. They're pretty solid. The eye double rucks and special there. Tribe Brigade, Faragee. I forget they put them in the front. So weird. Okay. That's a good secret room. You've got a ghost meets girl, Tribe Brigade, Karras, and then rares. Okay. Adding that's actually a good comment. Rookie for hire, lifeless leaf fish. Okay. Not about mega pack 2021. Okay. Ghosts from the past one. We could get a $600 dark magician out of here. Let's hope that you guys have some great polls when you guys open these up. Let's go. Time Thief, Sun Vine, Cypher, Splash Mage. You're gonna be like it. It's like it is actually pretty good. I think it used to be three bucks. I think it's still around there. Bones of Legend first edition. I haven't opened this at a long time. Can we get the Dragoon? Here we go. We have Rank Up Magic. We have Ghadarla. Very nice insect kaiju. We have Karibo, the alt-art and Altargeis, Fuji alert. All right. So our polls are not crazy right now, but that's okay. We're just showing off the packs inside. Hopefully you guys have better luck with the packs. Kingscourt. Will we pull something crazy? I don't know if it's two from the back. I'm just going to do it like this. I think it might be at the back in Kingscourt. We have the warrior converging wishes. Yeah. It seems like it's at the back. Unexpected die. Constellar told me. Queens Knight and... Oh, Chow Fang Phantom of the Yang Zang. All right. Super rare. Nothing too crazy there. Let's hit it off with the Battles of Chaos. Let's pull that Dark Magician real quick for you guys. Let's even get a Starlight. Same pack. Good happen. It's definitely possible. We have Flower Dino, Sword Soul Strife, Ghost Trick or Treat, Mad Hacker, Leaf Place Place, we got the Soul Sweeper, Clear New World, Mabel Maiden and... A Libro Mancer Fire Starter. All right. We're going into the final pack. It's a red pack. One in 10. Will we pull something crazy out of the Ruxton 2.0 Mr. Pack or will it be all left up to you guys?  Oh, speaking of, hobby CRV. Definitely is hobby, right? I don't actually know that. What is the hobby code? Okay. Is this hobby? I can't remember if that's hobby or not to be honest. I feel like it might be retail actually. So maybe we have a mix. I don't know. All right. Cybernetic Revolution. Let's go. If it is retail, you can get the Bubble Mail. Bubble Mail. Bubble Mailer. We got Bubble Man, Ultimate Rare. That's what we'd be looking for. Here we go. We have the Ultra Ruxton Special. Right to the rare. On the last pack. Come on, Ruxton. You've ruined it. That bolt pod of generosity. Mad Lobster Tyrant. Wow. I ruined the last pack. But you know, the Ultra Ruxton Special has just become a new thing that we do all the time now. If you guys are interested in these packs, make sure to check out the link down below. They will be available right now as soon as you're watching this video. And if you enjoyed the video, don't forget to subscribe. Shout out to ToneFo Show, Daxer, JTTO, Puffins of Doom, Ernesto D'Ande, Dizzy, Hoppus, Choice333, Missycle, James Jantz, TCG Trust of Cards, America Deutzer, Supreme Sage 21, and on the Thai Show, Ian Musa, Junior Barting, Mimic Gecko, and Thomas McClain. Thank you guys for supporting the channel. I'll see you guys in the next one. Peace. And if you enjoyed the video, don't forget to subscribe. Why can't I say it? Subscribe. Subscribe. I can't do it.


as we're embracing this strategic imperative of diversity, equity, and inclusion. Do you have any perceptions of where we are or how far we've come? I'm an old school teacher, so I will give the church a C-plus. I give the church a C-plus, which says that we're trying, we're working at it. We're not, we still have a ways to go, but we are working. We are working, which is, in some ways, good. In some ways, I remember my parents saying, you know, we're not going to see this inclusion thing happen in our lifetime, and my parents did not. So, and inclusion is, I struggle with that. I struggle with that word, because for me, I just want to be me. I just want to be a black female minister in Glenda Mayberry, and I think each individual should be who they are, and should be accepted for who they are. And I think that's the problem, is that we don't accept people for who they are. Are you hopeful that the United Methodist Church will live into reality of true diversity, equity, quality, as you mentioned, and inclusion? Am I hopeful? No. I would prefer it. Do I, I think it's possible. Do I think it's probable in the near future? No. Because I think we have a long way to go as a church, because we do not address the elephant in the room. We do not, as a church, we skim around it, we go over it or under it, but we don't actually address the elephant in the room. And as a matter of fact, Bishop McKee and I were talking one day, and I said to him, Bishop, until white men, until white men stand up and say, this is wrong, it's not going to change. I said because white men are in charge of the way things are today. And so until you stand up and say, not just you, but the blanket, until you all stand up and say, this is wrong, it's not going to change.


Hello, I'm Mark Thornton and welcome to another episode of the Minor Issues Podcast. In this episode, we look back at a previous episode about toilet paper. This episode aired in early March of this year and it covered the issue of very high toilet paper prices in the United States and elsewhere. In that episode, we discussed things like the Federal Reserve's inflation impact on toilet paper prices as well as softness in home construction in 2022 as contributing to supply side restrictions. We also hazarded a guess that toilet paper prices along with paper towels and other paper products would probably decline over the next year or so. Since that time, especially in the first half of 2023, stories in the news complained about the high prices of toilet paper and that toilet paper was expensive. It wasn't really until June of this year that there was a single story about a retailer who was cutting back on toilet paper prices in order to spur on sales in the UK. In the last couple of months, there's been almost no news about toilet paper prices. The only thing that I've seen is improved availability in the stores for toilet paper that prices have been stable and that in the wholesale market, allocations which developed in 2022 that limited wholesale buyers are now non-binding. In other words, they can order more than their allocations from the manufacturer. So what's happened in the meantime? Well, housing starts are up over 15% in recent months, which means they have to use more lumber, which means they have to cut down more trees, which means there's more raw product for things like paper towels and toilet paper. Since January of this year, the wholesale price of the raw materials for paper products has declined by almost 20%. In my own personal source of information regarding the wholesale price of shipping boxes indicates that those prices are already down 4% for typical size boxes. So while I don't have great prices for toilet paper and paper towel products at the retail level at this point, what we see is that economic conditions of supply in terms of the impact of the housing market and construction and demand in the sense of the decreased use of Amazon and other online retailers shrinking demand naturally leads to a fall in the price of the raw materials. And we suspect that those falling prices will impact the retail market in the near future. There are no news items that I found online in terms of falling prices for toilet paper and paper towels, but we suspect that unless media bias is present here that we should soon expect stories about falling prices of things like paper towels, toilet paper. That won't completely buck the trend towards higher prices in general, but it's a silver lining that is a result of a dependable market process.


Five sneaky ways to find content gaps and simple tips to fix them. Now, you could be a great writer and you could write great content, but the problem is content gaps. What are content gaps? Well, content gaps are areas that your reader would love to see some content on, but you're not providing it. In this video, I'm going to show you how to find those content gaps. And more importantly, I'm going to show you how to fix them so that you get more readers. Now, if you haven't subscribed so far, please do so. Click that button down there. Turn it from red to gray. Don't forget to ring the bell. Turn on all bell notifications so you're notified each and every time I go live. This video is going to help you fix those content gaps. Your content is one of the basics of your business. Those businesses that don't have content, that don't have blogs, that don't have videos, that don't have reviews, that don't have any of this stuff are going to be left in the dust. If you're not doing any of this now, you need to do it. Your content gap may be that you don't have any content, so you need to get on that and get started as quickly as possible. I know it seems like a huge mountain to have to climb, but the point is if you do a little bit each day or a little bit each week, it's going to build and overall you're going to have more business, and that's what you want to do. Now, according to Ahrefs, there are 31,000 keywords that get over 100,000 searches a month. That's a bunch. That's a lot to cover, but here's the scary part. There are over 200,000 keywords that get between 10 and 1,000 searches a month. So that's a lot of keywords to cover, and the key to this is figuring out which ones you're missing. Now, why is this so important? Well, the first thing it's going to do for you is it's going to improve your SEO. If you cover more areas, it's going to give you better chances to get on page one, and that's what most readers say is that they only go to page one of the search results. In addition to that, it's going to help you build a better relationship with your end user and user of your content, and this is important because 64% of the people surveyed said that they actually have a relationship with their brand. In addition to that, it's going to give a more optimized journey for the reader or viewer of your content. They're going to feel like they're more in sync with you because they're getting all of their itches scratched. So let's talk a little bit about the different types of content gaps. The first content gap would be keyword gaps, keywords that you're not serving. Now, there's a lot of tools out there that will help you find good keywords, but the simple and easiest one is the Google keyword tool. You could use this for free by logging into your account. It will help you find more keywords that are being searched for, and you can search by volume on related keywords. The next type of gap is media gaps. So you may have a great blog and you may have a lot of people reading it, but do you have videos? Videos is another area that you need to have content in. So this content gap could actually be a type of media that you're not producing. Maybe you could turn some of those articles you have on your site into videos or vice versa. Maybe you could turn some of those videos into articles. Google says that over the past two years there's been five times as much watch related content on products. Okay, so we know where the gaps are now. We know what they could be. Let's talk about fixing them. The first thing you can do, number one, is to run a content gap analysis. You can use a tool like Uber Suggest and actually go in there and try to figure out where your content gaps are. This is a paid service. However, you could run it once if you want, maybe go for a month or two months, get all your stuff fixed, and then you could drop that service if you wanted to. I don't recommend dropping it. It's a good service. I'm just saying if you don't want to spend a lot of money and pay for it every month, if you're not going to use it every month, you could just sign up for it and use it when you do need it. Number two, you could manually search your competitors to find gaps in your content. Look at what they're doing. What are they publishing? What are they writing about? Are they doing videos? And if so, what are those videos on? You can look at your competitors and figure out a lot of gaps that you currently have in your content that you don't even know. It's really a simple process. Make a list of all your competitors. And once you've done so, go through their content. Look at their videos, look at their channels, look at their blog. Make a list of the content that you see in their sites that you do not have in your site. Accumulate all of that and get to work and start filling in those gaps. You can also use number three, which is use Google Console. Google Console can help you find these content gaps. Let me walk you through it real quick. First thing you want to do is log into your Google Console. Once you've done that, you want to click on search results on the left-hand side of the page. Next, you want to click the plus new next to search type and date range, which you can actually modify to match whatever you want. When that box pops up, just click Page. Enter the URLs for your top performing pages one at a time. When the results populate, see what keywords you're ranking for on that page. Go to the page of your website or in your CMS and search for all of your top keywords from Search Console, either top clicks or impressions. Finally, go to the post on your website and make sure the content is related to your top performing keywords. Odds are some of the keywords you rank for will not actually be covered on the page. Once you identify the keywords you're ranking for that are not covered on the page, create content that does by either creating a new post or adding a section to your existing one. Number four, you can perform a self-content audit. What is that? Well, that's looking for gaps in your page, your website that you already have out there. And I think certain things you can add to it, which will give you content that you don't always think about, like a frequently asked question page. You can add a lot of content in there that can actually come up in the SEO results. People don't think of it that way. They just think of it as a question and answer page, but you can optimize that page to where those question and answers are actually going to give you good SEO. In addition to that, what about reviews? Reviews are great. If you can get enough reviews on your products and there's a whole lot of different ways to do that and a whole lot of softwares to do that, but those reviews on your products can actually be SEO'd also and that can help you in the rankings immensely. Think about all the people who look at these reviews. It's been proven that 72% of the people read the reviews before they buy a product, especially if it's a high-priced product. So if you've got a product out there and it doesn't have any reviews, that's the first thing you need to do is get reviews because that is actually a content gap that you may not have been thinking of. Number five, hire an SEO team. And I say team. I don't mean one person. I know that you can go to Upwork, you can go to Five or you can go to Freelancer.com and you can find people to work on your SEO, but you actually need to manage these people. And that's very difficult. We recently started working on our SEO on Survival K Food. You can see that right here. And we realized that one person wasn't going to be able to do it because one person would actually do the work, but there was nobody actually to oversee it. Having an SEO team, having somebody that can actually farm out all that information to all these different people really is helpful. Now, what they do is they actually do the keywords and the meta titles and the descriptions and all that. I always go in and I check behind them and make sure that the English is right and we're saying what we want to say, but it's nice to have them organize it and put it on Excel spreadsheets so that I can read it and see what needs to get done. And then we can farm it out to other people. They may suggest that we write some blogs on particular subjects. I can figure out what that is. I can farm that out to other people and have them write the blogs also. So you really need to have an entire team. In the beginning, you may not be able to do this. You may have to do everything yourself. But if you're in a position right now where you're actually making sales and you have some income, you need to start plowing it back into your business. Nothing would be more economically reasonable than to take that money you have and put it back in your business because it's just going to grow your business. Using content to grow your business is great because it's basically free outside of the fact that somebody's got to write it. Free content out there is going to get you rankings in SEO. It's going to get you more customers and it's going to get you more brand loyalty. I hope this helped you out. I hope you understand this a little bit better now. I know you'll get good results if you use all the tips I gave you. But if you have any other questions, feel free to put them in the comments section and I'll be happy to come back and answer those for you. Maybe you just want to say hello in the comments section. You can do that too. That would be great. Don't forget to subscribe if you haven't done so so far. Hit that red button down there. Turn it gray. Don't forget to ring the bell. Turn on all bell notifications so you're notified each and every time I go live or if I upload a video. I'm generally going live three days a week which is normally Monday, Wednesday and Friday and we have content coming out Tuesday, Wednesdays and Thursdays. So we're there for you every single day to help you start running Grow Your Online Business. I do have a course below. It's a $97 course. You can get it absolutely free. Just click the link and learn no credit card required. Thank you so much for watching this video and I'll see you in the next one.


This galaxy was one of several hosts of recent Type 1A supernova observed by astronomers in the 1930s Edwin Hubble made precise measurements of C-Feed variable stars in this galaxy, highlighted by green circles in the four inset boxes. These C-Feeds are used to calibrate the supernova that was observed in the galaxy in 1995.


Mark Fields, president and CEO of Ford. Nice to see you here at CES in Vegas. Great to see you again. And I heard you promise us a revolutionary year in the auto industry in 2016. You made several announcements here.  You know, we use CES today and the announcements today to really put an emphasis on how we're accelerating our transformation from an auto company to an auto and mobility company. And when you think about some of these emerging opportunities that are enabled by technology, we call that our Ford smart mobility strategy. You know, areas of connected cars and autonomous vehicles and using data analytics to better satisfy customers. And one of the announcements we had on the customer experience is around working with Amazon to integrate their Alexa voice activated assistant to not only connect to the vehicle but also as you're driving in the vehicle to connect to your smart devices in the home. And it's really about making customers' lives easier, giving them convenience where let's say in the morning they can get up and they can tell Alexa to start their car because it's cold out this morning. I want it nice and toasty by the time I get out there. But even as you're driving home, you can access your home security system, you know, turn the security system off or open the garage or turn the porch light on. It's really around empowering customers and just giving them a level of convenience and an experience and peace of mind and a Ford, they wouldn't get anywhere else. Another big announcement today had to do with autonomous cars and basically expanding the vehicles that'll be on the road. There was a lot of speculation that you were going to announce to deal with Google. Are you talking with Google at all? Well we have lots of discussions and we're working with a lot of different companies and around our Ford Smart Mobility Plan and I think as you can appreciate we keep those discussions private for competitive reasons so you know we're not going to comment on that speculation but what we have said is as we go forward and we provide mobility solutions in the future, there'll be some cases where we do things on our own and there'll be some cases where we work with great partners. And as you think about the announcements we made today on autonomous vehicles, we are shooting towards developing a fully autonomous vehicle. We've been at this for 10 years now. We're tripling our fleet of autonomous research vehicles. We'll have up to 30 on the road and we see a future where there'll be some cases in defined areas where the driver doesn't have to take control and that'll complement all the other great products that we'll be offering consumers or for consumers that do want to drive. Do we have a point yet of when that future occurs? Are we talking five years, 10 years? Because you also said that this is a technology that an average consumer will be able to afford. It will not be just for a high-end consumer. So what kind of year generally you're targeting for this to hit the market? Well, we haven't given a time frame on when we'll introduce a vehicle. We want to make sure that the vehicle operates correctly, is safe, but also is accessible to everyone and not just to folks that can afford luxury cars. Will there be an autonomous vehicle, a fully autonomous vehicle, in the next four years, by the end of the decade? Sure, there might be. Somebody might offer one. But from our standpoint, we want to make sure it's true to our brand. And our brand has always been about making people's lives easier and being accessible to everyone. Your sync technology obviously is getting a wider following, so that's something I know you're quite pleased with. Is it the industry standard in your view? Well, it is the industry's most successful communication and entertainment system. We have 15 million vehicles around the globe that have our sync system. By the end of the decade, we'll probably have over 43 million folks that'll be using that system. And what we used today, we talked about giving customers even more choice on how they connect their smartphones to our vehicles through sync. Apple CarPlay we're adding to all of our 2017 model year vehicles that have sync 3, Android Auto. And it's really emphasizing and showcasing the growth of sync and allowing people to stay connected 24-7. Because again, it's all about making people's lives easier. And that's something that every customer, they tell us they want. They want to stay connected. So how do we help do that through our vehicles? Mark, as you know, one of the big things at CES this year, drones. And you're getting in on the drone game as well. We saw drones coming out of F-150s in a video that you showed. So help me understand why this is an area that you want to venture into as well. Well, it's an important area. It's another part of our Ford Smart Mobility strategy around data and analytics. And we announced today a drone developer challenge. And essentially, we're working with the UN. And the UN goes into a lot of disaster areas. And they have to go in, they have to survey the area. Think about if we can provide a UN employee, and they're driving in their F-150. The drone can actually take off, do its survey. It's thermal mapping of the area so they can kind of figure out what's going on. The driver can actually be driving to another area in the disaster zone. And because the drone would be linked to the GPS system in the vehicle, it will know where to go back to land in the bed of the truck. And that way it may provide emergency services faster. But think about this. Think about applications potentially for agriculture, farmers. Think about construction. Think about even things like infrastructure inspection, bridge inspection. These folks are using trucks. So how do we combine the use of their trucks and these great drones to allow them to be more productive and more efficient in the jobs that they do? Mark Fields, a lot of announcements today. There'll be more in Detroit next week. We'll see you then. Yes, there will. Thank you.


Peer review is the evaluation of work by one or more people of similar competence to the producers of the work peers. It constitutes a form of self-regulation by qualified members of a profession within the relevant field. Peer review methods are employed to maintain standards of quality, improve performance, and provide credibility. In academia, scholarly peer review is often used to determine an academic paper's suitability for publication. Peer review can be categorized by the type of activity and by the field or profession in which the activity occurs, e.g., medical peer review. Professional peer review focuses on the performance of professionals, with a view to improving quality, upholding standards, or providing certification. In academia, peer review is common in decisions related to faculty advancement and tenure. Citation needed Henry Oldenburg 1619-1677 was a British philosopher who is seen as the father of modern scientific peer review. W.A. prototype clarification needed professional peer review process was recommended in the ethics of the physician written by Ishek Ibn Harli Al Ruhay 854-931. He stated that a visiting physician had to make duplicate notes of a patient's condition on every visit. When the patient was cured or had died, the notes of the physician were examined by a local medical counsel of other physicians, who would decide whether the treatment had met the required standards of medical care. Professional peer review is common in the field of healthcare, where it is usually called clinical peer review. Further, since peer review activity is commonly segmented by clinical discipline, there is also physician peer review, nursing peer review, dentistry peer review, etc. Many other professional fields have some level of peer review process, accounting, law, engineering e.g., software peer review, technical peer review aviation, and even forest fire management. Peer review is used in education to achieve certain learning objectives, particularly as a tool to reach higher-order processes in the effective and cognitive domains as defined by Bloom's taxonomy. This may take a variety of forms, including closely mimicking the scholarly peer review processes used in science and medicine.


Once I make my move, the Queen will take me, then you're free to check the King. No, Ron, no! What is it?


Our next speaker says that it's possible to avoid this by use of event streaming architecture. Let's welcome Kai Venner, Field CTO of Confluent and find out more. Kai, nice to see you. Okay, thank you. Let me just jam my screen and say hi everybody. We can certainly hear you Kai and we can see your screen so take it away. So I hope you can hear me now. We can hear you. We can see the screen. Yeah a little bit of sound problems but I will start now and I hope you can hear me. Well, we can hear you. We can hear you. And I'm also sharing my screen so I can't see that yet somewhere. So can you just give me a thumbs up? This is the problem of the live conferences, right? Kai, we can see the screen. Everything is okay. We hear you and see you perfectly. Okay now we see everything. I will start now and sorry for the technical problems. That's the co-founder. So here we go. Today as introduced I will talk about event streaming. Okay, we're going to take a five minute break. We're having a few audio problems here. So we'll just take a short break and we'll be back. Thanks. Hi, we're back again. Let's see if we're lucky this time around. Kai, can you hear me okay? I hope so. I can hear you very well now. Now you're streaming really well. I hear you perfectly. Okay, cool. So let's try it again. Try it. Can I get started? Yes, please. Okay, cool. So sorry for the technical problems. That's the COVID times, right? As introduced, I want to talk about event streaming and machine learning and a few different architecture options you have here. So let's directly get started. Thank you for our story. So no worries if you're not working in the automotive industry, you can adopt that to any industry like finance, insurance, telco or whatnot. But it's always easier to show you an example. And we will use the example of a connected kind for structure today to show you how you can enhance that with machine learning for real-time predictions. So the scenario in the setup today is that we already have a connected kind for structure to connect to hundreds of thousands of cars in real-time. That's what we have done with several customers already in the real world too, like Audi for example. And we want to enhance that with machine learning for predictive maintenance. That's in the end the use case I want to talk about today step by step. Definitely beginning just quickly. If you don't know Apache Kafka well, I really want to explain what I mean with event streaming. So it means to continuously process data, often at high volume. But the point is really that you correlate and aggregate data from different sources. This can be real-time sources like sensors from the car. Or this can be databases and backend systems like a customer relationship management tool or ERP system. And then you correlate that data in real-time. That's what I mean with event streaming and that's the heart of Apache Kafka. And based on that, this is such an architecture where we have a connected kind for structure. On the left side, we have hundreds of thousands of cars which produce data in real-time, continuously. And then the streaming platform is used to integrate with these systems with the sources and with the things and to process the data. And so this can be real-time systems like a monitor and alerting system. So that you send an alert to the driver or car that he should stop the car in the next 10 minutes. But it can also be batch system and analytics tools where you create reports maybe over the driving behavior of the last week or something like that. So this is a very common architecture, and often you have more than just one consumer of the data. So now this is again in this talk because we're not introducing Kafka yet, but really want to enhance it with machine learning. This is the status quo actually already. And with that now, we want to add machine learning to the story and see how it works together with Kafka and so many projects. Here's just a few different examples. I think at this conference here, you know what machine learning is and how it works. My key point here is really that translation for your Alexa, that's a one-predictable use case. But on the other side, it's also there to improve existing use cases like fraud detection or something like real-time tracking of the cars. And that's what we are working on today in this example. And we are using it for predictive maintenance using sensor analytics to do predictions on that in real time with analytic models. Again, I don't want to talk too much about machine learning here. My point is really it doesn't matter what algorithm you use. It depends on the use case. In this example today, and we have built that at a demo, so we're using an autoencoder, which is one kind of neural network or deep learning architecture. It's unsupervised and it's used to detect anomalies. So that's perfect for sensor data in the car engine because you can detect if things change and it's not expected to change. And then you can send an alert by that to the driver to stop the car soon. That's the main idea here. But again, no matter which algorithm and use case you have, it applies the same architecture you see today. So here we go again. This is the same picture as before with the architecture, but now we enhance it with machine learning. And the great news is you don't have to change your existing architecture. You just add additional components. In this case, in green you see on the left side, we do the model training, which is the first part of machine learning. You take historical data, either from a data lake or a picture without a data lake directly from the streaming platform to train a model on that, to find anomalies in historical data from the hundreds of thousands of cars. And then as a second part to leverage that, you deploy this model into an application for real-time scoring, as you can see on the right side. And again, so often this is not a news case, but you want to improve existing use cases. So you might already have an existing real-time learning system analyzing your ODB2 data from the cars. So the onboard detection data is that. But here now we embed an analytic model into the same architecture and application to do real-time scoring on every single event coming from the car. And this can happen parallel to all the other applications you have in place already. So let's talk a little bit more about how to do that. Because the big problem is that actually writing analytic models is just a small piece of the puzzle. And when you go to your favorite machine learning framework on the website and tutorials, you see that you can write 20 or 30 or 50 lines of Python code to great models, which are accurate and do great predictions. But then when you want to deploy that production, it's a little bit harder. So how do you do real-time scoring milliseconds end-to-end? How do you do that at scale for 100,000 cars? And how do you do that 24-7 without downtime? And that's typically not what the data scientists' toolings do, like a Jupyter notebook, right? They are used for development part, not for the deployment part. And that's in the end this impedance mismatch we need to solve. And we will solve today. So just to make this even more clear, this is a great paper by Google, if you have not seen that before, they had a technical depth in machine learning systems. And if you map this to our connected kind for structure, you can imagine that writing the Python code in a Jupyter notebook, that's still, of course, an important part and not easy. A small part of the overall architecture and solution. Because first of all, you need to collect all the car data from hundreds of thousands of cars in real-time at scale, 24-7. And then build a serving infrastructure with real-time monitoring and all these kind of things. And therefore, you see the ML code in the middle here, that's just a small part of the architecture. So the great thing is, we can learn from many other companies which already did this. And here's a few of the examples, like Netflix with Chaspil, the scalable recommendation engine, like Uber, which does all of their data and conversations with the customers in real-time, like for the taxi drivers, for estimated time of arrival, for cost predictions, traffic predictions, all these kind of things have to run in real-time at scale for millions of users. Or like PayPal, where you don't have just one model for fraud detection, but really plenty of models. So what's coming about all these reuse cases is that they all use Kafka heavily, because it's such a good fit for doing real-time processing at scale 24-7. Because all of these mission, these workloads are mission critical. And that's really what we also want to do now in our connected car infrastructure. Or if you're working in the finance field or in insurance or telco, it's the same principles, right? But we can learn from other companies which have already done this and have written blog posts and talked at conferences about this. So if you don't know Kafka or not well, then this is the one slide to introduce it to you. So Kafka is an event streaming platform. This means it's built, yes, for real-time messaging for millions of events per second. So it's a scalable messaging system. But it's really important to understand that Kafka has much more than just messaging. It's also a storage system. So all these events can be stored in the middle in Kafka. And the key difference is what you see in this picture. They are not stored like in a data lake, where you ingest everything like in a table, for example, and store it there at rest. But here it's stored in an event-based manner, with timestamps, with guaranteed ordering in the logs. So it's still an event-based system. But it's also storage to decouple all the producers and consumers from each other. The car sensors continuously process data, no matter if the consumers are falling behind or maybe even have downtime. And all of these consumers are also decoupled from each other. They don't know each other. They just connect to a Kafka topic to consume the data. Some of them in real-time, some of them in batch, and some others via a REST API from a mobile app with request response. And that's the key difference between Kafka and other systems like a data lake. The data is event-based and in motion, but it's still a storage to decouple the systems from each other. And in addition to that, Kafka also provides capabilities to integrate different sources and things with Kafka Connect. And it provides stream processing capabilities, like you maybe know it from Spark Streaming or Flink or Storm, with Kafka Streams. The key is that all of this is Kafka-native then. And this is really what Apache Kafka is. So it's much more than just a messaging system. And I really cannot emphasize enough the point about decoupling. So each client application can use their own technologies, like the CRM integration can run with Kafka Connect, while some legacy integration runs with an ESP or ETL tool, which also has a Kafka Connect trend in the meantime. And on the bottom right, you see in another domain, you can use your own code, like Python for a data scientist or Java for the production engineer. So it's totally up to you. And that's the great thing about Kafka and its ecosystem. And this is very different from traditional middleware tools like an ESP and ETL tool, where you would have to put everything into the one single infrastructure and platform. Here, you can really choose everything for the client what you need. And here you see, I mean, of course, you can also use other frameworks and combine that, like, instead of using Kafka Streams or KSQL, they be the native Kafka technologies, you can combine it with others. They all have trade-offs and pros and cons. There is no single best option. So as I said before, Kafka was built for scale. I don't want to talk about that here anymore. That's battle-tested in thousands of companies. What I really want to emphasize is that it's used everywhere in the meantime. And what's even more important is that it's not just used for big data. That's what I always point out to new people. So Kafka was built for scale, and it still hurts. But over 50% of the projects I see today are not for big data, but it's more about transactional data and really important messages like instant payment platforms, like fraud detection, like all of these customer conversations. That's more like transactional data. So that's not terabytes and more. That's maybe a megabyte per second or even less, right? So maybe 1,000 messages per second. But Kafka is still so great because of its characteristics, like 24-7 uptime, distributed highly-available system with rolling upgrades, with backwards compatibility, and all these things you need in such a microservice architecture. And therefore, unfortunately, in this talk, I don't have so much time to talk about all the Kafka use cases. So I really just want to point out there are so many, and that's why people use Kafka so much. There is not just one specific use case for Kafka. 10 years ago, people started using Kafka as a messaging and a gestion layer into Hadoop. That was the first prominent use case also in Europe. But today, we see really any kind of platform that can use Kafka for different reasons. So that's really the key point to know. It's much more than just an ingestion system in her data lake today. And now, with that, let's go back to the machine learning story and how that relates to Kafka. You maybe can already imagine that. So again, machine learning is two things. It's model training, model building, that's on the bottom right. And for that, you need data collection at scale in real time. And that's what Kafka is. And on the other side, when you want to deploy an application where you want to do real-time scoring, 24-7 in milliseconds, even for millions of messages. That's also what Kafka is. And therefore, it's not just a core of Kafka, the messaging and storage layer, but it's really the whole ecosystem that you can leverage here. Like in this example, we use Kafka Connect for the data integration, both to the producing applications, like for cars, we maybe use the MQTT connector, but also for the consuming application, like a data lake or like any other analytics tool. And the data scientist maybe uses the Python client to use the data from Kafka in his Jupyter notebook and combine it with other tools like NumPy and scikit-learn. So that's totally up to you, and you can mix that like you need it. So let me walk through this process in a little bit more detail here today, how we built it. So first of all, this data ingestion. In this case, mainly the car data. So we use Kafka Connect and maybe with MQTT in this case, when you have an MQTT gateway somewhere in the middle, and then you ingest the data in a Kafka cluster. This can be running on premise or in a cloud, or you can use a fully managed service like Confluent Cloud, for example. That's the first step. And what we see in practice in real world today is often that many customers even separate this so that they use a production Kafka cluster for the mission critical workloads, with SLA's like almost zero downtime and zero data loss and low latency. And then they replicate the data into a second analytics Kafka cluster. That often has very strong SLA. So there it might be okay if it's down for an hour. For the production cluster, it's not okay if it's down for an hour. And based on that, you can separate based on your SLA's and requirements, and also by your teams and business units. So no matter which of these architectures you use, you ingest the data into the Kafka cluster as first point. And then we do the data preprocessing. This is the second part. So typically, when you are a data scientist in machine learning, you use a Jupyter notebook, you do rapid prototyping, and use your Python code for that. And that's okay for the building the model and so on. But if you need to process the data for hundreds of thousands of cars in real time, then your Python script in Jupyter is not the right engine for that. And therefore, at least most of the parts can be outsourced into something like Kafka streams or KSQL, where you do these standard steps like filtering, transformation, aggregation, extracting features in a Kafka native scalable way. So that's a huge advantage of doing that with this, because this is also solving an impedance mismatch between the data scientist and the production deployment. I want to show you one example here. This is exactly where we solved this impedance mismatch in a very smart way. Here, as you see, we still use Python and our Jupyter notebook for data prototyping and rapid development. However, we also use Kafka native technologies. In this case, we use KSQL, where you write a SQL query, in this case for an ETL process like filtering. In the same way, you could use the Python client, where you consume the data into Python. So it's optional, but you can use Kafka native technologies for all these ETL processes and still do it in your Jupyter notebook and rapid prototyping with that. But then when you have developed something that works well, then you can deploy the same query into production, into a KSQL server, without any code changes or any additional code. And this is really solving this impedance mismatch. And no matter how you process data, then you can find ingested, like in a data lake, where we want to do a model training. The key piece here is that really, while in this case, we ingest the data into Google Cloud Storage, the data lake, an object store. In addition to that, and completely decoupled from that, we also use Kafka with other consumers. Some are real-time, some are near real-time, because they are all decoupled from each other. That's a great thing. But now let's focus more on the machine learning part with the batch mode, right? We have ingested it into the object store in our data lake. So finally, we can do all these things. For example, TensorFlow. And this is not Kafka, right? So this is your machine learning framework of choice. This is not related at all to Kafka. Here we now do our model training, which can take minutes, hours, days, whatever your scenario is. And then finally, we got our model, which we can use. As I said, in this case, I use an autoencoder, an unsupervised deep learning framework or algorithm for doing anomaly detection. But no matter what it is, it's just a binary. It's a model. In this case of Protobuf, in this case of TensorFlow, it's Protobuf. With other tools, it's binary, it's proprietary, whatever it is. It doesn't matter. It's a model, which you can ship them there and deploy it. However, now, and this is really a key part of what the title said in the beginning in introduction, the other option, and this is what we did in this architecture, we don't even use another data lake. In this case, as you can see, we directly consume the data from the event stream, from the Kafka log into our TensorFlow engine, which is, for example, running in Docker containers or in Kubernetes or something else. And then there we do our model training. So this is really key to your architecture, because it simplifies it. You don't need another data lake like HDFS or something like that, if you don't want to use it for other use cases, too. You can directly consume from Kafka to train your models, like we did here with TensorFlow I.O. in its Kafka plugin. And this really completely simplifies your architecture. And I'm not saying you shouldn't use other data lakes anymore, right?  And especially because now you can also leverage tier storage for Kafka, which means that this is really one platform where you don't have justice, the brokers with this attached, but you have a decoupled object store somewhere where you offload all the big data sets. And with this architecture now, you can have one simple solution with Kafka. You can now call Kafka the data lake if you want. So the term doesn't matter. It's the architecture, which is important. But with this now, you have one single platform for doing both real-time streaming and processing and long-term storage for machine learning, for batch, for reporting, for request response, and so on. So this is the huge advantage of this architecture here. So let's think about that in a little bit more detail. Because if we think about machine learning, we want to consume historical data. So we need to store data in Kafka long-term, right? And this is possible with Kafka since the beginning because you can't set a retention time and it can be only a few days. It can be a month. It can be a year. It can be forever. That's possible for the retention time, you said. Minus one means forever. The big problem with that is that, of course, the storage gets more and more expensive and Kafka brokers are attached to HDDs or STDs. And that can get very expensive when you have terabytes of data. And also scalability gets an issue when you have to re-synchronize because a disk is broken. So honestly with Kafka itself, it's not really ideal for big data sets and storing historical data forever in big data terms. And with tiered storage, most of the data from the Kafka broker into a remote object store, huge advantages. Look, the cost savings are there because now, like in an other data lake, you store the data in an object store like S3 or in MinIO on Kubernetes or pure storage or whatever your object store technology is, no matter on-prem or in the cloud. And with that, you have better scalability and you have reduced costs. And now you can really store petabytes of data in Kafka in a cost-efficient way. Here's a picture which shows that even better. So the main idea is to really offload most of the data into an object store. The great news is that this is not any change to the application. The application is still the same Kafka consumer API like before. So you will not have any breaking changes in your existing Kafka applications. It's just a back end under the hood which changes this. And that's the great benefit of that. And concurrent tier storage is already available today in GA. And we are also working with the community to add this tiered storage interface into Apache Kafka itself into the open source framework. Uber has the lead here and we're working with them. And it's expected that tiered storage, the open source interface comes into Kafka in the Apache Kafka 3.0 around the middle of the GA and available for several different object stores like S3 or Google Cloud Storage or there isn't any bottom right. For example, for compliance and regulatory reasons. We have customers, for example, which use Snowflake as a data warehouse. And every time they change the schema, they need to replay the data because they need to reprocess it and store in the table differently. And so they reprocess it again from Kafka and adjust it again into the data lake or into the data warehouse. So there's really plenty of different use cases of why people process data. And the significant difference again is with Kafka, the heart of it is real-time and event-based. So you can have a real-time consumer like here on the right in green, but also you can have consumers which consume historical data. Like your data scientist which uses a chip in a notebook and consumes historical data. Again, the data is order and it has time stamps. You can just take the data you need and process it in your favorite machine learning framework, for example. So I talked a lot about model training and data integration and processing here. But in addition to that, let me spend the last five minutes on the model deployment part because this is typically really separated from the model training part. And often this is where the mission-critical workloads really are. While model training is batch, model deployment often has to be real-time and scale and 24-7. But this is no problem because again, you get a model from model training. It's a binary and you deploy that somewhere. With Kafka, there's different options. So I had a Kafka summit talk where I spent 60 minutes just on this topic. So it's complex in the detail, but on a high level it's pretty simple. Either you use an external model server like your data scientist know it. In this case, we use TF serving for TensorFlow. And then on the left side, from the streaming application, you do an RPC call to do a request with the incoming data to the model server. And then the model prediction is done and the prediction is sent back to the streaming application. So this is okay for many use cases. But keep in mind that an RPC call in the middle is not really a streaming pattern. And so you have to worry about things like what happens in a case of error? Or what if there is latency problems? Or if the model server only supports HTTP and this doesn't scale well for your use case? So there are some tradeoffs. And therefore, the other option is to embed your model directly into your streaming application. No RPC call to any other model server. With this, you have the huge advantage that it's very robust and low latency. And it's just a single application which includes both the stream processing engine and the model predictions. And for an example, like a connected kind for structure where you want to score on every single event in milliseconds, this is probably the better approach. But again, both approaches have their tradeoffs. Just check out my Kafka summary talk where I talk about this in much more detail. Here is one example of model deployment. So in this case, we use a case SQL query with your rights, which just get the sensor data from the car. And then we apply a UDF on that. And in this user defined function, I embedded the TensorFlow model we have built before in the cloud. But this model now is really executing every single event coming from the car. And the great thing is again, this can be developed in your Jupyter notebook with Python or something else. But then you can deploy exactly this query here into a SQL server in production, either in your self-managed Kafka cluster or in a fully managed service like Confluent Cloud. And with that, you can really process millions of events per second by just deploying this query, no coding around it. And this is really exciting, I think. And the reason why that works well is because this also is just Kafka under the hood. This is Kafka native technology. And so this is really one of the coolest examples to combine machine learning together with Kafka for model scoring. Because you really get real-time scoring at scale highly reliable this way without using another data lake or another streaming engine. So this is also important for deploying this highly available because this is just one ecosystem and infrastructure you have to operate 24-7 now instead of two or three or four different engines. So now if we come back to our architecture from the beginning, this is what we have now implemented here step by step. Right, as I said, so we have some gateway to the cars, often via MQTT. And then we get the data into Kafka. Here in this case, we leverage tier storage so that we can store the data long-term. And then we also do real-time processing like with KC SQL, they'll be on the bottom left and on the top left, sorry. And then the TensorFlow model training happens here on the historical data. So this means either you directly stream the data in real-time into TensorFlow and wait until you have enough data in the TensorFlow engine to train a model or you start it up every day or every week and consume the historical data from the Kafka look and train your model based on that. Again, the key significant difference is you don't need another data lake anymore. And you have to ask yourself, do I need another data lake? Sometimes, yes. So if you want to do map reduce or shuffling or these things where Spark and Hadoop were built for, that's what you still need a data lake for, right? That's what they upgrade for. But for some other use cases like training an analytic model and doing real-time processing at scale. So that's where probably you get it much easier and better and simpler if you just use Kafka's infrastructure for that. And then completely independent of that. So no matter if you train your model with a data lake or directly from Kafka, you then deploy your model into another application. So you can still deploy it into a decoupled, lightweight, scalable Kafka application like on the top right here. And completely independent of that, you have any other consumer, like in this case in our example, we have built a digital twin in MongoDB with that. And this is completely decoupled from all the machine learning stuff. And that's a great thing about Kafka. You can combine whatever you want to do over time step by step. And we actually have built this, what I showed you today as a demo. So this is running on top of Kubernetes. And we're using Terraform to deploy this. So you can really check out this demo on GitHub. I also have a recorded demo 15 minutes about this. So check that out if you're interested. Just go to my GitHub site and take a look at the demo. Just take a look at a YouTube video there or maybe run it by yourself or anything else. So my last slide summary is really what I showed you today is one pipeline to rule them all. And this is of course, a little bit of provoking title here again. So again, I'm not saying don't use any other data lake. So if there are use cases, do that. But I hope you today understand, understood that you can simplify many architectures because Kafka is much more than just a messaging and suggestion layer. Kafka has capabilities for storage where you can store it long-term and reprocess the data. For example, from your Twitter notebook, the storage is also great for decoupling producers and consumers. Like if a consumer is down or if a consumer is falling behind, that doesn't matter. The producer produces the data. Our car doesn't ask. They produce that data continuously. And Kafka handles the back pressure for that. And in addition to that, Kafka provides Kafka Connect for data integration, both to legacy systems and modern technologies and streaming capabilities with Kafka Streams or KsqlDB. And so with that, you have a real event streaming platform. And that's in the end the main goal, which I wanted to show you today, especially on the example of machine learning. And I hope this worked well for you. And with this in mind now, here's the last page where you can ask questions in the chat window. And I'm happy to answer anything and also feel free to connect to me on LinkedIn and Twitter to stay in touch. So I hope you learned a lot today. And now if you have any questions, just let me know. And again, sorry for the technical problems in the beginning. That's the corona world, right? So let's now check any questions. And otherwise, thank you for listening and have fun at the conference. Kai, thank you so much. What a fascinating talk. And I see you like provoking or provocative titles. It's good to see that data lakes are not going to dry up completely. They still have their place. And I very much enjoyed the way that you introduced your talk with the story there, the use of the car industry, and the clarity with which you explained everything was fantastic, even for someone not so technical, such as myself, it was pretty easy to follow. So congratulations on the talk. Now you mentioned Uber, Netflix, PayPal, and some of the tech giants that are already using and benefiting from this real time processing with Kafka. And then you went on to say that actually you could see almost anyone benefiting from this, although you were reluctant to pick a specific use case. Are there other places or sectors that you see where this is not being used yet and where it should be or it could be used? Yes. So actually, I mean, what's really more the cutting edge part is really storing data longer and then using the old data like in this machine learning example. This is where still people use data lakes because they have them. But this is where the advantage comes of event streaming, as I talked about. And also, I really emphasized a lot that Kafka is not a messaging layer, but still many people don't understand this because they learn to ingest data into Hadoop and then process it there. So this is really where people have to do more around that. But as you said, I didn't cover many examples here. We have a public example for connected kind of structures with Audi. And also there's public content from Tesla where they process trillions of messages. And the great thing is really that there is examples from every industry, no matter if it's financial services, telco, media, whatever it is or gaming or whatever. So what I can recommend really is to take a look at the past Kafka summits. That's public, that's free, that's on demand, that's slides, that's video recordings. And there you will find use cases for Kafka in every single industry. Right, I was going to ask you, you mentioned that to check out your Kafka summit talk. So where do we find this? Yes, just a Kafka summit website, there is all on demand recordings from the last release. And it's really hundreds of recordings and slides from every industry and a lot of mine talks also. Right. If we had to, and I know I'm forcing you here to actually pick one particular example, but if you could pick one example as a way of demonstrating where we can see the benefits more clearly, the before and after and we can compare and perhaps see how this technology has really benefited the company, which one would you suggest we look at? I mean, the one example where you really more or less have to use Kafka there was the easiest to find it out and that's typically when you have to process high volumes in real time. That's actually why Kafka was built 10 years ago at LinkedIn. They had to process millions of messages in real time. That's what ETL tools cannot do because they were processed for high volume, but for batch. And on the other side, messaging existence also exists for 20 years and for real time, but they were built for 100 messages per second. And therefore, if you have to process higher volume at scale, then this is where Kafka is the only option. And that's often where people get started for that reason. Okay. We have a question here which asks, how do you train a model with Kafka with a year of historical data? Yeah. So again, so that's also very important to understand. So Kafka is not the machine learning part, right? Kafka is the streaming platform. And then you connect your machine learning framework to that. But the important thing to Kafka is it's a lock where you append events and every event has a timestamp. And so if you have one year of events, they are appended to this lock. And then when you have your machine learning framework, like in Jupiter with Python, you just say, give me all the events for a specific topic for the last year or for the last month or only for May 2019, for example. And you then get all these events into your machine learning engine. And then there you train your models or pre-process them with your favorite tools like NumPy and CyclicLearn or whatever you use, like with any other database from a CSV file, for example. So that's exactly the same. The only difference is instead of connecting to a file like CSV or to our batch system like Hadoop, instead you consume the data from the Kafka interface. That's actually the only difference. Okay. Kai, the time is against us, unfortunately. I would like to thank you once again for the amazing talk. There are lots of congratulatory comments here. So I think everyone really enjoyed it. And I encourage people who have more specific questions to contact Kai directly, maybe via the networking section on the website or via the addresses that Kai gave us earlier. So Kai, thank you once again. Thanks a lot. Goodbye, guys.


Ben, gweithio i Brathfodyd. Gweithio i ni fydd i'n cael eu rhai o'r ddangos ymlaen? Rwy'n gweithio'n gweithio gyd yn gyfer cwylio'r ddebyg o'r dynion ddiwyd. Rwyf wedi'u gyd yn gweithio'r ddangos o'r ddangos gyfnodydd yn gwahanol. Rwyf wedi'i gweithio'n gweithio'n gweithio'n gweithio'n gweithio'r ddysguysg. I'm delighted to be here, have the opportunity to work for him. So you've worked with other clubs in the past in the Football League, what can you tell us about that and what you're going to bring to the club? I've worked in professional football for 12 years, various different clubs. The last year I've been at Leedsbeck at university so I've got quite a lot of experience of working in professional football. Obviously we've been already started at the moment, we've had the boys in for testing already. So we've got a rough idea of where they're at and we're just looking forward to starting on Monday the pre-season training. And what have you done at other clubs, Doncaster, Sconefop etc, will you bring to Bradford? I'd like to get a good work ethic into the players, get them fit, get them strong, get them robust and ready for a 46 league game season. Try and make sure that we put things in place so that we have a team that's available for the manager for not an injured table. Try and make sure that we get every player to be able to peak at the maximum of the game really. And what will pre-season look like? It's going to be tough, it's going to be challenging. We're going to test for players mentally, we're going to test them physically obviously. We're going to try and do different things a little bit away from the training ground as well, put them into little bits of team building exercises. As well as obviously all the physical work that will be going on on the training pitch led by the coaching staff and obviously the fitness side led by myself. You can sense excitement building now, players coming through the door, the fixture list is now there. We've got some very big and interesting pre-season friendlies, although you're not going to be on the field, I guess you'll be looking forward to being a part of it off the field. Massively, the pre-season is already shaping up really well. As you said we've got the European champions coming, which will be a massive occasion for the club. We've got some really good local teams as well. It's a good mix of opponents to give us a good bit of experience of different playing styles so that when we hit the ground running on the 3rd of August. Obviously, League 2 is going to be real tough next season. We want to be at the top end of it. Is it these extra 1-2% that you can get out of the players that can make the difference? Of course, yeah. The little things that we can do, taking care of their recovery. We've just brought in a yoga teacher to work with the players. I think that's really important. So every Monday the players will be doing recovery work, they'll be doing a lot of hurdle work, but also making sure that we're in the gym two, three times a week as well. We're getting a lot of strength work into them, a lot of power work into them so that they are durable. As you know, the English football calendar is very demanding over 10 months. A lot of times you're playing two games a week, so there's not a lot of time to recover. We need to make sure that we can do everything we can to get a fit team onto a pitch every week. Obviously, you thought there's enough facilities down at Willhouse Grove where we train. We've got just about everything you need to get this team ready to go. Absolutely. It's unbelievable to have everything in one place. There's not many clubs outside the Premier League where you'd get a swimming pool, a state-of-the-art gym, a running track, fantastic football pitches. So we're really, really lucky that we've got that facility to use and we'll be absolutely making sure we make the most of it. You say you've been here as an opposition member of coaching staff. What would it be like to be on the other side of the French course? It'd be brilliant because it's always a hard place. Everybody knows it's a tough place to come. It's a big crowd, a very passionate crowd. Previously, we've always had teams that have competed so we want to make sure that we're getting that again this season so that teams are coming here and psychologically we'd like to think we're beating them before the game really if they're coming here and they'll be a little bit intimidated by the stadium and by the fact that hopefully we're playing good football and use that to our advantage.


Welcome to another week of watch a weekly Wednesday. I'm Aubrey part of the stamp and gel creative team today I'm excited to share with you a new bundle that's in the mini catalogue It's the ice cream swirl bundle in it comes the ice cream swirl stamp set and dies that coordinate So so cute so fun. I absolutely love this little ice cream cone There's the cone that goes with or you can make it into a little bowl.  These are fun I like to use the Tombow multi mono liquid glue it we call it green glue because it's green and I just kind of put a little dab on It and then stick a sprinkle on so nothing too crazy a little bit of this goes a long way We'll just decorate our chocolate ice cream up with some sprinkles This is also a great use for our take your pick tool. It has this little sticky end on it that can kind of help you grab those pieces To put them where you want just put These yummy-looking sprinkles all over okay, and then we're going to Stamp with melon mambo Just on the inside Make a wish and it will adhere this Right to the top of our ice cream cone just like that And then how cute is that for a little? topper on a gift bag Something like that So fun so cute. Let me show you some other card ideas that I've come up with using this stamp set You have the cute doughnuts that are in there this pattern reminded me of Tablecloth that I once bought for a birthday party for my kids the white background with all the colorful doughnuts I thought was so so cute Here's another one. This is a little bit Simple with just some cute chocolate doughnuts Here's one with that cute ice cream cone And then one more Hope your day is sweet and that cute strawberry and the strawberries in the background So much fun. I loved playing with this ice cream swirl bundle I love the little ice cream cone feature in the die set and I hope that you will too if you have any questions Make sure to email us at sudemonstrator at gmail.com and make sure to give our video a like Thanks so much for watching and we'll see you next time for another watch at weekly Wednesday


In a time of so much uncertainty, one thing that has remained constant is the high quality service of the UT library system. Despite being unable to access the physical library spaces, the amount of digital and remote services available makes any task possible. For me personally, I've been able to continue accessing the materials I need thanks to the vast online collection of eBooks and scholarly articles. The library's online chat service and one-on-one Zoom opportunities are also great for answering any questions you may have for an assignment.


Hello and welcome to the session. In this session we will discuss the following question and the question says, find the dividend due at the end of the year on 250 shares at $50 each if the half yearly dividend is 4% of the value of the share. Let's start the solution now. First we will calculate the half yearly dividend on one share. We are given that half yearly dividend is 4% of the value of the share. So half yearly dividend on one share is equal to 4% of the value of the share which is given to be $50. So $50. So this implies yearly dividend on one share is equal to 2 into 4% of $50. This is equal to 8% of $50 and this is equal to 8 upon 100 into $50. Now 52 times is 100 and 2 4 times is 8. So this is equal to $4. We have to find the dividend due at the end of the year on 250 shares. So therefore total dividend at the end of the year is equal to total number of shares which is 250 into yearly dividend on one share which is $4 and this is equal to $1000. Thus the final answer is dividend due at the end of the year is equal to $1000. With this we end our session. Hope you enjoyed the session.