Meeting Title: Analytics Engineering Daily Sync Date: 2025-03-12 Meeting participants: Luke Daque, Uttam Kumaran, Demilade Agboola, Caio Velasco
WEBVTT
1 00:00:45.540 ⇒ 00:00:46.199 Demilade Agboola: Oh, good!
2 00:00:46.810 ⇒ 00:00:48.320 Demilade Agboola: I see you’re in Spain.
3 00:00:50.215 ⇒ 00:00:56.140 Caio Velasco: Yes, theoretically, yes, I’m in between Spain, Portugal. But yeah, Spain is my official location.
4 00:00:57.180 ⇒ 00:00:58.642 Demilade Agboola: Oh, nice, nice
5 00:00:59.860 ⇒ 00:01:00.950 Demilade Agboola: What about you?
6 00:01:01.140 ⇒ 00:01:04.030 Demilade Agboola: I’m in Malta right now. So.
7 00:01:04.390 ⇒ 00:01:05.559 Caio Velasco: In a way to help.
8 00:01:05.720 ⇒ 00:01:06.840 Demilade Agboola: Malta.
9 00:01:07.823 ⇒ 00:01:08.950 Caio Velasco: Malta. Cool.
10 00:01:09.260 ⇒ 00:01:14.020 Demilade Agboola: Yeah, but actually, I haven’t been. I’ve done a lot of European countries, but I haven’t done Spain.
11 00:01:14.911 ⇒ 00:01:20.510 Demilade Agboola: I’ve done Portugal, Belgium, Luxembourg, hungry.
12 00:01:20.750 ⇒ 00:01:21.310 Caio Velasco: Nice.
13 00:01:21.310 ⇒ 00:01:29.789 Demilade Agboola: A bunch of them I should actually visit Spain. I’ve not done Spain. I’ve not done Germany. I’ve not done Netherlands. For some reason I’ve not done the like. The big quote, unquote big countries.
14 00:01:30.803 ⇒ 00:01:32.110 Demilade Agboola: But yeah.
15 00:01:32.110 ⇒ 00:01:36.134 Caio Velasco: Did you digital nomading, or did you live there? Work there.
16 00:01:37.120 ⇒ 00:01:38.150 Demilade Agboola: Okay, this will not matter.
17 00:01:38.710 ⇒ 00:01:39.660 Caio Velasco: Oh, cool!
18 00:01:40.380 ⇒ 00:01:41.280 Demilade Agboola: So.
19 00:01:41.780 ⇒ 00:01:43.340 Caio Velasco: And where are you from? Originally.
20 00:01:44.060 ⇒ 00:01:45.150 Demilade Agboola: Oh, Nigeria!
21 00:01:45.440 ⇒ 00:01:46.899 Caio Velasco: Nigeria. Okay, cool.
22 00:01:47.330 ⇒ 00:01:52.540 Demilade Agboola: I was born and raised in Nigeria, and I didn’t leave Nigeria until about 2 years ago.
23 00:01:53.210 ⇒ 00:01:59.290 Caio Velasco: Okay. Okay, yeah. I left Brazil 8 years ago, more or less. 7, 8.
24 00:02:00.040 ⇒ 00:02:01.130 Demilade Agboola: Oh, nice!
25 00:02:01.906 ⇒ 00:02:03.000 Caio Velasco: Also have.
26 00:02:04.780 ⇒ 00:02:06.260 Demilade Agboola: Why did you leave in Brazil.
27 00:02:07.330 ⇒ 00:02:10.090 Caio Velasco: Originally well, I grew up in Rio Janeiro.
28 00:02:11.355 ⇒ 00:02:18.969 Caio Velasco: My whole life, basically. And then I also lived for one year, one year and a half in in Sao paulo
29 00:02:20.645 ⇒ 00:02:26.330 Caio Velasco: which for us is just another like state. But you know, in Europe would be definitely another country.
30 00:02:26.810 ⇒ 00:02:27.769 Demilade Agboola: Yeah, yeah.
31 00:02:29.110 ⇒ 00:02:33.479 Demilade Agboola: I know, President, I have. I have. I met someone who used to live in Sao Paulo, actually.
32 00:02:35.050 ⇒ 00:02:41.709 Caio Velasco: Yeah. So, Brazil, it’s a bit. Let’s say, that’s a bit larger than Europe overall.
33 00:02:42.570 ⇒ 00:02:42.900 Caio Velasco: Yeah.
34 00:02:42.900 ⇒ 00:02:47.049 Caio Velasco: And Rio. Well, in Rio Janito.
35 00:02:47.220 ⇒ 00:02:51.679 Caio Velasco: Rio, Janet and Sao Paulo. We have the same name for the State and for the capital.
36 00:02:52.000 ⇒ 00:02:59.130 Caio Velasco: So in the capital of Rio Janeiro it’s about 6 to 7 million people, and in the capital of
37 00:02:59.370 ⇒ 00:03:02.500 Caio Velasco: but like 11 or 10 overall.
38 00:03:02.660 ⇒ 00:03:08.839 Caio Velasco: And in Sao Paulo, just the capital, is like, I think, 12 million people, and then
39 00:03:09.100 ⇒ 00:03:11.780 Caio Velasco: 30 or something in the whole state.
40 00:03:12.933 ⇒ 00:03:19.399 Caio Velasco: Yeah. But I think in the in the whole country, like 200 million people, it’s a quite like big
41 00:03:20.478 ⇒ 00:03:23.349 Caio Velasco: country, and there are many things going on.
42 00:03:24.310 ⇒ 00:03:25.519 Demilade Agboola: That’s a lot of people.
43 00:03:25.780 ⇒ 00:03:26.440 Caio Velasco: Yeah.
44 00:03:27.750 ⇒ 00:03:28.840 Demilade Agboola: And.
45 00:03:29.610 ⇒ 00:03:33.129 Caio Velasco: But yeah, but I lived in the Us. I lived in the Netherlands.
46 00:03:33.730 ⇒ 00:03:39.529 Caio Velasco: Then I moved down to Porto in Spain. Basically.
47 00:03:39.680 ⇒ 00:03:47.290 Caio Velasco: it’s a nice place it’s a bit similar to my to like Brazilian culture or food or weather.
48 00:03:47.960 ⇒ 00:03:51.259 Caio Velasco: So yeah, I feel a bit more home.
49 00:03:53.290 ⇒ 00:03:56.350 Demilade Agboola: Now is there as well.
50 00:03:58.530 ⇒ 00:04:04.989 Demilade Agboola: Also, I mean how many languages do you speak then? I mean Portuguese and English definitely.
51 00:04:05.210 ⇒ 00:04:10.579 Caio Velasco: Yes, Portuguese native English learning all my life, and
52 00:04:11.697 ⇒ 00:04:17.939 Caio Velasco: Spanish I can definitely hold a conversation in Spanish, but I’m not. I cannot consider myself fluent.
53 00:04:19.339 ⇒ 00:04:23.159 Caio Velasco: But yeah, I can definitely get by. It’s very similar to Portuguese.
54 00:04:25.800 ⇒ 00:04:26.850 Demilade Agboola: Interesting.
55 00:04:31.340 ⇒ 00:04:31.775 Caio Velasco: Sorry.
56 00:04:34.560 ⇒ 00:04:35.430 Demilade Agboola: That’s it.
57 00:04:37.790 ⇒ 00:04:38.470 Caio Velasco: Hi Luke.
58 00:04:43.100 ⇒ 00:04:44.590 Luke Daque: Yeah, how’s everyone doing?
59 00:04:45.090 ⇒ 00:04:48.879 Luke Daque: You are like interviewing Kyle. Then.
60 00:04:49.320 ⇒ 00:04:52.510 Demilade Agboola: I’m just trying to get to know him, you know.
61 00:04:52.510 ⇒ 00:04:53.950 Luke Daque: Yeah, yeah.
62 00:04:55.140 ⇒ 00:04:57.259 Caio Velasco: No, that’s nice. I also like to know people.
63 00:04:58.030 ⇒ 00:04:58.530 Caio Velasco: Okay.
64 00:04:58.530 ⇒ 00:05:00.350 Luke Daque: Yeah, it’s always good.
65 00:05:01.148 ⇒ 00:05:07.509 Caio Velasco: So you you live in Philippines, but you have like stuff in the Us. Or how is it with you?
66 00:05:08.346 ⇒ 00:05:11.910 Luke Daque: No, I yeah, I’m I’m I live in the Philippines.
67 00:05:12.300 ⇒ 00:05:16.389 Luke Daque: I’ve I lived. I’ve lived in the Philippines my whole life. Basically.
68 00:05:17.060 ⇒ 00:05:18.500 Caio Velasco: Okay. Nice.
69 00:05:21.410 ⇒ 00:05:30.730 Luke Daque: But yeah, I’ve been to the Us a couple of times before. For yeah, I was working at Lexmark before, and I did have
70 00:05:30.900 ⇒ 00:05:35.410 Luke Daque: like some travels to the Us. For work purposes.
71 00:05:35.590 ⇒ 00:05:40.649 Luke Daque: but only for a couple of weeks, and then come back here again, and then stuff like that.
72 00:05:41.640 ⇒ 00:05:42.979 Luke Daque: That’s pretty cool.
73 00:05:44.240 ⇒ 00:05:51.859 Caio Velasco: Nice, are we all in the understand? What’s her name?
74 00:05:52.880 ⇒ 00:05:54.730 Caio Velasco: Farms?
75 00:05:55.520 ⇒ 00:05:59.070 Caio Velasco: Yeah. Pull parts total meeting today.
76 00:05:59.070 ⇒ 00:05:59.880 Luke Daque: Bytes.
77 00:06:00.980 ⇒ 00:06:06.529 Luke Daque: I think so. Yes, yeah, I am in that meeting as well.
78 00:06:07.840 ⇒ 00:06:08.979 Demilade Agboola: One meeting, Sir.
79 00:06:09.870 ⇒ 00:06:14.665 Luke Daque: The pool parts one. I don’t think
80 00:06:16.784 ⇒ 00:06:18.299 Caio Velasco: Yeah.
81 00:06:18.300 ⇒ 00:06:19.630 Luke Daque: Yeah, yeah.
82 00:06:21.650 ⇒ 00:06:28.129 Luke Daque: And I believe, yeah, I think that’s the only meeting I have actually for today.
83 00:06:28.560 ⇒ 00:06:29.670 Luke Daque: After this.
84 00:06:31.710 ⇒ 00:06:32.500 Caio Velasco: Okay.
85 00:06:32.630 ⇒ 00:06:33.270 Luke Daque: Yeah.
86 00:06:35.020 ⇒ 00:06:37.317 Caio Velasco: Have you? Have you worked with?
87 00:06:38.630 ⇒ 00:06:41.759 Caio Velasco: the recharge data by any chance?
88 00:06:43.640 ⇒ 00:06:47.230 Luke Daque: Me before. I think I did.
89 00:06:47.900 ⇒ 00:06:52.370 Luke Daque: I did even create, I think, staging models for those before recharge
90 00:06:52.470 ⇒ 00:06:54.419 Luke Daque: can’t quite remember. I’ll have to like
91 00:06:54.670 ⇒ 00:06:58.379 Luke Daque: review that one again. Cause, I believe.
92 00:06:59.290 ⇒ 00:07:02.962 Luke Daque: From what I can recall, recharge was added, using
93 00:07:04.330 ⇒ 00:07:06.320 Luke Daque: What was the portable right.
94 00:07:06.820 ⇒ 00:07:07.640 Caio Velasco: Yes.
95 00:07:07.640 ⇒ 00:07:13.990 Luke Daque: And then, yeah, I think we did create a couple of models related to recharge. But hmm.
96 00:07:14.330 ⇒ 00:07:17.849 Luke Daque: no definite March models, for from what I can remember.
97 00:07:20.210 ⇒ 00:07:21.250 Caio Velasco: Okay. Okay?
98 00:07:22.345 ⇒ 00:07:26.199 Caio Velasco: Yeah, because I have to check. Now, stuff from gorgeous
99 00:07:26.420 ⇒ 00:07:31.070 Caio Velasco: that seems to be connected, or that should be connected with recharge data.
100 00:07:31.700 ⇒ 00:07:34.810 Caio Velasco: but I haven’t touched it yet. So.
101 00:07:36.710 ⇒ 00:07:39.110 Luke Daque: Yeah, I think I remember that, too.
102 00:07:40.730 ⇒ 00:07:45.789 Luke Daque: like, but I can’t quite remember, like how they are connected.
103 00:07:46.060 ⇒ 00:07:51.089 Luke Daque: whether it’s like there’s a user id that can be joined or something like that.
104 00:07:51.980 ⇒ 00:07:58.470 Luke Daque: But yeah, we can discuss that later in the meeting may perhaps like what needs to be done.
105 00:08:00.800 ⇒ 00:08:01.630 Luke Daque: Yeah.
106 00:08:03.660 ⇒ 00:08:15.519 Caio Velasco: How do you guys usually approach like when there’s, for example, Javi has multiple sources come like with multiple. Well, they are coming from multiple places.
107 00:08:17.140 ⇒ 00:08:32.669 Caio Velasco: whoever built the back end and front end, they never document like what they’re doing like what, for example, if there is a customer table coming from recharge, which is like financial data, or whatever that is, subscriptions or something.
108 00:08:32.950 ⇒ 00:08:39.649 Caio Velasco: And then there is gorgeous, which is the tickets that the company has on the customer relationship
109 00:08:39.929 ⇒ 00:08:43.260 Caio Velasco: side. But whoever do you?
110 00:08:43.669 ⇒ 00:08:48.009 Caio Velasco: I mean, I don’t know. Like I’m always interested in. I never see
111 00:08:48.630 ⇒ 00:08:54.929 Caio Velasco: on the client side, like how they themselves connect stuff to make the business work.
112 00:08:58.540 ⇒ 00:09:01.440 Caio Velasco: Not sure if I had any idea.
113 00:09:03.170 ⇒ 00:09:05.270 Luke Daque: You mean from the client side.
114 00:09:06.420 ⇒ 00:09:17.150 Caio Velasco: Yeah, if they if they have subscriptions from recharge, and then customer reaching out via tickets on the before we exist.
115 00:09:17.290 ⇒ 00:09:19.599 Caio Velasco: how did they do the work.
116 00:09:20.880 ⇒ 00:09:21.510 Demilade Agboola: Usually no.
117 00:09:21.510 ⇒ 00:09:22.490 Luke Daque: But I guess.
118 00:09:22.660 ⇒ 00:09:24.860 Demilade Agboola: Or usually in like silo
119 00:09:26.120 ⇒ 00:09:26.470 Luke Daque: Yeah.
120 00:09:26.470 ⇒ 00:09:32.130 Demilade Agboola: So people responsible for setting data, just kind of knew what’s what was needed.
121 00:09:32.540 ⇒ 00:09:33.290 Demilade Agboola: Okay, fine.
122 00:09:33.600 ⇒ 00:09:42.619 Demilade Agboola: Don’t do the background work, which is kind of the problem, because you then realize one person has the knowledge of how everything works, or and that person leave.
123 00:09:44.110 ⇒ 00:09:47.410 Luke Daque: Yeah, it’s like, it’s like, just they, they sign up.
124 00:09:47.710 ⇒ 00:09:50.909 Luke Daque: create an account in recharge, for example.
125 00:09:51.210 ⇒ 00:09:54.160 Luke Daque: so that they can create subscriptions there and then.
126 00:09:54.810 ⇒ 00:10:00.309 Luke Daque: yeah, they sign up for, like gorgeous or oakend, or something for ticketing system.
127 00:10:00.790 ⇒ 00:10:08.650 Luke Daque: and maybe shopify for orders or whatever. And then so they are like different systems, right? So they are most likely siloed.
128 00:10:09.677 ⇒ 00:10:12.799 Luke Daque: But I believe there’s like some way to connect
129 00:10:13.418 ⇒ 00:10:18.129 Luke Daque: subscription to shopify or something like that. But yeah, it’s not
130 00:10:18.830 ⇒ 00:10:26.899 Luke Daque: like nobody really knows from their end most of the time. If they don’t have like technical expertise, like how they are actually connected. And that’s
131 00:10:27.370 ⇒ 00:10:34.290 Luke Daque: yeah. That’s like part of the challenge there for us to understand what’s going on on, how to connect the team.
132 00:10:36.150 ⇒ 00:10:48.290 Caio Velasco: Okay, interesting? Yeah, because I also, I always see 2 layers of problems like, yeah, I understand that it can be on on silos, or different people with with like so much knowledge
133 00:10:48.986 ⇒ 00:10:58.770 Caio Velasco: but I always intrigued that they themselves didn’t document like how things are, because otherwise they could just hand us like some. I don’t know whatever they use like a
134 00:10:58.870 ⇒ 00:11:04.360 Caio Velasco: word document, or anything where there is stuff about the project.
135 00:11:05.790 ⇒ 00:11:12.370 Caio Velasco: And this is one, and the second is how well
136 00:11:13.530 ⇒ 00:11:19.019 Caio Velasco: we are. We are, I mean, most of the time we are text tasked with building
137 00:11:19.300 ⇒ 00:11:25.060 Caio Velasco: like dashboards, reports, even if it’s something regarding their own operations like
138 00:11:25.180 ⇒ 00:11:37.500 Caio Velasco: I don’t know how many subscriptions we had, or how many people canceled. Those things are fine as metrics, but I’m always interested in. How could we actually be
139 00:11:39.190 ⇒ 00:11:45.550 Caio Velasco: making their their business run more smoothly.
140 00:11:45.660 ⇒ 00:11:52.889 Caio Velasco: you know. Like, if if even if they’re not connecting subscription with tick, where there is many tickets from a person.
141 00:11:53.050 ⇒ 00:12:00.239 Caio Velasco: and then that person was not satisfied, and then they cancel the subscription. It’s like 2 sources, different, like gorgeous and
142 00:12:00.510 ⇒ 00:12:01.700 Caio Velasco: and recharge.
143 00:12:01.850 ⇒ 00:12:10.909 Caio Velasco: I mean even their own work. I would always think like, how could we help on that matter? And not only on the, you know, metrics or
144 00:12:11.120 ⇒ 00:12:15.129 Caio Velasco: looking stuff on the on the business end.
145 00:12:16.280 ⇒ 00:12:19.180 Caio Velasco: I don’t know just thinking out loud.
146 00:12:20.470 ⇒ 00:12:21.220 Luke Daque: Yeah.
147 00:12:27.062 ⇒ 00:12:29.050 Caio Velasco: Yes, it benefits.
148 00:12:29.700 ⇒ 00:12:31.370 Caio Velasco: If Tom is joining.
149 00:12:33.820 ⇒ 00:12:37.810 Luke Daque: He, yeah, he just message. It’s gonna be later, I guess.
150 00:12:38.830 ⇒ 00:12:40.350 Luke Daque: But yeah, I think he’s joining.
151 00:12:44.250 ⇒ 00:12:50.660 Luke Daque: I guess, for the pool parts one later. This is, I guess this is a different topic. I think that’d be
152 00:12:51.010 ⇒ 00:12:55.030 Luke Daque: like, at least from from the from the Ae Perspective.
153 00:12:55.580 ⇒ 00:13:04.729 Luke Daque: We have a lot of models in full parts already, but we did not really necessary, because that I believe that was one of the 1st clients that we had
154 00:13:05.150 ⇒ 00:13:10.769 Luke Daque: in Brain forge, and we didn’t really have any standard practices, best practices and stuff like that. And it
155 00:13:11.000 ⇒ 00:13:12.920 Luke Daque: it was just new, Tom and me
156 00:13:13.040 ⇒ 00:13:16.349 Luke Daque: creating all the all the modules and stuff. So
157 00:13:16.880 ⇒ 00:13:22.199 Luke Daque: yeah, for the meeting. Later, maybe we’ll have to discuss things about the tech depths like
158 00:13:22.500 ⇒ 00:13:31.799 Luke Daque: maybe refactoring most of the models to follow our current standard practice like maybe
159 00:13:33.880 ⇒ 00:13:36.830 Luke Daque: I don’t know even just creating the
160 00:13:37.520 ⇒ 00:13:41.140 Luke Daque: putting everything in the analytics database.
161 00:13:41.420 ⇒ 00:13:44.980 Luke Daque: because even in Snowflake it’s like everywhere, all the models are
162 00:13:45.270 ⇒ 00:13:49.569 Luke Daque: like everywhere, even the naming conventions and stuff like that. So
163 00:13:49.990 ⇒ 00:13:59.329 Luke Daque: that’s probably one of the things we can do for pool parts, especially considering there’s nothing there’s there’s new, no new models that need to be created. So
164 00:13:59.610 ⇒ 00:14:05.479 Luke Daque: we can spend more time on tech depths, adding tests and stuff like that.
165 00:14:05.660 ⇒ 00:14:07.450 Luke Daque: And maybe we can also ask
166 00:14:07.740 ⇒ 00:14:12.000 Luke Daque: help from Demi ladi on like the tests and stuff, especially.
167 00:14:12.300 ⇒ 00:14:15.160 Luke Daque: yeah, like, he has lots of experience with that.
168 00:14:16.130 ⇒ 00:14:20.000 Luke Daque: That’s what. Also I am trying to do for stack bits at the moment.
169 00:14:22.070 ⇒ 00:14:25.909 Luke Daque: It’s yeah. The data validation piece is basically the
170 00:14:28.560 ⇒ 00:14:34.490 Luke Daque: the one that’s like pretty challenging at the moment for stack. Let’s especially considering
171 00:14:34.790 ⇒ 00:14:41.799 Luke Daque: like they are using bare metrics for their matrices. And I did also create my own metrics.
172 00:14:42.418 ⇒ 00:14:47.510 Luke Daque: And they don’t seem to match. So I’m not sure, like, if there are any filters that they’re doing
173 00:14:47.720 ⇒ 00:14:49.740 Luke Daque: and stuff like that.
174 00:14:51.440 ⇒ 00:15:00.469 Luke Daque: And so yeah, it’s pretty challenging, especially you don’t know like what’s going on like, there are subscriptions that
175 00:15:00.920 ⇒ 00:15:09.293 Luke Daque: are being upgraded or downgraded in the same month. And then, yeah, that’s gonna yeah, it’s it’s pretty
176 00:15:10.330 ⇒ 00:15:15.480 Luke Daque: yeah, difficult to to determine which subscription we would allocate that
177 00:15:15.970 ⇒ 00:15:21.009 Luke Daque: specific user for a specific month if they just upgraded or downgraded.
178 00:15:21.910 ⇒ 00:15:27.069 Luke Daque: Okay? So yeah, those are like questions that need to be asked. Right?
179 00:15:28.410 ⇒ 00:15:35.579 Caio Velasco: And for for all all our clients so far I mean, we are ingesting everything with portable.
180 00:15:35.720 ⇒ 00:15:49.830 Caio Velasco: but I think portable is just in. I mean, I I haven’t checked, but it seems to be just in ingesting whatever data they have portable is not like, for example, creating Sd type. 2 tables
181 00:15:50.090 ⇒ 00:15:51.240 Caio Velasco: like historical data.
182 00:15:51.240 ⇒ 00:15:51.940 Luke Daque: Yeah, I,
183 00:15:52.150 ⇒ 00:16:02.609 Luke Daque: yeah, I don’t think it’s we’re using portable for all clients like for pool parts. For example, we’re using 5 Tran, we initially used 5 tran for everything, but it’s pretty expensive.
184 00:16:02.790 ⇒ 00:16:05.119 Luke Daque: So that’s why for
185 00:16:07.140 ⇒ 00:16:14.640 Luke Daque: I believe. Javi, we switched to portable. Because, it’s the 5 China was getting too expensive for them.
186 00:16:14.770 ⇒ 00:16:21.460 Luke Daque: and portable is a lot cheaper. And even for Stack Blitz, we’re using a different one polyatomic.
187 00:16:22.250 ⇒ 00:16:26.529 Luke Daque: It’s another 3rd party ingestion platform.
188 00:16:26.800 ⇒ 00:16:32.449 Luke Daque: So yeah, we have multiple, we, we don’t just necessarily use one or the other or the other.
189 00:16:35.360 ⇒ 00:16:39.540 Caio Velasco: But all the tables in raw are historical tables.
190 00:16:41.870 ⇒ 00:16:43.870 Caio Velasco: I mean, are we keeping history?
191 00:16:44.350 ⇒ 00:16:55.539 Caio Velasco: For example, if a customer is deleted, do we know we do. We have the information about that customer? Or if a subscription subscription is canceled, do we still have information.
192 00:16:56.180 ⇒ 00:16:58.829 Luke Daque: Yeah, most of the time it’s still there. Yeah.
193 00:16:59.550 ⇒ 00:17:00.410 Caio Velasco: Okay, cool.
194 00:17:05.819 ⇒ 00:17:08.510 Caio Velasco: Yeah. A lot lot of work for sure.
195 00:17:15.920 ⇒ 00:17:20.030 Luke Daque: How about you? Do, Meladi? What are you like working on at the moment.
196 00:17:24.150 ⇒ 00:17:34.349 Demilade Agboola: so you can hear me. Okay, yeah. So for me, I’m working on the alien project. There seems to be a disparity in the numbers that
197 00:17:34.450 ⇒ 00:17:40.199 Demilade Agboola: on the dashboard and the numbers that our model is creating. So I’m going to look into the
198 00:17:40.420 ⇒ 00:17:45.339 Demilade Agboola: tableau today, I’m just trying to figure out why that is that they is like
199 00:17:46.300 ⇒ 00:17:55.569 Demilade Agboola: cause I know working on it. Are there any like filters being added, or anything’s been done to them to the dashboard so that there is no match.
200 00:17:56.431 ⇒ 00:18:03.259 Demilade Agboola: So for context, the the revenue over the last 10 months on the dashboard is 15 million.
201 00:18:03.830 ⇒ 00:18:16.899 Demilade Agboola: But the revenue, when you run the model and you sum up revenue is 64 million. So it’s a huge difference, and it’s something the CEO has called us out about that. You know, it definitely cannot be 15 million
202 00:18:17.623 ⇒ 00:18:21.830 Demilade Agboola: so just basically trying to figure out where that drop is coming from.
203 00:18:22.697 ⇒ 00:18:41.410 Demilade Agboola: And then we also need to kind of just ensure that what we, what we’re doing in terms of membership plans, and how we are ascribing things like ad spend and revenue into those membership plans as as accurate as possible. So that will be my focus for today and just trying to get that over the line.
204 00:18:42.880 ⇒ 00:18:43.650 Luke Daque: Nice.
205 00:18:49.189 ⇒ 00:18:57.509 Luke Daque: Yeah, it’s the same for me, like for stack, which is, it’s also like, I’m spending most of the time doing data validation at the moment.
206 00:18:58.340 ⇒ 00:18:58.960 Demilade Agboola: Hold on!
207 00:18:58.960 ⇒ 00:19:00.109 Luke Daque: Trying to figure out.
208 00:19:01.870 ⇒ 00:19:07.549 Demilade Agboola: I think that’s the hardest quote. Unquote, hardest part of the work is just ensuring that, like
209 00:19:09.680 ⇒ 00:19:13.850 Demilade Agboola: the numbers actually match or the numbers are what they should be.
210 00:19:14.530 ⇒ 00:19:21.670 Luke Daque: Yeah, I I guess. So i i i agree like creating data models. Isn’t
211 00:19:23.070 ⇒ 00:19:26.949 Luke Daque: that hard? I guess, like, sometimes it’s it’s a bit hard, but
212 00:19:27.290 ⇒ 00:19:33.900 Luke Daque: but doing the data, validation is usually more challenging.
213 00:19:34.440 ⇒ 00:19:37.020 Demilade Agboola: Oh, definitely, okay. I think.
214 00:19:40.250 ⇒ 00:19:43.680 Demilade Agboola: I think for me, it’s it’s always interesting.
215 00:19:43.800 ⇒ 00:19:56.030 Demilade Agboola: I think, for me. I I enjoy it in the sense that it feels like a detective work. So you kind of you’re sitting down and figuring out, okay, so where do things go wrong? And you’re trying to figure out whether it’s the tables, whether it’s logic.
216 00:19:56.230 ⇒ 00:20:04.839 Demilade Agboola: whether it’s how it’s being used. And I think that for me is a. It’s pretty fun. It can be frustrating on some days, but most of the time I find it fun.
217 00:20:05.320 ⇒ 00:20:08.620 Demilade Agboola: Oh, so yeah.
218 00:20:10.570 ⇒ 00:20:13.567 Caio Velasco: Yeah, I agree with that part. Same for me.
219 00:20:14.590 ⇒ 00:20:17.220 Caio Velasco: I. Also, I’m also very interested in
220 00:20:17.390 ⇒ 00:20:24.309 Caio Velasco: after like data science work, because at the end of the day, even though we can do everything correct.
221 00:20:27.780 ⇒ 00:20:29.490 Caio Velasco: I don’t know. Maybe they are
222 00:20:30.890 ⇒ 00:20:36.699 Caio Velasco: reading the data in a naive way, or making some
223 00:20:36.960 ⇒ 00:20:41.380 Caio Velasco: like statistical pitfalls, and I’m also very interested in those things.
224 00:20:43.710 ⇒ 00:20:44.810 Demilade Agboola: Definitely.
225 00:20:44.920 ⇒ 00:20:46.029 Demilade Agboola: I think it’s
226 00:20:47.600 ⇒ 00:20:54.210 Demilade Agboola: it feels like, yeah, it just feels like you’re like Sherlock Holmes. I guess I think that’s the best way to put it.
227 00:20:54.600 ⇒ 00:21:03.219 Demilade Agboola: Okay, so where did we go wrong? And you’re trying to walk back? Go back from like back to front and figure out where exactly the numbers don’t add up.
228 00:21:06.050 ⇒ 00:21:06.650 Caio Velasco: I agree.
229 00:21:09.590 ⇒ 00:21:11.780 Uttam Kumaran: Hey, guys, good morning. Sorry for the delay.
230 00:21:12.260 ⇒ 00:21:12.770 Demilade Agboola: No problem.
231 00:21:12.770 ⇒ 00:21:13.330 Caio Velasco: Good morning!
232 00:21:13.330 ⇒ 00:21:14.040 Luke Daque: Yeah, with them.
233 00:21:15.130 ⇒ 00:21:19.370 Demilade Agboola: So we’re talking about being short when it comes to data figuring out like.
234 00:21:19.370 ⇒ 00:21:20.000 Uttam Kumaran: Haha!
235 00:21:20.500 ⇒ 00:21:23.070 Demilade Agboola: Line, just in case.
236 00:21:23.070 ⇒ 00:21:29.490 Uttam Kumaran: That’s the that’s the job. Have you read any of those books like I? There’s a famous author here.
237 00:21:29.620 ⇒ 00:21:34.170 Uttam Kumaran: Her name is Agatha. Christie.
238 00:21:35.030 ⇒ 00:21:35.600 Demilade Agboola: Yeah.
239 00:21:35.670 ⇒ 00:21:36.870 Uttam Kumaran: Have you heard of her?
240 00:21:37.210 ⇒ 00:21:38.319 Caio Velasco: Yeah, yeah.
241 00:21:39.320 ⇒ 00:21:43.269 Uttam Kumaran: Those books are great like mystery books.
242 00:21:43.860 ⇒ 00:21:47.495 Uttam Kumaran: It just like sucks. It just traps you in.
243 00:21:48.430 ⇒ 00:21:49.190 Demilade Agboola: So let me.
244 00:21:49.370 ⇒ 00:21:55.659 Demilade Agboola: So let me just grind on me. So my dad was he like, especially when he was much younger. He used to love reading.
245 00:21:55.870 ⇒ 00:22:02.920 Demilade Agboola: so I and my siblings are much older than I am, so I kind of grew up as an only child, even though I have 2 siblings.
246 00:22:03.359 ⇒ 00:22:13.989 Demilade Agboola: So I also fell in love with reading, because my dad had, like bookshelves with different books. And so my dad had a book that had a complete collection of Sherlock Holmes. He had the complete collection of, like Shakespeare’s works.
247 00:22:14.500 ⇒ 00:22:17.509 Uttam Kumaran: Wow! Wait! What what did your dad do for work?
248 00:22:18.490 ⇒ 00:22:22.456 Demilade Agboola: He was an accountant. Actually, that’s the funny part. But he was an accountant.
249 00:22:23.080 ⇒ 00:22:23.770 Uttam Kumaran: Wow!
250 00:22:23.970 ⇒ 00:22:30.350 Demilade Agboola: Yeah. But again, that’s a different story. He didn’t. Necessarily, he didn’t necessarily have a choice in what he wanted to become. He just.
251 00:22:30.350 ⇒ 00:22:31.000 Uttam Kumaran: Okay.
252 00:22:31.450 ⇒ 00:22:37.680 Demilade Agboola: You know it was filled on a form for him, and he became an accountant. When I asked him what he wanted to become, he said he would love to become a lawyer.
253 00:22:38.270 ⇒ 00:22:39.410 Demilade Agboola: you know, but.
254 00:22:39.410 ⇒ 00:22:40.390 Uttam Kumaran: Wow!
255 00:22:40.810 ⇒ 00:22:44.559 Caio Velasco: It might be from the movie. The accountant with Ben Affleck.
256 00:22:44.560 ⇒ 00:22:55.243 Uttam Kumaran: I love that movie, too. That’s how I feel. My job is when I think about our job, I feel like, that’s like, Okay, the peak of like. What we do is like the accountant.
257 00:22:58.950 ⇒ 00:23:08.299 Uttam Kumaran: I I don’t know like the reason why. You know I that’s why I find what we do to be different than most data jobs. And this is what I’m pushing
258 00:23:08.550 ⇒ 00:23:20.829 Uttam Kumaran: this group. And like every group, is that like, I want us to not many data teams that I’ve been on fail because they think about data as the last mile like.
259 00:23:21.190 ⇒ 00:23:25.870 Uttam Kumaran: And just like you guys said, that’s not the mile. The last mile is the decision.
260 00:23:26.200 ⇒ 00:23:31.230 Uttam Kumaran: right? And ideally, the the impact that we can measure.
261 00:23:32.301 ⇒ 00:23:38.979 Uttam Kumaran: I think it’s 1 thing to be able to get the data in the warehouse modeled in a dashboard.
262 00:23:39.160 ⇒ 00:23:45.959 Uttam Kumaran: But many people cannot bridge the gap, and as you guys talked about the even the business folks can’t bridge the gap.
263 00:23:46.820 ⇒ 00:23:50.309 Uttam Kumaran: But this is where, like in my career, it’s been.
264 00:23:50.910 ⇒ 00:24:07.589 Uttam Kumaran: you know, it’s been impacted because there have been times where I’m like, well, I know the data the best. This is what you guys should do. And it’s like, Okay, like, there’s no one else in the room that knows what to do. And I want us to move towards that, because for 2 reasons, there’s a business reason, and there’s
265 00:24:07.870 ⇒ 00:24:15.550 Uttam Kumaran: like a fun reason. Fun. Reason is that that’s the fun, right? We get an opportunity to
266 00:24:15.710 ⇒ 00:24:22.589 Uttam Kumaran: be part of like 5 or 6 businesses. Help them grow and help them make decisions.
267 00:24:23.000 ⇒ 00:24:29.804 Uttam Kumaran: You know. I don’t know for me, that’s what that’s more, that’s fun. That’s actually the fun in in a lot of this
268 00:24:30.380 ⇒ 00:24:41.670 Uttam Kumaran: of course, in addition to the all the engineering work. That’s my! That’s my first, st my 1st love of in all of this. But the second piece is, it’s much more
269 00:24:42.427 ⇒ 00:24:50.930 Uttam Kumaran: lucrative meaning. There’s not many companies on Earth that can do the engineering work.
270 00:24:51.240 ⇒ 00:24:55.130 Uttam Kumaran: build the dashboards, and then also offer the strategic guidance.
271 00:24:56.960 ⇒ 00:25:02.830 Uttam Kumaran: And that that last piece is like a huge unlock. In fact.
272 00:25:02.960 ⇒ 00:25:10.709 Uttam Kumaran: that is the reason why we’ve been able to take on clients like Eden. Take on clients like like a future client like urban stems
273 00:25:10.850 ⇒ 00:25:21.129 Uttam Kumaran: is, we’re not just coming in and throwing the dashboards together and saying, Here’s a dashboard. They actually are like, what should we do? And we’re like, okay, we’ll help you. We’ll tell you what to do.
274 00:25:21.746 ⇒ 00:25:25.313 Uttam Kumaran: And we’re able to increase our prices because of that.
275 00:25:26.610 ⇒ 00:25:32.240 Uttam Kumaran: hopefully, that adds a little bit of context. But I, I really, yeah, I really agree.
276 00:25:37.310 ⇒ 00:25:38.210 Caio Velasco: I mean.
277 00:25:41.330 ⇒ 00:25:46.830 Caio Velasco: yeah, it’s definitely like a lot of work. I can see a lot of work in a lot of challenges.
278 00:25:48.070 ⇒ 00:25:49.390 Uttam Kumaran: That’s where we’re gonna go, though.
279 00:25:49.390 ⇒ 00:25:50.499 Caio Velasco: Talking about like.
280 00:25:50.920 ⇒ 00:25:51.570 Uttam Kumaran: Yeah.
281 00:25:54.580 ⇒ 00:25:55.120 Uttam Kumaran: Go ahead.
282 00:25:56.650 ⇒ 00:26:00.179 Caio Velasco: No, I was just gonna say that we were I I was asking them like
283 00:26:01.830 ⇒ 00:26:09.209 Caio Velasco: But like what I what I have to do now like, look into gorgeous stuff, and now also recharge stuff, and then I will. I’m always asking like.
284 00:26:09.890 ⇒ 00:26:19.350 Caio Velasco: who was the person in the client side that built the product. And you know the the back end. Everything like, did they document anything out?
285 00:26:20.010 ⇒ 00:26:24.879 Caio Velasco: They themselves know how to solve issues when happen when it happens.
286 00:26:25.648 ⇒ 00:26:36.050 Caio Velasco: only by starting with that very small thing like, can we help them do that better instead of only, for example, dashboard, which, of course, that has its own.
287 00:26:36.050 ⇒ 00:26:43.990 Caio Velasco: Yes, but you know, there’s even like daily stuff that, aren’t they doing it? Well, Google.
288 00:26:44.670 ⇒ 00:26:54.079 Uttam Kumaran: No, you’re totally right. And and for us, this is where it’s like we think about other services to add right like, for example, Eden just asked, can you help set up their mix panel?
289 00:26:54.570 ⇒ 00:27:05.240 Uttam Kumaran: I don’t know how to do that. We do have one akash on our team knows how to do that. So we’re like, okay, what’s the price? Right? The thing. This is what happens is that when people.
290 00:27:05.610 ⇒ 00:27:08.469 Uttam Kumaran: it’s just like in your life. And this is what I described
291 00:27:09.010 ⇒ 00:27:13.100 Uttam Kumaran: I described. This is like, when you have someone come to your house and fixes.
292 00:27:13.592 ⇒ 00:27:17.789 Uttam Kumaran: You know the cabinets you’re like, hey? Can you also go check the roof or something?
293 00:27:17.970 ⇒ 00:27:18.710 Uttam Kumaran: Right?
294 00:27:18.860 ⇒ 00:27:41.230 Uttam Kumaran: Well, you just find someone that can do the job. And you’re like, what else can you do? And that’s us. That’s us right. That’s everything is, has a lot of parallels, like many people, never have anyone that comes in and does a job right and as soon as they meet someone with confidence that can get it done and can do something that is as complicated as what we do.
295 00:27:42.030 ⇒ 00:27:44.839 Uttam Kumaran: They’re like, what else can you do right for me?
296 00:27:45.120 ⇒ 00:27:51.129 Uttam Kumaran: The math is okay. Should we do that? And at what price?
297 00:27:51.380 ⇒ 00:27:57.360 Uttam Kumaran: Because, of course, as you guys know, there is a there is a challenge to
298 00:27:57.660 ⇒ 00:28:03.679 Uttam Kumaran: like having too many services and not being able to do one of those right.
299 00:28:03.890 ⇒ 00:28:22.880 Uttam Kumaran: So for me, I’m always sales. And me, or whoever is like sort of running execution, are always gonna have this back and forth. Sales are gonna say, Okay, can we go do this. And can we do this thing? I’m gonna say, can could we probably do it? Yes. Can we do it at our level of quality?
300 00:28:23.270 ⇒ 00:28:25.080 Uttam Kumaran: That’s gonna be my question.
301 00:28:26.300 ⇒ 00:28:40.420 Uttam Kumaran: But even in within our small sector of data, right from data engineering to analysis, I think we have a lot. There’s a lot just to do there. I think there’s a huge amount for us for each of us to start to grow, to
302 00:28:40.560 ⇒ 00:28:45.760 Uttam Kumaran: be able to actually run the meetings where we’re giving the feedback right?
303 00:28:45.870 ⇒ 00:28:50.910 Uttam Kumaran: Like, here’s what you guys should take action on. That is something that we
304 00:28:51.080 ⇒ 00:29:07.229 Uttam Kumaran: like to to get to give you guys what the future is, I have to go hire that person right? But I I prefer that to come out of our team is, I don’t know a better person on the team that’s gonna know what the data says than our analysts or or us. So like
305 00:29:07.890 ⇒ 00:29:10.390 Uttam Kumaran: for me, that’s what I’m I’m thinking a lot about.
306 00:29:20.290 ⇒ 00:29:25.110 Demilade Agboola: Sorry. I kind of zoned off like 2 min, because I figured out what the problem is in Eden. So I I was.
307 00:29:25.110 ⇒ 00:29:27.659 Uttam Kumaran: Yeah, yeah. Okay, go. No, that’s fine. Don’t worry.
308 00:29:28.567 ⇒ 00:29:31.659 Demilade Agboola: But I have. I think I figured out why the revenue doesn’t match.
309 00:29:32.120 ⇒ 00:29:33.730 Uttam Kumaran: Okay, yeah. What’d you? What’d you find.
310 00:29:35.030 ⇒ 00:29:43.090 Demilade Agboola: So it’s a function of the formula that is being used. On the revenue. 1st things. First, st there’s a formula being applied
311 00:29:43.280 ⇒ 00:30:01.379 Demilade Agboola: that makes it only for certain types of membership plans. The revenue is only 95%. So there’s a 0 point 9 5 multiplier on that. That’s 1. But the second thing, and, more importantly, and what is the huge disparity is that we’re looking at revenue as total revenue. However.
312 00:30:01.560 ⇒ 00:30:07.099 Demilade Agboola: there isn’t end. Revenue and end revenue is defined as the 1st time revenue, so not the
313 00:30:07.470 ⇒ 00:30:12.060 Demilade Agboola: the 1st time revenue. And so that is obviously much smaller done
314 00:30:12.850 ⇒ 00:30:17.410 Demilade Agboola: than that. So I’m writing that up, but I’m trying to just do the sums
315 00:30:17.550 ⇒ 00:30:22.259 Demilade Agboola: in bigquery so that it matches what my like. What I have seen.
316 00:30:30.750 ⇒ 00:30:36.590 Uttam Kumaran: Yeah, probably a better question for a wish. And Robert and Robert, I’m not exactly sure
317 00:30:37.340 ⇒ 00:30:38.979 Uttam Kumaran: which is the right one.
318 00:30:44.880 ⇒ 00:30:49.360 Demilade Agboola: Honestly, it’s not the model. Actually, it’s the the tableau dashboard itself. I’ve had to dig in.
319 00:30:49.360 ⇒ 00:30:50.530 Uttam Kumaran: Oh!
320 00:30:50.720 ⇒ 00:30:52.740 Demilade Agboola: Yes, it’s the tableau dashboard!
321 00:30:59.620 ⇒ 00:31:01.419 Uttam Kumaran: It’s just pulling the wrong metric.
322 00:31:01.830 ⇒ 00:31:10.700 Demilade Agboola: Yeah, it’s using the column 1st time revenue instead of total revenue, like, there’s a total revenue column, so that disparity
323 00:31:12.426 ⇒ 00:31:13.980 Demilade Agboola: that disparity.
324 00:31:13.980 ⇒ 00:31:16.330 Uttam Kumaran: Yeah, I mean this. So my.
325 00:31:17.310 ⇒ 00:31:25.890 Uttam Kumaran: yeah, I think I think these guys are just they’re not really clear on where to even what? What columns are the appropriate ones. So they’re just picking randomly.
326 00:31:26.090 ⇒ 00:31:28.720 Uttam Kumaran: like, I feel like, there’s a couple of those types of issues.
327 00:31:30.470 ⇒ 00:31:31.300 Demilade Agboola: Yeah,
328 00:31:43.530 ⇒ 00:31:46.070 Demilade Agboola: okay. So I guess we can move this.
329 00:31:48.020 ⇒ 00:31:50.760 Demilade Agboola: We can move on from this, because I feel like this was a
330 00:31:51.660 ⇒ 00:31:57.329 Demilade Agboola: sort of low level problems we have, because, like, I think, the the models were fine
331 00:32:02.230 ⇒ 00:32:03.070 Demilade Agboola: is about.
332 00:32:08.670 ⇒ 00:32:11.459 Caio Velasco: Utm. I have a question regarding recharge.
333 00:32:11.820 ⇒ 00:32:12.690 Uttam Kumaran: Yes.
334 00:32:13.972 ⇒ 00:32:17.649 Caio Velasco: So I was checking the
335 00:32:18.450 ⇒ 00:32:28.360 Caio Velasco: the record for Javi. And I saw that there’s some the data was modeled. At least there are like some Dean tables, type tables
336 00:32:28.500 ⇒ 00:32:30.480 Caio Velasco: and tables.
337 00:32:33.220 ⇒ 00:32:39.659 Caio Velasco: I well, I will check if I can answer, because Robert has a question that he canceled orders and
338 00:32:39.890 ⇒ 00:32:43.120 Caio Velasco: tickets and stuff that goes together.
339 00:32:43.869 ⇒ 00:32:47.960 Caio Velasco: But I didn’t know that there was already recharge data.
340 00:32:49.550 ⇒ 00:32:54.259 Caio Velasco: so I’ll I’ll I’ll check, and I can use those right. Those are not like old things like they are being used.
341 00:32:54.930 ⇒ 00:33:00.299 Uttam Kumaran: Do you want to share screen? So I can see? Sorry. I just, I just switch contacts a little bit.
342 00:33:01.490 ⇒ 00:33:03.861 Uttam Kumaran: And then, yeah, I think also after
343 00:33:05.330 ⇒ 00:33:15.410 Uttam Kumaran: honestly, after today. But maybe even later this afternoon, I can. Probably I’m gonna start running probably daily standups for Javi as well.
344 00:33:17.080 ⇒ 00:33:20.320 Uttam Kumaran: So we can start to talk about that as well.
345 00:33:20.550 ⇒ 00:33:26.960 Uttam Kumaran: Eden seems now that them a lot is here, and I think there’s more motion. I feel a lot better there. So.
346 00:33:28.430 ⇒ 00:33:28.790 Caio Velasco: Okay.
347 00:33:29.490 ⇒ 00:33:32.220 Uttam Kumaran: Yeah, so, yeah, these are.
348 00:33:32.640 ⇒ 00:33:37.359 Uttam Kumaran: I feel like, these are fine. I mean, Luke, you’re on the call. I think you wrote these original
349 00:33:37.470 ⇒ 00:33:38.630 Uttam Kumaran: recharge.
350 00:33:39.400 ⇒ 00:33:40.170 Luke Daque: Yeah.
351 00:33:40.530 ⇒ 00:33:41.150 Uttam Kumaran: Models, right.
352 00:33:41.770 ⇒ 00:33:44.869 Luke Daque: I believe they are. Just.
353 00:33:45.690 ⇒ 00:33:50.200 Luke Daque: Yeah, it looks like I have ink models. I thought, I just created staging ones.
354 00:33:51.000 ⇒ 00:33:57.119 Luke Daque: Yeah, I can take a look again, like how I created those.
355 00:33:57.300 ⇒ 00:34:01.190 Luke Daque: because maybe I already created aggregates in the intermediate models.
356 00:34:04.380 ⇒ 00:34:05.170 Luke Daque: Yeah.
357 00:34:08.020 ⇒ 00:34:08.850 Caio Velasco: Okay.
358 00:34:08.850 ⇒ 00:34:14.149 Luke Daque: Yeah, there’s even March models for prejudge customer dimensions. Looks like.
359 00:34:17.289 ⇒ 00:34:20.109 Caio Velasco: Yeah, they’re just pulling from the end.
360 00:34:21.409 ⇒ 00:34:21.949 Luke Daque: Yep.
361 00:34:22.610 ⇒ 00:34:27.219 Caio Velasco: And the hint is pulling from direct directly from the source. Oh, okay, cool.
362 00:34:28.429 ⇒ 00:34:28.969 Luke Daque: Yeah.
363 00:34:29.340 ⇒ 00:34:30.210 Caio Velasco: We’ll check it.
364 00:34:30.320 ⇒ 00:34:31.010 Caio Velasco: Okay.
365 00:34:34.750 ⇒ 00:34:39.459 Luke Daque: Yeah, it looks like they’re aggregates for the orders. Order quantity
366 00:34:42.610 ⇒ 00:34:44.710 Luke Daque: coming from the line items.
367 00:34:45.400 ⇒ 00:34:47.230 Luke Daque: No, I’m cute.
368 00:34:49.791 ⇒ 00:34:55.040 Caio Velasco: And sorry for the well, for the stupid question, but orders order lines. What? What is the difference.
369 00:34:57.920 ⇒ 00:35:04.500 Luke Daque: You know, when there’s like orders is the actual order. But you know, in an order, there could be multiple
370 00:35:05.220 ⇒ 00:35:08.000 Luke Daque: lines like multiple products are being ordered.
371 00:35:08.460 ⇒ 00:35:09.190 Caio Velasco: Okay.
372 00:35:10.410 ⇒ 00:35:17.429 Luke Daque: So yeah, so one order can have multiple order lines that’s for each brought up being ordered.
373 00:35:20.170 ⇒ 00:35:20.860 Caio Velasco: Great.
374 00:35:22.750 ⇒ 00:35:28.619 Luke Daque: That’s that’s also why we did aggregates on like the quantity. For example.
375 00:35:28.800 ⇒ 00:35:36.300 Luke Daque: because, for example, an order has 2 lines, and the 1st line has a quantity of one, the second line has a quantity of 2,
376 00:35:36.690 ⇒ 00:35:42.800 Luke Daque: so if we aggregate that at an order level, the total quantity would be 3,
377 00:35:43.230 ⇒ 00:35:46.590 Luke Daque: because it would be like the sum of the of both quantities.
378 00:35:47.160 ⇒ 00:35:52.940 Luke Daque: So same with the price and grams, for example, the weight.
379 00:35:53.870 ⇒ 00:35:54.540 Caio Velasco: Okay.
380 00:35:55.870 ⇒ 00:36:01.640 Caio Velasco: Okay, cool, cool, nice. I’ll check it in. Thank you.
381 00:36:04.970 ⇒ 00:36:08.649 Uttam Kumaran: Yeah, basically, it’s like, when you order an Amazon, you order multiple items per order.
382 00:36:09.120 ⇒ 00:36:18.089 Uttam Kumaran: So there are properties of each item, but also properties of the order. The big thing to understand is like similar on Amazon. You may have discounts at the order level.
383 00:36:18.270 ⇒ 00:36:24.300 Uttam Kumaran: but no discounts at the order item level. You also may have those. For example, if you do like a free
384 00:36:24.550 ⇒ 00:36:26.379 Uttam Kumaran: like, buy one, get one free.
385 00:36:26.660 ⇒ 00:36:31.260 Uttam Kumaran: That is a free item, but that’s not a discount at the on the order level.
386 00:36:31.460 ⇒ 00:36:33.429 Uttam Kumaran: There’s some nuances like that.
387 00:36:33.880 ⇒ 00:36:37.230 Uttam Kumaran: This happens across all of all of our E-com sources.
388 00:36:37.810 ⇒ 00:36:38.630 Caio Velasco: Okay.
389 00:36:39.710 ⇒ 00:36:40.850 Luke Daque: That’s where it’s
390 00:36:41.372 ⇒ 00:36:51.039 Luke Daque: pretty annoying sometimes, because, like there are, sometimes there’s an order level discount, and then there are also some that are item level discount. So you’ll have to figure out
391 00:36:51.240 ⇒ 00:36:52.639 Luke Daque: which one is which.
392 00:36:53.720 ⇒ 00:36:55.080 Caio Velasco: Oh, well, that’s tricky!
393 00:36:56.340 ⇒ 00:36:56.960 Luke Daque: Yeah.
394 00:37:00.940 ⇒ 00:37:04.499 Luke Daque: like, even in refunds or returns
395 00:37:04.950 ⇒ 00:37:11.009 Luke Daque: could be an order level refund. Or it can just be a line item, refund.
396 00:37:11.830 ⇒ 00:37:13.110 Luke Daque: Yeah, yeah.
397 00:37:15.650 ⇒ 00:37:19.509 Caio Velasco: Okay, okay. Now, I think I understand Robert’s question better
398 00:37:25.320 ⇒ 00:37:26.100 Caio Velasco: and.
399 00:37:26.400 ⇒ 00:37:29.100 Uttam Kumaran: I have some time later, Kyle, to to meet.
400 00:37:29.350 ⇒ 00:37:36.590 Uttam Kumaran: I’m like I have 2 in person meetings today for sales, but in between. Then I I may have some a little bit of time to chat.
401 00:37:37.840 ⇒ 00:37:40.420 Caio Velasco: Okay, cool ping you.
402 00:37:44.200 ⇒ 00:37:46.220 Caio Velasco: And just last question, like.
403 00:37:46.220 ⇒ 00:37:46.730 Uttam Kumaran: Yeah.
404 00:37:47.268 ⇒ 00:37:55.180 Caio Velasco: 8 months. Right? Like the yeah from come back from Javi Aman.
405 00:37:56.700 ⇒ 00:37:57.960 Uttam Kumaran: Yes, I’m on.
406 00:37:58.310 ⇒ 00:38:05.999 Caio Velasco: No, okay. Is he by any chance on the engineering side or business side? Is it.
407 00:38:06.000 ⇒ 00:38:08.769 Uttam Kumaran: Kind of in. He’s he’s kind of in both.
408 00:38:12.230 ⇒ 00:38:18.239 Uttam Kumaran: Like he he on some on many of these systems he set them up.
409 00:38:18.400 ⇒ 00:38:22.470 Uttam Kumaran: but like setup is not like what we do. Setup is like clicking buttons in a ui.
410 00:38:22.870 ⇒ 00:38:23.480 Caio Velasco: Yep.
411 00:38:23.960 ⇒ 00:38:33.070 Uttam Kumaran: You know. So I think all the context you’re giving it’s helpful.
412 00:38:35.370 ⇒ 00:38:39.220 Uttam Kumaran: So just keep talking to him, cause he’s he’s he’s really curious about our work.
413 00:38:39.610 ⇒ 00:38:49.860 Uttam Kumaran: So I think, like feel free to just answer the questions with as much detail as you need. You’ll answer. I think we’re what most likely we’re gonna have them start to come to stand ups and stuff
414 00:38:50.497 ⇒ 00:38:52.760 Uttam Kumaran: because that will save us like
415 00:38:53.340 ⇒ 00:38:55.540 Uttam Kumaran: a bunch more time. I’m just waiting for
416 00:38:55.730 ⇒ 00:38:58.850 Uttam Kumaran: Steven to start on the project management side. Because
417 00:38:59.867 ⇒ 00:39:02.230 Uttam Kumaran: I want him to own that process.
418 00:39:03.460 ⇒ 00:39:05.749 Caio Velasco: That’d be great if he can just join, you know.
419 00:39:06.080 ⇒ 00:39:06.650 Caio Velasco: Yep.
420 00:39:08.620 ⇒ 00:39:15.730 Caio Velasco: yeah, no nice. Because he asked me something about like, What is March? Because I mentioned, and I was like, Okay, but then I gave like a brief
421 00:39:15.730 ⇒ 00:39:18.430 Caio Velasco: no, it’s good. It’s I think your explanation was great.
422 00:39:19.461 ⇒ 00:39:25.778 Caio Velasco: I always feel like I should. I er on the on which side.
423 00:39:26.230 ⇒ 00:39:32.699 Uttam Kumaran: It’s like, it’s, it’s also like, well, yeah, I mean, sometimes it’s a judgment call, for example, on pool, on on another client.
424 00:39:32.850 ⇒ 00:39:36.870 Uttam Kumaran: This is where it’s like we don’t want to just do the things that are
425 00:39:37.140 ⇒ 00:39:44.364 Uttam Kumaran: that are in our in our purview. But
426 00:39:45.610 ⇒ 00:40:08.250 Uttam Kumaran: sort of not that doesn’t, doesn’t mess up anything on the timeline. If I give you an example, there’s another. There’s another message where I’m on was like, Hey, where is this, hey? Like, are we gonna like, I have, I got good feedback about this thing like, what’s next here? And then Pius was like, Yeah, we could do Xyz xyz xyz. And I’m like dude. You can’t sign up for all that work like we’re not gonna do any of those things.
427 00:40:08.500 ⇒ 00:40:09.549 Uttam Kumaran: you know. And
428 00:40:09.900 ⇒ 00:40:10.950 Caio Velasco: For sure.
429 00:40:11.394 ⇒ 00:40:14.949 Uttam Kumaran: That is like that’s probably the opposite. But
430 00:40:15.080 ⇒ 00:40:25.610 Uttam Kumaran: ultimately these aren’t like we’re not like this isn’t do or die work like it’s always about salvage, I would say, be, my inclination is to be more open.
431 00:40:25.780 ⇒ 00:40:32.079 Uttam Kumaran: because Co. Consultants always will be like oh, I am sorry I can’t tell you that I have to run it through somebody else.
432 00:40:32.250 ⇒ 00:40:42.330 Uttam Kumaran: That’s the difference. That’s like bureaucracy. I want us to be open to it, but also all of us should have, we should work as a team on like, okay, what are we comfortable? Sharing versus? Not
433 00:40:43.100 ⇒ 00:40:47.880 Uttam Kumaran: but sort of think of them. They’re a boss, right, so you don’t want to throw yourself under the bus.
434 00:40:48.120 ⇒ 00:40:48.820 Caio Velasco: You bet!
435 00:40:48.820 ⇒ 00:40:50.669 Uttam Kumaran: You know, and say the wrong thing, but.
436 00:40:52.920 ⇒ 00:40:54.080 Caio Velasco: Okay, got it?
437 00:40:58.960 ⇒ 00:41:01.390 Uttam Kumaran: Cool, I guess. Maybe one more thing.
438 00:41:01.810 ⇒ 00:41:07.840 Uttam Kumaran: Luke. I had a couple of questions about the real for stack. Blitz.
439 00:41:09.505 ⇒ 00:41:11.959 Uttam Kumaran: Yeah, I guess.
440 00:41:12.570 ⇒ 00:41:20.286 Uttam Kumaran: yeah. One, I may think about doing the the templating. Second, yeah, for stripe customers, can I?
441 00:41:21.540 ⇒ 00:41:25.390 Uttam Kumaran: can I remove that? Is that all in is that all that data in customers.
442 00:41:27.510 ⇒ 00:41:31.319 Luke Daque: Stripe customers, was the one. Wait! Let me check.
443 00:41:31.720 ⇒ 00:41:32.680 Uttam Kumaran: Yeah, double, check.
444 00:41:34.160 ⇒ 00:41:36.069 Luke Daque: Because we have users.
445 00:41:37.290 ⇒ 00:41:39.520 Luke Daque: We have stripe users, and we have
446 00:41:42.520 ⇒ 00:41:44.150 Luke Daque: stripe customers.
447 00:41:46.070 ⇒ 00:41:50.449 Uttam Kumaran: Yes, no, we have. We have just dim customers and stripe users.
448 00:41:58.540 ⇒ 00:42:02.940 Luke Daque: Wait in in real, you mean, or the the fact table.
449 00:42:03.210 ⇒ 00:42:04.120 Uttam Kumaran: In real.
450 00:42:07.870 ⇒ 00:42:09.620 Luke Daque: Stripe users
451 00:42:19.410 ⇒ 00:42:25.340 Luke Daque: you remove. Oh, well, I think the customers one is the latest that I showed last
452 00:42:25.962 ⇒ 00:42:30.110 Luke Daque: Friday with Mitch and it
453 00:42:30.580 ⇒ 00:42:33.980 Luke Daque: shows it should show, like the subscriptions.
454 00:42:34.140 ⇒ 00:42:40.110 Luke Daque: active subscriptions for each customer, lifetime value and stuff like that.
455 00:42:46.590 ⇒ 00:42:50.069 Uttam Kumaran: But like, can we move that all? Can we move that all to customers like.
456 00:42:52.130 ⇒ 00:42:54.960 Luke Daque: Yeah, there’s yeah. It looks like there’s 2,
457 00:42:56.970 ⇒ 00:43:02.740 Luke Daque: the the users. One is also coming from stripe, and maybe we can remove the users, one, the stripe users.
458 00:43:03.580 ⇒ 00:43:05.210 Uttam Kumaran: That’s what I mean. That’s what I mean.
459 00:43:05.210 ⇒ 00:43:12.270 Luke Daque: Customers. Yeah, yeah, yeah, we can remove the because it’s it’s essentially
460 00:43:12.500 ⇒ 00:43:15.740 Luke Daque: the same, but different. But yeah, they’re the same sources.
461 00:43:16.030 ⇒ 00:43:18.030 Luke Daque: And it’s yeah, it’s confusing.
462 00:43:18.370 ⇒ 00:43:22.069 Luke Daque: Yeah, let’s let’s remove the users. Let’s retain the customers.
463 00:43:22.780 ⇒ 00:43:23.390 Uttam Kumaran: Okay.
464 00:43:28.200 ⇒ 00:43:29.150 Uttam Kumaran: okay, cool.
465 00:43:29.380 ⇒ 00:43:33.410 Uttam Kumaran: And then, I guess, last item, Devin Lotte, I just checked in one password.
466 00:43:34.055 ⇒ 00:43:37.089 Uttam Kumaran: You you have. You should have added access to that vault.
467 00:43:37.930 ⇒ 00:43:43.376 Demilade Agboola: Yeah, I figured out so I’ve added the a new user with
468 00:43:44.250 ⇒ 00:43:45.330 Uttam Kumaran: Cool one time.
469 00:43:46.650 ⇒ 00:43:54.280 Uttam Kumaran: Nice. Okay? And then I guess while I have you, Kyle, I you’re you’re invited to one password, but you haven’t accepted
470 00:43:54.410 ⇒ 00:43:57.899 Uttam Kumaran: or wait. Maybe I’m wrong. Hold on.
471 00:43:59.360 ⇒ 00:44:00.770 Uttam Kumaran: Someone spelled your name.
472 00:44:01.110 ⇒ 00:44:03.619 Uttam Kumaran: Someone spelled your name. Okay, never mind. You’re here. You’re here. Sorry.
473 00:44:03.620 ⇒ 00:44:04.265 Caio Velasco: Okay.
474 00:44:05.480 ⇒ 00:44:09.649 Demilade Agboola: In a bunch of systems. I’m seeing your name with a CIAO.
475 00:44:10.230 ⇒ 00:44:15.839 Uttam Kumaran: And I’m like yo. He hasn’t accepted any any of his stuff, and then I’m like, Oh, never mind, sorry! So I canceled that.
476 00:44:16.380 ⇒ 00:44:17.460 Caio Velasco: Of course.
477 00:44:20.250 ⇒ 00:44:30.209 Uttam Kumaran: Okay, cool, I think, yeah, this week. I don’t know if we’re gonna get too much stuff about like our data platform. Maybe next week we can start to talk a little bit about
478 00:44:30.390 ⇒ 00:44:33.639 Uttam Kumaran: running Dbt across every client and a few other things.
479 00:44:35.820 ⇒ 00:44:41.909 Uttam Kumaran: And then I think, yeah, Kyle, I’m gonna start to run to some sort of daily meeting for
480 00:44:43.390 ⇒ 00:44:53.039 Uttam Kumaran: for Javi as well. Most likely Steven will come in and and start to run that moving forward. But for each of our clients it seems like that’s a good cadence.
481 00:44:53.340 ⇒ 00:44:59.689 Uttam Kumaran: until unless we find that we’re coming every day and we’re like, hey? There’s nothing to talk about. Then we’ll we’ll sort of move on.
482 00:45:01.300 ⇒ 00:45:02.790 Uttam Kumaran: The lookout for that as well.
483 00:45:03.590 ⇒ 00:45:04.443 Caio Velasco: Okay. Cool.
484 00:45:05.060 ⇒ 00:45:05.610 Uttam Kumaran: Okay.
485 00:45:06.040 ⇒ 00:45:08.130 Uttam Kumaran: Okay, thanks, guys. We’ll talk in slack.
486 00:45:09.430 ⇒ 00:45:10.779 Luke Daque: Sounds good. Thanks. See you.
487 00:45:11.570 ⇒ 00:45:12.150 Caio Velasco: Thank you.
488 00:45:12.150 ⇒ 00:45:12.720 Luke Daque: Bye, bye.