Welcome to tomorrow. We are Carrie Ann and Jamie Higginbotham. We will be your hosts for this show. This week we had a really fun live interactive conversation with you, the fellow space nerds. We start off by talking about Dragon One, it's final mission. Memories. Exactly the memories of Dragon One. We then moved on to Boeing and what in the world is happening over there? And then somehow that kind of led us into the space launch system. Yeah. Like that was just organically sort of happened. People were like, also this. So if you're a space nerd and you're interested in all of the space geek re-stay tuned because tomorrow begins right now. Good morning. How's everything up in the sky? So Zach actually gets us into one of the topics that we wanted to talk about today. And that is thoughts on the last flight of Dragon One. Right? Yeah. Because this has a special place near and dear to us because we both work, we always say company X we work at SpaceX, which means we're not allowed to talk about SpaceX. Right? So we don't represent the views and opinions of SpaceX. We are not official spokespeople for SpaceX. So we just choose not to talk about... Nor would we want to be. No, yeah, we really don't want to be. So we choose not to talk about SpaceX and we bring other people on board. So we can't really, we can't talk about any of like the non, like the technical specific stuff to this or anything truly SpaceX related, but because this is around the time frame, Dragon One spinning up is around the time frame. Both of us came online. We have like personal unique views of Dragon One that I think are a little bit maybe different. And I just want like this was for those who don't know, CRS 20, which just flew to the international space station about what would have been 48 hours ago. Yeah. I mean, it's, it's birthing with the space station soon. Yeah, I won't get into that or already has. I've lost track of time. No, I thought it was the ninth and she's the eighth. Okay. So last Dragon One. It's an end of an era. Yeah. So like, it's also, so it's the end of the contract. It was CRS 20 was the last one. It just happens to also be the last of the Dragon Ones. We're moving on to human rated and cargo rated Dragon. So Dragon two, V2, Mark two, whatever it is you want to call it, because apparently it's been called everything in the universe right now. But this is the one that can carry not just humans, but as well as cargo in a lot of different configurations. And I came up with a fantastic analogy because my department is currently hiring. So I've been doing a lot of these interviews and this young lady that I was trying to explain this to, hey, I'd never watched Doctor Who before. So when I was, I know. So when I was joking, that's a little bit like the TARDIS, because you can actually fit seven humans in there. She gave me a blank stare and I was like, okay. But then I was trying to explain like it can do humans and cargo. And she also still kind of gave like a blank stare to me. We didn't hire this girl, it doesn't matter. The point is, the point is here though that I said that this dragon is a lot like a minivan, right? So sometimes you need to go to the grocery store and it's you and one other person, but you got to get a lot of groceries.  But like either way you can reconfigure the minivan, however you need to. And there's sometimes you're taking a longer trip and you've got five people and you've got five suitcases but you don't have room for three suitcases inside but you'd put two more on the roof. And that's like our, what? This is brilliant. It's, right? I was so- I love this so much. I have not actually heard this until now. So she is, you're watching her break my brain. Keep going. Cause that was the unpressurized cargo section cause we were trying to explain that. Cause like you got the, cause you got the pressurized part where the people have to go. And there's some stuff where you would wanna have like the fruit and vegetables and stuff like those have to be inside.  I was- TARDIS is from me, right? Cause I'm like a TARDIS, I'm like, it looks small, yeah. And a lot of people just don't, even if you have seen Doctor Who, it's still difficult. You look at Dragon and you're like, there's no way humans can fit in there. There's just, there just isn't. And you're like, but you start explaining it that it is though Dragon Two is Spaceman Ivan. You start explaining it that way and it becomes a lot simpler. You're like, oh right. You take a couple of seats out, you can get more cargo in. Sometimes like, you know, you have the beef jerky and the sodas on the inside, but you can put the clothes on the outside and those can be unpressurized. It's not a big deal. And yeah, thankfully she got it then. So questions like this, can Dragon Two have more than four seats now though? This is where we have to kind of like, like any technical questions like this, we're gonna have to just skip. Like we can bring on other people who can answer these for you, but they can't be us. But first off, that's brilliant. Also, I just want to point out your hiring. So if anyone wants to, this is the cool thing about space is everyone thinks you need to be like an engineer or a rocket scientist and that is very untrue. You're hiring baristas, right? Yeah, I'm hiring baristas right now, as well as dining room attendants. So I mean, if you are in remote, anywhere close to being food service industry, like there's a whole entire department inside of SpaceX that is hiring. And you're hiring country wide I think, right? Or at least California, Texas. Florida. Yeah, I think maybe also Seattle, not positive. So basically if you live in the United States and you want to work for SpaceX, check out their jobs posting page because it's aerospace is so much more than just the engineering of the rocket. Oh, absolutely. It's everything because I can't function without my coffee. Right. I bet a lot of people at SpaceX would say the same thing. For sure. So you would be helping to do that. For sure. So I would just recommend going to their careers page. What is it? SpaceX.com slash careers. Yep. Easy as that. In any case, so there's those sorts of things. So that's a major differentiator between Dragon 1 and Dragon 2. But yeah, we both came on right around the very beginning of the COTS program and CRS missions and what have you. It was one of the first missions that I got to see inside Dragon. Yeah. Spaceship. Yeah. Oh my, I have that funny story of Dragon 2 too. The COTS for those who don't know, that was the contract that SpaceX won along with North, not North Programman, or Orbital, oh man. Who was it at the time? It was Orbital. I think it was Orbital at the time. It was Orbital, yeah. To fly payload. It was the commercial orbital transportation services contract, I think is what it was. And that was a contract to send, to basically become the FedEx of space, right? You send your payload up to the International Space Station. SpaceX won that. I got hired just after Falcon 9 Flight 1. So this is nearly nine years ago. So I was there for Falcon 9 Flight 2, never Falcon 1, Falcon 9, which was the COTS 2 plus contract and that's where they took, there were supposed to be three COTS missions. And then once that part was done, then they would consider it like operational and they moved to CRS, which is the commercial resupply services. Orbital Sciences, then Orbital ADK and now part of Northrop Grumman. And now yeah, Northrop Grumman. This is why we can't remember what it was called at the time. Orbital Sciences ATK, a space innovations division of Northrop Grumman. There you go. I think is what we ended up, what we jokingly called. Yeah, so I've been there since COTS 2 plus and COTS 2 plus was the combination of COTS 2 and COTS 3, so two different missions that they kind of moved together so that they could kind of push the program along. And I mean, I remember sitting there and working on that basically full time. Alls I did at that time, we were so laser focused on COTS 2 plus at like 12, 6, you remember when I first started, it was crazy long day. It was crazy, crazy. And it was just constantly working on Dragon and just like this incredible machine that no one had really ever done before. It'd always been like the purview of giant governments. So there wasn't a huge, great blueprint for something like this. Did they change it again? Now it's in Northrop Grumman space sector? Or is it? I mean, it's a lot easier to say, let's be fair. It's a lot easier to say. That's funny though. Anyhow, and then I remember, I remember the pucker factor of Falcon 9 Flight 2 and like we, at least in my role, I'm now a principal video systems engineer. So I was responsible for making sure that all of the video worked everywhere, including from the pad to the consoles and from just everywhere. And the webcast and I was just, I was so, so worried that time came to a halt on that first launch on COTS 2 plus. And I remember like lift off through Dragon separation was about a four hour event. And then once Dragon separated, I was able to breathe a sigh of relief, but now I'm worried about the space to ground video legs and how they're gonna perform. And they did pretty decent. And then I think I didn't actually like relax until we were attached to the space station. Yeah, I bet. And I think the entire company was like that at that moment, just kind of like this tense moment of like, you know, what don't you know, what don't we know, what did we think about, what did we forget? Just like that kind of paranoid, like how can we do this better? What, what didn't we do? And it went off without a hitch. And so that is my introduction. My introduction into working at an aerospace company is Dragon 1. So for me, getting rid of Dragon 1 is a little bit bittersweet. Because I love Dragon 2 as well, or Crew Dragon, whatever we're ever calling it. I banged my head on it many times. Like... I know, boohoo, that's what I said to you, don't worry about it. I literally, I think I bled in Dragon 2. And accidentally. But yeah, I'm going to miss Dragon 1. It was really cool. And those missions were always something special. Just something a little bit more than the satellite missions. Not that the satellite missions aren't important, but there was something just a little more magical about the Dragon missions. The thing that I'm really excited for though, with Dragon 2, I think it's going to take that magic up a notch, right? Because it's the vehicle that we can send humans on. I think that's going to be pretty cool. I appreciate that this last CRS-20 mission is a refurbished Dragon. She's got her little stamp of approval, so her couple different ISS tattoos on her, which I think is kind of cool. But yeah, I think it'll be interesting to kind of see, kind of like people put on football helmets, the number of like, I don't know, it's a sports thing, so I guess I don't really know. But I just know that you mark how many times you've been somewhere, what have you. Almost like a passport stamp, I guess it's more like that. So it'll be kind of neat to see the first crew Dragon with her all of her different ISS stamps on it. That will be cool, that'll be really cool. They might have to make them smaller, because they're quite large right now. Like, they're really about this big. Would they change the stamp based on whether it's sent to crew up or cargo? Ooh, maybe we could change the color. That's a good idea, that's great. Small scale wants to know, can you buy a non-flight capable Dragon 1 capsule as like a backyard shed for, or fort for kids asking for a friend? Wouldn't that be incredible? It would be, but the number of times that just Jamie has hit her head on one, I'm not sure that you want your kids tumbling around in one. Yeah, I don't know. To be fair, that was an unfinished Dragon 2. That was the mock-up that we had for the release, the unveiling of Crew Dragon. And it was like very early stages, and I had to get in and like wire up my infrastructure. And there was like, there was literally nothing in there. It was an empty shell. And so you have to kind of like, I don't know how to describe this, but you've got the door opening, you have to like reach in and grab a handle kind of up and then like pull yourself through the door and swing in and like jump down. Cause again, the floor is not complete either. So you have a little bit of a fall in there.   And I remember, so this is Dragon 2 now, not Dragon 1. I remember we haven't even released Dragon 2 to the public. No one knows we're working on it really. No one has seen this thing. It's a fairly secret project, even inside of SpaceX. Let me quasi-secret, you still have engineers working on it. But like, you know, only a handful of people that are actually like looking at it, let alone going inside of it. And I'm going in and out constantly. And I come home to carry it. I know, trust me. I come home to carry it.  I had to go in the spaceship today. And like, I had to work inside of Dragon all day. I was like, can you hear my shoulders? They kind of heard I was in and out of the spaceship all day. And I was like, no. Why would I want to do that? Like, how dare you? Boo-hoo.  Seriously, boo-hoo. Do you hear yourself right now as in and out of a spaceship all day? Ooh. Like, that's not how I meant it back then. I just, I can't sometimes. You guys, like, this is not a thing. Yeah, don't have that problem on the new direct. Like, now they actually have walkways into it and it's much, much easier. Like, there's a floor, right? So stuff like that.  I'm the Breeze's supervisor. I saw the question. I'm a Breeze's supervisor. I wish I could bring it up on screen, but yes. Yeah. So for those who don't know this SpaceX layout, the main coffee bar, oh, there you go. Thank you. The main coffee bar for SpaceX is literally right in front of Mission Control. And hanging in front of Mission Control is the very first hot one dragon.  You are literally looking at Serial 001, the very first spaceship that SpaceX ever sent to orbit, all day, every day. What are your, what's your memories and thoughts? Well, first of all, she's dirty. She needs to be dusted. She doesn't need to be dusted. They just dusted the leg, thankfully. And I didn't realize how dirty that was. That's a rocket factory. Like, they kick up dirt. Yes, they do. That's funny. Yeah, notes. Yeah, my very first experience of being able to watch a launch from inside the building, because everything else has been like how everybody else sees it, right? You watch it on YouTube. Or you watched it on the space broadcast. Was really, it was, it was really nerve-wracking. It was the feeling around. It was just so different. It reminded me a lot of watching Space Shuttle launches. We've had the fortunate opportunity to be able to go down to the media site and broadcast the last few Space Shuttle launches from there. And that was really amazing. And so it was interesting, though, because right when I first started, the Food Services Department didn't really exist. Oh, yeah, that's true. We were barely just sort of. Kitchen. Yeah, we were barely spinning up at the time. And so everyone who worked there were Food Service workers. But I was the only person who was like a space nerd, too, or at least knew a little bit about what was going on. And once they started to understand that I understood what was happening, then they just started asking me a lot of questions. And a lot of questions, like, I didn't know. I couldn't answer. I was like, well, I mean, I don't, obviously, I don't work on it. So like, what's happening now? Like, I don't know. For a while, it has to do stuff for a while. And then the solar panels come out. Is that solar panels? Yeah, this is solar panels. So for all, this is solar panels. That's what they do. Yeah, they kind of, they do. Actually, they're kind of more like. We are one at a time. But I was getting a lot of questions like that. And so I firmly established myself as being the space nerd, I think. Nerd. In my department. In a space company. Yeah. But I mean, in my department, because a lot of people they just didn't know. They didn't know. It's great though, because then you can kind of see people now. They know a lot more now, which is really kind of cool. It's been nice to see that growth. But it was, yeah, it was really exciting. Thoughts on Dragon 1, like, not anymore? You know, I think it's time to a certain extent. It's kind of sad that we didn't get to refurbish this one more times and use it for more things, right? She's only been to station twice. This will be our third time. But that's kind of cool that we could do that. But that was like even a thing. Yeah, I did this too early. I'm sorry. Like, I meant to hit blue and I hit red. No, I know. But yeah, it smells too. We're a little bit rusty. What is the most popular coffee during it's service at SpaceX? So.  Yeah, we go through about 300 gallons of drip coffee a day at one location. We have multiple locations there. It's hard to say. Our menu is kept very small for a lot of different reasons. I only have seven things, items on my menu. So lattes, cappuccinos, americanos, and mochas are probably all tied. I just named four out of seven items on my menu, right? So like, you know what I mean? Maybe it's eight items on my menu now that I think about it. In any case, it doesn't matter. Like, we just, we have a really, really small menu. So instead of walking into a Starbucks or a Pete's coffee or a Caribou or, you know, your favorite coffee shop and have like three pages worth of items, we have like six or seven items. Yeah, so they're all pretty popular and we only have three, six flavors. So there's always so many combinations you can make of anything, anyway, so. I have a fun game I play now which is when I go down to get a drink from Carrie Ann, I play the, I'll have what they're having game. And so whatever the last drink. Sometimes a backfire is great. Backfire last time severely. Whatever the last drink that she made for the last person that was right before me, that's the drink I get. Cause the thing is like, we also have a bunch of different milks as well. So, and that's really what killed you on that one, was the milk. Don D asks, Jamie, what do you do there? I'm the principal video systems engineer. So I don't edit any video. My primary role is taking photons, converting them to electrons and moving them around the planet. And so that's what I do. So when you're watching the live webcast, I'm not necessarily creating the content of the webcast, but the fact that you can see the webcast and you can see those pad cameras, the ability for us to paint and shade those cameras in the webcast control room, that's all me. So I'm moving video around. I'm working on making sure all of that works so that you can watch things. And then, you know, also, I mean, I would say the webcast is maybe five to 10% of what I do and then the rest of it is just, you know, all the other video that goes into a major aerospace company and there is a lot. Whoops, I missed, I missed again. Kathy says, I'm seriously about to move out of, I believe it's Albuquerque and Gosling Coffee Company X. You should, like, if this is something you're passionate about, again, I can't stress it. And also it's not Company X. Did you see recently, Long Beach, every aerospace company and their friends seem to be within like a one mile radius in Long Beach. And I was talking to Lisa the other day, I think we were both, and they've got like this really cool hanger bar thing going on over there where it's just like a bunch of different restaurants in a hanger that has a lot of history. Was it a Grumman? No, it was a, not a Hughes hanger. Anyhow, it's just like, it's not just SpaceX. You've got Virgin Galactic, Virgin Orbit, actually, I'm sorry, just Virgin Orbit over there. Virgin Orbit, Rocketlast down there. Blue just opened up an area, I don't know if they're in Long Beach, but Blue just opened up an office that's in LA. SpaceX, of course, is in LA. It's a Douglas hangout. There you go, Douglas, thank you, Tim. Yeah, there's just a lot of aerospace in LA just generally speaking, and they all have different needs. I know that a couple of the bigger companies have coffee and food services that would have you, like, yeah, if that's like something that you guys want to do, there's definitely plenty of openings all over, and you want to work at a space company, that's definitely an option. A human person asked, so is Jamie the person to be mad at when we don't get to see a drone ship landing? To be blunt, yes. I mean, at the end of the day, it is a team effort, and I believe we basically fixed that problem now, for the most part, and again, I'm not gonna take credit for fixing it because the final solution that got that working wasn't me, but yeah, I worked on that for years, and we tried, you have no idea, and I can't really talk to it, but we tried basically every launch for, I think it's been four or five years, I've forgotten the timeframe, since we've started landing, I have been trying to solve that, and we tried something new, every single landing to get that to work, and yeah. I cannot tell you what allowed us to fix it, I can't really even talk about what causes the problem, it is way more complicated than you probably think it is, no, a buoy won't help, no, you can't do ship to ship, so that's all I'll say. Oh, that's funny. In case. Yep, yeah, we got off topic with the topic. A little bit, yeah. Sorry. Yeah, did we have other topics on Dragon 1, or on a good transition point? Because I wanted to, the other thing I wanted to talk about today is, actually this is a good one, Alemur, any suggestions for where a lifelong space geek who's pivoting from tech support, going back to college for software engineering, ought to look to relocate? Well, I mean, if it's software engineering, I feel like Silicon Valley is probably still, Silicon Valley, kind of the Seattle area has got a bunch of stuff, and actually Austin, Texas is another surprising area. Yes. I would say those three. And Austin's great. And I think that was kind of. Not that there's anything wrong with Seattle or Silicon Valley necessarily, but I think you're right, I think it's a much overlooked city for that kind of thing. Yeah, and actually, this is what I was just about to get into, Boeing and their failure is another good topic to talk about. And I don't, I've always taken kind of an optimistic view of everything, right?  Why can't we be pro humanity? And if we're pro humanity, then we're also pro Boeing, and we're pro SpaceX, we're pro Northrop, we're pro all of them. We want everyone to succeed because we're not doing enough in space right now. But like also saying that, oh no, Boeing's doing great, would be disingenuous. They are not doing great. Well, as a company, they will survive. Oh, absolutely. But like, it feels like they've got major engineering issues. I have been thinking about this a lot because we know some engineers at Boeing. They're scary, smart people. Oh yeah, look, ridiculously smart. So, yeah, and actually, ah, being pro space doesn't mean you shouldn't be pointing at things that go wrong. Exactly, exactly. And so I've been really thinking about Boeing and like, what happened here? Because it's not just aerospace.  Right, right, right. You know, they had issues with their Starliner and then they had issues with communications on their Starliner, like literally communicating to us what was going on. There was a lack of transparency and now they're trying to be too transparent and like, it's like, they have just lost their way and it's like, what happened? Because I don't believe it to be an actual engineering issue. And actually, I think Lupi got, I think Lupi got to where I kind of was going, which is, is it an operational culture thing more than an intelligence thing, like the shuttle program in 1986 and OTT3? I think that's it. I think specifically it might be a management issue. So this is all personal opinion at this point, right? And I would love your comment and feedback below and if you're watching live, like, let's work through some of this stuff because we know some of the engineers are scary smart. Obviously some of the issues are engineering issues, but what seems to have changed is a management shift from building really great engineering components or really great engineering designs from what we'll call that probably 10 to 20 years ago, is that that Boeing of 20 years ago versus the Boeing of today, it feels like it's more centered on shareholder profit and making sure that the people who have stocks get a return on their investment. And so that means minimizing costs, cutting everywhere where possible, not necessarily caring about the engineering, but caring about the bottom line. Oh, that's interesting because I actually went a slightly different way. Oh, where did you go? Well, I was wondering if they started to get, like just literally too big, right? I think there are some times that you hear about some companies that you, you're trying to retain the best talent all the way around. And so then you hire, for instance, three people to do the job of one person. And then you start like dividing out the one person's job amongst three people, et cetera, et cetera, et cetera, to the point where you're just, you're so big, you're so bloated, you're so gigantic that like just trying to make decisions on even simple things, you have to get the okay from like six different people and they all have to agree in order to move forward that like things have just gotten to this like weird glacial pace that you just are unable to move at all. You just, you're not dynamic, you're not flexible because you have too many, too many cooks in the kitchen, effectively. See what I'm saying? Yeah. That could be it too. I mean, I hadn't actually taken that approach either. And I'm wondering. I was gonna say, I mean, I like, it's an interesting theory. It's not something I had even thought of before. And then, so you're saying too big and then I'm wondering if kind of spinning off of that a little bit, it's complacency, right? So when you have years and years of success, which to be blunt, let's remember, Boeing has had decades of success. Oh, absolutely. Their failures are fairly recent issues. They've had failures too. Like let's not pretend like there were no issues in the past, but the major, major failures of recent are fairly recent. So maybe they just got complacent and it's not just Boeing that got complacent. It's also in part NASA's fault on oversight. They admitted it in a recent conference. It's not just something mere spacemen say, it's something that NASA themselves say. Yeah, it could be one of those things of like, look, they've been doing well for so long. Like we're friends, right? I'm sure you'll get it. No big deal. This, that, and the other. Like we don't know about that new guy, but fine. Like I'm sure you'll figure it out. I'm not gonna worry about it because you always do. That could be oversight. Yeah, it just makes me wonder. The chat room is really big on McDonald Douglas merger and saying it was actually the McDonald Douglas management coming in that broke Boeing, which could be true too. Interesting. So I think it's easy for folks to think engineering issue. And actually, I think that's fundamentally my point. Anytime you have a complicated thing like a spacecraft like this, you're always going to be able to point to the engineers and say, you failed. But you need to take a step back because engineering isn't just one person building a part. Oh, for sure. The whole lot of decisions and compromises and like a game of kind of tug of war with the rest of the vehicle and the rest of the teams which is maintained, not by the engineer, but those wars are fought by management. Yes. And so it's actually the decisions of management that ultimately create a lot of these problems, which is why that's where I went. Well, and that's why you need the transparency inside of the company. They all need to be communicating with each other and you all need to be transparent about like what your timelines are and like how long do you think that's going to take? And you can't go, oh, that's going to take me two days when it's really going to take six weeks. Like that doesn't make any sense in any way, shape or form, right? And so then the next team is going to, oh, well, they're doing that in two days and like I need to get my stuff done in a single day and like, you know, timelines on that are very specific. And then yeah, that's when you do start to cut corners because like, well, I mean my manager promises in two days and he normally has to take six weeks and 20 of us, but now there's only going to be three of us working on it. Like there could be a bazillion different reasons of a bazillion different ways that this could be getting out of hand, just generally speaking. Bright Byron mentioned they wanted the McDonnell Douglas government contracts and ended up with the McDonnell Douglas profit drive that caused McDonnell Douglas to fail in the first place. And I think that's really important. And actually someone was Jen's right after that said, that's why like Elon's take on engineering issues. Email me. And I tend to agree, like the way that the new space, it's not just space science, but like- Again, it's the transparency about all of it, right? Like you have to, like if one group is going to say, hey, this is actually going to take us six weeks to do, everyone else has to go, okay, it's going to take you six weeks to do and you need to like back up for a minute. Now it shouldn't probably always take you six weeks, see if you can whittle that down to four maybe, but like either way, like you have to have a transparency within the company all the way around. And yeah, that's why I was like, if you have too many people working on the exact same project, like that could really mess you up hard. How would you pronounce that? Aviello, Aviello? Sorry, I'm if I'm mispronouncing your username very badly. Saying that Bowen's going to have a very hard time to adapt to fast iterative design. I think a lot of the old space companies are going to have that issue. Yeah. Yeah. And that isn't to say that fast iterative design is always a win either, right? Right. So there are pros and cons to all of these different pros.  At the same time, you've 30 people looking at the exact same thing and you must make sure that they all agree on the exact same thing. That's also going to take you way too much time. Like there has to be some compromise somewhere in the middle for sure. Mike brings up a good point. Engineering is all about compromising to make every part just good enough to do the job, which is very true. But the thing that people forgets is that there's that layer above the engineers of management that also dictates certain requirements that can have a negative impact on those parts. Yes. That the engineers oftentimes will say, engineering will come back and be like, no, and management will go, yeah, you're doing it anyhow. And it's that mentality that can break stuff. I do not know if this is what, there's all pure speculation, but you look at Boeing of 20 years ago, what a proud and amazing company. Like it would be a place where as an engineer you would want to go to work. Oh, for sure. It would be, it was the top of the pyramid. And you look at Boeing today, not as much. Yeah, I forget who it was with somebody much earlier said something like, could it just be that all the good engineers are going to Blue Origin in SpaceX? I mean, I guess that's an option. I don't know. Great question from Kenneth. Would the management bloat also be the cause of the slowdown on the space launch system? Again, I think that's just them trying to potentially maximize profits. And I think it's partly NASA's problem too. NASA has the tendency to want perfection upfront and like all of the paperwork that goes with perfection. I think another entity that will struggle with iterative rapid design is NASA. Like that is just not how they think. That's not how they work. To them, failure is not an option. You cannot fail. And as soon as failure is not an option, your program's going to take forever and it's going to be very expensive because you're not allowed to fail. And for all of the other new space companies, all of them, SpaceX, Blue Origin, Mastin Space Systems, Virgin Orbital, I'm missing a bunch, Rocket Lab. Just keep going, just keep going. Failure is an option. Don't fail at your primary mission, but like fail on the ground, fail early, fail often, figure it out, fix it, move on. And move on. And that is not how NASA works. And that same culture exists at Boeing, in my opinion, I know that culture exists at NASA. I do not know for sure it exists at Boeing, but it may. It feels the same. Sure. You know? So now we're pivoting to Space Launch System. I'm wondering if at this point, if the Space Launch System will ever be used for human spaceflight, do the growing launch options in the coming years? Yeah, Awesome Astronaut makes a really good point on that one. If you take too long to do the thing, and then other people do the thing before you, then are you still the one who's going to do the thing? I mean, it's a really good point. By the time Space Launch System comes online, and not even just in America though, right? Yeah. Like China is definitely getting up there. Like they want to build their own space station, which more power to them. They want to fly humans either to their space station and or to the moon and or beyond. And they're probably going to do it, right? So, and India's been working on a human spaceflight program, like there's a lot of other places that have been working on human spaceflight programs with their own stuff for a really long time. I think this is specific to the super heavy lift class of vehicles. Sure. And I can on one hand count and have extra fingers count the number of launchers. Actually, I think China does have one, and I don't remember what it is. Yeah, I think China does. But other than that, you're looking at ultimately at super heavy with Starship. Is the integrated stack still called Starship? I don't even know. Actually, I don't know how that works. I should know how that works, I don't know. I think it's still just called Starship. You've got New Glenn slash New Armstrong. I guess I should count that as two separate rockets, but ultimately New Glenn. Because they're going to different places. Followed by New Armstrong. Yeah. You've got Space Launch System, right? Right. But you do still have options. So the question is, A lot of options. Will New Glenn and will Starship fly before Space Launch System is actually ready? I don't know the answer to that. Well, it almost, it almost doesn't matter if it's before it's ready. Like if, I feel like if you both reach the finish line at approximately the same time, you know what I'm saying? Yeah, I guess, but this is over a billion per flight. It is not reusable at all. That's what I'm saying. Starship and New Glenn are both reusable rockets. No, that's precisely what I'm saying. What the, what guys? So as a customer, if you're looking at both of those options and they're done at basically the same time. Oh yeah. Like it almost doesn't matter if they're done at that time.   NASA's not the only customer out there. That's fair, that's fair. Vax Headroom has an interesting point. The Space Launch System should, no, I didn't intend for this to be a topic. This is how live shows go, by the way. Space Launch System should never have been a human rated rocket. It should have been a cargo only like the Aries-5 was going to be. It would have been flying already. And for those who don't know, the Space Launch System is derived from the Aries-5, which was part of the constellation program. Oh man, what a series of mistakes. It's like looking at Space Launch System, it was a series of mistakes after a mistake, after a mistake to get where we're at right now. And I mean, having a super heavy lift rocket is a good idea. We need to have redundant pathways to that. But like, man, I'm just, Peter Quinn does mention it's not a race. No, it's not. Peter Quinn, not wrong. It's not. No matter what Time Magazine says, by the way, it's not a race. Also, Loopy brings up a good point. The price tag for the Space Launch System doesn't matter because the way NASA works, Space Launch System isn't for the market. It's the side product of NASA's main goals. And NASA's main goals are to get humans back on the moon, right? They want to establish a permanent, that's their current goals. I think, fundamentally, this is part of the issue. Again, this goes back down to not, this is not an engineering issue, right? SLS is the perfect, perfect example of not an engineering issue. This is a management problem, and in many cases, a congressional issue, which we're just gonna roll into management, because most companies don't have Congress to deal with. This is true. Like, mandating crazy, silly things that don't make sense and forcing the engineers to make certain decisions that they have no control over. The Space Launch System, well, I'm pretty sure the Space Launch System, at least the areas five, Space Launch System had a congressional mandate to use solid rocket boosters. Yep. Required by law. Yep. To use solid rocket boosters on your rocket. How crazy is that? That you don't let the engineers decide that. Fundamentally, those are some of the flaws that I don't, I'm not a huge fan of, in Space Launch System. That's hilarious. Home, which one? Fabiano. I feel that Space Launch System is now like a poker table. The stacks are so high and it's harder to leave that table. You know, you could be right. Could be right. Absolutely. Somehow we got from Boeing to Space Launch System. I feel like they are in the same bucket though. Yeah, yeah, yeah. Yeah, absolutely. Tim's data basically said, can you imagine what the delay would have been had the Space Launch System needed to be recovered or reusable? First launched in 2053 while they figured out the whole recovery thing and that others have nailed already. You know, maybe. Here's the thing. NASA does have reuse a bit. Well, they have refurbishability down, right? NASA had the space shuttle. I'm not a huge fan of the space shuttle. Sorry. I'm not a huge fan of the space shuttle. They had the solid rocket motors that they did recover and reuse. They didn't, what NASA never got down wasn't the recovery part of it. They didn't get down the pricing part of it because they don't, they never needed to worry about that, right? They, to someone else's previous comment, NASA doesn't build these things to put into the market to try to sell. NASA builds these things for NASA so they don't really care for cost more money. They'll just add it into their yearly budget, right? Sure. It's tax dollars. Right. But they did actually figure out refurbishability, reusing stuff, right? What they didn't get down to was rapidly refurbishable and they didn't get down to lower cost access to space, which was the fundamental goal of Shuttle. So Shuttle fundamentally, as a system architecture, failed in its primary goal of reducing the cost of access to space. It actually increased the cost of access to space. Yep. But they did have the ability to bring this stuff back and I think they could use a lot of those lessons learned in a reusable space launch system. Although you're not wrong, I think it would have been 2053, right? Yeah. Yeah, that's kind of funny. SmokeSkills says on a different topic, I'd like to hear more about Astra. I haven't been able to keep track of what's been going on there. I think that's really a great topic for our space news. I think we're kind of winding down a little bit for this particular live news show. I think this was a lot of fun. I'm sorry that it was so long for us to get back to doing live stuff with you guys again. Hopefully everyone enjoyed their time with us today. So housekeeping notes on the show. I know that it's difficult when we don't have a set time and date for the show. Totally. Understand, heard and agreed. However, we've got crazy schedules at SpaceX right now. They're just crazy. And I'm trying to figure out how to make this so that you know when and where to tune in, but we're just not there yet and I need some more time to figure this out.  The best thing I can say right now is I'm tweeting it on Twitter. I'm putting it on Discord with an ad here tag and I'm trying to publish your show as soon as I know on YouTube so if you've got the notification bell you should get a notification as soon as we go live. And then once we do go live we're giving people like 30 minutes or so to kind of like get that notification show up. So we're actually going live as we're still setting up. So you'll see like an empty set and everything else. We're doing what we can to make this as easy on you as possible, full well understanding. It needs to be fixed and it needs to change. I hear you, I understand, it's in work. I just don't have a solution yet. The second housekeeping note I wanted to make was we've been doing ask me anything with me. The goal is to get those going with as many people as possible. So I wanna have AMAs with you. I'd love to have it with Jared and Jade, Lisa, everyone. Everyone associated with the show, I think should be able to sit down and just have and these AMAs go on for hours. They go on for however long the host is able to sit there and however long you guys have compelling content. Wade is worth the quality of the show. Well, thank you. I hope you guys enjoy the slightly updated format of the live show. Also, so the AMAs. I've been doing the last few AMAs, I'll probably do one or two more. Part of the AMAs because I'm undergoing a gender transition like half to two thirds of the AMAs have been focused on gender transition and that is not what tomorrow is about. That's very Canadian of me. Well, it's not that it's not what tomorrow is about but it's not going towards what tomorrow started as, ironically. So tomorrow's about getting you interested in space in the cosmos and transition while still very interesting and having a lot to do with Ben's transition to Jamie does have something to do with tomorrow. It doesn't have anything to do with tomorrow if that makes any sense. It's my personal tomorrow and not our collective tomorrow. Yes. You're the deal. I'm going to, I'm not gonna put a hard band on transition talk, obviously we're doing it right now at the end of the show but what I am gonna say is I'm going to move all of that to its own channel and I'm calling that channel Jamie Bitz. Jamie's Bitz. Jamie's Bitz, B-I-T-S just in case you're wondering it makes her laugh every single time. Every time. I think it's hilarious. Yes, it was my name. Thank you. I know I'm funny. In any case, but the thing is that all of those are gonna be cut out so not that they won't happen necessarily because obviously you guys are gonna ask questions and we're gonna answer them. That's why we are doing these things but it's all gonna be posted. That part of it is gonna be posted on Jamie's Bitz. I can't even say it. It's hard. Yeah. So all of that goes over there. So the AMAs for tomorrow will be focused on space and not about me and my personal transition. I will have my own personal AMAs on my own other channel. So if you are interested in transition go subscribe to that channel. Instead I will put a link in the description below. If you are not interested in transition, good news for you. Totally fine. Totally fine. It's getting removed from this channel so you don't have to deal with that anymore because I just don't feel like it belongs here. So I don't wanna harp on that too much. All I wanna say is I know a lot of you are interested. I will still be transparent. I will still do all that stuff but I'm gonna do it all over there. So go over there for that. This stays focused on space. And if you're like, I will do an AMA over there as well. Oh, that's a great idea. But you need to do an AMA here. I know. So we're gonna continue to do AMAs here. I might do the next one. Maybe we should do you for the next one. That might be fun. Yeah, there you go. All right, that's it for this month. I'd like to thank everyone so much for watching. I'd also like to thank everyone who helps contribute to the show. These are the citizens of tomorrow. These are our YouTube members. These are people who contribute anywhere between $50 per month and $1 per month. And I love that on YouTube you can actually get all the way down to $1 per month. Yes, which is awesome. Absolutely awesome and incredible. And just using advertising on the show hasn't really been something that we've been able to use to fund it. So your memberships absolutely help a great deal. Absolutely. So if you'd like to figure out how you can help crowdfund and contribute to the shows of tomorrow, head on over to youtube.com slash T-M-R-O slash join. And even at $1, like $1, that's like one soda, it's like one coffee. I don't know what you can get for a dollar anymore, honestly. If we had every single person. But you can get us for a dollar. You can get us for a dollar. We're chief like that. Yeah, exactly. Exactly. Or whatever dollar value you think it is worth for you. One final note. We are gonna try sometime in the very near future rolling in an advertisement from a third party. So we're gonna have a quick little thing. We're just trying it out. It's not like a permanent thing. I just wanna see. Cause you know, we are trying to keep the show funded cause it takes money to keep the lights. Keeps the station on orbit, right? Quite. We're gonna try that out. I love your constructive feedback on that. Let me know what you actually think. The first group or the first advertiser we're looking at is NordVPN. NordVPN, and they've got kind of a little special deal like you get 70% off of a one year membership. And then it's VPN server.  So still kind of contemplating all of that. But yeah, I'll buy that for, hang on. I gotta push that to the screen. I'll buy that for a dollar. Anyhow, yeah, yeah. All right. I'd like to thank everyone so much for watching and we will see you in about a month.


Julian exposed what the US in particular, but other countries as well, we're doing all around the world, as others have pointed out, on torture, on extraordinary rendition, on so many other things. And so it is important that we don't get totally concentrating only on the process, but also on the political message, because an awful lot of people around the world simply don't understand the importance of Julian's work in exposing all of that horrible stuff about foreign policy and what it really means. And if it's not exposed effectively, then it will be repeated over and over again. I'm speaking at the Stop the War Coalition this afternoon and will obviously continue our calls for support for Julian Assange. Secondly, a global conversation is a really important one. A sort of 24-hour speak out in support of Julian from people all around the world will be really, really valuable, because that would help to give it publicity, help to give it some real strength. And we could ask all of the alternative media channels to run it continuously, I'm sure many of them would, but also that would in turn force a lot of the mainstream media to take seriously this issue, because I continue to be shocked and appalled at the failure, the complete failure of the British media to adequately report what's happened to Julian, the injustice surrounding it, or how wrong the government's position is. I mean, whilst they have so far said the case is going to be heard again in court, they could quite easily say they're simply not going to agree to his removal to the USA. And so I think the role of the British media, and this has been appalling, in particular the failure of the Guardian and the BBC to adequately report what's going on here. I noticed my friend Pat Gavin and others were on the call today. We're all members of the Parliamentary Assembly, the Council of Europe, which meets next week. If there's some way that we can get it on the agenda next week and at least try and get some statements of support for him, because it's a good opportunity to do that. And so Parliamentarians around the world have a huge role to play, but above all, it's about making the campaign big, open, popular, and fully understood around the world that Julian has told us the truth about what the viciousness of the war on terror was all about.


Hi there, I'm Sandy Olnok, artist and paper crafter here on YouTube, and today I got my Inktense pencils out and some MFT stamps for a lesson packed with tips. First the supplies, I had the little doctor and nurse from MFT and you can stamp them in either direction, left or right, and they will interact differently and you can see a different orientation of them on the blog. Inktense pencils, this is my set that I have and I swatched out a few colors that I thought I might use. Got out my quiet sharp pencil sharpener that sucker needs empty big time and my brushes. There's a 12 and an 8 and the 8 is the one I use the most in paper crafting so I think that is the one I will be using throughout this entire video. So there are the colors and I don't use them all in this but I decided for some skin tone one would start with a 1720 and one would start with a 1740 and see how that goes. I stamped one of them, the one on the left is on the Kansen XL and the one on the right is on Arches cold press and I wanted kind of a direct comparison of the two. I knew that the Kansen XL cold press and they still call it cold press in some on some websites that that paper is a student grade paper and it's I don't know if it's not made of maybe as much cotton as the other one but the regular Kansen cold press has a bumpier texture to it. You can feel the cotton, it's a much softer surface but it's bumpier and it's just more fibrous. You can really tell the difference when you touch the two of them but I know that for a lot of people who have trouble with watercolor pencils and watercolor sometimes the cheaper paper is easier and sometimes it's harder and I wanted to have a real direct comparison to see both of them. So I did my coloring onto their skin tones just to see that I got both of them about the same with the same colors and then using my brush to apply the water I'm pushing the color toward the shadow area and allowing that to be kind of that's where my brush ends so it doesn't dump a lot of color in the highlight area and the little girl I put the pink in before I started applying anything. Sometimes we try to put the pink in afterward in the cheeks if you want pink and that can end up actually being a little bloopy on top but if you end up doing it while you're water coloring it and while you're doing your original coloring it sort of blends in a little bit nicer. The XL paper is easier for blending you don't end up having to work at it quite as hard and you'll see the difference as we go but the Kansen XL paper also takes the color differently and here you can see it's a different tone on the one on the right-hand side on the arches cold press. The reason for this I think this is the reason for this is because of the texture of the paper. The cold press will hold more of the color on the paper itself because it has those nooks and crannies to hold it all in there and it also scrapes more off of your pencil because it's got all those bumps on it if that makes sense and it's just going to hold things differently than the smoother paper. The smoother paper however is going to give you less of those little bumps and for some people trying it on that will work better and it's entirely up to you what you decide you want to do on your cards. If you have both papers try them out and see just see what the difference is but I found throughout this whole project all the coloring that I did the colors were definitely richer on the arches cold press even if some of them blended more easily on the Kansen XL. So I put that out there for you to just think about whatever you're interested in doing or what your struggles are. If you want that color to be really rich then use your your cold press paper instead and look at the difference in that one I mean it's not that much more pencil you saw when I colored them I didn't press really hard on that one but look how much more color is on the cold press than was on the Kansen XL.  One of the big differences you'll be able to see here too is in her little outfit her little nurses scrubs and the kinds of color I mean even just look at the color on the paper and then look at the color on on the Kansen XL versus the arches it looks purplier it just has this look because of that surface it is going to be about the same color when we start watercoloring it but that is one of the reasons for the difference between them is is the texture of that paper and again it will be easier to to blend it over here on the XL but a lot more of that pigment is going to leave the paper and be on my brush so you're going to see how much lighter that one's going to come out than the one on the other paper same as the skin tones on the face and that sort of thing and so I'll just spread that color around a little bit and you can compensate for this of course if you want darker richer color by putting more pigment on the Kansen XL and trying to be more intentional about really loading it up with color you get a similar effect with other brands of watercolor pencil but I find that I have the most trouble with these inktense pencils and trying to water them out a little bit more completely because they tend to hold the lines more so the ones that I find that I like the best I think are the Albrecht Durer watercolor pencils they just seem to work really great for me and there's a video either just happened recently or coming up soon with a fine art piece that I did in the Albrecht Durer so you'll be able to see what those look like on cold press so finishing coloring out these guys you probably noticed I did her shoes a little bit I used the pink the paint or the pigment that was already on my brush to add pigment to her shoes rather than actually coloring any on there and I'll do the same thing here with the shadows on the jacket I already have color on my brush and so I can get this really soft grayish color to put shadows on a white coat by picking up pigment from the pants that are there because the pants are really heavily laden with color and while they're still wet I can pick up some of that color and use it almost like a little palette and so rather than jumping up to do the hair right now I decided to do the jacket because the pants were still wet and I was waiting on doing the hair by the way so that I didn't end up touching the hair and having it bleed into the faces so just tried to jump around to different areas of the picture and I'll have some dark hair for the little boy I'll do some lighter hair for the little girl of course he needed a nice dark tie to go with his nice dark pants and notice how that dark color makes the white jacket pop and be more of a white jacket because it's got that contrast same thing with his pants if he was wearing white pants his he wouldn't be standing out much at all he would just be disappearing into the paper I used a dark bark brown for his hair and gonna watercolor that out and again the same thing with the other the other colors it's just gonna be much richer and darker on the right hand side on that at Canson arches Canson arches I can't they own both of them so I try not to use the word Canson when I'm using the word arches because it gets confusing Canson XL and Canson arches are both manufactured by the same folks so I used the lighter colors some of the color that was used on her face for her hair as well just in different proportions and again it will get much darker on the one on the right hand side and it takes a little more scrubbing sometimes to get the color to fully move and get rid of some of those little little bits if you struggle with some of those little bits not watering out let them sit for just a second don't let it completely dry and then go back and try to move the color again scrub around because sometimes just that water will allow it to break up a little bit and here's another one of those areas where you're going to be able to see the difference between the two papers and how they react by looking at this shadow that I'm putting behind them drawing the shadow out from their feet going out behind them and then creating a shadow that kind of has the shape of that shoe and then just goes off into the distance in a very generic kind of flat shape behind the two of them but look at how dark this one's going to get just immediately it is much richer in color than on the kanson xl the last finishing touches since i now have some of that black pigment still left on my brush is to add a shadow here on the left side of the jacket which i realized i had forgotten and trying to pick up some color the one on the left dried too much by then so i picked up some from the card on the right helpful to have two cards going at once and then just threw a tiny bit of that color onto the clipboard itself finished off the card by die cutting it with an a2 stitch die from mft putting it on some pink card stock and then i also decided to stamp the card with the opposite orientation of the two little doctor and nurse so you can see how they interact differently whether you're doing the left to right or right to left and that card as well as all the other cards are over on the blog all that good stuff as always if you need to pin something to your pinterest page to remember it and i will see you guys again later have a really awesome day go out and make something beautiful and i will see you later on thanks bye bye


Here we go. Hello and welcome. My name is Shannon Kemp and I'm the Chief Digital Manager of DataVersity. We would like to thank you for joining this DataVersity webinar today, which is Data Warehouse or Data Lake. Which do I choose? Sponsored today by Ahana. Just a couple of points to get us started due to the large number of people that attend these sessions, you will be muted during the webinar. For questions, we will be collecting them by the Q&A section or if you'd like to tweet. We encourage you to share highlights or questions via Twitter using hashtag DataVersity. And if you'd like to chat with us or with each other, we certainly encourage you to do so. And to find the Q&A and the chat panels, you can click those icons on the bottom middle of your screen to activate those features. And just to note, Zoom defaults the chat section to send to just the panelists, but you may absolutely change it to chat with everyone. And as always, we will send a follow-up email within two business days, containing links to the slides, the recording of the session, and any additional information requested throughout the webinar. Now, let me introduce to you our speaker for today, Allie LeClerc. Allie has over a decade of experience in open-source software, product, and community marketing. She's currently the head of community at AHANA, the software as a service for Presto Company, where she works closely with the Presto Foundation to drive open-source programs. Prior to AHANA, Allie held community and marketing positions at Alexio, Cashbase, and Time Warner. She holds a degree from Yale University in political science. And with that, I will give the floor to Allie to get the webinar started. Hello and welcome. Thank you, Shannon, for quite a nice introduction. And welcome. Thanks, everybody, for joining. Looking forward to talking more about data warehouses or data lakes, which do I choose? So why don't we jump into what we'll actually be talking about today? So I'm going to start with just a quick introduction. What is data warehouse? What is a data lake? And then I'm going to touch on the data lake house. Maybe it's a term that you have heard about lately. I think in the last six to eight months, the data lake house has certainly been picking up quite an amount of buzz. So I'll be talking more about that. And then I'm going to talk about Presto for the data lake house and do a quick intro on what Presto is and why we think Presto is the best SQL engine for that data lake house. I'll touch on some real-world use cases. And then I have some time for Q&A at the end. So with that, let's get started. So let's start the traditional data warehouse. Typically, it was a columnar structure. If we look back to the traditional data warehouse days, it had an in database analytics, very performance focus. And really, it was built for structured data. And what that really meant was that data had to be modeled. And data modeling was somewhat of an endless task.  And then finally, it was all primarily accessed through SQL. So data warehouses were made up of a few different sources, a few different outputs. But for the most part, if you look at the challenges, which I have here, they were, and they still kind of are, they're expensive, very expensive actually. They can be a little bit slower. They can be difficult to manage and very expensive to maintain. And they do provide access to just a limited amount of data. So a lot of challenges, yes, a lot of benefits, but a lot of challenges as well. So let's look at the data lake. If we move to the data lake and take a look at just the data lake in and of itself, let me just take a step back. We all remember Hadoop, right? Hadoop was going to replace the data warehouse. Years ago, everyone was reading that the warehouse was dead and Hadoop was taking over the world. And just a quick primer on Hadoop. So Hadoop was a file system, data storage. It was really inexpensive to use. And loading data into that Hadoop data lake was really cheap. And you could store all types of data, structured, semi-structured, unstructured. And it was all about ingesting data, loading it in and creating the structure once you had all of that data in there. And you could keep all of your data in there. And one of the big things that Hadoop helped was with that separation of storage and compute. So really for the first time, it separated out those two layers where you had storage with HDFS and then compute went through many iterations and generations, even in that Hadoop time frame. So it started with MapReduce and then it was Hive. But the point here is that you separated those two tiers out and you can do a lot more compute on your storage. So primary use cases for the data lake, discovery, text analytics, data science, mostly notebook and Python and other languages but became the primary way to access, although there was some SQL access as well. So while the data lake was less expensive, certainly than the data warehouse, there was limited performance, especially when it came to complex analytics, there was limited SQL access. And in general, the data lake was kind of hard to govern. Hadoop didn't really have those enterprise capabilities at first. And I think one of the biggest challenges with Hadoop was that it was really hard to use. It was never really simplified. And technology, it could be really great, but if it's complicated and it takes too long to get value from then it doesn't really serve its purpose. And over time, a lot of people struggled with it. So what were the drivers behind the modernization of the data warehouse and data lake? Let me start by saying that cloud is probably one of the biggest drivers of this. And I'll cover that in more detail later on. But what we've seen since the inception of the data warehouse and the rise in the data lake with the advent of Hadoop, there's been this really rapid modernization of those platforms and it's driven by three things. So the first is digital transformation. Everything's moved to digital now. There's massive upticks of things like mobile technology. There's more interactive web and mobile apps. And those are way more engaging. And there's tons of new data and data types. And along with that is an uptick in engagement. So think about engagement with your employees, with your customers, with your partners, et cetera. Second is real-time events. Everything's moving closer to real-time and that's created a need to respond quickly to business events of any kind. Third is really being on the cusp of seeing everything automated. So in the world of robotics, for example, there are these like massive plants for everything's being automated. And in order to automate everything that requires incredible intelligence delivered to machines, to sensors and all kinds of devices in the IoT in order to automate everything. So to smart data has really been a driver to modernize. So if we look at the data warehouse versus the data lake, these trends have really pushed us to the modernization of both of them. If we look at this slide, modernization is happening in both places, but in all areas. So the most modern of both is Cloud First, which I alluded to earlier. And there are increasingly more companies who are moving to the cloud and there are increasingly more companies that are born in the cloud.  It could be one cloud, it could be multiple clouds. And then going on one step deeper on the cloud first, today we're seeing a lot of containerization. So Kubernetes, I'm sure many of you are a part of it, you may even be using it, super popular, very stable. And a lot of companies are leveraging containers like Kubernetes to run their business. And even at the HANA where I work today, we were born in the cloud. We built a HANA as Cloud First and we built completely on Kubernetes and containerize it to take advantage of all the flexibility you get with that in the cloud and the availability of the cloud and the scalability of the cloud. So moving to the next bullet here, there's a move to more in-memory capabilities. And on the data warehouse side, they're now bringing in more complex data types than ever before. And the modern data lake is now bringing in columnar data types. So I talked earlier about the separation of compute and storage. And we're seeing that more so now. And so I won't go through every single one of these bullets in detail, but it kind of gives you a good sense of where we're at today when it comes to looking at the data warehouse and the data lake. I will touch on open formats as well. So formats like Apache ORC, Apache Parquet, specifically within the data lake, they can be consumed by many different engines. So what's really important about open formats is that you're not locked into one specific technology and you can move from one engine to another. For example, Spark supports it or Presto supports it and TensorFlow just added support for it. So with the data lake, you can leverage open formats, which are highly performant and have multiple types of processing on top of it. And of course the data warehouses are trying to expand and extend themselves to the data lake. But what happens is when you have a critical path for any product, it's built for a specific type of data. So with data warehouses, it's proprietary formats. Cloud formats like S3, there might be an extension. With data lakes, they're built for the open formats and not for these proprietary formats. So these are some of the considerations to think about as you're looking between the data warehouse and the data lake. How open do you want it to be? Is cost a factor? We have the adoption of AWS Amazon S3, which is one of the most popular data lakes today. Tons of companies, tons of data getting moved to S3 and with the advent of this simple, very cheap commoditized storage, S3 is becoming a big driver in the use of the data lake and moving to the cloud. So combining the both of both worlds. Let's talk about, I talked about the data lake house, but what does it mean to really merge the data warehouse and the data lake and the workloads? How does that work and what does that even mean? And so, we've talked about the data warehouse, we've talked about the data lake, the differences between the two, but let's talk about what's going on today in the market and we're at a point where these two things are converging. And I'll talk in a lot more detail on the data lake house stack and the architecture later on. But first, let's talk about why we're seeing this trend and specifically how a distributed query engine like Presto is helping drive this. So which one do you want to bet on in the future? Where's your primary path that you want optimized for? And the reason this is important is because it's going to tell you where your data is going to live. Is 80% of your data in the warehouse or is it in the lake? And that's an important decision and it's driven by the business requirements you have. So what we're seeing is if you have some dashboards or some reports and you need really high performance access and the data warehouse is a good fit, but there's an emerging trend of a different kind of analysis and consolidating that into a lake. It gives you the ability to run these future proof technologies on a lake. There's so much innovation happening on the lake today. So that becomes a fundamental decision. The next path, even if you choose one way or the other, the good part is that you do have a layer on top and that you can abstract that and give you access to both. And that's with SQL access. So querying across multiple data sources. At Ahana, we see most people, most of our customers leveraging the data lake and they query maybe one or two other data sources. So let's look at the list I have here. So first is SQL access, which is important. Most companies have data teams that know SQL, that can run SQL queries and that can use SQL to access data. So leveraging those resources to get better insight into your data lake and your data warehouse, that's bringing the best of both here. Then you have unified analytics, which means you can support more of your business use cases with a distributed query engine. Distributed query engines means you can leverage your existing platforms and your data sources with limitless scale for all data types, which is incredibly powerful. So our hypothesis is that the next enterprise data warehouse is the open data lake house. This convergence of the data warehouse and the data lake, what does it actually look like? So if we look at the last few decades, if you wanted to do SQL processing at a fairly large scale, you'd probably be using the enterprise data warehouse. Which makes sense, it was purpose built for SQL because it's widely adopted. And it's a great language for interacting with the abstractions you have, which are things like tables and doing things like reports or dashboards. So some of those characteristics, usually it's very coupled.  And this is usually some sort of proprietary format that the vendor stores your data in. And it would have a highly optimized engine on top of that. And that gives you the ability to interpret that data in that format. Typically you get really good performance in the data warehouse, so it can get really expensive. So this is where we see a lot of companies start to have challenges with their enterprise data warehouse. The cost or price performance starts getting really high as they're using more compute resources for their data. Plus it's a really locked in type of system, data format or proprietary. So now to address those things, we're seeing what we call the open data lake house getting a lot of steam. The stack has been separated out. So the storage is no longer coupled to the compute and it's scalable. So you can leverage the cloud and be elastically scalable and it's pretty low cost, relatively low cost. So the amount you're paying for the storage is very cheap compared to the data warehouse. And now that your storage is no longer tied to a compute engine, you can leverage many different types of compute. And by the way, that storage can be anything, text, images, videos, files, many different formats. So those engines, different engines can read it.  Then you can have Presto, which we'll spend some time talking about. Presto allows you to access the data in your data lake like S3 expose it with a SQL interface. So you can work with that data as if you were working with tables in a data warehouse and you could flex star from some table and it'll access the data in S3 data lake and then return the results in the table. And that's how you get your reporting and your dashboarding, et cetera. So you have your reporting and dashboarding on top of that.   Those are technologies like Apache hoodie, iceberg, Delta Lake, et cetera, Amundsen. They give you more parity with the data warehouse. And I won't go into a ton of detail into this, but I wanted to show kind of the whole picture here on how we're looking at the open data lake house. And really like I mentioned earlier, the reason we're seeing this shift to the open data lake house is to solve for those two key challenges with the enterprise data warehouse, cost and flexibility. Price performance is typically much better on the open data lake house. And you have a lot more flexibility with open formats, open data formats, open technologies, open source, open clouds and the like. So considerations for choosing a data warehouse or data lake, I'm guessing or hoping many of you joined today to learn more about what you should be thinking about as you're making some architectural decisions around unified analytics or choosing a data warehouse or data lake. So what we put together are just some considerations to think about as part of that process. And there are eight areas to drill into and I won't go really deep into each of these, but I hope they help kind of frame it for you. So starting with data, what kinds of data do you need to support? Can your approach support the breadth of that data, structured, complex data types, textual, streaming, analytics? You wanna be able to support a broad range of analytics, not just SQL, but Python, notebook, search. So you wanna plan for the future, set yourself up with a solution that can support a broad range of analytics, not just what you need today, but what you might need in the future. Users, does your solution support a broad set of users on a single platform? Can your data engineer, your data analyst, your data scientist, your line of business owner, can they all access the data that they need to do their daily tasks, their daily jobs? What is your platform support? Is cloud critical? Does it support your enterprise requirements? Is it cost efficient? Drilling more into cloud. Is it elastic? Is it automated? Can you scale it? Is there mobility? Is there globality? What can you do as you expand into new regions? Does your platform support that? Drilling into the enterprise, security, privacy, governance, unification, a lot of enterprise requirements. Will your platform support those things? Business, looking at business. Does it support business semantics and the logic I wanna include in that? Will it allow me to create measurable value for my organization and optimize? Can you create more value over time? And finally cost. Will you be able to forecast your costs accurately over time? What is the cost at scale? As it grows, anyone that's doing analytics, and as anyone that's doing analytics or will your analytics be growing over time? Will you be able to scale as your business grows without having to break the bank? So I think what's important to note here is that when it comes to costs, the way data platform teams today are evaluating technologies is really changing. And at HANA, what we're seeing is a lot more people wanting to start pay as you go so that you only pay for what you use as you decide what technologies to leverage. So it's usually in a consumption model and my recommendation is make sure you have that option because it gives you the flexibility to try things out and you don't have to have that exorbitant cost to try it out. And the cloud is allowing you to do that. That's definitely an enabler of this model. All right, so a little bit more about us. Why am I in here today talking about data warehouse or data lake and where does HANA and Presto fit in? And so let's start first with open source Presto. I don't know how many folks have heard about Presto. Let me do a quick introduction here. So Presto is an open source project. It's a distributed SQL query engine for the data lake and the lake house, which you saw in the data lake house architecture slide. And it was built for fast analytic queries against data of any size. The project came out of Metta Facebook back in 2013. And today it's being used at scale at internet giants like Uber and Twitter and TikTok by dance in addition to Metta. And you can query data in place so you don't need to move or ETL data. It supports federated querying so you can join data from different source formats. So super high level, that's why Presto is so awesome. Today, Presto is governed by the Linux Foundation. So it's an open, neutral open source project. And there's a lot of companies. I named a bunch of the bigger companies but big and small that are leveraging Presto. So I won't go into a ton of technical detail today on what Presto is, but if you are interested in going deeper there are a ton of resources available on ahana.io that will take you more in depth into the architecture, et cetera, if you wanna go deeper into that. But I will go into some of the more popular use cases and how we see customers using Presto for the data lake and the data lake house today. So starting with interactive ad hoc querying is right in Presto's sweet spot. You can run interactive queries on your data as you need to and that's all in your data lake. There's a ton of reporting and dashboarding which should be no surprise given what we've been talking about today. So more of like kind of the traditional data warehouse workloads. But we also see federation across different sources or different data lakes. And some of these applications are powering customer-facing applications. So taking advantage of the power of SQL in that sort of way is becoming more popular. And then more advanced functionality the data lake house can provide with data lake house analytics and transformation using SQL. So high level, these are kind of the five key use cases that we see across our customers and the Presto user community. And let's talk about Ahana. So what do we do? So our mission at Ahana is to simplify the use of Presto and make it accessible and usable for data platform teams of all sizes. As Shannon mentioned in the introduction we are the SaaS for Presto company. So what we've done is we've built it to be fully integrated and cloud native. It gives you the best of both worlds. You have full visibility into your clusters, into your nodes. You don't have to worry about installing and configuring anything. So given the vast potential that the open data lake house provides there's still a lot of challenges in standing up that kind of environment. And really what we do at Ahana is we wanna create the easiest managed service for Presto. We wanna enable you to do your SQL compute on top of your data lake, reporting and dashboarding, your lake house type of applications and supporting data teams of all sizes. So it's free to get started. It's just pay as you go. It's all in the AWS marketplace. It's a really easy way to get started with Presto on your data lake or for your data lake house as you begin your journey to the data lake and to the data lake house. So let's talk about some real world use cases. Starting with Blinkit. So for folks who aren't familiar, Blinkit is one of India's top instant delivery services. Their motto is everything delivered in 10 minutes. So one of the challenges Blinkit had prior to moving to Ahana was they were in a data warehouse, a cloud data warehouse and their price performance, specifically the costs were just starting to get unmanageable. They are, they kind of, they grew very fast exponentially. The amount of data that they were processing, the amount of compute resources they needed, it just grew significantly over a short period of time. And so what they found was their price performance, they just, it wasn't making sense for what they needed. So what they did is they moved from their data warehouse to the open data lake house approach and use Ahana Cloud for Presto at the core. So Presto has the SQL query engine on top of S3. And today they use this architecture to power over 200,000 orders per day at a much, much better price performance. And we actually did a customer presentation with them a few weeks ago at the AWS startup showcase. Really great video, check it out if you're interested in learning more about their architecture and why they chose the data lake house approach and specifically Presto to power the SQL queries for that data lake house. But one of the things they said is that Ahana is providing blanket with the SaaS managed service for Presto and giving the company the advanced data management capabilities it needs to meet its instant delivery promise. So fantastic use case here on a company facing challenges, facing limits with their data warehouse in the cloud and moving to a more open architecture, open data lake house approach. And then next is Securonix. So Securonix is a security information and event management software company. They use Ahana for their in-app SQL analytics. So what they call it is threat hunting. And every single day they'll get millions of potential threats events to stream in. They'll stream it into their S3 data lake. And on top of S3, they wanna be able to run quick queries to see where potential threats may be lurking. And that's why they chose Ahana for Presto. Those billions of events that get stored in S3. They, again, similar to Blinkit were facing very significant challenges when it came to price performance with their cloud data warehouse moving to this approach. They saw three X better price performance with Ahana for Presto on AWS. So just a few use cases there, some validation on the challenges folks are facing, real customers are facing in the cloud with their data warehouses and why they're moving to this more open approach, open architecture, open data lake house. So with that, I know we have plenty of time for questions and it looks like there's quite a few here. So Shannon and I will turn it over to you to see the questions. Thank you so much. Yes, lots of questions. And just to answer the most commonly asked questions, just a note, I will send a follow-up email to all registrants by end of day Thursday for this webinar with links to the slides and links to the recording. So diving in here, so Ali from environmental, social and governance perspective, focusing on the environmental aspect specifically. Can you provide any insight on the impact of the data warehouse versus that of the data lake with regard to energy consumption and carbon footprint? So I don't have any metrics or statistics top of top of hand here. And you know, I've never really gotten this question. So Indra, I'm sorry, I don't have very good and good answer for you, but let me take that and follow up with my engineering team to see if we can get you something that I just want to make something up here. So we'll take that and we'll follow up directly with you. Good, it's an interesting question. So can we have a star scheme of data warehouse inside the data lake? If not, what's the reason for that? Yeah, so we're seeing a lot of folks actually leveraging what data marks on top of the data lake. And Roshan, I hope this kind of answers your question, but one of the things that Amazon, the AWS talks a lot about is building kind of your line of business or your data marks on top of the data lake. And an architecture that a lot of enterprises are moving to are basically setting up line of business data marks on a data lake so that each line of business has full access to all of their data. So that means that for an example, the marketing team has their marketing analyst, has their marketing ops, has their marketing engineer, all having access to one specific data mark on the company's data lake. And HR might have a tool or a data mark. And so we're seeing this idea of kind of a mesh on top of the data lake a lot more. And so I think in that way, it's a little bit of a star schema in that the data lake is kind of the central repository and then you can set up your data marks on top of it. So it does kind of create a star schema in that sense. And there's some really great articles that AWS has written on that, Roshan. So I highly recommend checking those out. I love it. So it seems like a data lake is more of an ODS but has a cleaner data. Is this the fair assessment? So when I think of ODS, yeah, I guess so. I think it kind of depends on the use case, right? And so a lot of companies are very organized in their data lake approach and they have their meta data catalogs and they have their data catalog sitting right on top and that does help it become much more organized and clean. But keep in mind, a data lake is a repository for any and all types of data. So you can have your files in there, you can have your photos in there, your videos in there and then that's the beauty of the data lake and specifically with the open data lake house is that you can run your SQL workloads on top but you can also run your AI and your ML workloads on top. And so that's one of the really powerful characteristics of the open data lake house is that you can run various types of workloads, workloads that you can't necessarily run on the data warehouse. And that is why we're seeing a big shift to this data lake house approach. So are there tools to build a lake house available in the on-prem world? Yeah, absolutely. So I showed, let me see if I can find this architecture again. I showed our view because we are native, we are fully in cloud. Here it is. But you can build a lake house on your own. I mean, that's the beauty of the open data lake house is that each one of these components is self-managed, right? And so instead of a cloud data lake, you could do an on-prem Hadoop. You could run Presto on top of that. You could run your ML and AI on top of that. You could set up your own governance and security. So absolutely. I think it would be more of a do it yourself approach but it's certainly possible. So how does Presto leverage existing identity and access management infrastructures? Yeah, so there's a few different things. So let me talk about it from an AHANA perspective. So one is we actually just announced a lot of security updates and enhancements from an AHANA for Presto perspective. Specifically, we now have integration with AWS Lake Formation. If you're not familiar, AWS Lake Formation is an obviously AWS service that provides the identity and access management. It helps you build a secure data lake in literally hours. And so through that integration, you can do a lot of very fine-grained data access privacy control. Additionally, we also integrate with Apache Ranger, which is an open source security tool, also giving you identity and access management, very fine-grained permissions, et cetera. So there are a few different routes you can take depending on what you want to do. And that's from a AHANA perspective. Of course, from a Presto open source perspective, you can roll your own, right? So Presto integrates with Apache Ranger. And so you have the ability to do that as well from an open source perspective. So are you multi-cloud or just AWS? Yeah, good question. So from an AHANA perspective, we are today just in AWS, just available on AWS. However, Presto, the open source project is available in all clouds or on-prem. So you can run Presto, do it yourself, wherever you want. Is AHANA Cloud FedRAMP certified? I feel like we get this question quite a bit. So I need to bring it back to my product team. We're not FedRAMP certified. But because we get this quite a bit, I think it's something we need to look into. Definitely worth it. So yeah, besides cost consideration, when do you recommend using Presto S3 versus? Redshift. Yeah. Well, and we've got a couple of... We've got a couple of a comparison. And though we are a vendor-neutral company, really when should you use Presto? Yeah, and I think this kind of goes back to some of the use cases I was describing. And so for those who don't know, Redshift is Amazon's cloud data warehouse. And so I think it's, besides the cost consideration, what kinds of workloads, and I talked about this earlier, future-proofing yourself, what are the use cases that you're going to need to run? What are the kinds of workloads that you're gonna need to run? Not today, not just today, but in the future, right? And so, yeah, I talked a lot about price performance and that's where we see immediate, where you'll see immediate results, but down the road, are you gonna be needing to do AI and ML at any given point in time? And I think having the flexibility to do that is something to really consider, not to mention open data formats, right? The ability to run various compute engines on top of your data, those are the kinds of things to think about if you're looking at a data lake house approach versus a data warehouse approach. And specifically here with Ernesto asks about Presto S3 and Redshift. That's what I talked about because I live in the Presto world, but there's other technologies that, other data warehouse technologies out there and other data lake house technologies out there that are gonna kind of solve for the same sorts of problems. But Redshift to Presto S3 is certainly one that we see quite a bit. Awesome, thank you. So why is Presto performance on a HANA cloud three times faster than Presto on AWS? I love that question, Carmen. So we've built some proprietary features in a HANA, one of which is what we call data lake caching. So we actually have a cache built in directly into a HANA that is giving you that performance boost. So it's not available in the open source Presto project today. So that's typically why we see a HANA cloud for Presto faster, Presto on AWS. And then of course not to mention all the other benefits you get from a managed service, right? So, you know, resource, engineering resource overhead, managing and deploying and tuning Presto. I mean, I think there's literally like a thousand different configuration parameters that you can throttle back and forth with on Presto, which is a lot. And so we've kind of abstracted away all those complexities and made it very easy to get Presto up and running in the cloud literally like in 30 minutes, you're up and running. Oh, that's impressive. So it sounds like you're using virtual star schemas and that people aren't necessarily materializing them. What are they using to do that virtualizing? Does Presto allow us to define schemas and present these to reporting tools? Yeah, so Richard, I think let me try to answer your question. So I think, you know, typically what we see is Presto running on top of the data lake and then I don't have it in this slide. So, but your metadata catalog and your data catalog, those are gonna be important components to organizing your data, materializing it, being able to, you know, use that as the crawler, I guess, in some sense of your data. And then I'm not sure exactly virtualizing is kind of an overloaded term. So I'll take a guess by virtualizing, you mean being able to, I think, identify where your data is sitting and underneath the data lake, I'm not sure. But anyways, with Presto, so you'd actually be using the attached catalog to define schemas and then from there, that's right, your reporting and dashboarding would use that to actually pull that in into the reported dashboard. I love it. So many great questions coming in and following up on that, you know, most enterprises use Active Directory, maybe Octa for identity and access management. AWS is a separate world isolated in that respect. Can we integrate with Active Directory? Yeah, I think you can with open source Presto. So you, if you go to the docs, there's I think a whole thing on Active Directory and LDAP authentication. So check that out and that will give you more detail. What was that URL again? Just go to the Presto docs, PrestoDB.io, it's the open source projects. And in the docs, you'll be able to see more about Active Directory. And what do you use for metadata management? Yeah, so from an AHANA perspective, AHANA comes pre-bundled with an AHANA managed Hive Metastore. So that is part of the product itself and that's all self-managed and comes pre-bundled. So Hive and then Glue is the other one that you can also integrate with from an Amazon perspective. Some great questions coming in here. So when is the perfect moment to move from data warehouse to a data like taking into consideration the amount of data and the amount of sessions connected at X time in my infrastructure and what do I need to measure in terms to take the decision? Yeah, it's a good question. So what we see in our kind of customer base is when those costs start escalating. And so if you, as you scale your business, as your data starts growing, as your compute resources, resource needs start growing, that's when you're gonna start seeing some price jumps. And that's from our perspective, what we're seeing is the biggest pain for our customers. All of a sudden, they're paying 10 X more in some cases every month because of the compute. And so when you start seeing that pain, that's a good time. Obviously before that is even better, so you don't have to pay it, but if you have an expectation that your business is going to grow, your data is gonna grow and your compute needs are gonna grow, that's when you should start exploring the data lake. And not to mention, we talk to a lot of folks that are kind of in their journey of moving from a data warehouse to a data lake because they have hit those pains or they're even before the data warehouse. They're pre-data warehouse and they're trying to figure out, should I choose a data warehouse or a data lake? And our take is the data lake is gonna give you much more flexibility, much more openness and help you future-proof your use cases. And our belief is that 80% of the data is gonna be in the data lake. So take advantage of the data lake, build that data lake house in the right way so that you're set for today and you're set for the future. Perfect, and so in the fabric versus mesh debate, which we used to talk about as data federation, where do you place Presto? Yeah, so it's funny. So the fabric versus mesh debate, I think there's kind of, I think these terms all get completed. You have data fabric, you have data mesh, you have data virtualization, right? And so Presto is powerful in that you can query data where it lives. And so Presto sits kind of on top of your data sources and that's kind of the beauty about Presto is you don't need to move data, you don't need to ETL your data, copy your data, et cetera, right? You can query your data where it lives. Now that said, our perspective at Ahana is that like I just mentioned, we think like up to 80% of data is going to live in the data lake. And so if that's the case and if that's where your primary workloads and use cases are going to run, then Presto just on top of S3 is gonna take care of about 80% of workloads. And that's what we're building. We're building the best engine for the data lake, for the data lake house. And so it's for us, it's less about fabric and mesh and accessing a bunch of different data sources and more about building the best performing, best SQL engine on the market for the data lake. A lot of questions on schema, schema changes and data updates are always incredibly painful. Does Presto make this easier? So when we talk about the data lake house, Presto works very well with technologies like Apache hoodie and Delta Lake and iceberg and those and it's that layer that would typically be kind of handling the inserts, the upserts, the updating. And so from that perspective, Presto plus Apache hoodie, for example, on top of S3 is how we are recommending to our customers to do that in the right way. I love it. So I'll give everyone a couple more minutes to put questions in the Q&A, but in the meantime, you know, is Presto asset compliant? So from, I see it cause we're in the chat now. So from an asset compliant perspective, you can, you can use asset tables in Presto. And this goes back to what I was just talking about with, excuse me, sorry. Oh, excuse me. So at a high level, you can support it, it is supported, but there are some nuances there and I am not an expert in that. So I won't go into detail, but I recommend checking out the Presto documentation. And can you speak to your data quality approach? I'm not exactly sure what that question means. Katie, maybe you can elaborate a little bit more. I'll give Katie a moment to elaborate there. In the meantime, can you operate against Delta Lake? Yes. So Richard actually next week, there is a really great virtual event happening where Denny from the Delta Lake community will be talking about, we just, the Presto open-source community just announced an integration, Presto plus Delta Lake. So he'll be diving more into the architecture on that and there are integrations with both Presto, Hoodie and Presto in Delta Lake. And so yes, the answer is yes, you can. And check out the meetup next week, next Tuesday. It does sound yummy. Sounds great. So back to Katie's question on data quality, tools for using data quality, data cleansing, governance. Sure. So from an AHANA perspective, we talk a lot about Lake, AWS Lake formation, which gives you governance on your data lake. If you are an AWS user, I highly recommend checking out Lake formation. It's an easy service that gets you up and running on building a secure data lake on S3. And then AHANA for Presto integrates very seamlessly with AWS Lake formation. Open source, like I mentioned earlier, Apache Ranger is good for security and governance. So there are a few tools that are pretty baked in that you can use. And then you can also roll your own as long as you can connect JDBC, ODBC or on top of your data lake, you should be able to use it with Presto. All right. Well, that's all the questions we have, Ali. Thank you so much for this great presentation and thanks to AHANA for sponsoring today's webinar. And again, just a reminder to everybody, I will send a follow-up email by end of day Thursday with links to the slides, links to the recording and the additional links here that we've posted throughout the webinar and to y'all. So again, thank you all for a great joining us today. And Ali, thank you so much. Hope y'all have a great day. Thank you.