2023-12-22_uttam--tom--byang_-_fema-dataset_e49076f4

Meeting Title: Uttam—Tom—BYang - Fema-Dataset Date: 2023-12-22 Meeting participants: Brian Yang, Tom Bescherer, Uttam Kumaran

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WEBVTT

1 00:03:24.890 ⇒ 00:03:26.060 Hey, Tom.

2 00:03:28.030 ⇒ 00:03:29.209 Tom Bescherer: hey? Nice to see you!

3 00:03:29.420 ⇒ 00:03:30.700 Uttam Kumaran: What’s up?

4 00:03:31.380 ⇒ 00:03:33.679 Tom Bescherer: Oh, man, I got the same lim.

5 00:03:33.870 ⇒ 00:03:41.760 Uttam Kumaran: There we go. Oh, nice! Well, I always used to go to the library in New York, and I love like these lamps.

6 00:03:42.090 ⇒ 00:04:06.109 Tom Bescherer: And this one’s like really bright. And it’s great. I always I just want. I like, I like those vintage like library lamps. I know me, too. It makes you feel like very studious, like I met. It’s been a long time. Yeah, it’s been a while. Let’s see what’s new. Yeah, I have, baby. I think you probably know months ago.

7 00:04:07.000 ⇒ 00:04:11.820 Tom Bescherer: Now you got your own firm. Is that right? Are you doing full time?

8 00:04:12.150 ⇒ 00:04:18.139 Uttam Kumaran: I know I so I was like jumping between a couple of companies, and then

9 00:04:18.160 ⇒ 00:04:23.179 Uttam Kumaran: I was like, I kind of got like I got a little bit to contracting. And I was like.

10 00:04:23.240 ⇒ 00:04:28.669 Uttam Kumaran: Oh, this is like everything that’s fun about the job, except like no politics.

11 00:04:28.930 ⇒ 00:04:30.620 Uttam Kumaran: and like.

12 00:04:30.670 ⇒ 00:04:58.919 Uttam Kumaran: it’s just like doing a lot of engineering. And I was like, cool. And then I was working with Lee and Gabby on their their recent company. And then II left. And then I was like, Okay, let me go see if I can just go contract for a couple of companies directly. So then II got a couple of clients, and I started like an official company, and then, now, I’m just kinda like doing whatever is fun or like new. One of the things like I wanted to try to figure out eventually was like a lot of snowflake listing stuff

13 00:04:59.150 ⇒ 00:05:07.979 Uttam Kumaran: like I called Brian Young, who’s still at Snowflake. It’s still at we work, and me and him always talk about like new snowflake Updates. I’m like

14 00:05:08.060 ⇒ 00:05:24.409 Uttam Kumaran: I was like, Yo, Tom needs something like, why don’t just like pair and try to like work on it? And like we can list it all through. My company is like, yeah, I was like, so we just like, been messing around for like couple of weeks. Yeah. And it’s like, it’s really nice, because we can run everything within Snowflake. Now you can

15 00:05:24.640 ⇒ 00:05:31.729 Uttam Kumaran: run python the request to actually get the data and run that on a task. So we don’t need any

16 00:05:31.980 ⇒ 00:05:36.860 Uttam Kumaran: real external stuff to do it like everything’s on a store procedure running on a task.

17 00:05:37.110 ⇒ 00:05:45.699 Tom Bescherer: Snow park, python worksheets. Yeah, I was looking at the day. I just think it’s like super underrated.

18 00:05:45.920 ⇒ 00:05:53.209 Uttam Kumaran: But the but it’s like a little. The documentation is like, this is a lot of new stuff. So let me just message him to tell him to hop on.

19 00:05:54.100 ⇒ 00:06:11.040 Uttam Kumaran: But yeah, it’s like, it’s been fun playing around with that. And then, yeah, I just wanted to see whether it works. And then we were gonna try to like, push out a couple of more listings and like and, like, just, you know, get more established on there. And so, yeah, that was the main thing. But it’s been fun, like, I pretty much

20 00:06:11.060 ⇒ 00:06:15.839 Uttam Kumaran: like, just do go and do analytics, engineering or data engineering stuff directly for people. So

21 00:06:15.960 ⇒ 00:06:24.019 Uttam Kumaran: you know, companies who just like have their data scattered across a bunch of stuff need to establish snowflake. I bring in 5 train. And then I model everything on Dbt.

22 00:06:24.140 ⇒ 00:06:30.549 Uttam Kumaran: kinda just like running out and then been also messing around with a ton of generative AI type stuff, too. So

23 00:06:31.910 ⇒ 00:06:43.800 Tom Bescherer: yeah, Brian, Brian, right? Says he told me that you guys are just play league or like all the time, or I don’t know what what he’s like

24 00:06:43.980 ⇒ 00:06:58.979 Tom Bescherer: trying to bring me in on the totally stuff. And it sounded like there were Co clients that were. It’s it’s slowly, I mean, like I would love to bring every all my friends in stuff as I as I get them. I think like this is one thing that I think we’re trying to see whether has legs.

25 00:06:59.130 ⇒ 00:07:06.909 Uttam Kumaran: I’m I’m like slowly getting a couple more clients. So just trying to bring as many homies in. Hey, Brian? Nice to see you.

26 00:07:07.470 ⇒ 00:07:10.259 Brian Yang: hey? Been a while? Yeah.

27 00:07:11.060 ⇒ 00:07:12.089 Brian Yang: How are you?

28 00:07:12.500 ⇒ 00:07:17.400 Tom Bescherer: Good? How are you? You still? Who, Thomas? Just saying you’re still that we work? Is that right

29 00:07:18.070 ⇒ 00:07:19.810 Brian Yang: I am. You know that

30 00:07:19.820 ⇒ 00:07:30.490 Brian Yang: hosting life when we work I love it. I’ve got one of those.

31 00:07:30.590 ⇒ 00:07:33.840 Tom Bescherer: Is rice still open? That place is cool.

32 00:07:33.850 ⇒ 00:07:35.429 Uttam Kumaran: I have no idea

33 00:07:41.250 ⇒ 00:07:42.870 Tom Bescherer: but the t-shirt is on.

34 00:07:43.370 ⇒ 00:07:48.639 Uttam Kumaran: I all my t-shirt people like are like, Oh, dude you should keep, though those are like, gonna be vintage.

35 00:07:48.830 ⇒ 00:07:58.649 Uttam Kumaran: I’m like these are just with my Gym T-shirts, like, 6 years. Yeah, I’ve got my like summer camp 2019 t-shirt. Feel like I should be able to sell that for

36 00:07:58.770 ⇒ 00:08:09.370 Tom Bescherer: 200 bucks. Yeah, at least 50 bucks. Adam Newman on this shirt. Yeah.

37 00:08:09.740 ⇒ 00:08:10.570 smile.

38 00:08:10.710 ⇒ 00:08:13.520 Tom Bescherer: But What’s new with you, Brian?

39 00:08:14.910 ⇒ 00:08:20.200 Brian Yang: Well, after Covid, I kind of I became a nomad. So I’m not in New York anymore. It’s kinda

40 00:08:20.370 ⇒ 00:08:21.759 Brian Yang: all over the place.

41 00:08:21.950 ⇒ 00:08:23.309 Tom Bescherer: How nice! Where are you?

42 00:08:24.070 ⇒ 00:08:27.199 Brian Yang: I’m in the Us. Now, right now. But I just came back.

43 00:08:27.240 ⇒ 00:08:30.220 Brian Yang: after 3 months in South Africa.

44 00:08:30.800 ⇒ 00:08:32.360 Tom Bescherer: Oh, wow! Cool

45 00:08:32.480 ⇒ 00:08:34.130 Tom Bescherer: in like Cape Town or

46 00:08:34.260 ⇒ 00:08:36.039 Brian Yang: yeah. Kate Town.

47 00:08:37.659 ⇒ 00:08:40.849 Tom Bescherer: heading to heading to Vietnam on the fourth.

48 00:08:41.640 ⇒ 00:08:42.510 Uttam Kumaran: Damn

49 00:08:42.750 ⇒ 00:08:51.690 Tom Bescherer: wow! That’s cool. Is it? Grueling to like work? The time zone difference, or is it not as bad as it’s like people would think

50 00:08:52.410 ⇒ 00:08:54.949 Brian Yang: it’s not. It’s not so bad.

51 00:08:55.750 ⇒ 00:09:03.679 Brian Yang: I don’t really keep Usc stars. I kind of just do the basing stuff and then hop onto meetings. So it’s not. It’s not too bad.

52 00:09:04.280 ⇒ 00:09:05.400 Tom Bescherer: Oh, nice. Okay.

53 00:09:06.460 ⇒ 00:09:08.879 Brian Yang: yeah. You moved to Boston. Right?

54 00:09:09.440 ⇒ 00:09:24.929 Uttam Kumaran: Yeah. I live like 30 min west of Boston. I had my daughter came like 7 months ago, so we wanted. I grew up in Massachusetts, so I wanted to come back home to force my parents to do unpaid child care labor.

55 00:09:25.080 ⇒ 00:09:26.729 Tom Bescherer: Yeah, thank you.

56 00:09:26.950 ⇒ 00:09:29.070 Uttam Kumaran: How’s it going so far? Dude?

57 00:09:29.200 ⇒ 00:09:38.309 Tom Bescherer: It’s been good. It’s like a lot of work, you know, the sleeping was rough for a while. She actually just finally, like stabilized on sleep, which has been

58 00:09:38.470 ⇒ 00:09:42.440 Tom Bescherer: amazing. But overall, it’s been really good experience.

59 00:09:42.870 ⇒ 00:09:50.540 Tom Bescherer: It’s an easy way to like stop work at 6 pm. Because people will respect you a lot more when you’re like, okay, I gotta go take care of my kid versus, just like.

60 00:09:50.550 ⇒ 00:09:52.489 Uttam Kumaran: I gotta go.

61 00:09:52.610 ⇒ 00:10:00.909 Uttam Kumaran: Yeah, yeah, I should just make up. And I have a kid or something. But yeah.

62 00:10:01.180 ⇒ 00:10:12.529 Tom Bescherer: what? How’s my work been dude, Brian said. Other. Brian said, that you’re interviewing and stuff. What? What do you? What do you? Oh, yeah. My current company is like a a garbage fire there. You know

63 00:10:12.540 ⇒ 00:10:26.859 Tom Bescherer: they were. They just sort of are on the we work path not quite as like exciting, but they raised a shit load of money, and like early 2021, and then spend it all the wrong ways. And now they’re just like laying people off left and right.

64 00:10:27.130 ⇒ 00:10:33.810 Tom Bescherer: So I’m looking around. But it’s been good. I’ve been there for like year and 3 months I like my team.

65 00:10:34.280 ⇒ 00:10:37.149 Tom Bescherer: That’s all I can really say.

66 00:10:38.500 ⇒ 00:10:49.440 Tom Bescherer: yeah. You know, it’s like, I feel every tech company and 2023 is going through some of these pain points, unless I guess if you’re on the consultant lifestyle, you don’t have to worry about this stuff.

67 00:10:49.730 ⇒ 00:11:09.909 Uttam Kumaran: It is nice, it’s tough, but because I can’t really slack, though. So I I’m like working like my ass off. But yeah, I don’t really go to any meetings or anything that I don’t wanna be in. So that’s the nice part, we’ll see how long this stuff lasts. I’m hoping for a while like, are you trying to go back into data and stuff? Or what are you trying to do

68 00:11:10.340 ⇒ 00:11:17.359 Uttam Kumaran: I mean, I’ve been like a manager for almost 5 years now. So I’m sticking with that. I think nice

69 00:11:40.710 ⇒ 00:11:45.300 Tom Bescherer: being crazy about people’s matrix. So they’re like, okay.

70 00:11:45.320 ⇒ 00:11:59.959 Tom Bescherer: everyone needs to C code 4 days a week. And so I’m my job is to like, go on their github and like, see that they’ve committed. Are you serious? No, they really do this and like it’s not really natural to me. But I, as a manager, I get like

71 00:12:00.010 ⇒ 00:12:20.300 Tom Bescherer: forced to swap people on that, and like, Hey, your metrics are way down. You got it’s all commit like comments, or some student I would have. I would have committed just straight comments for like weeks. Got around that shit. Yeah. People like definitely fake it a little bit. What’s that?

72 00:12:20.660 ⇒ 00:12:44.589 Tom Bescherer: Just space out your commits. There’s definitely ways to fake it. But the CTO is kind of like a psycho. He’ll go on and like, look at every single like level. 2 engineers. Github profile like this commit doesn’t seem sophisticated enough kind of a crazy place, and that’s why we have a 2.1 on glass door.

73 00:12:45.080 ⇒ 00:12:52.979 Uttam Kumaran: Oh, what’s Weworks glass door these days? Probably a lot of that I don’t know. Let me check

74 00:12:55.490 ⇒ 00:13:01.910 Uttam Kumaran: yeah, like a lot of the a lot of the companies. Bird just went out of business to like

75 00:13:02.250 ⇒ 00:13:09.099 Uttam Kumaran: a lot of the companies that were like raising around. We work time or raise a ton of money, or like. yeah, getting really screwed.

76 00:13:10.040 ⇒ 00:13:13.529 Brian Yang: We’ve got a 3.6 somehow.

77 00:13:13.690 ⇒ 00:13:15.070 Brian Yang: That’s way too high

78 00:13:15.880 ⇒ 00:13:17.010 Tom Bescherer: hanging in there.

79 00:13:18.530 ⇒ 00:13:19.900 Tom Bescherer: that is impressive.

80 00:13:20.530 ⇒ 00:13:25.839 Brian Yang: What’s your car? What’s your car company. so called, built technologies

81 00:13:28.310 ⇒ 00:13:30.230 Tom Bescherer: based on national knowledge.

82 00:13:31.750 ⇒ 00:13:34.449 Brian Yang: Oh, you have a 4.1 Google rating

83 00:13:35.360 ⇒ 00:13:41.030 Tom Bescherer: alright. That’s probably a restaurant with the same name dude you got a

84 00:13:41.040 ⇒ 00:13:44.470 Uttam Kumaran: that’s the

85 00:13:45.890 ⇒ 00:13:50.040 Tom Bescherer: let’s see 2.4. Actually, okay, II slightly over dramatic.

86 00:13:52.650 ⇒ 00:13:55.479 Brian Yang: Pumped up a little bit. Yeah.

87 00:13:55.870 ⇒ 00:13:59.409 Tom Bescherer: So where are you gonna be staying in Vietnam? Have you like picked that place?

88 00:13:59.880 ⇒ 00:14:03.910 Brian Yang: Yeah, we’re in for a month. Then

89 00:14:04.360 ⇒ 00:14:09.439 Brian Yang: after that, I want to do like the Northern Vietnam kind of the motorcycle loop

90 00:14:10.020 ⇒ 00:14:13.330 Brian Yang: you go through like the countryside. Then

91 00:14:13.410 ⇒ 00:14:15.620 Tom Bescherer: have you seen that episode? Top gear?

92 00:14:16.490 ⇒ 00:14:17.600 Uttam Kumaran: Yes.

93 00:14:17.810 ⇒ 00:14:21.879 Brian Yang: that’s the top gear where they do a motorcycle race through Vietnam.

94 00:14:22.040 ⇒ 00:14:32.119 Brian Yang: that’s wild cause people people drive like crazy. They’re like, you go on a grab. And then, like you think you’re gonna die constantly because these guys just leaving through like traffic. It’s nuts.

95 00:14:32.810 ⇒ 00:14:34.240 Brian Yang: That’s how everyone gets around.

96 00:14:34.980 ⇒ 00:14:37.559 Tom Bescherer: Yeah, right? That sounds cool.

97 00:14:39.420 ⇒ 00:14:42.720 Tom Bescherer: Vietnam. But I’d like to go sometime.

98 00:14:44.350 ⇒ 00:14:48.590 Brian Yang: Yeah, Dunning is a nice, like, nice little beach resort. You can get like.

99 00:14:48.950 ⇒ 00:14:55.599 Brian Yang: really nice. We got like, really nice apartment, like for like 1,800 us for like a month. It makes like on water and

100 00:14:55.680 ⇒ 00:14:56.980 Uttam Kumaran: pam

101 00:14:58.340 ⇒ 00:15:05.280 Tom Bescherer: like a lot of those countries have such amazing like cost of living

102 00:15:05.300 ⇒ 00:15:08.750 Tom Bescherer: like when Taurus moved to. I think Taraji was in Bali.

103 00:15:08.810 ⇒ 00:15:19.810 Tom Bescherer: and he was living in like a mansion with several servants, paying like significantly less than I was paying for my tiny Queen’s apartment.

104 00:15:19.960 ⇒ 00:15:21.510 Tom Bescherer: It’s like, Okay, huh?

105 00:15:22.220 ⇒ 00:15:34.459 Uttam Kumaran: Smart, smart move. I think if you’re like, if you’re just like yeah, me, my, my, like close flaming. We’re like, fuck it. We’re out. You could do it. But if you’re like, Oh, I wanna hang out my friends like it’s tough.

106 00:15:34.490 ⇒ 00:15:42.269 Uttam Kumaran: but like I don’t know. I feel like me and Brian. We talk all the time like I don’t know. It’s not like the worst thing.

107 00:15:42.700 ⇒ 00:15:47.750 Uttam Kumaran: but he is. He has a Us job. So like, that’s the that is the game, like, you know.

108 00:15:48.950 ⇒ 00:15:57.549 Brian Yang: which Brian Brian pay you, Brian, you Brian? Oh, okay. yeah, yeah, you need like that, us salary just to go abroad. And

109 00:15:58.110 ⇒ 00:16:08.150 Brian Yang: yeah, if they let you do it, then do it. But it’s I mean, it’s just different. I’m not gonna say it’s like a lot of no matter like the life and stuff. It’s like the only way to do it. I don’t really believe that it’s like

110 00:16:08.600 ⇒ 00:16:11.579 Brian Yang: it’s just different cause, like you miss your friends and stuff like that

111 00:16:12.150 ⇒ 00:16:13.950 Brian Yang: so definitely pros and cons.

112 00:16:14.750 ⇒ 00:16:28.740 Uttam Kumaran: I mean, I live in the middle of nowhere in Massachusetts, and I miss my friends. Where are you like II go through my! I’ll fit a lot of time in like Dover, New Hampshire, and like Concord and stuff like that. But how close

113 00:16:29.110 ⇒ 00:16:35.310 Uttam Kumaran: are you to the actual? Like the city, Boston, they 30 min west of Boston?

114 00:16:35.410 ⇒ 00:16:37.850 Uttam Kumaran: Okay? I think.

115 00:16:37.880 ⇒ 00:16:41.729 Tom Bescherer: Yeah. Well, it’d be like an hour to get to conquer New Hampshire.

116 00:16:42.400 ⇒ 00:16:48.139 Tom Bescherer: yeah, it’s nice area for like raising kids. But it’s a little. No, Worsville.

117 00:16:49.940 ⇒ 00:16:55.580 Tom Bescherer: I’m in Austin. It’s it’s been nice. I like Texas a lot. Actually. Oh, yeah. When did you move there

118 00:16:55.690 ⇒ 00:16:59.180 Uttam Kumaran: last year around April.

119 00:17:00.500 ⇒ 00:17:06.100 Uttam Kumaran: I really like it. I think it’s like the weather’s really nice. Things are pretty cheap.

120 00:17:06.339 ⇒ 00:17:19.050 Uttam Kumaran: It’s like I go. My parents are in the Bay area, so I fly home to see that more. I fly back to New York. So yeah, it’s it’s nice. I like it a lot. It’s hot as fuck during the summer. But

121 00:17:19.349 ⇒ 00:17:22.079 Tom Bescherer: yeah, yeah, Texas seems like a cool place.

122 00:17:22.119 ⇒ 00:17:24.849 Uttam Kumaran: Yeah, opportunity. There. Yeah.

123 00:17:26.400 ⇒ 00:17:29.709 Tom Bescherer: should we try this stuff out? Let’s do it.

124 00:17:30.670 ⇒ 00:17:36.899 Uttam Kumaran: I think. Yeah, the last thing we’re talking about, Tom is like the Us. East, like

125 00:17:37.160 ⇒ 00:17:45.499 Uttam Kumaran: whatever the last like signifier was. But I don’t know, Brian, do you want it like, what do you need on your side? So the again, we can. Maybe we can

126 00:17:45.780 ⇒ 00:17:50.019 Uttam Kumaran: also just walk through generally like the entire process be cool to share.

127 00:17:51.650 ⇒ 00:17:52.540 Brian Yang: Yeah.

128 00:17:53.640 ⇒ 00:17:56.329 Brian Yang: you want me walk through the code or what? I just like the

129 00:17:56.520 ⇒ 00:17:58.040 Uttam Kumaran: I’m sure it’s not.

130 00:17:58.530 ⇒ 00:18:05.729 Uttam Kumaran: Yeah. I guess. Like, while we have Tom here. Maybe we could get the like all of his account stuff. And then we can walk through the code.

131 00:18:07.290 ⇒ 00:18:10.170 Uttam Kumaran: And then we can even see.

132 00:18:12.850 ⇒ 00:18:14.829 Brian Yang: yeah, let me see?

133 00:18:16.190 ⇒ 00:18:17.530 Brian Yang: So

134 00:18:19.660 ⇒ 00:18:21.080 Brian Yang: so

135 00:18:21.400 ⇒ 00:18:32.219 Brian Yang: okay, so to do, do like a snowflake, share the annoying part is, you have to be on the same like us East like availabilities like availability. Zoom so like

136 00:18:32.340 ⇒ 00:18:34.769 Brian Yang: there’s like a couple of different places your server

137 00:18:34.840 ⇒ 00:18:45.970 Brian Yang: can be. And that’s why I couldn’t like. I made a snowflake account, and I couldn’t get it to work with Sam’s account, because we’re in like different, like, you’re I’m in Virginia, and you’re in probably like Ohio, or something like that.

138 00:18:46.270 ⇒ 00:18:51.639 Tom Bescherer: But I thought, you guys are planning to put on marketplace you. So you want to do snowflake share?

139 00:18:52.900 ⇒ 00:19:07.649 Uttam Kumaran: Oh, yeah, this is the it’s like the marketplace is driven by the share I can. So we’re doing. We’re doing a public listing, but we’re waiting for that to get approved. But we could do a private listing that I’ll just share to you.

140 00:19:09.380 ⇒ 00:19:16.290 Uttam Kumaran: because we just wanted to see like what it looks like from your end. And then, if you’re good with it, we’re just gonna publish it. Publish that and publish a couple more.

141 00:19:17.590 ⇒ 00:19:21.839 Tom Bescherer: Yeah, I think I can do that. I’m just gonna use our dev snowflake. So I feel like.

142 00:19:22.020 ⇒ 00:19:26.499 Tom Bescherer: I don’t need our compliance team to approve or anything like that hopefully.

143 00:19:27.960 ⇒ 00:19:32.189 Brian Yang: yeah, so this is just the private share. So I can just share this with.

144 00:19:33.910 ⇒ 00:19:37.529 Brian Yang: share this with you.

145 00:19:39.620 ⇒ 00:19:43.210 Brian Yang: I don’t know. Supervisor studio listings.

146 00:19:45.920 ⇒ 00:19:47.590 Brian Yang: Okay, so this is the private one.

147 00:19:49.100 ⇒ 00:19:51.810 Brian Yang: So I can just share this thing

148 00:19:53.570 ⇒ 00:19:59.539 Brian Yang: Now for trial.

149 00:20:00.390 ⇒ 00:20:06.079 Uttam Kumaran: There, this is this the private one, or is this the public one?

150 00:20:06.360 ⇒ 00:20:10.929 Brian Yang: This is a private one, just need, like a placeholder for this.

151 00:20:12.150 ⇒ 00:20:14.010 Tom Bescherer: Yeah. Hook me up with the free trial.

152 00:20:14.130 ⇒ 00:20:23.419 Uttam Kumaran: Well, but also the thing we wanted to ask you, Tom. Maybe like, after you see, it is like, I don’t even we don’t even know what to price these things. As

153 00:20:23.740 ⇒ 00:20:25.739 Uttam Kumaran: so it’d be interesting to hear like

154 00:20:26.690 ⇒ 00:20:40.029 Uttam Kumaran: like what it takes for you guys to even like host and do this. Then we we went through a bunch of marketplace things people are pricing like all over the place. I don’t even know what we would. Yeah, the marketplace product seems like a good idea, but I feel like they haven’t

155 00:20:40.190 ⇒ 00:20:44.339 Tom Bescherer: nailed it yet, like, II don’t know. You guys, have you ever actually used?

156 00:20:44.510 ⇒ 00:20:52.989 Uttam Kumaran: I use it for like, so, for example, there’s like some stuff like where you want. You want like that long to zip code.

157 00:20:53.070 ⇒ 00:20:59.420 Uttam Kumaran: That’s like an easy one that like I would just get off Github or something. But they’re for free.

158 00:20:59.480 ⇒ 00:21:09.799 Uttam Kumaran: So that’s nice, or they have like weather data and stuff like that. I think the problem is trying. They have, like all types of pricing. So you could price them like query. You could do like

159 00:21:09.970 ⇒ 00:21:21.859 Uttam Kumaran: 20 bucks a month. so I’m don’t know, like everyone has it kind of like all over the place. so I don’t know it was kind of I can’t. Some people, a lot of people have it where you can’t even buy. You have to talk to them to buy it.

160 00:21:22.020 ⇒ 00:21:38.980 Uttam Kumaran: But I’m trying to get a general sense of like even I was talking to Brian. I was like, how much time do we spend doing that? And it’s like probably worth 20 bucks. So like, get that set up and like running that pipeline. But I don’t know like, but we could talk about that I mean, 20 seems

161 00:21:39.210 ⇒ 00:21:50.050 Tom Bescherer: about right, like, like, right? If I think about your business model, what I had hope is that you are finding data sets that are like a lot of people have interest in. So then you kind of like

162 00:21:50.110 ⇒ 00:21:59.629 Uttam Kumaran: defraying the costs for them. So everybody doesn’t have to build themselves. They pay you and the refresh right like. So if there’s another disaster declaration.

163 00:21:59.700 ⇒ 00:22:06.799 Uttam Kumaran: we get it immediately within the hour of it getting published. and then it it shows up in your pipeline

164 00:22:07.520 ⇒ 00:22:12.979 Uttam Kumaran: and like there’s nothing like that’s it’s like having your own Etl for that on your own right.

165 00:22:13.820 ⇒ 00:22:14.540 Tom Bescherer: right?

166 00:22:15.120 ⇒ 00:22:20.119 Tom Bescherer: Like we we have each for it now, but then it breaks every once in a while.

167 00:22:20.280 ⇒ 00:22:28.480 Uttam Kumaran: Yeah, but you’re totally right, is like, what I would do is we would go through. For example, if if this works today, we were gonna go through, get all the fema ones.

168 00:22:28.560 ⇒ 00:22:31.569 Uttam Kumaran: and then was gonna kind of work towards other ones. But

169 00:22:31.610 ⇒ 00:22:34.180 Uttam Kumaran: I don’t know. It’s interesting to just brainstorm, like, what

170 00:22:34.280 ⇒ 00:22:47.739 Uttam Kumaran: good price? Because I don’t want to do something where you have to contact us to get it. I would rather just be like 20 bucks a month while you use it, if you don’t use it doesn’t charge you like per query. Seems like, not great

171 00:22:48.560 ⇒ 00:23:00.470 Uttam Kumaran: like, I think that’s like a query is like a very hard to reason about. And but you can trial. So, for example, for the public listing. We were gonna put up like the top 50 rows so you could see the data. And then

172 00:23:00.580 ⇒ 00:23:06.430 Uttam Kumaran: you could even like get it. And then pretty much stop paying us. But if you need the updates.

173 00:23:06.790 ⇒ 00:23:09.780 Uttam Kumaran: then that’s what you would probably pay for. Right?

174 00:23:10.850 ⇒ 00:23:13.040 Tom Bescherer: Yeah, that makes sense to me.

175 00:23:13.750 ⇒ 00:23:17.110 Tom Bescherer: Like for us. We have a looker dashboard

176 00:23:17.290 ⇒ 00:23:23.939 Tom Bescherer: that pulls this data in. And it’s supposed to be like our our customers are expecting it to be up to date like every day.

177 00:23:24.020 ⇒ 00:23:27.690 Uttam Kumaran: Yeah, that’s why we have this pipeline. And so we would definitely be

178 00:23:27.780 ⇒ 00:23:30.469 Tom Bescherer: needing that like up to date information.

179 00:23:30.560 ⇒ 00:23:35.510 Uttam Kumaran: Yeah, we have the task run like every hour. and

180 00:23:35.800 ⇒ 00:23:45.139 Uttam Kumaran: but that that cost doesn’t even come like that. Cost sits on us like our task and our compute beats that. And yeah, we just we run the request and host that

181 00:23:45.340 ⇒ 00:23:57.820 Uttam Kumaran: so. But yeah, I don’t know, Brian. Maybe what else do we need from from Tom’s side, if it shows up. Can you? Can you go here and paste your account identifier?

182 00:23:57.880 ⇒ 00:24:00.600 Tom Bescherer: So I just want to add your account. Here, let me grab that

183 00:24:09.580 ⇒ 00:24:10.280 Tom Bescherer: oops.

184 00:24:12.040 ⇒ 00:24:16.990 Tom Bescherer: Yeah, right? Is that the right identifier? I was kind of surprised that

185 00:24:17.720 ⇒ 00:24:19.400 Uttam Kumaran: that

186 00:24:19.920 ⇒ 00:24:21.560 Brian Yang: that

187 00:24:22.690 ⇒ 00:24:24.900 Tom Bescherer: is it. The yeah. URL.

188 00:24:25.470 ⇒ 00:24:29.629 Uttam Kumaran: You see, you see, like on on Brian’s screen that little like.

189 00:24:30.290 ⇒ 00:24:33.909 Uttam Kumaran: if you go nice.

190 00:24:34.310 ⇒ 00:24:36.930 Uttam Kumaran: how’d you guys get like a custom one? I want a custom one.

191 00:24:37.370 ⇒ 00:24:38.420 Tom Bescherer: I don’t know.

192 00:24:39.250 ⇒ 00:24:42.470 Tom Bescherer: Yeah, that’s why I was confused. Because

193 00:24:42.860 ⇒ 00:24:45.360 Tom Bescherer: seems special

194 00:24:47.810 ⇒ 00:24:52.669 Brian Yang: cool. And you’re in the same like region like the north region here uses. So it works

195 00:24:53.850 ⇒ 00:24:56.380 Brian Yang: how to add. that’s well.

196 00:24:58.700 ⇒ 00:25:01.019 Brian Yang: okay. And now you should be able to see it.

197 00:25:01.030 ⇒ 00:25:02.690 Brian Yang: I just give you access.

198 00:25:03.140 ⇒ 00:25:13.710 Uttam Kumaran: Okay, so I go, what do you want me to share? I want to record a video like walking through what it looks like.

199 00:25:14.370 ⇒ 00:25:17.820 Tom Bescherer: Alright. So I’m going.

200 00:25:18.150 ⇒ 00:25:20.000 Uttam Kumaran: And you go to private sharing

201 00:25:20.840 ⇒ 00:25:23.359 Uttam Kumaran: and shared with you. Oh.

202 00:25:24.050 ⇒ 00:25:25.889 Uttam Kumaran: let me refresh this.

203 00:25:29.660 ⇒ 00:25:30.749 Uttam Kumaran: What? The fuck?

204 00:25:31.450 ⇒ 00:25:38.380 Brian Yang: Oh, wait, I have to publish. No, I have to do something I just have to save. Give me one. Sec.

205 00:25:42.630 ⇒ 00:25:44.439 Brian Yang: Okay, try again.

206 00:25:46.840 ⇒ 00:25:48.990 Tom Bescherer: Yeah, there you go. All right.

207 00:25:49.270 ⇒ 00:25:50.270 Uttam Kumaran: Nice.

208 00:25:51.780 ⇒ 00:25:55.650 Tom Bescherer: Verify your email to get data products. Okay?

209 00:26:00.030 ⇒ 00:26:01.830 Tom Bescherer: Alright, that’s pretty good.

210 00:26:02.670 ⇒ 00:26:06.060 Tom Bescherer: It’s my personal email. whatever.

211 00:26:10.960 ⇒ 00:26:17.819 Tom Bescherer: Okay? But where’s my verification email? Is it from Brainforge, or is it from? Oh, no, no, it’s from Snowflake.

212 00:26:23.000 ⇒ 00:26:25.010 Brian Yang: Sometimes it takes a minute to come in.

213 00:26:32.650 ⇒ 00:26:34.160 Uttam Kumaran: Did you hit resend

214 00:26:34.560 ⇒ 00:26:35.420 Tom Bescherer: yeah.

215 00:26:35.600 ⇒ 00:26:39.740 Tom Bescherer: mean look again.

216 00:26:48.880 ⇒ 00:26:51.100 Tom Bescherer: an email. No, it seems

217 00:26:52.100 ⇒ 00:26:54.690 Brian Yang: it just takes a minute. It took a minute as well.

218 00:26:54.830 ⇒ 00:26:55.840 Uttam Kumaran: Oh, okay.

219 00:26:57.420 ⇒ 00:26:59.670 Uttam Kumaran: he’s got 16 now.

220 00:26:59.880 ⇒ 00:27:00.780 Uttam Kumaran: yeah.

221 00:27:02.050 ⇒ 00:27:03.600 Tom Bescherer: they are perfect

222 00:27:07.920 ⇒ 00:27:10.489 Uttam Kumaran: nice. Okay.

223 00:27:11.890 ⇒ 00:27:13.250 Tom Bescherer: go back.

224 00:27:17.150 ⇒ 00:27:18.160 Uttam Kumaran: This place.

225 00:27:25.140 ⇒ 00:27:28.579 Uttam Kumaran: Have privileges. Oh, my unassume. Read, okay.

226 00:27:37.420 ⇒ 00:27:39.329 Tom Bescherer: 10 min. Okay.

227 00:27:59.070 ⇒ 00:28:00.930 Tom Bescherer: right? Alright. It’s replicating

228 00:28:02.820 ⇒ 00:28:06.100 Brian Yang: cool. And the looks like the data dictionary went through as well

229 00:28:08.160 ⇒ 00:28:10.579 Uttam Kumaran: like the button part of it. Yeah.

230 00:28:11.980 ⇒ 00:28:12.950 Uttam Kumaran: Nice

231 00:28:14.560 ⇒ 00:28:15.360 Tom Bescherer: cool.

232 00:28:16.030 ⇒ 00:28:21.580 Uttam Kumaran: Brian. W. Well, one. Once he runs a query, Brian. I want to see whether, if I can see the usage on our end.

233 00:28:23.740 ⇒ 00:28:26.190 Brian Yang: the usage shouldn’t hit our.

234 00:28:26.950 ⇒ 00:28:28.689 Uttam Kumaran: I thought it does. It does.

235 00:28:30.290 ⇒ 00:28:34.540 Brian Yang: No, it uses their usage. it uses their warehouse.

236 00:28:34.660 ⇒ 00:28:35.910 Uttam Kumaran: You sure

237 00:28:38.230 ⇒ 00:28:41.549 Tom Bescherer: yeah, makes sense to me like the data is being replicated on to our

238 00:28:42.080 ⇒ 00:28:46.490 Uttam Kumaran: system. Right? I feel like it’d be weird if you could.

239 00:28:46.970 ⇒ 00:28:50.549 Uttam Kumaran: The I thought it was like a secure view.

240 00:28:50.630 ⇒ 00:28:51.750 Tom Bescherer: Okay.

241 00:28:54.720 ⇒ 00:29:01.679 Brian Yang: no, it’s it shouldn’t just be like copying the database from from our database to their database. Now.

242 00:29:01.780 ⇒ 00:29:02.800 okay.

243 00:29:05.860 ⇒ 00:29:09.829 Tom Bescherer: the thing I really want from all of these marketplace listings is just like.

244 00:29:10.180 ⇒ 00:29:16.919 Uttam Kumaran: how often is this actually updating? Oh, but I put it, I have it in. I have it in there, but

245 00:29:17.620 ⇒ 00:29:29.470 Uttam Kumaran: on. So this is just one table, I think, on the actual listing I have a big description of like. Here’s how often we run it. And it it comes up on the right side, too.

246 00:29:30.580 ⇒ 00:29:34.659 Uttam Kumaran: But maybe I’ll even send that to you once it’s published, you can be like.

247 00:29:35.470 ⇒ 00:29:40.460 Uttam Kumaran: cause I don’t. Even a lot of them are so bad they don’t have a lot of info, so

248 00:29:40.500 ⇒ 00:29:48.709 stuff to know like how much to put in there. I mean, I know what I would want. So that’s what I kind of do. And then I’m pretty much like, reach out. If you have questions.

249 00:29:49.310 ⇒ 00:29:50.110 Tom Bescherer: Yeah.

250 00:29:51.770 ⇒ 00:29:55.930 Uttam Kumaran: right? I feel like, wanna know, like, Okay, is it actually running every day?

251 00:29:56.090 ⇒ 00:30:09.190 Uttam Kumaran: I guess our our question would be like, so what would be? Would it would it be like, do you want to sign up for email to get alerted like I don’t know how to verify that other than a column in there

252 00:30:09.200 ⇒ 00:30:11.610 Uttam Kumaran: that’s like last refresh day

253 00:30:13.300 ⇒ 00:30:17.169 Tom Bescherer: of of so like last referendum date every day, or like.

254 00:30:17.800 ⇒ 00:30:24.809 Uttam Kumaran: like the yeah, both the most recent data. And like the last day, the last timestamp of the refresh

255 00:30:24.930 ⇒ 00:30:30.020 Uttam Kumaran: II could put it here, but I don’t know if there’s any other verification that like might be helpful

256 00:30:30.470 ⇒ 00:30:38.249 Brian Yang: if you hit refresh again. This thing. I just added some attributes. I wanna see if it, I just added, saying, it’s updated daily.

257 00:30:38.400 ⇒ 00:30:40.040 Brian Yang: how does this show up here?

258 00:30:42.030 ⇒ 00:30:44.180 Brian Yang: Scroll down? Is there anything done there?

259 00:30:48.110 ⇒ 00:30:51.010 Tom Bescherer: I feel like you could get almost have like a status page.

260 00:30:51.020 ⇒ 00:30:55.519 Uttam Kumaran: Yeah. So that’s what we were thinking is like having like a external status page

261 00:30:56.360 ⇒ 00:31:01.209 Tom Bescherer: at the end of day. This data says, not really a big deal. There’s like.

262 00:31:01.280 ⇒ 00:31:12.699 Tom Bescherer: you know, 50 customers who check the dashboard, that’s, you know, using this data. But like we’re supposed to keep it updated every day. So if it’s not up to date, I’m supposed to like kinda

263 00:31:13.220 ⇒ 00:31:15.269 Tom Bescherer: scramble and figure out how to fix it.

264 00:31:15.660 ⇒ 00:31:18.999 Uttam Kumaran: So I mean, we should do, maybe both like

265 00:31:19.210 ⇒ 00:31:23.290 Uttam Kumaran: all these listings we could go subscribe to, perhaps

266 00:31:23.820 ⇒ 00:31:29.610 Uttam Kumaran: and we add we add a thing in here. So if you have like Dvt test or something.

267 00:31:30.180 ⇒ 00:31:34.140 Uttam Kumaran: You can. You can have that data as well. That’s probably what we’ll do.

268 00:31:34.680 ⇒ 00:31:40.419 Tom Bescherer: Yeah, I think that would be cool. That’s I think my as my biggest concern, using marketplaces like.

269 00:31:40.530 ⇒ 00:31:45.740 Tom Bescherer: okay, if it is broken in some way. I don’t have any power to fix it.

270 00:31:46.850 ⇒ 00:31:50.829 Uttam Kumaran: We should. I mean, we should pretty much be able to back if it breaks.

271 00:31:52.210 ⇒ 00:31:58.510 Brian Yang: Yeah, yeah. like, we were talking about this, too. I think, yeah, the biggest problem people have in marketplaces is like.

272 00:31:58.900 ⇒ 00:32:05.320 Brian Yang: you know, if the provider could just disappear because there’s no like accountability on the other side, it just could just be like

273 00:32:05.830 ⇒ 00:32:08.399 Brian Yang: disappear on the other side, like, you know, you know that word

274 00:32:08.470 ⇒ 00:32:18.139 Brian Yang: bringing this one up. But like, if I’m I’m not gonna trust like a marketplace thing where now is on the other side. So at least, if you have like a status page or something, just to show the hey, we’re like

275 00:32:18.160 ⇒ 00:32:19.970 Brian Yang: serious about this stuff and

276 00:32:20.410 ⇒ 00:32:24.999 Uttam Kumaran: but ex, but I could even give you like you can slack us

277 00:32:25.330 ⇒ 00:32:29.819 Uttam Kumaran: like we’ll be in a shared slack like. However, you interact with the other vendors. I don’t care.

278 00:32:31.060 ⇒ 00:32:31.880 Tom Bescherer: Yeah.

279 00:32:32.320 ⇒ 00:32:35.470 Uttam Kumaran: Why is this thing taking it forever?

280 00:32:37.290 ⇒ 00:32:39.149 Tom Bescherer: It said. Takes up to 10 min.

281 00:32:39.570 ⇒ 00:32:48.039 Uttam Kumaran: How big is the dancing? Be that big right? Not that big at all. No, it’s not that big 6,000 roads or something.

282 00:32:49.210 ⇒ 00:32:51.129 Tom Bescherer: Where does it go once? I

283 00:32:51.820 ⇒ 00:32:55.179 Tom Bescherer: but I should see it like this in your databases.

284 00:32:58.760 ⇒ 00:33:00.290 Brian Yang: You might have to.

285 00:33:02.450 ⇒ 00:33:04.890 Brian Yang: Okay, so could you open a query window?

286 00:33:05.390 ⇒ 00:33:06.150 Tom Bescherer: Yeah.

287 00:33:07.730 ⇒ 00:33:12.540 Brian Yang: Then, under account, Admins like, Look show show show shares an account

288 00:33:15.600 ⇒ 00:33:16.530 Tom Bescherer: like that.

289 00:33:17.310 ⇒ 00:33:18.959 Brian Yang: Yeah, I sure shares.

290 00:33:19.170 ⇒ 00:33:19.940 Tom Bescherer: Okay.

291 00:33:23.800 ⇒ 00:33:29.079 Brian Yang: okay, it should pop up there, and then you’ll have to create a database from the share if it works like. I think it does.

292 00:33:30.010 ⇒ 00:33:31.709 Brian Yang: That just hasn’t popped up yet.

293 00:33:31.990 ⇒ 00:33:35.419 Uttam Kumaran: I thought it was gonna replicate as a as a dB.

294 00:33:37.300 ⇒ 00:33:47.169 Brian Yang: You have to. I think if it works at normal shares, you’ll have to create the database cause. What if they have like a source TV? It’s not gonna overwrite your your existing one. Right?

295 00:33:47.850 ⇒ 00:33:54.980 Uttam Kumaran: So like, though, you’ll you’ll have to do like a create database whatever from share whatever.

296 00:33:59.130 ⇒ 00:34:03.260 Brian Yang: And I think, once you’ve created it. It’ll update like, you don’t have to keep doing that.

297 00:34:06.300 ⇒ 00:34:07.380 Tom Bescherer: Make sense.

298 00:34:16.000 ⇒ 00:34:18.760 Tom Bescherer: Let’s see what other data sets we want.

299 00:34:21.820 ⇒ 00:34:24.400 Tom Bescherer: Oh, do you guys remember like a clear bit

300 00:34:24.580 ⇒ 00:34:27.339 Uttam Kumaran: from. I mean, this is probably much harder to set.

301 00:34:28.190 ⇒ 00:34:41.369 Tom Bescherer: But like, I think there’s this really common like use case we have it again at at, built of like. Okay, we have leads. We have customers instead of like all the different companies that exist

302 00:34:41.650 ⇒ 00:34:54.690 Tom Bescherer: so that we can like tie them in. although that I think there’s also kind of like a matching element that requires a little extra. But like just having a data set of like here, all are all the companies that

303 00:34:54.710 ⇒ 00:35:00.040 Tom Bescherer: are registered in the Us. Or whatever is one thing that would be helpful.

304 00:35:02.680 ⇒ 00:35:09.640 Uttam Kumaran: Do you have any other data sets in mind that you’re thinking about for, like, well, I well, we wanted to run through all Fema, just because

305 00:35:10.110 ⇒ 00:35:18.910 Uttam Kumaran: I don’t know we’re on the topic. And we’re gonna we’re starting to like parameterize a lot of the behind the scenes of like setting up the shares. So

306 00:35:19.080 ⇒ 00:35:34.469 Uttam Kumaran: we wanted to do that. And then I don’t know was gonna kind of just poll people on like what interesting stuff or what stuff is like, really painful like, there’s a lot of flashy stuff we could do. But I was. I really am. Gonna go through the marketplace and then see what people are already doing.

307 00:35:34.670 ⇒ 00:35:45.410 Uttam Kumaran: and especially if there’s some of them who they have there. It’s like it’s like half baked. So I don’t know I was gonna go look! I was also gonna go look at Caggl, and like try to see some of those.

308 00:35:46.380 ⇒ 00:35:52.900 Uttam Kumaran: but I don’t know if you have any other things especially cause yours. You may see some interest. I didn’t never heard of this fema data set. So there’s anything that you’re

309 00:35:53.180 ⇒ 00:35:56.470 Uttam Kumaran: the government won’t seem like good to just go after.

310 00:35:56.730 ⇒ 00:35:59.369 Tom Bescherer: Yeah, a lot of the government stuff.

311 00:36:00.440 ⇒ 00:36:03.849 Tom Bescherer: would be super helpful

312 00:36:04.310 ⇒ 00:36:07.760 Tom Bescherer: especially like. When I was at Beckett I was at Aclu prior to this job.

313 00:36:07.800 ⇒ 00:36:19.300 Tom Bescherer: and they were constantly trying to get like government data sets and a like, a lot of government entities just have, like some shitty Api that you’re supposed to hit. And it’s like really slow?

314 00:36:20.060 ⇒ 00:36:23.339 Tom Bescherer: So there’s a lot of opportunity

315 00:36:23.620 ⇒ 00:36:26.090 Uttam Kumaran: for those data sets. Okay.

316 00:36:27.370 ⇒ 00:36:29.840 Tom Bescherer: like, what’s what’s an example?

317 00:36:33.270 ⇒ 00:36:36.210 Tom Bescherer: like court records is one that always comes up.

318 00:36:37.350 ⇒ 00:36:39.309 Uttam Kumaran: Oh, interesting. Okay.

319 00:36:39.960 ⇒ 00:36:47.770 Tom Bescherer: maybe more morally grey. But one thing at Aclu that they always wanted was like prisoner records from alright

320 00:36:48.440 ⇒ 00:36:55.970 Tom Bescherer: like prison institutions, basically like prisons have their website where they’ll put up like a

321 00:36:56.040 ⇒ 00:36:59.669 Tom Bescherer: HTML table. Of all the people who are incarcerated there.

322 00:36:59.940 ⇒ 00:37:03.810 Tom Bescherer: Really not performing at all.

323 00:37:04.270 ⇒ 00:37:10.230 Tom Bescherer: It’s it’s really hard to keep track even of like, who is actually at that prison.

324 00:37:10.610 ⇒ 00:37:20.710 Tom Bescherer: So that’s one where we had actually a a python job. There that would go and scrape the HTML from the website every day and like, turn that into a

325 00:37:20.750 ⇒ 00:37:22.510 Tom Bescherer: table. And in redshift.

326 00:37:22.940 ⇒ 00:37:24.140 Uttam Kumaran: Oh, damn!

327 00:37:25.720 ⇒ 00:37:34.939 Uttam Kumaran: That’s what I’m talking about, though, like, that’s what I wanna try like the government stuff. Maybe we can tackle because a lot of their formatting is pretty standard.

328 00:37:36.230 ⇒ 00:37:37.400 Tom Bescherer: See? South

329 00:37:37.880 ⇒ 00:37:39.890 Tom Bescherer: California prison

330 00:37:41.430 ⇒ 00:37:42.719 Tom Bescherer: in the search.

331 00:37:43.920 ⇒ 00:37:46.630 Tom Bescherer: What was the website that I was using at the time?

332 00:37:56.290 ⇒ 00:37:57.859 Uttam Kumaran: No, I don’t think it’s this one.

333 00:37:59.250 ⇒ 00:38:02.209 Tom Bescherer: Yeah. But all government stuff is really bad.

334 00:38:04.150 ⇒ 00:38:07.210 Tom Bescherer: and, like all the websites, were made in the early nineties.

335 00:38:11.910 ⇒ 00:38:18.090 Tom Bescherer: I don’t know how many like consumers there are, for this data, though, is the only problem they use. Red shift. So like

336 00:38:18.210 ⇒ 00:38:19.890 Tom Bescherer: kind of doesn’t matter

337 00:38:20.440 ⇒ 00:38:21.609 Tom Bescherer: for your person.

338 00:38:38.290 ⇒ 00:38:39.909 Tom Bescherer: Alright. See? Am I?

339 00:38:39.920 ⇒ 00:38:42.000 Uttam Kumaran: Yeah. See how it’s going?

340 00:38:43.250 ⇒ 00:38:46.150 Tom Bescherer: Oh, yeah. Might never get away. Let’s keep it here for that.

341 00:38:50.450 ⇒ 00:38:51.870 Tom Bescherer: Still. No.

342 00:38:52.140 ⇒ 00:38:52.820 Brian Yang: Umhm.

343 00:38:53.540 ⇒ 00:38:55.750 Uttam Kumaran: maybe it shows up in a different spot than

344 00:38:56.310 ⇒ 00:38:58.699 Brian Yang: yeah. Maybe it’s more of these Ui things.

345 00:38:59.950 ⇒ 00:39:04.009 Tom Bescherer: So where do I go again? Check data

346 00:39:05.400 ⇒ 00:39:07.990 Brian Yang: still replicating, yeah.

347 00:39:08.740 ⇒ 00:39:12.150 Uttam Kumaran: is it on our end? Or something dude like, should I increase the

348 00:39:16.030 ⇒ 00:39:20.339 Brian Yang: I don’t think so. I don’t even think our warehouse is running. I think it’s just simply internal.

349 00:39:22.830 ⇒ 00:39:24.070 Uttam Kumaran: What the fuck

350 00:39:42.360 ⇒ 00:39:44.779 Brian Yang: oh, actually, our snowflake is running interesting.

351 00:39:47.440 ⇒ 00:39:49.859 Uttam Kumaran: That’s what I’m saying. I think it is on our end.

352 00:39:56.320 ⇒ 00:39:58.790 Uttam Kumaran: There’s something like on reporting.

353 00:40:08.270 ⇒ 00:40:17.210 Tom Bescherer: I forgot that Amazon even has a similar concept to still fake marketplace. Let’s see, they release all sorts of nobody knows like any of like.

354 00:40:17.410 ⇒ 00:40:19.349 Uttam Kumaran: I don’t know anyone that’s ever used. This

355 00:40:19.480 ⇒ 00:40:24.789 Tom Bescherer: that’s wrong with both of them is, I feel like everything is just like garbage that you can’t tell one way or another

356 00:40:24.930 ⇒ 00:40:25.960 Uttam Kumaran: if it’s

357 00:40:26.810 ⇒ 00:40:29.860 Tom Bescherer: usable or not. Fire factor.

358 00:40:34.720 ⇒ 00:40:36.550 Tom Bescherer: Yeah, like, where do I see.

359 00:40:40.400 ⇒ 00:40:50.820 Uttam Kumaran: it’s just so clunky. It’s like, I don’t know. That’s why I think on Snowflake. My bed is like, Yeah, if we’re actually like established. And we have. You can either get alerts through slack or through a web page.

360 00:40:50.900 ⇒ 00:40:54.020 Uttam Kumaran: We kind of bridge the gap and build the feature that they don’t have.

361 00:40:55.620 ⇒ 00:40:59.940 Uttam Kumaran: Maybe they just buy my ass. Yeah, that’d be great.

362 00:41:00.290 ⇒ 00:41:06.049 Tom Bescherer: And then I can. I could go manage.

363 00:41:06.390 ⇒ 00:41:10.480 Uttam Kumaran: I’ll be. I’ll be yeah lead manager of marketplace products.

364 00:41:12.480 ⇒ 00:41:21.149 Tom Bescherer: Yeah, there’s a lot of these like data, observ observability tools, like, have you ever seen elementary? Yeah? And like, Atlant or something?

365 00:41:21.590 ⇒ 00:41:24.050 Tom Bescherer: Well, Allen’s more like a catalog. Right?

366 00:41:24.460 ⇒ 00:41:25.919 Brian Yang: Yeah, we use that one.

367 00:41:28.090 ⇒ 00:41:31.060 Tom Bescherer: If you guys found like one of these open source.

368 00:41:31.360 ⇒ 00:41:34.930 Uttam Kumaran: Oh, we like provide like a package or something I don’t know.

369 00:41:35.900 ⇒ 00:41:37.049 Tom Bescherer: or just like

370 00:41:39.340 ⇒ 00:41:45.370 Tom Bescherer: What am I looking for here? There’s like some page where I can go see?

371 00:41:45.530 ⇒ 00:41:50.149 Tom Bescherer: You know your sla for freshness and your

372 00:41:50.280 ⇒ 00:41:59.850 Tom Bescherer: whatever anomaly detection for the primary key, and Utah and Brian usually respond within 3 h to

373 00:42:00.080 ⇒ 00:42:10.129 Tom Bescherer: reports on issues so like that kind of data would make me willing to use some of these days. That’s in a way that I’m not for a lot of these things.

374 00:42:20.060 ⇒ 00:42:24.459 Tom Bescherer: If we just did a demo, this one which is helpful. Maybe they have a better picture of what it looks like.

375 00:42:25.080 ⇒ 00:42:27.320 Uttam Kumaran: Oh, heard about metal plane, too.

376 00:42:29.150 ⇒ 00:42:32.470 Tom Bescherer: Yeah, I this product, the cool. I wish I still had

377 00:42:32.930 ⇒ 00:42:37.650 Tom Bescherer: the demo they they gave for us.

378 00:43:04.160 ⇒ 00:43:06.370 Tom Bescherer: Oh, yeah, you’re right. Atlanta is a

379 00:43:08.960 ⇒ 00:43:14.809 Tom Bescherer: what is Alan? All I know about Alan is they harass me on Linkedin like constantly.

380 00:43:15.360 ⇒ 00:43:19.779 Uttam Kumaran: I know the to the elementary stuff is what I see on Linkedin a lot.

381 00:43:20.470 ⇒ 00:43:32.600 Brian Yang: Oh, really, it’s like a data catalog, but we still haven’t. Oh, we worked as on our page. We still haven’t really got no one uses. It’s it’s pretty bad, would not recommend.

382 00:43:38.730 ⇒ 00:43:44.310 Brian Yang: because, like it, it runs, it runs these scrapers on on a parasymflic instance to like scrape stuff. And

383 00:43:45.040 ⇒ 00:43:47.150 Brian Yang: sometimes they fail. And like.

384 00:43:47.980 ⇒ 00:43:48.820 Brian Yang: yeah.

385 00:43:54.700 ⇒ 00:44:01.629 Brian Yang: I’m one set as the open source version is the and my wife works at

386 00:44:01.940 ⇒ 00:44:05.259 Tom Bescherer: Instacart. And we use a month. And and she seems to actually like it.

387 00:44:05.380 ⇒ 00:44:12.369 Brian Yang: Yeah, months in is pretty good. I tried to get. We worked together, use the month, and I failed. We went.

388 00:44:12.500 ⇒ 00:44:18.079 Brian Yang: They bought lunch with her, and then suddenly, we’re on.

389 00:44:24.730 ⇒ 00:44:32.539 Tom Bescherer: Oh, yeah, if you guys had like a public a month, sentences that’d be cool. or all your data sets. Well, I guess it’s

390 00:44:34.890 ⇒ 00:44:35.830 Tom Bescherer: that work.

391 00:44:43.110 ⇒ 00:44:45.210 Alright. Let’s see any luck.

392 00:44:50.180 ⇒ 00:44:54.740 Brian Yang: Still, no replicating what is going on? I don’t know.

393 00:44:55.760 ⇒ 00:44:57.599 Brian Yang: Should I crank up this warehouse?

394 00:44:57.990 ⇒ 00:44:59.030 Uttam Kumaran: Yeah.

395 00:44:59.240 ⇒ 00:45:01.010 Brian Yang: something is running on it.

396 00:45:01.990 ⇒ 00:45:05.420 Uttam Kumaran: Well, I’ve been looking at queries and stuff. So maybe it’s like mine

397 00:45:15.060 ⇒ 00:45:18.970 Brian Yang: thankfully, this should just this should just be the first first time it should.

398 00:45:20.080 ⇒ 00:45:23.690 Brian Yang: Oh, you think it’s replicating like globally?

399 00:45:27.280 ⇒ 00:45:38.540 Brian Yang: well, you never really know what Snuffy is doing under the hood. But that’s not what we told it to do. We just said private listening for one person.

400 00:45:49.330 ⇒ 00:45:57.830 Brian Yang: Munson has a paid version, too. The open source guys like they made like a Saas product based on months months of just like kind of a Dvt route.

401 00:46:00.730 ⇒ 00:46:03.720 Uttam Kumaran: What was that called again?

402 00:46:09.550 ⇒ 00:46:11.710 Brian Yang: Oh, this is pretty cool, is this yours?

403 00:46:12.240 ⇒ 00:46:13.040 Tom Bescherer: It’s fine.

404 00:46:13.600 ⇒ 00:46:21.309 Tom Bescherer: Is this some who’s a month, and this is, I was on at Lands Page, and they had like a demo a month in.

405 00:46:22.350 ⇒ 00:46:23.620 Brian Yang: Oh, okay.

406 00:46:24.600 ⇒ 00:46:28.679 Uttam Kumaran: because this Covid data set is like a public dataset that’s on Snowflake as well.

407 00:46:30.230 ⇒ 00:46:32.980 Tom Bescherer: Yeah. although it doesn’t give you like.

408 00:46:33.060 ⇒ 00:46:35.630 Tom Bescherer: I still need to see like

409 00:46:35.980 ⇒ 00:46:43.060 Tom Bescherer: so the status page information and like freshness, which I don’t see here. But maybe they just don’t have that turned on

410 00:46:43.970 ⇒ 00:46:46.539 Brian Yang: right. It should have it

411 00:46:46.660 ⇒ 00:46:49.570 Brian Yang: And you could always throw it into like the description, or something like.

412 00:46:49.910 ⇒ 00:46:51.630 Uttam Kumaran: even if it’s

413 00:47:04.680 ⇒ 00:47:05.920 aye

414 00:47:07.870 ⇒ 00:47:09.090 Brian Yang: love you right back

415 00:47:09.660 ⇒ 00:47:10.490 Tom Bescherer: sounds good

416 00:47:10.860 ⇒ 00:47:12.680 Uttam Kumaran: and no luck.

417 00:47:16.960 ⇒ 00:47:19.990 Tom Bescherer: You can come in if you want to tell you town and Brian.

418 00:47:20.360 ⇒ 00:47:21.020 Yep.

419 00:47:21.620 ⇒ 00:47:24.580 Tom Bescherer: Tom and Brian. Yeah. Hi, everyone.

420 00:47:24.680 ⇒ 00:47:25.880 Uttam Kumaran: Hi.

421 00:47:26.320 ⇒ 00:47:29.239 Tom Bescherer: good to see you.

422 00:47:29.800 ⇒ 00:47:30.860 Tom Bescherer: Bye.

423 00:47:38.870 ⇒ 00:47:48.770 Uttam Kumaran: Yeah, dude. I’ll definitely let you know if you’re still interested in doing like basic Ae stuff. As like, I’m I’m just like, Re, I’m just like, been pitching to a bunch of companies. But

424 00:47:48.840 ⇒ 00:47:55.210 Uttam Kumaran: lot of the work is just like setting up snowflake and doing like analytics. But the money is pretty good. So

425 00:47:55.340 ⇒ 00:47:56.609 Tom Bescherer: yeah, I mean.

426 00:47:56.710 ⇒ 00:48:14.080 Uttam Kumaran: Brian was telling me about his some of his like earnings last year. Brian is hilarious, but I’m but me. I’m happy because one of the clients I was working on. I’m like Brian, I need help with something like, I’m can you just take it over? And then, yeah, he’s helping me do some sales stuff.

427 00:48:14.320 ⇒ 00:48:16.750 Uttam Kumaran: And yeah, it’s like the easiest word ever

428 00:48:16.780 ⇒ 00:48:34.189 Uttam Kumaran: because a lot of these guys are in advance. So it’s like, once you get like all the stuff figured out, then it’s like tougher problems. These guys are just getting established a lot of the times. Right? Set up Snowflake. Show them what Dvt is exactly, exactly. And it’s like, just make sure dashboards and data models work. And

429 00:48:35.060 ⇒ 00:48:39.769 Uttam Kumaran: it’s like, not too bad. I think the hard. The hardest part is the sales side. Frankly.

430 00:48:41.140 ⇒ 00:48:41.980 Tom Bescherer: yeah.

431 00:48:55.350 ⇒ 00:48:57.660 Tom Bescherer: I like this. This whole like data set

432 00:48:58.170 ⇒ 00:49:08.790 Tom Bescherer: marketplace angle, though, I feel has a lot of potential as a business. Yeah, I mean, go on. Go on the marketplace. I’ll just even show you a couple of interesting things that I’ve been looking at.

433 00:49:08.920 ⇒ 00:49:12.670 Uttam Kumaran: So if you go there, if you tip and browse data products at the top.

434 00:49:13.720 ⇒ 00:49:23.570 Uttam Kumaran: They have like organization. So for example, I’m gonna we’re gonna listen to government. If you go under government at, if you go under browsed. So one, there’s only 2,000 listings.

435 00:49:23.720 ⇒ 00:49:25.230 Uttam Kumaran: which is not that much.

436 00:49:26.340 ⇒ 00:49:29.969 Uttam Kumaran: And there’s only like a 31 government related listings.

437 00:49:31.550 ⇒ 00:49:42.399 Uttam Kumaran: So I’m gonna totally SEO. Get myself to the top of every single thing. And if you click on it like, click on this this one.

438 00:49:43.240 ⇒ 00:49:44.219 Tom Bescherer: this, yeah.

439 00:49:44.990 ⇒ 00:49:45.910 Uttam Kumaran: like.

440 00:49:46.710 ⇒ 00:49:51.529 Uttam Kumaran: it’s a lot of text, but you can see on the right, it says, refreshes daily time coverage.

441 00:49:51.820 ⇒ 00:49:53.350 Uttam Kumaran: It’s free.

442 00:49:53.780 ⇒ 00:50:01.289 Uttam Kumaran: So this is just like a they just like check this as an option, though, right? I will show you my end. It’s literally just

443 00:50:01.470 ⇒ 00:50:11.649 Uttam Kumaran: put. You just put that I’ve been talking to Brian about like we wanna have a data set hosted on like the brain Forge website that, like, maybe you can subscribe to. Or

444 00:50:11.720 ⇒ 00:50:17.009 Uttam Kumaran: maybe you email, maybe after you cause, I think we will get your email if you

445 00:50:17.150 ⇒ 00:50:23.610 Uttam Kumaran: request it. And then I can email you and be like, here’s this like, here’s our shared slack, slack connect, do you? Wanna

446 00:50:24.560 ⇒ 00:50:33.690 Uttam Kumaran: right? So and then that way, I can also begin to engage with those folks and ask if you scroll all the way down to the bottom like these guys only have 3 other datasets.

447 00:50:35.150 ⇒ 00:50:44.649 Tom Bescherer: Yeah, nobody must be making any money off of this whole like marketplace feature. I don’t know. And if you go under providers. The top right?

448 00:50:46.500 ⇒ 00:50:49.410 Uttam Kumaran: There’s only like 529 providers.

449 00:50:50.700 ⇒ 00:50:53.140 Tom Bescherer: Star Schema’s one. A lot of these only have one.

450 00:50:55.110 ⇒ 00:51:06.540 Uttam Kumaran: I was one. Snowflake really wants to push this, and nobody, I think, has really saturated it.

451 00:51:07.130 ⇒ 00:51:19.620 Uttam Kumaran: Yeah, hasn’t it? Hasn’t this been around for like 5 years at this point? No, no, no, it’s only been around for like 2 years. Oh, okay, yeah, it’s not been around for that long. this is like weather data.

452 00:51:19.680 ⇒ 00:51:26.550 Uttam Kumaran: But like. I don’t know. They they haven’t, really. There’s there’s I don’t think anybody’s really like. There’s probably a couple of people that are raking it in.

453 00:51:27.950 ⇒ 00:51:35.779 Uttam Kumaran: So I’m like dude if we can, if and the nice thing is, we don’t need airflow or lambdas. We can run now everything with tasks and python.

454 00:51:35.850 ⇒ 00:51:40.399 Uttam Kumaran: 3 8 like I don’t. I can have everything run on Snowflake.

455 00:51:40.760 ⇒ 00:51:50.800 Uttam Kumaran: That was the thing that was tough before is like I had that external stuff running. Now it’s like. have everything running snowflake tasks, everything on one. Then we just pump these out right?

456 00:51:52.240 ⇒ 00:51:54.510 Tom Bescherer: Yeah, I think it’s pencil. It’s like.

457 00:51:55.430 ⇒ 00:51:59.930 Tom Bescherer: but it’s probably mostly gonna be a marketing thing. And like, I made people aware that this data exists.

458 00:52:00.490 ⇒ 00:52:09.779 Uttam Kumaran: Yeah, pre one. But also again, if they’re able to come back to my site or like, give me their email. Then they’re probably in Snowflake. They’re in the Snowflake world, and I can engage with them

459 00:52:09.970 ⇒ 00:52:21.069 Uttam Kumaran: for other stuff. So that’s kinda what we’re hoping, and it honestly like II say, it took a while just to figure how the stuff works, but fit, getting ready the function to get the data. And some of that is not that bad.

460 00:52:21.270 ⇒ 00:52:25.249 Uttam Kumaran: It’s just like I have. I have to go talk to Snowflake to get approved

461 00:52:25.270 ⇒ 00:52:32.739 Uttam Kumaran: with this. Do bunch of stuff there. I almost had to go tell them like, you know, we’re like legit people like, can we please get access to this.

462 00:52:33.760 ⇒ 00:52:39.140 Uttam Kumaran: So there’s there’s there’s other providers on here who like, have nothing. There’s no information about stuff. And it’s like.

463 00:52:39.930 ⇒ 00:52:41.450 Uttam Kumaran: I gotta report them.

464 00:52:43.070 ⇒ 00:52:47.660 Tom Bescherer: Yeah, I feel like, right, you can just drive, drive everyone else out of the market by

465 00:52:47.960 ⇒ 00:52:57.530 Tom Bescherer: showing that they have low quality data status page. And the something like a data catalog like a month in, or one of those feel like that would put you so far ahead of

466 00:52:57.750 ⇒ 00:53:05.630 Tom Bescherer: any other of these companies in terms of being able to trust the data updates at some reasonable frequency.

467 00:53:05.640 ⇒ 00:53:22.240 Uttam Kumaran: Yeah, I think that that makes sense. I think I’m gonna try to do that. And then one thing I’m gonna try to do is have, like an outsourced person, go through every listing. I’m gonna be like, I want every listing the name, the company and categorize. And that way we can get a date. Full data set of like

468 00:53:22.850 ⇒ 00:53:30.049 Uttam Kumaran: what? Like, how many listings there are across a bunch of different categories and then be able to find out like, okay, cool. There’s a lot of competition here.

469 00:53:30.160 ⇒ 00:53:31.200 Uttam Kumaran: So

470 00:53:32.630 ⇒ 00:53:34.339 Brian Yang: hey, Matt, did it go through?

471 00:53:35.280 ⇒ 00:53:37.529 Tom Bescherer: Let’s check it out.

472 00:53:42.040 ⇒ 00:53:44.580 Tom Bescherer: Oh, okay, so wait. Did it work?

473 00:53:45.330 ⇒ 00:53:49.129 Brian Yang: can you just can you just hit share shares again?

474 00:53:53.680 ⇒ 00:53:56.300 Uttam Kumaran: Well, what is again? Do you see this before

475 00:53:56.580 ⇒ 00:54:00.550 Brian Yang: I forget what are the

476 00:54:00.660 ⇒ 00:54:02.060 Uttam Kumaran: Oh, thanks, ma’am.

477 00:54:02.100 ⇒ 00:54:04.449 Tom Bescherer: Alright, this is new.

478 00:54:05.390 ⇒ 00:54:08.939 Tom Bescherer: alright, this is fine. Fema! Yeah.

479 00:54:09.180 ⇒ 00:54:14.369 Tom Bescherer: Updated daily United States at City level. Nice.

480 00:54:15.190 ⇒ 00:54:16.919 Uttam Kumaran: I wrote that yesterday.

481 00:54:19.520 ⇒ 00:54:20.860 Brian Yang: Co-created.

482 00:54:21.870 ⇒ 00:54:24.579 Uttam Kumaran: Okay, now, Brian, we’re gonna see who’s right.

483 00:54:27.650 ⇒ 00:54:29.850 Brian Yang: Don’t put a snowflake credit on it.

484 00:54:31.120 ⇒ 00:54:32.819 Uttam Kumaran: Yeah, you want to bet $2 on it.

485 00:54:34.340 ⇒ 00:54:35.849 Brian Yang: I’ll bet you $2 on it.

486 00:54:47.460 ⇒ 00:54:48.400 Tom Bescherer: Nice!

487 00:54:51.170 ⇒ 00:54:52.430 Tom Bescherer: What are you betting on?

488 00:54:52.700 ⇒ 00:54:55.749 Uttam Kumaran: On? Who who got here with the experience?

489 00:54:55.920 ⇒ 00:54:56.730 Brian Yang: Yeah.

490 00:55:00.110 ⇒ 00:55:02.020 Brian Yang: Can you see that query on our side.

491 00:55:03.520 ⇒ 00:55:04.940 Uttam Kumaran: No.

492 00:55:06.450 ⇒ 00:55:10.580 Uttam Kumaran: really. Yeah.

493 00:55:14.400 ⇒ 00:55:21.690 Uttam Kumaran: Okay, nice. Wait. Did you see the stuff? Oh, yeah, let’s I guess we’ll see what what you’re currently doing, and see if it matches.

494 00:55:23.230 ⇒ 00:55:27.080 Tom Bescherer: Where is our fema table even living? Oh, Fema.

495 00:55:29.450 ⇒ 00:55:32.900 Tom Bescherer: we have like, declare, yeah, declaration state

496 00:55:34.850 ⇒ 00:55:38.899 Tom Bescherer: where you go? Yeah. Looks like all the same thing. Basically

497 00:55:41.090 ⇒ 00:55:42.389 Tom Bescherer: nice. Yeah.

498 00:55:43.740 ⇒ 00:55:46.730 Tom Bescherer: into the end date. That’s because that date.

499 00:55:55.120 ⇒ 00:55:57.099 Tom Bescherer: Yeah, yeah, great

500 00:55:57.200 ⇒ 00:56:03.659 Brian Yang: data sites should be right as well. We check that to make sure the data, the dates or dates, and the stuff

501 00:56:04.590 ⇒ 00:56:06.520 Brian Yang: stuff like that, like

502 00:56:07.010 ⇒ 00:56:09.839 Brian Yang: and all of our cars. Fix all that stuff.

503 00:56:10.440 ⇒ 00:56:14.669 Uttam Kumaran: comments any comments on every column

504 00:56:15.270 ⇒ 00:56:18.270 Uttam Kumaran: when you’ll in case you wanna hear new engineer and they need

505 00:56:18.340 ⇒ 00:56:20.510 Uttam Kumaran: assistance.

506 00:56:24.430 ⇒ 00:56:26.530 Tom Bescherer: Declaration, title.

507 00:56:27.150 ⇒ 00:56:31.199 Uttam Kumaran: Brian. We should we should add a last refresh.

508 00:56:32.020 ⇒ 00:56:44.609 Brian Yang: There’s one there’s there’s one internal that’s like an internal last refresh. For when when, like fema refers to data set. But yeah, we can add like underscore like last last reset or something like that.

509 00:56:45.000 ⇒ 00:56:48.340 Brian Yang: Hmm, brand everything. I wanna brand everything

510 00:56:50.750 ⇒ 00:56:55.960 Brian Yang: I mean, 5 train has 5 train has a 5 train sync. We could do brain forward synced.

511 00:56:56.310 ⇒ 00:56:57.160 Brian Yang: Yeah.

512 00:57:03.460 ⇒ 00:57:05.630 Uttam Kumaran: So this is their last refresh.

513 00:57:06.200 ⇒ 00:57:07.060 Brian Yang: Right?

514 00:57:09.270 ⇒ 00:57:14.430 Uttam Kumaran: Okay? So it’s it’s like recent data. Well, what’s that mean? That must have been today.

515 00:57:16.030 ⇒ 00:57:19.940 Brian Yang: Yeah, that’s a new Tc, yeah. Yeah. Cause we pull that every day.

516 00:57:22.300 ⇒ 00:57:24.449 Brian Yang: It’s like 1 41 Utc.

517 00:57:25.660 ⇒ 00:57:32.529 Uttam Kumaran: it can’t be 141. You? Oh, yeah, it can be actually 8, 48, 40, am, yeah.

518 00:57:32.740 ⇒ 00:57:34.150 Uttam Kumaran: yeah. 7 am.

519 00:57:34.340 ⇒ 00:57:36.550 Tom Bescherer: wait. So how does it update? Like?

520 00:57:37.680 ⇒ 00:57:41.850 Brian Yang: So we updated on ours, we updated on our side which should update it for you.

521 00:57:43.250 ⇒ 00:57:47.469 Tom Bescherer: Okay, it just replicates, replicates.

522 00:57:48.030 ⇒ 00:57:49.390 Tom Bescherer: nice. That’s cool.

523 00:57:50.600 ⇒ 00:58:01.070 Brian Yang: So we can. We have that we have the thing running as right now. So like, if you just check it tomorrow when there’s a new data set, then that last updated should be, you know, 1223,

524 00:58:02.690 ⇒ 00:58:06.459 Brian Yang: because we’ve got we’ve got stuff running on our site to replicate updated.

525 00:58:08.620 ⇒ 00:58:09.640 Tom Bescherer: Very nice.

526 00:58:11.400 ⇒ 00:58:17.929 Tom Bescherer: Yeah, I feel like there’s real. There is a lot of potential in these kind of data sets being easy at the marketplace.

527 00:58:18.540 ⇒ 00:58:21.660 Tom Bescherer: So this is exciting. Cause. Yeah, I was gonna show you. We have, like.

528 00:58:21.930 ⇒ 00:58:27.749 Tom Bescherer: you know, a ton of bullshit. Yeah, I think there’s even more like

529 00:58:29.430 ⇒ 00:58:31.020 Tom Bescherer: we’ve got a

530 00:58:32.370 ⇒ 00:58:44.140 Tom Bescherer: jobs it does the fetch. And this is broken like a couple of times. So it’s definitely more than what if you if we ran your $20 a month for 3 years? That’s like

531 00:58:45.070 ⇒ 00:58:46.280 100 bucks

532 00:58:47.200 ⇒ 00:58:55.470 Tom Bescherer: that might be roughly bad math. But like are you? Are you? Is this running on airflow? No, this is a itibus batch job.

533 00:58:57.050 ⇒ 00:59:01.460 Uttam Kumaran: which is like bootleg bootleg

534 00:59:02.690 ⇒ 00:59:06.920 Tom Bescherer: we never got we’ve had batch for a lot of these things, and

535 00:59:07.410 ⇒ 00:59:09.529 Tom Bescherer: have never gotten around to setting up your flow.

536 00:59:11.150 ⇒ 00:59:20.969 Uttam Kumaran: Yeah, II like, I don’t know. I’m clearly biased. But like, if you’re, you have an engineer that spends 1 h on this. you pay for a few months of usage.

537 00:59:21.290 ⇒ 00:59:22.310 Tom Bescherer: Right? Yeah.

538 00:59:22.420 ⇒ 00:59:26.109 Uttam Kumaran: But you’re you are right, I think. to contrast.

539 00:59:26.220 ⇒ 00:59:34.080 Uttam Kumaran: If you have an internal person, at least you have some confidence. So, Brian, what I think we should do is like on the brain Forge website, we can have like

540 00:59:34.100 ⇒ 00:59:35.670 Uttam Kumaran: an open page

541 00:59:35.690 ⇒ 00:59:38.440 Uttam Kumaran: about each of our

542 00:59:39.930 ⇒ 00:59:42.320 Uttam Kumaran: I’ll set a page with each of our data sets

543 00:59:42.460 ⇒ 00:59:46.590 Uttam Kumaran: and the ability to like. See when it lasts, refresh or something.

544 00:59:47.770 ⇒ 00:59:52.079 Uttam Kumaran: And we can. We could even run that as a streamlit application directly on softly.

545 00:59:54.310 ⇒ 00:59:55.130 Brian Yang: yeah.

546 00:59:55.980 ⇒ 00:59:59.680 Uttam Kumaran: And then and then again, right there, people can maybe like.

547 01:00:00.360 ⇒ 01:00:07.280 Uttam Kumaran: Oh, II think frankly, it’s just a good also way to get people to subscribe, and then we get more business for the consulting stuff.

548 01:00:07.620 ⇒ 01:00:19.239 Brian Yang: Yes, this pretty much detail as a service, right? Like you’re offering each color as a service. You don’t have to worry. The other thing is thinking this morning, because I’m like looking at 5 tram pricing for one of my clients, and I’m like fuck. They’re robbing.

549 01:00:19.580 ⇒ 01:00:22.830 Uttam Kumaran: It’s a robbery on some of these tables

550 01:00:22.960 ⇒ 01:00:24.260 Tom Bescherer: so expensive.

551 01:00:24.870 ⇒ 01:00:26.200 Uttam Kumaran: And

552 01:00:26.820 ⇒ 01:00:31.479 Uttam Kumaran: I wonder if for some really easy things we could just like

553 01:00:31.520 ⇒ 01:00:40.409 Uttam Kumaran: you can all. Maybe you could even run a we could even provide you a function that like if you give your Api key to the function it like brings in their data. I think there’s interesting ways of like

554 01:00:40.700 ⇒ 01:00:41.730 Uttam Kumaran: doing

555 01:00:42.080 ⇒ 01:00:46.829 Uttam Kumaran: us just owning that that replication for very, very cheap

556 01:00:48.670 ⇒ 01:01:00.590 Brian Yang: right? And stuff like just building out the tools to to kind of do some of that stuff because they wanna make all that money off of that compute right? So they’re they’re doing like the coffee, connect stuff. The the major thing they haven’t built right now is like

557 01:01:00.980 ⇒ 01:01:02.160 Brian Yang: the

558 01:01:02.430 ⇒ 01:01:15.059 Brian Yang: the the Cdc kind of reading from like a Postgres server like a sequel server like there’s no good open source stuff right now for that. But if you’re just like dropping us 3,000 to a bucket, then that can easily all be on.

559 01:01:16.150 ⇒ 01:01:24.689 Brian Yang: They’ll all be on the stuff like, yeah, everybody is the the open source, like version of of kind of fire trends. So

560 01:01:24.740 ⇒ 01:01:31.759 Brian Yang: I think they’re probably offering like platform as a service for Airbnb as well. It’s like one of those open core like business models.

561 01:01:32.160 ⇒ 01:01:33.980 Tom Bescherer: Yeah, it’s much cheaper, but

562 01:01:35.260 ⇒ 01:01:41.380 Uttam Kumaran: running anything yourself has hidden costs more expensive than Snowflake for like for me on this other thing.

563 01:01:42.450 ⇒ 01:01:44.340 Uttam Kumaran: Hi, Shrek.

564 01:01:44.590 ⇒ 01:01:46.530 Brian Yang: yeah, fisher’s so expensive.

565 01:01:48.130 ⇒ 01:01:55.540 Tom Bescherer: I was talking to this guy. Taylor Murphy recently. He they’re doing this interesting product

566 01:01:56.910 ⇒ 01:02:02.980 Tom Bescherer: it’s not called. It’s expanded version of Mintano.

567 01:02:10.510 ⇒ 01:02:16.590 Tom Bescherer: I don’t know. Anyway. the a Milton is also like kind of a solution for this, although it’s not hosted right on Snowflake.

568 01:02:17.380 ⇒ 01:02:21.050 Uttam Kumaran: Yeah, the the thing I was talking to Brian a lot about was like.

569 01:02:21.310 ⇒ 01:02:32.519 Uttam Kumaran: we are just like, you know how like sometimes there’s those fish that sit underneath the shark, and then just eat all their shit like eat all the stuff that kind of like falls out of their mouth. That’s who I want to be for Snowflake.

570 01:02:32.990 ⇒ 01:02:38.759 Uttam Kumaran: And they’re pushing this stuff so hard. And I’m like, we might as well just like, ride that wave.

571 01:02:41.260 ⇒ 01:02:48.250 Tom Bescherer: Yeah, that makes sense. marketplace product is clear, clearly not like

572 01:02:48.310 ⇒ 01:02:54.209 Tom Bescherer: mature. Yet. So they need people like you. That yeah, I think that totally seems that’s the point which we have leverage.

573 01:02:54.350 ⇒ 01:03:09.239 Uttam Kumaran: And again, like, II just think there’s like, we’re probably one of like a couple of 1,000 people who’ve ever gone through this journey. How many providers like there’s only 500 providers, and there’s only 2,000 listing.

574 01:03:10.210 ⇒ 01:03:13.829 Tom Bescherer: And it’s a global product, a global product.

575 01:03:14.850 ⇒ 01:03:20.199 Uttam Kumaran: I think we use 500 data sets at like one company like I.

576 01:03:20.650 ⇒ 01:03:22.740 Uttam Kumaran: So yeah, I think

577 01:03:22.800 ⇒ 01:03:28.769 Uttam Kumaran: I wanna see, when we publish this, what it’s like, how they can help promote it, too. Because.

578 01:03:29.180 ⇒ 01:03:37.430 Uttam Kumaran: like, I wanna run ads on here, I think they could. Really, yeah, we wanna look at Kaggle and hugging face. Yeah, exactly.

579 01:03:39.760 ⇒ 01:03:42.259 Tom Bescherer: You pay. How does hugging face work?

580 01:03:43.100 ⇒ 01:03:46.760 Uttam Kumaran: I think you can run stuff on there, but a lot of their stuff is free.

581 01:03:49.740 ⇒ 01:03:51.110 Tom Bescherer: You should just like

582 01:03:51.290 ⇒ 01:04:04.050 Uttam Kumaran: everything from hugging face and put it on. Yeah, probably violation of their terms. Maybe I don’t know cause the fema terms were just. You got a right that we took it from Fema

583 01:04:06.050 ⇒ 01:04:08.840 Uttam Kumaran: dude like, who owns Wikipedia

584 01:04:09.830 ⇒ 01:04:15.819 Uttam Kumaran: right? True? Yeah. Click on that like is, can you look up? See if there’s like a license? Oh, look! There’s a license at the top.

585 01:04:16.630 ⇒ 01:04:19.089 Uttam Kumaran: CC, what does that mean?

586 01:04:21.200 ⇒ 01:04:24.900 Tom Bescherer: No idea. something you can click on for the license.

587 01:04:26.080 ⇒ 01:04:27.340 Brian Yang: Oh, yeah, okay.

588 01:04:29.340 ⇒ 01:04:32.220 Tom Bescherer: All textual content is licensed under

589 01:04:33.260 ⇒ 01:04:39.269 Tom Bescherer: new free documentation license, creative Commons share like license.

590 01:04:52.090 ⇒ 01:04:54.170 Uttam Kumaran: Like, can I? Can I charge money on this?

591 01:04:56.010 ⇒ 01:04:57.559 Tom Bescherer: I don’t know. I can’t tell.

592 01:04:58.320 ⇒ 01:04:59.959 Uttam Kumaran: I’ve been refused.

593 01:04:59.970 ⇒ 01:05:01.680 Tom Bescherer: even commercially. Okay.

594 01:05:02.950 ⇒ 01:05:04.010 Uttam Kumaran: Word.

595 01:05:05.420 ⇒ 01:05:07.140 Tom Bescherer: yeah, you should just like.

596 01:05:08.170 ⇒ 01:05:11.610 Tom Bescherer: ingest all of huggy face and put it on Snowflake workplace.

597 01:05:14.320 ⇒ 01:05:23.969 Uttam Kumaran: I wanted to. Oh, cause dude, I wanted to own the word fema on there, cause if you go search fema and marketplace. There’s there’s like nothing that comes up. That’s like, appropriate

598 01:05:24.410 ⇒ 01:05:26.040 Tom Bescherer: right? Which has to be

599 01:05:26.230 ⇒ 01:05:28.940 Tom Bescherer: fairly interesting data set for some like.

600 01:05:29.280 ⇒ 01:05:31.970 Uttam Kumaran: I mean, you guys. So

601 01:05:38.870 ⇒ 01:05:40.950 Uttam Kumaran: there’s only this one. This is the only one.

602 01:05:43.460 ⇒ 01:05:45.959 Uttam Kumaran: And they just did like one. Yeah.

603 01:05:46.460 ⇒ 01:05:49.440 Uttam Kumaran: I don’t even bother adding the description, what are these columns.

604 01:05:49.990 ⇒ 01:05:54.240 Uttam Kumaran: and they didn’t do any. They didn’t do any documentation

605 01:05:56.510 ⇒ 01:05:57.910 Uttam Kumaran: fuck these guys.

606 01:06:00.320 ⇒ 01:06:01.940 Tom Bescherer: I think it’s a great idea.

607 01:06:10.830 ⇒ 01:06:17.330 Uttam Kumaran: Yeah, I wanna win the marketplace award of the like provider of the year award next year.

608 01:06:17.640 ⇒ 01:06:19.450 Uttam Kumaran: No, but they gotta make one.

609 01:06:19.460 ⇒ 01:06:22.750 Brian Yang: Yes.

610 01:06:25.140 ⇒ 01:06:33.769 Uttam Kumaran: you’ve given us. So much money. Yeah, this also fun. I don’t know. It’s fun trying the new products out and like, see what do happens.

611 01:06:35.300 ⇒ 01:06:37.930 Tom Bescherer: I see. Yeah, this is what we wanted company to set.

612 01:06:39.480 ⇒ 01:06:44.869 Tom Bescherer: Oh, country. See, everyone’s giving away for free, though that’s a problem, is like.

613 01:06:44.980 ⇒ 01:06:50.189 Uttam Kumaran: no, but this is only one table. And look, it’s not. This is just. This is just about crunch based.

614 01:06:50.910 ⇒ 01:06:53.509 Uttam Kumaran: This is like, this is like one data set

615 01:06:55.470 ⇒ 01:07:02.200 Uttam Kumaran: because you’re not gonna get any of the enrichment information. I think. See? For example, look at that one, the company funding data that’s paid one.

616 01:07:03.470 ⇒ 01:07:07.079 Tom Bescherer: So what do they want for this? 15 a month?

617 01:07:08.460 ⇒ 01:07:13.070 Uttam Kumaran: So random top 10 KUK companies like.

618 01:07:14.760 ⇒ 01:07:16.500 Tom Bescherer: yes, anyone paying for this

619 01:07:21.130 ⇒ 01:07:26.269 Uttam Kumaran: C-suite turnover transition affiliation. See? 500 a month.

620 01:07:34.300 ⇒ 01:07:36.460 Tom Bescherer: Yeah. wow.

621 01:07:37.360 ⇒ 01:07:39.540 Uttam Kumaran: That’s Tripoli per month.

622 01:07:42.840 ⇒ 01:07:44.139 Tom Bescherer: That’s wild.

623 01:07:47.680 ⇒ 01:07:50.420 Tom Bescherer: Equal. R, okay, so they’re actually.

624 01:07:51.530 ⇒ 01:07:56.180 Tom Bescherer: oh, this is so. This is like, okay, this is the like sales targeting use case.

625 01:07:59.640 ⇒ 01:08:00.370 Tom Bescherer: Huh?

626 01:08:03.740 ⇒ 01:08:11.650 Uttam Kumaran: But again, I wanna treat it like if you search for anything, we should have some way of getting the top. And I wanna people request

627 01:08:11.890 ⇒ 01:08:17.110 Uttam Kumaran: like, don’t see what you want like. literally go submit this form

628 01:08:18.380 ⇒ 01:08:21.820 Uttam Kumaran: or call me on my text. Me

629 01:08:25.810 ⇒ 01:08:31.259 Uttam Kumaran: and a lot of these are by request, meaning someone has to call you. what are we talking about?

630 01:08:31.830 ⇒ 01:08:40.430 Uttam Kumaran: Yeah, right? I feel like the whole off. The whole value proposition is ease of use like I click one button.

631 01:08:40.520 ⇒ 01:09:01.040 Tom Bescherer: and I like close my 3 points. The deer ticket and go play legal legends. Yeah, like you, you’re like, fuck this Fema said, I gotta like write this thing you’re like, I wonder if they’re having a marketplace. 20 bucks fuck it. I don’t even know who. I don’t even know who gets charged for this. Yeah, whatever it’s on corporate card.

632 01:09:03.510 ⇒ 01:09:05.430 Tom Bescherer: Yeah, I think it’s a good idea.

633 01:09:05.609 ⇒ 01:09:06.880 Uttam Kumaran: Okay.

634 01:09:07.729 ⇒ 01:09:11.180 Tom Bescherer: cool. Thanks for setting it up.

635 01:09:11.550 ⇒ 01:09:22.459 Uttam Kumaran: Yeah. So maybe once we publish the public one. we’ll send it to you. I think I’m gonna I’m gonna probably hit. Publish this weekend or Monday.

636 01:09:22.920 ⇒ 01:09:26.109 Uttam Kumaran: And then yeah, could be our first customer. If it works

637 01:09:27.149 ⇒ 01:09:30.709 Uttam Kumaran: one of those things reasonable. Right? Like

638 01:09:31.830 ⇒ 01:09:34.660 Tom Bescherer: 20 bucks. Yeah. sure.

639 01:09:36.130 ⇒ 01:09:42.960 Tom Bescherer: I mean, we’ve if we ever have to debug that fema job again. That’s like

640 01:09:42.970 ⇒ 01:09:44.950 Tom Bescherer: pays for itself. If we prevent that.

641 01:09:45.810 ⇒ 01:09:46.660 Yeah.

642 01:09:48.170 ⇒ 01:09:52.740 Uttam Kumaran: And we did a documentation for you. Think about how many minutes that would have taken

643 01:09:53.520 ⇒ 01:10:00.849 Uttam Kumaran: right to write all the calls. That’s like an extra 5 bucks right there on the house, on the house.

644 01:10:01.180 ⇒ 01:10:05.949 It’s so funny pricing these things I was like, I don’t even know.

645 01:10:06.130 ⇒ 01:10:13.879 Uttam Kumaran: I guess, like I was like, Yeah, how long did it take us to write the script and like, make sure it’s right. And like, get all this stuff like 20 bucks

646 01:10:14.570 ⇒ 01:10:16.469 Uttam Kumaran: seems fairly reasonable.

647 01:10:16.740 ⇒ 01:10:20.529 Uttam Kumaran: I use the co-pilot for this kind of like

648 01:10:20.640 ⇒ 01:10:29.550 Uttam Kumaran: documentation generation yet. Well, what I fema has some, and then I

649 01:10:29.650 ⇒ 01:10:34.660 Uttam Kumaran: also for the to stop. So when you see the listing, a lot of that I wrote with with

650 01:10:34.820 ⇒ 01:10:49.379 Uttam Kumaran: with co-pilot because it asked you for like a description examples, what I did is I gave Copilot the entire fe my page. I gave it the table like Ddl and

651 01:10:49.800 ⇒ 01:11:04.140 Uttam Kumaran: I was like curious all the stuff stuff like asks I was like, write me 6 query examples. Write me descriptions. All this stuff I had to write. So yeah, I mean, II want it’s I think it’s pretty easy to to do a lot of this across a lot of data sets.

652 01:11:05.530 ⇒ 01:11:06.400 Tom Bescherer: Yeah.

653 01:11:07.470 ⇒ 01:11:10.990 Tom Bescherer: right? That’d be really useful. Use cases just like

654 01:11:12.550 ⇒ 01:11:15.940 Tom Bescherer: super easy. Everything’s there. You just click it and it’s downloaded

655 01:11:16.010 ⇒ 01:11:22.490 Tom Bescherer: the whole like 10 min. Wait sucks. I feel like, obviously it’s not under your control, but like.

656 01:11:23.640 ⇒ 01:11:27.320 Uttam Kumaran: I don’t think I think that was a one. I think that’s because we did

657 01:11:27.790 ⇒ 01:11:29.139 Tom Bescherer: cause the first one.

658 01:11:29.650 ⇒ 01:11:32.440 Brian Yang: I think it’s because we I think it’s just a

659 01:11:32.560 ⇒ 01:11:42.220 Brian Yang: it’s just like that. II remember trying to get like a sample data set of like baseball stats. And it was like a tiny tiny table and also take 10 min for the thing to show up in my stuff. Like.

660 01:11:43.990 ⇒ 01:11:44.750 Tom Bescherer: yeah.

661 01:11:46.300 ⇒ 01:11:48.630 Uttam Kumaran: I’m gonna ask for property there.

662 01:11:48.830 ⇒ 01:11:51.069 Uttam Kumaran: Yeah, that’s for Fast Lane access.

663 01:11:52.190 ⇒ 01:11:54.549 Brian Yang: That’s like, preferred. Partner.

664 01:11:54.660 ⇒ 01:11:57.440 Uttam Kumaran: Yeah, easy pass. Yeah.

665 01:11:58.790 ⇒ 01:12:01.440 Alright. Well, maybe we’ll send it to you next week.

666 01:12:01.780 ⇒ 01:12:09.019 Tom Bescherer: Yeah. Sending my way. I’ll I’ll try to get it set up. I can. Data sets you think are like y’all need

667 01:12:09.560 ⇒ 01:12:19.870 Uttam Kumaran: let me know. And then hopefully, sometime we’ll we’ll finish up like the ui for, like, taking a look at it, or I’ll just draft something out. Maybe you could be like, yeah, that works

668 01:12:21.260 ⇒ 01:12:21.990 Tom Bescherer: cool.

669 01:12:23.040 ⇒ 01:12:24.930 Tom Bescherer: Appreciate it. Yeah, this is awesome.

670 01:12:25.150 ⇒ 01:12:39.489 Uttam Kumaran: Looking forward to seeing like your names on the Snowflake preferred marketplace partner awards data marketplace preferred program.

671 01:12:39.900 ⇒ 01:12:43.419 Brian Yang: I’ll be like free trial on the house like

672 01:12:46.560 ⇒ 01:12:49.509 Brian Yang: alright. We’ll catch you both later. Alright!

673 01:12:49.870 ⇒ 01:12:51.699 Brian Yang: See, you guys.