Meeting Title: Pool-Parts-AE-Download-Meeting Date: 2024-07-12 Meeting participants: Bryce Codell, Brian Pei, Ryan Luke Daque, Nicolas Sucari


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1 00:00:24.370 00:00:25.190 Nicolas Sucari: Hi! Ryan.

2 00:00:41.830 00:00:42.560 Ryan Luke Daque: Hello.

3 00:00:43.960 00:00:44.740 Ryan Luke Daque: Hi, Nicholas!

4 00:00:44.740 00:00:45.900 Nicolas Sucari: Hi, Ryan, how are you?

5 00:00:45.900 00:00:47.930 Ryan Luke Daque: There you go. I’m doing well. How are you.

6 00:00:48.620 00:00:49.410 Nicolas Sucari: And fine.

7 00:00:50.770 00:00:51.560 Ryan Luke Daque: Nice.

8 00:00:51.560 00:00:53.448 Nicolas Sucari: Already already on Saturday.

9 00:00:54.260 00:00:54.780 Ryan Luke Daque: Yeah.

10 00:00:55.650 00:00:57.659 Ryan Luke Daque: It’s already 2 in the morning. Here.

11 00:00:58.270 00:00:59.070 Nicolas Sucari: Oh.

12 00:00:59.300 00:01:00.830 Nicolas Sucari: terrible! Yeah.

13 00:01:00.970 00:01:05.680 Nicolas Sucari: let’s hope to do this one quick, so you can go rest. Enjoy your weekend.

14 00:01:35.870 00:01:36.959 Nicolas Sucari: hey, Brian?

15 00:01:37.890 00:01:41.140 Nicolas Sucari: We we can’t hear you, or I can hear you.

16 00:01:43.760 00:01:45.860 Ryan Luke Daque: I can’t hear anything either.

17 00:01:46.430 00:01:47.510 Bryce Codell: I was muted.

18 00:01:47.550 00:01:49.120 Bryce Codell: I still can’t hear you, Brian.

19 00:01:58.220 00:01:59.929 Bryce Codell: Well, it was fun while it lasted.

20 00:02:00.430 00:02:01.590 Bryce Codell: See you, Brian

21 00:02:07.080 00:02:10.269 Bryce Codell: Ryan, what’s up? I’m Bryce. It’s nice to meet you.

22 00:02:10.270 00:02:11.429 Ryan Luke Daque: Nice to meet you.

23 00:02:11.970 00:02:14.700 Bryce Codell: I think I may have met you one other time. I think I.

24 00:02:14.700 00:02:15.849 Ryan Luke Daque: So like when you are like.

25 00:02:15.850 00:02:19.009 Bryce Codell: Your team a while back. And yeah, I think you were on that call.

26 00:02:19.510 00:02:21.950 Ryan Luke Daque: Yeah, you were like, showing.

27 00:02:22.230 00:02:25.809 Ryan Luke Daque: like, some new data modeling stuff. Yeah, cool.

28 00:02:28.000 00:02:28.960 Brian Pei: Hello!

29 00:02:29.320 00:02:30.060 Brian Pei: Thank you.

30 00:02:30.060 00:02:31.060 Bryce Codell: Oh!

31 00:02:32.180 00:02:33.690 Brian Pei: Nice beard, Bryce.

32 00:02:34.740 00:02:36.440 Bryce Codell: Nice face. Brian.

33 00:02:37.480 00:02:38.999 Brian Pei: Thank you. Haven’t changed.

34 00:02:39.000 00:02:41.599 Bryce Codell: You don’t have a beard, so you just you know you got a face.

35 00:02:42.010 00:02:44.339 Brian Pei: I can’t grow a beard. It’s hurtful.

36 00:02:45.430 00:02:49.499 Bryce Codell: No like it hurt like it hurts to grow a beer like physically. It’s painful.

37 00:02:49.730 00:02:52.260 Brian Pei: No, it hurts to be reminded that I can’t grow a beard, but I.

38 00:02:52.260 00:02:53.819 Bryce Codell: Every one. I’m sorry.

39 00:02:54.650 00:02:55.760 Bryce Codell: very nice.

40 00:02:56.200 00:02:57.830 Brian Pei: I’m just jealous.

41 00:02:59.172 00:03:05.070 Bryce Codell: And I’m just lately this thing is unwieldy. But how’s everybody doing.

42 00:03:06.230 00:03:07.003 Ryan Luke Daque: Doing well.

43 00:03:08.310 00:03:09.050 Brian Pei: Pretty good.

44 00:03:09.290 00:03:10.390 Brian Pei: Can’t complain.

45 00:03:10.690 00:03:11.729 Brian Pei: I’m here.

46 00:03:13.570 00:03:13.880 Bryce Codell: Man

47 00:03:14.750 00:03:18.060 Bryce Codell: Ryan, how long have you been working with Brain Forge?

48 00:03:18.730 00:03:25.339 Ryan Luke Daque: I’ve been working with them since December last year, so like 6 months, 7 months already.

49 00:03:25.340 00:03:26.869 Bryce Codell: Alright, very tender.

50 00:03:26.870 00:03:27.260 Ryan Luke Daque: Fast.

51 00:03:27.260 00:03:30.690 Bryce Codell: And Brian and Brian, he said, you’re a few weeks in.

52 00:03:31.800 00:03:36.029 Brian Pei: Few weeks in I I helped him right in the beginning.

53 00:03:36.450 00:03:37.689 Brian Pei: and then I

54 00:03:38.050 00:03:44.779 Brian Pei: then I did my own thing. I pop in every now and then. But as of the last couple of weeks I’m I’m I’m in in.

55 00:03:47.600 00:03:50.289 Bryce Codell: But also at spotify also at spotify.

56 00:03:50.700 00:03:54.364 Brian Pei: Yup, that was a secret. But it’s fine. Now I’m kidding. Yeah.

57 00:03:54.670 00:03:56.619 Bryce Codell: And we don’t. Joe, go. Sorry.

58 00:03:56.620 00:03:57.100 Brian Pei: No, it’s.

59 00:03:57.100 00:03:58.570 Bryce Codell: We don’t really talk about that.

60 00:03:58.840 00:04:00.151 Brian Pei: No, we don’t talk about that.

61 00:04:00.610 00:04:01.250 Bryce Codell: Yep.

62 00:04:01.732 00:04:04.149 Bryce Codell: but you’re kinda like an indoor outdoor cat.

63 00:04:04.870 00:04:05.570 Bryce Codell: You’re anyone.

64 00:04:05.570 00:04:06.000 Brian Pei: They disapp.

65 00:04:06.000 00:04:07.050 Bryce Codell: You’re out when you’re out.

66 00:04:07.330 00:04:08.100 Bryce Codell: Yeah.

67 00:04:08.780 00:04:10.083 Brian Pei: Do mornings and nights

68 00:04:10.460 00:04:12.826 Brian Pei: on Brain Forge. And then,

69 00:04:13.630 00:04:15.889 Brian Pei: I’ll write a little code for spotify. No problem.

70 00:04:17.620 00:04:20.059 Bryce Codell: The the joys are over, employment.

71 00:04:20.490 00:04:21.806 Brian Pei: It still inspired me so.

72 00:04:23.630 00:04:24.979 Bryce Codell: I mean, that’s the dream

73 00:04:25.890 00:04:27.570 Bryce Codell: get paid to work minimally.

74 00:04:29.370 00:04:31.989 Bryce Codell: You’re the hero. I don’t really need them.

75 00:04:32.690 00:04:44.220 Bryce Codell: I haven’t left yet, so I am you and I are very similar in that in that regard. I did put in my notice at my day job. So they are ramping my

76 00:04:44.340 00:04:52.399 Bryce Codell: workload down. But yeah, I will shamelessly spend more time on this kind of stuff, cause it is a greater interest to me.

77 00:04:53.440 00:04:53.880 Brian Pei: Same.

78 00:04:54.277 00:04:55.470 Bryce Codell: But yeah, data.

79 00:04:55.470 00:04:56.690 Brian Pei: Science and machine learning.

80 00:04:58.070 00:05:19.460 Bryce Codell: More like analysis and data science. I suppose if there’s if there’s some Ml stuff to do, then I’ll do it. But I definitely gone down more of like a data and analytics and track over the last few years with like snippets of data size stuff. So I know I’m a. I’m a dbt kind of guy now, I guess.

81 00:05:20.570 00:05:27.260 Brian Pei: That’s fine. I’m pretty sure that if you know how a linear regression works, then you can shock every single client

82 00:05:27.270 00:05:29.360 Brian Pei: on earth with just that.

83 00:05:30.140 00:05:30.740 Brian Pei: What’s up.

84 00:05:30.740 00:05:31.610 Bryce Codell: Good point.

85 00:05:31.850 00:05:32.410 Brian Pei: Yeah.

86 00:05:32.590 00:05:38.403 Bryce Codell: It’s a good point. Well, it’s it’s just the number of syllables in that phrase that just that’s the shocking thing.

87 00:05:38.680 00:05:41.139 Brian Pei: She’s like Whoa, this guy, said Regression. That’s crazy.

88 00:05:41.789 00:05:43.089 Bryce Codell: Yeah, cool.

89 00:05:43.090 00:05:45.100 Brian Pei: Correlation, holy Shit.

90 00:05:45.670 00:05:46.420 Bryce Codell: Death.

91 00:05:46.800 00:05:53.390 Bryce Codell: It’s it’s a lot but but anyway, alright, we could banter like this all day that should probably be good.

92 00:05:53.390 00:05:54.910 Brian Pei: And we got 30 min.

93 00:05:55.560 00:05:58.099 Brian Pei: We got 30 min. I slightly prepped.

94 00:05:58.450 00:05:59.580 Brian Pei: or

95 00:06:00.470 00:06:04.589 Brian Pei: brain dumping as much from top to bottom as I possibly can.

96 00:06:05.876 00:06:09.629 Brian Pei: Infrastructure, which also kind of relates to.

97 00:06:09.670 00:06:15.100 Brian Pei: I think everything in here U, Tom and Nico have playbooked, so that

98 00:06:15.150 00:06:19.529 Brian Pei: for future clients it’ll all probably be the same tools.

99 00:06:19.690 00:06:23.263 Brian Pei: and probably the same style. Guide as well.

100 00:06:24.470 00:06:32.260 Brian Pei: You could use improvements like as as anything can and sometimes we slip up the style guide and blah blah blah, but is

101 00:06:32.340 00:06:35.079 Brian Pei: pretty written down so that they can.

102 00:06:35.220 00:06:38.490 Brian Pei: And you’ve you’ve heard this phrase before, plug and play

103 00:06:38.787 00:06:43.679 Brian Pei: so instead of going to a client and figuring it all out from scratch, at least like

104 00:06:44.010 00:06:48.459 Brian Pei: they they have packaged kind of like a lot of this kind of stuff.

105 00:06:49.270 00:06:51.100 Brian Pei: To. Yeah.

106 00:06:51.180 00:06:54.261 Brian Pei: which makes things easier and it’s also good for

107 00:06:54.770 00:07:00.080 Brian Pei: for advertising the the service because of how many times, which is pretty impressive. I mean, I think.

108 00:07:00.180 00:07:08.733 Brian Pei: through brain forge like it’s it’s already been like fiber through like before brain forge time was contracting. And then, when Braden Forge was

109 00:07:09.370 00:07:12.300 Brian Pei: created like 6 or 7 clients.

110 00:07:13.650 00:07:20.770 Brian Pei: and probably more than 10, if you count like the people on Utam’s team who have done contracts in the past

111 00:07:21.020 00:07:21.890 Brian Pei: so

112 00:07:22.090 00:07:23.159 Brian Pei: pretty

113 00:07:25.120 00:07:28.209 Brian Pei: general things that you’ve seen before, Bryce.

114 00:07:28.671 00:07:33.198 Brian Pei: We’re talking 5, Tran, we’re we’re talking Snowflake. That’s basically it. But I’ll try to.

115 00:07:33.500 00:07:34.190 Bryce Codell: A.

116 00:07:34.190 00:07:38.149 Brian Pei: Or do some specific pull parts to go stuff, since you’re.

117 00:07:38.446 00:07:46.459 Bryce Codell: Yeah, that’d be great. Can I ask a couple of questions just from like my self guided exploration that I’ve done over the last few days?

118 00:07:46.890 00:07:47.300 Brian Pei: Please, do.

119 00:07:47.300 00:08:04.130 Bryce Codell: So, okay, in Snowflake there are a few different databases related to a few different Yl providers. So 5 Tran is the only resource that we’re using to replicate data from source systems into snowflake right now, is that correct?

120 00:08:04.970 00:08:09.789 Brian Pei: It’s 5 tran and then a little bit of brain forge, Etl for

121 00:08:10.330 00:08:17.660 Brian Pei: for things that didn’t need a 5 tran connector. And I believe, also like Google sheet stuff. I think

122 00:08:18.360 00:08:19.189 Bryce Codell: So back.

123 00:08:19.190 00:08:24.349 Brian Pei: So if it’s not, if for source data, if it’s not 5 Tran, it’s brain Fourgtl.

124 00:08:24.530 00:08:25.650 Brian Pei: No, thanks.

125 00:08:26.030 00:08:30.793 Brian Pei: I think, like Walmart and Primus were custom connectors. That

126 00:08:33.690 00:08:38.339 Brian Pei: your other friend, whose name is escaping me because I’ve only met him once, I think, set up the data engineer.

127 00:08:38.940 00:08:40.240 Brian Pei: I only talked to him one time.

128 00:08:40.240 00:08:40.780 Nicolas Sucari: Patrick.

129 00:08:40.780 00:08:41.760 Bryce Codell: Patrick.

130 00:08:41.760 00:08:44.343 Brian Pei: Patrick, I believe. And

131 00:08:45.070 00:08:48.678 Brian Pei: so so yeah, anything else, I believe would be

132 00:08:49.280 00:08:51.400 Brian Pei: actually don’t know what next is.

133 00:08:51.990 00:08:52.730 Brian Pei: But everything.

134 00:08:52.730 00:08:55.179 Bryce Codell: He’s a 5 giant competitor. Yeah.

135 00:08:55.340 00:08:56.990 Brian Pei: Oh, cool.

136 00:08:57.758 00:09:04.750 Brian Pei: interesting, probably. Figure out why? Why we have these 2. But yeah other. Otherwise they are transformations.

137 00:09:05.360 00:09:08.430 Bryce Codell: Okay, cool. Yeah. That makes sense.

138 00:09:09.270 00:09:13.889 Brian Pei: Yeah. And I believe, everyone has a dbt dev.

139 00:09:14.070 00:09:17.599 Brian Pei: and you can have one, too, if you would like. I don’t know what.

140 00:09:17.918 00:09:19.830 Bryce Codell: I already got one. My friend.

141 00:09:20.560 00:09:22.688 Brian Pei: Hey? Okay, nice, perfect.

142 00:09:24.490 00:09:27.258 Brian Pei: yeah. So that’s pretty self explanatory.

143 00:09:27.790 00:09:32.020 Brian Pei: dbt, dev is for dev. And then in Dbt, the

144 00:09:32.120 00:09:38.679 Brian Pei: tables that are considered reporting ready and source of truth would be in Dbt. Mart

145 00:09:38.950 00:09:39.870 Brian Pei: pulled out.

146 00:09:40.130 00:09:41.520 Brian Pei: which has

147 00:09:42.020 00:09:57.469 Brian Pei: orders and order lines and all that stuff and orders being union of all of the different order stream. That’s not the right word, Amazon, and shopify and

148 00:09:57.900 00:10:01.729 Brian Pei: direct mail, like all those like kinds of orders are

149 00:10:01.890 00:10:07.140 Brian Pei: kind of like union together in a very classic union. Dbt, script.

150 00:10:07.504 00:10:11.855 Brian Pei: so consolidated data across different sources, I would say, would be in Dbt.

151 00:10:12.967 00:10:22.369 Brian Pei: And then deep. Anything that’s like Dbt shopify is probably just a staging layer to clean up like shopify objects out of

152 00:10:22.510 00:10:26.909 Brian Pei: shopify 5 tran and like, do some joining, and some, you know, cleaning up stuff

153 00:10:27.483 00:10:31.659 Brian Pei: to make like shopify orders before shopify orders is

154 00:10:31.760 00:10:34.289 Brian Pei: unioned into all orders.

155 00:10:35.190 00:10:37.720 Brian Pei: So you got Dbt shopify is a big one.

156 00:10:38.347 00:10:45.052 Brian Pei: Dbt. Walmart. It’s not in here. But we can look at that in a second.

157 00:10:45.800 00:10:54.709 Brian Pei: let’s see, yeah, Klaviyo is marketing Google ads, Facebook ads, marketing and then Dbt, elementary is where we store

158 00:10:55.470 00:10:56.870 Brian Pei: logs and

159 00:10:57.160 00:10:59.890 Brian Pei: run or failures from Dbt tests.

160 00:11:00.800 00:11:04.760 Brian Pei: And this is in. Dbc, right? Yeah. And then.

161 00:11:06.760 00:11:12.190 Brian Pei: yeah, I said this in another meeting before. But if you ever wanted to look at the actual run history.

162 00:11:13.540 00:11:15.519 Brian Pei: think just this one is good enough.

163 00:11:15.830 00:11:18.690 Brian Pei: Dbt, elementary. Dbt, run results

164 00:11:19.030 00:11:22.321 Brian Pei: should have all the Dbt runs

165 00:11:23.240 00:11:24.700 Brian Pei: pass or fail.

166 00:11:25.390 00:11:26.420 Brian Pei: And

167 00:11:27.080 00:11:31.310 Brian Pei: I actually haven’t been using this now. So I don’t have to go to like different windows.

168 00:11:31.510 00:11:32.810 Brian Pei: Yeah.

169 00:11:33.420 00:11:38.089 Brian Pei: where it will also compile the sequel for you, for

170 00:11:38.380 00:12:00.359 Brian Pei: for anything, for for successes, and for failures, and so it also kind of bypasses having to go into Github, if you’re like me and just, I have add, and I just want to be in one place at one time. So this table ran, and if I wanted to see what the compiled sequel is, I can just copy, paste it, and the compiled sequel just shows up for me. It’s pretty big.

171 00:12:00.360 00:12:03.070 Bryce Codell: Super slick man. That’s awesome.

172 00:12:03.070 00:12:05.238 Brian Pei: But that’s really nice to have

173 00:12:05.640 00:12:07.760 Brian Pei: So that’s elementary.

174 00:12:08.344 00:12:10.400 Brian Pei: Do, do I guess?

175 00:12:10.480 00:12:15.879 Brian Pei: Yeah, you know, 5 trend. So I was gonna show you 5 grand, but you know 5 trend.

176 00:12:15.880 00:12:16.690 Bryce Codell: Yeah.

177 00:12:16.730 00:12:18.154 Brian Pei: It’s faith, we got Facebook.

178 00:12:18.440 00:12:19.310 Bryce Codell: 5, 10.

179 00:12:19.650 00:12:27.062 Brian Pei: Ship station is the platform that handles shipments. So this is something this is the company that I didn’t know about before.

180 00:12:27.680 00:12:28.550 Bryce Codell: But.

181 00:12:28.550 00:12:30.430 Brian Pei: Shipment logistics

182 00:12:30.886 00:12:47.549 Brian Pei: so every order through shopify or Amazon or Walmart there the shipping metadata gets sent to ship station, and then ship station does the logistics for the actual shipping. And then that data comes through ship station and so

183 00:12:47.910 00:12:48.790 Brian Pei: believe,

184 00:12:49.990 00:12:55.100 Brian Pei: sometimes shipping costs through like. So shopify would be the website.

185 00:12:55.441 00:13:09.679 Brian Pei: It has a shipping cost and depending on if it changes, or there’s like more orders involved in it. And the wait changes because they’re so big. The true shipping amount, the source of truth for shipping mount would be ship station.

186 00:13:10.130 00:13:10.900 Bryce Codell: So guy.

187 00:13:11.050 00:13:16.796 Brian Pei: But you know all the order detail is fine to get through, shopify and.

188 00:13:17.180 00:13:17.980 Bryce Codell: Is.

189 00:13:17.980 00:13:18.590 Brian Pei: Yeah.

190 00:13:19.050 00:13:19.790 Bryce Codell: Oh, sorry

191 00:13:21.230 00:13:24.570 Bryce Codell: So all the are there, like logistical detail.

192 00:13:24.600 00:13:27.480 Bryce Codell: From that we can get from ship station like

193 00:13:27.620 00:13:37.839 Bryce Codell: when the product leaves the facility, like the manufacturing facility, like where it or where it is in transit, like when it’s like hitting all of its fulfillment, checkpoints and stuff as well.

194 00:13:38.040 00:13:44.530 Brian Pei: Yes, I believe so. Yeah, like all of that extra sexy shipping stuff that shopify wouldn’t have is in Chip station.

195 00:13:45.685 00:13:46.060 Bryce Codell: Guy.

196 00:13:46.440 00:13:50.552 Brian Pei: Through here, and I believe there is.

197 00:13:51.570 00:13:58.269 Brian Pei: well, I specifically know of one ship station model. Yeah. Ship station order items.

198 00:13:59.650 00:14:04.590 Brian Pei: does the by order. Item, it joins orders. Product shipment, item.

199 00:14:05.530 00:14:06.900 Brian Pei: warehouse

200 00:14:07.270 00:14:10.132 Brian Pei: package. So this this one’s pretty good. But

201 00:14:10.670 00:14:11.363 Brian Pei: I know

202 00:14:13.640 00:14:18.340 Brian Pei: crafty data. Scientists like yourself don’t get scared of like jumping into the 5 trend stuff, which is totally fine.

203 00:14:18.340 00:14:19.150 Bryce Codell: Yes.

204 00:14:19.150 00:14:22.370 Brian Pei: There could definitely be more ship station

205 00:14:23.940 00:14:27.840 Brian Pei: tables in a ship station, mart. We only have one right now, but there’s a lot more.

206 00:14:27.840 00:14:28.630 Bryce Codell: More information.

207 00:14:29.532 00:14:33.229 Bryce Codell: So what would be the impetus for using

208 00:14:33.310 00:14:43.600 Bryce Codell: the ship station? This ship station data, like the ship station order items, data versus like the Union, like Mark, like all order items, table.

209 00:14:44.805 00:14:47.990 Bryce Codell: So they have the same content. Or is there are there cases.

210 00:14:47.990 00:14:49.040 Brian Pei: I

211 00:14:49.220 00:14:52.080 Brian Pei: I think all orders might

212 00:14:52.480 00:14:53.270 Brian Pei: have

213 00:14:53.380 00:15:05.510 Brian Pei: all the ship station stuff in it, and so like, which would mean all order, all orders and all order items is an extension of this, anyway. So you would have it. But if this didn’t exist that wouldn’t have it. But there are

214 00:15:05.710 00:15:16.579 Brian Pei: things there are fun things that I don’t know too much about, but things that wouldn’t be in shopify or Amazon would be like the the height and length and and width of a box, and.

215 00:15:16.580 00:15:17.280 Bryce Codell: And the.

216 00:15:17.280 00:15:32.779 Brian Pei: The rate of which the shipping provider, like Fedex, or whatever charges for that kind of box. And so then you can do price predictions for if you got a pump, and you got like 2 other things, and it’s all in one box, and it’s heavier.

217 00:15:33.520 00:15:36.690 Brian Pei: Else orders that same, you know, package

218 00:15:37.352 00:15:45.579 Brian Pei: you can probably predict rates between delivery competitors and also by state

219 00:15:45.981 00:15:51.109 Brian Pei: so where the customer is, and where the warehouse is obviously it’s gonna be

220 00:15:51.776 00:15:53.960 Brian Pei: varied, pricing, depending on

221 00:15:54.850 00:15:58.389 Brian Pei: they fly it, or if it’s a truck or whatever. So

222 00:15:58.880 00:16:05.949 Brian Pei: so the client, I know really cares about shipping, because shipping is really expensive for such a big

223 00:16:05.960 00:16:09.079 Brian Pei: thing that weighs like sometimes like

224 00:16:09.260 00:16:10.660 Brian Pei: 8,000 pounds

225 00:16:11.306 00:16:17.369 Brian Pei: which is like shipping, is, which is probably why I’m talking about it like I’ve never had to deal with

226 00:16:17.580 00:16:21.199 Brian Pei: shipping in my in my life or think about it. And it’s.

227 00:16:21.530 00:16:23.239 Brian Pei: Live. We talk about ship

228 00:16:23.570 00:16:25.830 Brian Pei: shipments and shipping a lot here.

229 00:16:27.600 00:16:32.020 Brian Pei: But that was just a big tangent, because I was in 5 train, and I saw ship station, and that was like the one.

230 00:16:32.020 00:16:32.920 Bryce Codell: Yeah, I’m doing it.

231 00:16:32.920 00:16:34.109 Brian Pei: What that was.

232 00:16:34.340 00:16:37.610 Bryce Codell: Yeah, and 5 trend syncs. All the ship station data.

233 00:16:40.790 00:16:45.211 Brian Pei: Who Tom’s been mad about this before, so I don’t know specifically.

234 00:16:45.580 00:16:48.890 Ryan Luke Daque: It is. Yeah, it should be work. It should be from there.

235 00:16:48.890 00:16:53.498 Bryce Codell: Everything. Can we go? Take a quick look at the schema. Yeah, okay, that looks like everything.

236 00:16:53.770 00:16:54.130 Brian Pei: What did?

237 00:16:54.130 00:17:03.269 Ryan Luke Daque: But I don’t think like shift station is in all orders, though, because I think, though it was before. But then we removed it because there were there were like duplicates

238 00:17:03.330 00:17:05.182 Ryan Luke Daque: just like one

239 00:17:05.829 00:17:12.030 Ryan Luke Daque: there could be, like us, one order with multiple shipments, and that was like causing all of a aggregation.

240 00:17:12.030 00:17:12.569 Bryce Codell: Right.

241 00:17:12.579 00:17:13.119 Ryan Luke Daque: Stuff,

242 00:17:13.660 00:17:14.230 Brian Pei: That makes sense.

243 00:17:14.230 00:17:15.410 Ryan Luke Daque: Failing. So yeah.

244 00:17:15.869 00:17:18.029 Bryce Codell: Yeah, gotcha, that makes sense.

245 00:17:18.030 00:17:21.669 Ryan Luke Daque: So they, we split it into 2 different models. Basically one.

246 00:17:21.960 00:17:22.739 Ryan Luke Daque: you know.

247 00:17:23.730 00:17:24.690 Bryce Codell: Hmm, okay.

248 00:17:24.690 00:17:32.240 Nicolas Sucari: I think that’s why we have the the All order items where you can see the shipping for all of the items inside of the order.

249 00:17:33.310 00:17:37.320 Ryan Luke Daque: There’s even another one like a shipments model, I believe, or like.

250 00:17:37.320 00:17:38.179 Bryce Codell: Something like that.

251 00:17:38.180 00:17:38.720 Ryan Luke Daque: Yeah.

252 00:17:38.720 00:17:40.670 Brian Pei: Yeah, there’s a there is a fat

253 00:17:40.990 00:17:44.010 Brian Pei: shipments straight up, just shipments.

254 00:17:44.160 00:17:46.249 Ryan Luke Daque: Yeah, I think that’s 1 that’s the one.

255 00:17:46.420 00:18:02.679 Brian Pei: That aggregates Amazon and ship station and shopify and tries to get all this. Yeah. So what Ryan said I totally forgot about. So like if you if you get something and it’s wrong and you send it back, and then they send you a new one. It’s 1 order, but it’s 3 shipments, and the shipments cost a lot of money.

256 00:18:04.110 00:18:04.770 Brian Pei: That’s

257 00:18:05.130 00:18:06.810 Brian Pei: the the reason for that.

258 00:18:07.260 00:18:07.960 Bryce Codell: Yeah.

259 00:18:08.946 00:18:18.069 Bryce Codell: Okay, that makes sense. Do we have any data on changes to the statuses of the of an individual shipment

260 00:18:18.110 00:18:21.520 Bryce Codell: like when it’s like when it goes from like

261 00:18:22.030 00:18:25.490 Bryce Codell: order received, to like being fulfilled, to

262 00:18:25.710 00:18:32.029 Bryce Codell: like comp like fulfillment completed, to like it’s been shipped. It’s in transit. It’s been delivered.

263 00:18:34.750 00:18:35.808 Brian Pei: Ryan, do you know this.

264 00:18:36.100 00:18:38.769 Ryan Luke Daque: I don’t even also know the.

265 00:18:38.770 00:18:40.140 Brian Pei: That would like. I would have.

266 00:18:40.140 00:18:41.350 Ryan Luke Daque: Like I was gonna make.

267 00:18:41.350 00:18:43.269 Brian Pei: I was gonna make something up.

268 00:18:44.620 00:18:47.029 Ryan Luke Daque: I guess we can do a query and like should.

269 00:18:47.270 00:18:59.849 Nicolas Sucari: Yeah, I I haven’t seen it yet. Until now, but probably if if there is, we should go into the station and see if that exists. But we are we. We haven’t been using that information.

270 00:19:01.660 00:19:03.899 Bryce Codell: You haven’t been using that information. Okay.

271 00:19:03.900 00:19:04.640 Nicolas Sucari: No.

272 00:19:05.810 00:19:06.139 Brian Pei: Soon as.

273 00:19:06.140 00:19:10.800 Nicolas Sucari: Probably probably there should be something about that. I don’t know if you’re getting it.

274 00:19:10.930 00:19:15.519 Nicolas Sucari: But if we want that information, I think we should. Yeah. Ask

275 00:19:15.650 00:19:18.099 Nicolas Sucari: ship station, or, yeah, I don’t know.

276 00:19:19.750 00:19:23.190 Brian Pei: Probably never use tags before. But I have to assume.

277 00:19:23.320 00:19:25.820 Brian Pei: yeah, there’s a there’s an order.

278 00:19:26.230 00:19:37.539 Brian Pei: There’s an order tag which is like a change log of like one order could have like 20 tags on it by date, and the tags look like it’s stuff like that

279 00:19:37.720 00:19:38.810 Brian Pei: delay

280 00:19:39.360 00:19:41.848 Brian Pei: possible fraud. That’s funny.

281 00:19:42.490 00:19:44.629 Brian Pei: I don’t know. Yeah. So I’ve never looked into this. I just

282 00:19:45.290 00:19:47.480 Brian Pei: checked out what tags is, because usually

283 00:19:47.660 00:19:50.269 Brian Pei: tags is like a change log in my head.

284 00:19:50.560 00:19:51.810 Bryce Codell: Hmm! But.

285 00:19:51.810 00:19:54.930 Brian Pei: I haven’t really dove in as deep cause.

286 00:19:55.500 00:19:57.649 Brian Pei: It’s only been like couple of months. So.

287 00:19:57.650 00:19:58.679 Bryce Codell: Yeah, yeah.

288 00:19:58.968 00:20:00.991 Brian Pei: It’s probably there, but we don’t do.

289 00:20:01.787 00:20:06.099 Brian Pei: we don’t do change, change log stuff for shipments yet, but we probably we should.

290 00:20:06.100 00:20:06.870 Bryce Codell: Yeah, atmosphere.

291 00:20:07.910 00:20:08.840 Bryce Codell: Yad.

292 00:20:09.070 00:20:12.740 Bryce Codell: like it would be helpful. That kind of content would be helpful for

293 00:20:14.430 00:20:22.353 Bryce Codell: what’s the what’s the word like for the analysis that I’m trying to do related to like weather. So

294 00:20:22.820 00:20:41.950 Bryce Codell: like seeing if we’re able to see like the different steps in the fulfillment process, then that’s great. But worst case scenario. I’m sure there’s probably like a like a shipment created Timestamp, and then a shipment fulfilled timestamp, and I can just use that duration as a reasonable, as a reasonable proxy.

295 00:20:42.720 00:20:44.539 Brian Pei: Yeah, looks like fulfillment

296 00:20:44.670 00:20:48.809 Brian Pei: and has a created date and a ship date in order.

297 00:20:48.860 00:20:50.499 Brian Pei: Oh, it has a lot of stuff in here.

298 00:20:51.350 00:20:56.980 Brian Pei: Yeah, I’m not. Gonna I’m not being very helpful here. I’m kind of just like looking like what you would do and stuff like.

299 00:20:57.980 00:21:01.725 Brian Pei: But so let me get out of my add brain

300 00:21:02.420 00:21:12.270 Brian Pei: some. Okay. So I don’t know enough about this. There’s some shipment data that is in Google sheets, and only in Google sheets

301 00:21:13.140 00:21:18.430 Brian Pei: like fright. I know this is really important. I don’t know why this is in a Google sheet and not in a system. I just

302 00:21:18.850 00:21:21.470 Brian Pei: don’t. But there, there are

303 00:21:21.760 00:21:26.269 Brian Pei: like any company, very important Google sheets that people do user input in that we ingest.

304 00:21:26.270 00:21:27.090 Bryce Codell: Yeah.

305 00:21:27.090 00:21:32.789 Ryan Luke Daque: Yeah, there’s a lot. I don’t think that we have like those are like we don’t have

306 00:21:32.970 00:21:39.250 Ryan Luke Daque: like 5. Grand doesn’t support like Ltl, for example, data integration. So I think that’s 1 of

307 00:21:39.350 00:21:43.140 Ryan Luke Daque: license a couple. There’s a couple of there things there are like

308 00:21:43.410 00:21:45.770 Ryan Luke Daque: things to do from Patrick’s.

309 00:21:46.580 00:21:49.739 Ryan Luke Daque: And basically data engineering, just basically

310 00:21:50.370 00:21:53.219 Ryan Luke Daque: ingesting all those into

311 00:21:54.170 00:21:57.610 Ryan Luke Daque: Snowflake. But for now it’s like manually being pulled.

312 00:21:57.610 00:22:00.809 Nicolas Sucari: Yeah, we we talked a little bit about that with Patrick.

313 00:22:03.890 00:22:04.590 Bryce Codell: That makes sense.

314 00:22:04.590 00:22:05.930 Brian Pei: So the same with

315 00:22:06.090 00:22:07.120 Brian Pei: Primus.

316 00:22:07.400 00:22:14.430 Brian Pei: and I also don’t know between Primus and ship station. But I probably should. But I know that this is another

317 00:22:14.590 00:22:16.140 Brian Pei: right now, Google sheet

318 00:22:16.270 00:22:17.960 Brian Pei: shipping solution.

319 00:22:19.530 00:22:27.400 Brian Pei: So might have to put that on the list of things to clarify from from mute some. I just get orders and I fulfill them. I don’t know context.

320 00:22:28.110 00:22:32.699 Brian Pei: Speaking of context, at least, I can talk about our Dbt project

321 00:22:32.790 00:22:34.719 Brian Pei: at the very least. So

322 00:22:36.080 00:22:38.030 Brian Pei: let’s start here.

323 00:22:38.770 00:22:39.450 Brian Pei: It’s

324 00:22:40.070 00:22:58.210 Brian Pei: unlike other dbt projects, it’s 1 directory in. So if you set up a Vm. And try to run dbt stuff in pull parts ago, it won’t work. You have to. Go, you have to CD into Dbt project, which the reason for that is because there are other integrations and packages outside of Dbt, that we use.

325 00:22:58.210 00:22:58.950 Bryce Codell: Reasonable.

326 00:22:58.950 00:23:03.243 Brian Pei: Ago like real, which is the bi solution, which is really nice.

327 00:23:04.218 00:23:22.049 Brian Pei: To be honest. And then integrations is because we run dbt, not using Dbt. Cloud or anything like that. We run it via github actions. So if you ever want to see the runs they’re all synced up to github action. So we don’t have to pay for Dbt cloud, which is nice.

328 00:23:25.020 00:23:30.879 Brian Pei: so that’s how Dbt runs. And then, yeah, if I go into Dbt project.

329 00:23:31.050 00:23:35.170 Brian Pei: The probably biggest ones are macros and models.

330 00:23:36.287 00:23:40.939 Brian Pei: There aren’t too many macros. And our macros aren’t really crazy. It’s

331 00:23:41.070 00:23:42.809 Brian Pei: it’s stuff like like this.

332 00:23:42.820 00:23:44.859 Brian Pei: if it’s the Us. And united

333 00:23:45.210 00:23:49.078 Brian Pei: some pretty basic stuff that just like is used all over

334 00:23:51.750 00:24:02.349 Brian Pei: and this one which sucks because between all the systems, sometimes they you’ve probably seen this before, too. Sometimes they do the whatever. And so this is just custom in here.

335 00:24:02.977 00:24:08.249 Brian Pei: And then models is broken out between staging and marts.

336 00:24:08.620 00:24:15.349 Brian Pei: Everything in marts goes Dbt. Mart, I believe, and everything staging goes to everywhere else.

337 00:24:15.410 00:24:22.749 Brian Pei: I I assume. So if there isn’t a designated schema, I think it by default goes to Dbt.

338 00:24:23.303 00:24:27.289 Brian Pei: For staging, and then but I think we want

339 00:24:27.410 00:24:29.780 Brian Pei: staging models to have their own database.

340 00:24:29.780 00:24:29.990 Ryan Luke Daque: Yeah.

341 00:24:30.510 00:24:40.069 Brian Pei: Because staging model shouldn’t be used in reporting everything in Dbt mark should be used in reporting. But you gotta do some data cleansing every once in a while.

342 00:24:40.320 00:24:41.280 Brian Pei: so I’ll.

343 00:24:41.280 00:24:46.009 Ryan Luke Daque: They’re they’re in the Dbt staging database. If you go. Yeah, it should be.

344 00:24:47.220 00:24:47.790 Brian Pei: Yeah.

345 00:24:48.170 00:24:50.070 Ryan Luke Daque: Yeah. And then somewhere there.

346 00:24:50.070 00:24:52.100 Brian Pei: Oh, there’s oh!

347 00:24:52.100 00:24:53.680 Ryan Luke Daque: Yeah. Should be all there.

348 00:24:53.680 00:24:56.019 Brian Pei: Oh, perfect. Okay, so that makes sense.

349 00:24:56.450 00:24:58.800 Brian Pei: So let’s look at staging.

350 00:24:58.850 00:25:03.489 Brian Pei: It is broken out by application. Basically.

351 00:25:04.930 00:25:05.999 Brian Pei: Which is also nice.

352 00:25:06.160 00:25:15.040 Brian Pei: So I think pretty self explanatory. All this shopify, all the Amazon stops, names on all the shopify, stops in shopify, and then, at some point or another

353 00:25:15.280 00:25:20.129 Brian Pei: Amazon orders and shopify orders, and Walmart orders our union together for all orders.

354 00:25:20.610 00:25:22.610 Brian Pei: So pretty good

355 00:25:22.990 00:25:24.550 Brian Pei: directory layout

356 00:25:25.050 00:25:26.260 Brian Pei: better than we work.

357 00:25:26.320 00:25:34.660 Brian Pei: We work was just monolithic. It’s all there. and then if I go to prod.

358 00:25:35.140 00:25:44.077 Brian Pei: yeah, that makes sense so staging, broken out by application, or whatever you want to call it, and then marts is broken out by

359 00:25:44.480 00:25:46.560 Brian Pei: core object like

360 00:25:46.600 00:25:51.930 Brian Pei: you’ve cut. I keep seeing you saying you’ve seen this stuff before, because Bryce and I work together for like 2 years

361 00:25:51.970 00:25:54.340 Brian Pei: customers inventory Kpi marketing. It’s all this.

362 00:25:54.340 00:25:55.060 Bryce Codell: Yeah.

363 00:25:55.060 00:25:59.050 Brian Pei: Paging. You need it all together. It makes for a very happy, complete data set

364 00:25:59.648 00:26:08.409 Brian Pei: so pretty well organized, in my opinion, and something that I know Tom wants to continue for future clients

365 00:26:08.990 00:26:11.639 Brian Pei: especially because since we’re

366 00:26:11.680 00:26:17.209 Brian Pei: consulting at some point, we hand it off, and we want those engineers to be happy. So the more organization.

367 00:26:17.210 00:26:18.010 Bryce Codell: Yeah, the better.

368 00:26:18.820 00:26:20.220 Bryce Codell: Yeah.

369 00:26:20.220 00:26:20.840 Brian Pei: Yep.

370 00:26:22.040 00:26:30.990 Brian Pei: I think that that’s kind of all I had. I just wanted to show you the layout of stuff, and we have 5 min left perfect cut somebody off. Sorry somebody wanna save me.

371 00:26:33.100 00:26:35.000 Brian Pei: Or was that? Bryce, grunting.

372 00:26:35.000 00:26:40.919 Bryce Codell: And I I don’t know. Yeah, he might have been me grunting, but no. So okay. So just to make sure that I’m like

373 00:26:41.020 00:26:46.690 Bryce Codell: understanding this correctly. So ship station in theory

374 00:26:46.970 00:26:55.200 Bryce Codell: should is basically like the spine that contains all order, information, and then we can left join

375 00:26:55.920 00:27:05.280 Bryce Codell: like Amazon shopify. I don’t know who the other, for, like Walmart Primus, like the other, feel like the other individual, like

376 00:27:05.350 00:27:08.709 Bryce Codell: sellers to ship stations so like

377 00:27:09.060 00:27:14.720 Bryce Codell: ship station handles all of the like the fulfillment for all the Amazon orders, and for all of the

378 00:27:14.750 00:27:19.349 Bryce Codell: shopify orders, and for all the Walmart orders. Am I thinking about that correctly, or

379 00:27:19.770 00:27:21.179 Bryce Codell: is that

380 00:27:21.600 00:27:23.979 Bryce Codell: or my misunderstanding.

381 00:27:27.980 00:27:28.980 Brian Pei: Done that

382 00:27:29.130 00:27:32.570 Brian Pei: in in theory it should. But I know that like

383 00:27:32.960 00:27:34.859 Brian Pei: it doesn’t right now.

384 00:27:34.930 00:27:37.079 Brian Pei: and I don’t know the specifics of why

385 00:27:37.970 00:27:38.760 Bryce Codell: Sounds good.

386 00:27:40.310 00:27:40.790 Brian Pei: So dude.

387 00:27:40.790 00:27:41.730 Bryce Codell: So.

388 00:27:41.730 00:27:44.489 Brian Pei: Like what I’m trying to get at like Kenosha.

389 00:27:44.620 00:27:46.330 Brian Pei: I don’t know. I’m not.

390 00:27:46.580 00:27:47.979 Nicolas Sucari: I’m not sure, too.

391 00:27:48.462 00:27:52.390 Nicolas Sucari: Yeah, probably something we need to ask to some. But

392 00:27:52.570 00:27:56.569 Nicolas Sucari: yeah, I mean, i i i’m I’m not sure about it.

393 00:27:57.050 00:28:02.720 Brian Pei: So it it should. But I believe that, like potentially like bulk freight

394 00:28:03.360 00:28:04.270 Brian Pei: stuff

395 00:28:04.760 00:28:09.519 Brian Pei: is somewhere else. I think that’s the reason that we have like a whole fright section

396 00:28:09.680 00:28:18.380 Brian Pei: or like, or maybe maybe it’s like the shipments of the materials to be built is somewhere else.

397 00:28:19.767 00:28:20.942 Brian Pei: I just

398 00:28:21.950 00:28:24.060 Brian Pei: I don’t want to say yes, in case I’m wrong

399 00:28:24.430 00:28:36.270 Brian Pei: in theory. 95 coverage for orders that go to customers, I want to say will come from ship station, and there’s a there’s a small like 5 that will probably need to ask Patrick or Utah for clarity.

400 00:28:36.820 00:28:38.904 Bryce Codell: Okay? Alright, that’s

401 00:28:39.660 00:28:48.329 Bryce Codell: That sounds good. And then, do we have like, do we have anything on delivery dates? I was, I did a quick

402 00:28:48.846 00:28:57.239 Bryce Codell: like cursory glance, and I didn’t see anything. But if that is not available, yeah, go ahead, Brian.

403 00:28:57.390 00:29:02.270 Ryan Luke Daque: I don’t think so as well like I haven’t seen any delivery dates. We do have

404 00:29:02.560 00:29:04.650 Ryan Luke Daque: only up to ship dates. I guess

405 00:29:04.920 00:29:07.649 Ryan Luke Daque: we haven’t seen any delivery dates.

406 00:29:08.600 00:29:11.050 Bryce Codell: Yeah, okay. But even at a minimum.

407 00:29:11.500 00:29:16.660 Brian Pei: Yeah, if that part’s confusing, because the orders are split out between like shopify and Walmart and Amazon, and stuff.

408 00:29:17.290 00:29:20.449 Brian Pei: Like shopify does have like estimated delivery date.

409 00:29:21.270 00:29:24.660 Brian Pei: But I don’t think that’s actual. And then, like that’s called something else in Amazon.

410 00:29:24.700 00:29:26.520 Brian Pei: You know what I mean. It’s like their Apis are.

411 00:29:26.520 00:29:27.210 Bryce Codell: Just different.

412 00:29:27.210 00:29:35.229 Brian Pei: Sometimes they have that data there, and so I believe there. But I don’t know if we bring it in, because they’re estimated delivery dates.

413 00:29:35.330 00:29:38.409 Brian Pei: but for what you’re doing that might be better than nothing

414 00:29:39.525 00:29:39.830 Bryce Codell: Him.

415 00:29:40.510 00:29:41.270 Brian Pei: But yeah.

416 00:29:41.530 00:29:46.189 Brian Pei: or maybe we just haven’t found it yet. I don’t know. I I also don’t know much about delivery

417 00:29:46.270 00:29:53.650 Brian Pei: delivery dates it. I wanna say, it’s probably like, if you take the shipment date and take whatever estimated day it is. There.

418 00:29:53.650 00:29:54.610 Bryce Codell: Yeah, I, probably.

419 00:29:54.610 00:29:56.979 Brian Pei: To the shipment date. But you know

420 00:29:57.770 00:30:00.949 Brian Pei: trucks fall over sometimes. I don’t know. I don’t know if that’s in the data.

421 00:30:01.210 00:30:01.900 Bryce Codell: Yeah.

422 00:30:03.030 00:30:05.919 Bryce Codell: no, that is, that is fair. So

423 00:30:07.803 00:30:09.810 Bryce Codell: okay. So

424 00:30:10.040 00:30:12.608 Bryce Codell: alright, do we have?

425 00:30:14.910 00:30:18.868 Bryce Codell: Okay, that’s helpful to know.

426 00:30:19.660 00:30:22.390 Brian Pei: Tired truckers get tired is what I should have said. Not that they.

427 00:30:22.390 00:30:23.220 Bryce Codell: Yeah, it’s

428 00:30:24.230 00:30:26.741 Bryce Codell: they simultaneously combust. Yeah.

429 00:30:27.370 00:30:28.570 Brian Pei: Right.

430 00:30:28.570 00:30:29.150 Bryce Codell: Yeah.

431 00:30:29.580 00:30:30.009 Brian Pei: Question.

432 00:30:30.030 00:30:35.100 Bryce Codell: There was nice. There was one other thing that was

433 00:30:35.690 00:30:42.980 Bryce Codell: I wanted to ask, but it is now escaping me, so I will just I will follow up once I once that comes back to

434 00:30:43.130 00:30:45.279 Bryce Codell: my old brain. But

435 00:30:45.811 00:30:47.978 Bryce Codell: yeah, this stuff is super helpful.

436 00:30:48.580 00:30:49.800 Bryce Codell: I

437 00:30:49.920 00:30:57.559 Bryce Codell: think I have everything that I need. Oh, right? So there’s only one warehouse currently. Is that correct?

438 00:31:00.950 00:31:03.050 Brian Pei: Snowflake, warehouse, or physical warehouse.

439 00:31:03.050 00:31:07.396 Bryce Codell: Sorry physical warehouse for Ppt. Bouvarts to go.

440 00:31:08.650 00:31:14.669 Brian Pei: Nico. You can keep me honest there. When we 1st started there was one main warehouse, New York.

441 00:31:14.750 00:31:17.879 Brian Pei: but now they might be storing stuff in Texas

442 00:31:17.990 00:31:18.660 Brian Pei: right.

443 00:31:18.660 00:31:24.810 Nicolas Sucari: No, I don’t think it’s yet in Texas like we’re trying to open one in Texas.

444 00:31:25.159 00:31:28.879 Nicolas Sucari: But I think there is 1 1 in Florida. Now that it’s.

445 00:31:28.880 00:31:29.330 Brian Pei: Oh!

446 00:31:29.330 00:31:29.859 Nicolas Sucari: Start, eating.

447 00:31:29.860 00:31:30.480 Brian Pei: Yeah.

448 00:31:30.480 00:31:31.060 Nicolas Sucari: I’m the one.

449 00:31:31.060 00:31:31.999 Brian Pei: You’re worried about.

450 00:31:32.000 00:31:32.650 Nicolas Sucari: It?

451 00:31:32.800 00:31:33.570 Nicolas Sucari: Yeah.

452 00:31:34.010 00:31:34.430 Brian Pei: Yeah.

453 00:31:34.430 00:31:41.100 Nicolas Sucari: And also I think there will be one in California that is also being set up.

454 00:31:41.583 00:31:47.320 Nicolas Sucari: I’m not sure yet if if they are fully operational yet. But yeah, we can confirm that with with them, too.

455 00:31:48.450 00:31:54.539 Bryce Codell: Gotcha. So then given that there are, is more than one warehouse that’s was involved in the fulfillment process.

456 00:31:54.560 00:31:56.450 Bryce Codell: How do we tell

457 00:31:56.510 00:31:59.439 Bryce Codell: which warehouse fulfilled a given order.

458 00:32:00.060 00:32:06.339 Nicolas Sucari: I think most of the orders for now are being sent by through the New York warehouse.

459 00:32:06.850 00:32:07.299 Bryce Codell: So, then.

460 00:32:07.300 00:32:11.510 Nicolas Sucari: From there. Yeah. And the other ones are just like, really recent.

461 00:32:12.431 00:32:18.529 Nicolas Sucari: They just open. So yeah, I think the majority of the orders are shipped from New York. I think.

462 00:32:18.530 00:32:18.850 Bryce Codell: Okay.

463 00:32:19.898 00:32:21.272 Brian Pei: There. There is

464 00:32:21.800 00:32:25.649 Brian Pei: in the ship station 5 tran database.

465 00:32:25.760 00:32:27.659 Brian Pei: There’s a warehouse id

466 00:32:27.740 00:32:31.749 Brian Pei: that joins to a table that’s just called Warehouse.

467 00:32:32.340 00:32:34.100 Bryce Codell: So slick.

468 00:32:34.470 00:32:35.779 Nicolas Sucari: Yeah, but I think.

469 00:32:36.170 00:32:36.730 Brian Pei: It’s weird.

470 00:32:36.730 00:32:46.350 Nicolas Sucari: Yeah, yeah, yeah. The names of the warehouse that we have there. There are some issues we were looking with Jacob yesterday. So I think we need to clean that up.

471 00:32:47.950 00:32:48.600 Bryce Codell: Yeah. The.

472 00:32:48.600 00:32:53.890 Brian Pei: There’s a lot of like user. There’s a lot of user input where people are just like typing random shit in in the house.

473 00:32:53.910 00:32:58.093 Brian Pei: So it exists. But yeah, it needs a little bit of cleaning, which is why I hesitated.

474 00:32:59.970 00:33:05.210 Bryce Codell: Gotcha duly noted. Yeah? I asked, because we need to pull in weather data.

475 00:33:05.280 00:33:06.350 Bryce Codell: And

476 00:33:06.500 00:33:16.874 Bryce Codell: we like, it’s pretty straightforward to think like we need weather data, and like 2 different places like where the shipments originate and where they end up as well.

477 00:33:17.570 00:33:29.110 Bryce Codell: The like. Finding the shipping destinations is relatively straightforward. But yeah, it was. This was a little more opaque. But I see this now. Thank you very much. This is exactly what I was looking for.

478 00:33:32.940 00:33:33.660 Brian Pei: Sweet.

479 00:33:33.870 00:33:34.240 Bryce Codell: And.

480 00:33:34.240 00:33:39.209 Brian Pei: Actually, I have to jump. I didn’t realize we’re 3 min over, but I’m here and available.

481 00:33:39.990 00:33:42.690 Bryce Codell: Sounds good man. I’m here, and available, too.

482 00:33:43.560 00:33:45.248 Bryce Codell: It’s good to see you, Brian.

483 00:33:45.530 00:33:46.236 Brian Pei: Appreciate you.

484 00:33:48.260 00:33:53.300 Bryce Codell: Alright thank you, everybody, Ryan. Good to see you again. Nico. Been real

485 00:33:53.490 00:33:56.108 Bryce Codell: lot a lot of hanging out. It’s been a lot of fun.

486 00:33:56.370 00:33:57.819 Bryce Codell: Alright, everybody! Thanks, thanks.

487 00:33:57.820 00:33:58.260 Ryan Luke Daque: Guys.

488 00:33:58.260 00:33:58.660 Bryce Codell: To the end.

489 00:33:58.660 00:33:59.099 Ryan Luke Daque: Have a nice.

490 00:33:59.100 00:34:00.040 Nicolas Sucari: Thanks guys.

491 00:34:00.040 00:34:00.430 Ryan Luke Daque: Goodbye!

492 00:34:00.430 00:34:01.509 Bryce Codell: Alright! See you.