Meeting Title: Luke Daque’s Zoom Meeting Date: 2025-04-08 Meeting participants: Luke Daque, Caio Velasco
WEBVTT
1 00:01:01.310 ⇒ 00:01:03.189 Caio Velasco: Hey, Luke, how’s it going.
2 00:01:06.600 ⇒ 00:01:09.900 Luke Daque: Hi, Kyle, can you hear me?
3 00:01:10.490 ⇒ 00:01:11.069 Caio Velasco: Yes, I can.
4 00:01:11.780 ⇒ 00:01:13.400 Luke Daque: Oh, cool, nice.
5 00:01:13.980 ⇒ 00:01:15.790 Luke Daque: Yeah. I’m doing. Well. How are you.
6 00:01:16.330 ⇒ 00:01:21.130 Caio Velasco: I’m good. I’m also. I was also practicing food, so.
7 00:01:21.789 ⇒ 00:01:23.769 Luke Daque: Yeah, makes sense.
8 00:01:24.110 ⇒ 00:01:25.709 Luke Daque: Yeah, they’re the same thing.
9 00:01:26.640 ⇒ 00:01:38.606 Luke Daque: But anyway, yeah, so yeah, I can get you up to speed with what both parties
10 00:01:39.740 ⇒ 00:01:42.859 Luke Daque: like the request, or like the the tasks. Or
11 00:01:43.140 ⇒ 00:01:45.680 Luke Daque: I guess there’s stuff that we need to do for
12 00:01:47.270 ⇒ 00:01:47.810 Caio Velasco: Sure.
13 00:01:47.810 ⇒ 00:01:48.750 Luke Daque: Bullet points.
14 00:01:50.170 ⇒ 00:01:51.369 Caio Velasco: Okay, just to give
15 00:01:51.780 ⇒ 00:01:59.910 Caio Velasco: like an overview. And then we can dive into the test. And let’s see what you where you’re doing, so that I can at least learn a bit.
16 00:02:01.350 ⇒ 00:02:03.960 Caio Velasco: See how can help to to review those stuff.
17 00:02:05.320 ⇒ 00:02:06.530 Luke Daque: Oh, yeah, sure.
18 00:02:06.950 ⇒ 00:02:14.999 Luke Daque: So I guess. Yeah, I don’t know how we can start this.
19 00:02:19.010 ⇒ 00:02:26.050 Caio Velasco: Maybe. Well, maybe we can start with the sources or the overall id. I don’t even remember like.
20 00:02:26.320 ⇒ 00:02:29.560 Caio Velasco: what is poop bars trying to do at home what it sells.
21 00:02:29.920 ⇒ 00:02:33.549 Caio Velasco: I know that we were trying to do like some forecasting models for them.
22 00:02:34.600 ⇒ 00:02:35.570 Luke Daque: Yeah.
23 00:02:35.790 ⇒ 00:02:36.350 Caio Velasco: Perfect.
24 00:02:36.350 ⇒ 00:02:45.620 Luke Daque: Yeah, that. But that’s really not really what I’m I’m working on at the moment, so I can show you. So there was this, let me share my screen.
25 00:02:49.260 ⇒ 00:02:50.590 Luke Daque: You see my screen.
26 00:02:51.990 ⇒ 00:02:52.900 Caio Velasco: Yeah, thank, you.
27 00:02:54.350 ⇒ 00:02:57.780 Luke Daque: So yeah, last week, I believe. Monday.
28 00:02:59.655 ⇒ 00:03:08.960 Luke Daque: There was this message from Ben, basically saying that he wasn’t
29 00:03:11.270 ⇒ 00:03:16.990 Luke Daque: like there was no way for him to be able to drill down, do
30 00:03:17.130 ⇒ 00:03:20.900 Luke Daque: for the specific dashboard, the Daily Kpis?
31 00:03:21.060 ⇒ 00:03:24.025 Luke Daque: There was no, it was aggregated
32 00:03:24.890 ⇒ 00:03:30.999 Luke Daque: for everything, basically. And he was, there was no way for him to drill down which selling platform, or
33 00:03:31.140 ⇒ 00:03:41.250 Luke Daque: which fulfillment channel, or what? Which product class like, whether it’s a heat pump or whatever item, basically.
34 00:03:41.440 ⇒ 00:03:49.550 Luke Daque: right? So like, it was like, the total sales will be just all total total sales for everything. Total calls for everything. So yeah.
35 00:03:49.660 ⇒ 00:03:50.990 Luke Daque: And that was like
36 00:03:51.420 ⇒ 00:04:03.680 Luke Daque: his main ask was to be able to provide a breakdown, at least from a selling platform, so they would know which sales are coming from shopify Amazon or Walmart and stuff like that.
37 00:04:04.860 ⇒ 00:04:12.780 Luke Daque: So essentially, that’s what I did. We did last week. I I added a couple of dimensions here.
38 00:04:13.355 ⇒ 00:04:17.709 Luke Daque: specifically, the selling platform, the fulfillment channel and the product class.
39 00:04:18.290 ⇒ 00:04:24.774 Luke Daque: But then that that made the numbers like incorrect, inaccurate, because,
40 00:04:26.330 ⇒ 00:04:30.152 Luke Daque: like the the this daily Kpi is from
41 00:04:30.850 ⇒ 00:04:37.939 Luke Daque: The source would be all orders. But then product class, for example, is coming from an order line item
42 00:04:38.130 ⇒ 00:04:41.369 Luke Daque: because we wouldn’t. 1 order can have multiple
43 00:04:41.550 ⇒ 00:04:48.820 Luke Daque: products in it. So that was like duplicating the numbers for marketing, for example, because instead of
44 00:04:49.110 ⇒ 00:04:53.139 Luke Daque: of just one order. It’s like
45 00:04:53.820 ⇒ 00:04:56.610 Luke Daque: shown twice for each ordered item.
46 00:04:57.030 ⇒ 00:04:58.510 Luke Daque: So something like that.
47 00:05:00.470 ⇒ 00:05:02.333 Luke Daque: So yeah, I did a couple of
48 00:05:03.280 ⇒ 00:05:16.279 Luke Daque: like, I reverted basically the code. And like instead, I just like left the selling platform. I actually removed fulfillment channel. I’ll have to remove this in real as well. That’s why like it’s showing now, because I removed it from
49 00:05:16.600 ⇒ 00:05:22.149 Luke Daque: the code, the the sequel, the model. So yeah.
50 00:05:22.530 ⇒ 00:05:26.899 Luke Daque: And then. But still I think like they came.
51 00:05:27.420 ⇒ 00:05:29.990 Luke Daque: was saying, it’s still not the same number.
52 00:05:30.927 ⇒ 00:05:35.740 Luke Daque: So I did a deep dive or like, investigate, basically compare
53 00:05:35.850 ⇒ 00:05:40.870 Luke Daque: real and like what they were doing manually, or what Kim was doing manually.
54 00:05:41.070 ⇒ 00:05:49.190 Luke Daque: and this is where she did send a like a link here to
55 00:05:51.050 ⇒ 00:05:55.490 Luke Daque: through Amazon for the month of March, and then
56 00:05:56.040 ⇒ 00:06:04.290 Luke Daque: like this was the supposed to be the correct numbers for Amazon for the month of March, March, and then she also has.
57 00:06:04.730 ⇒ 00:06:07.660 Luke Daque: She’s more familiar with shopify.
58 00:06:08.180 ⇒ 00:06:14.220 Luke Daque: So she did give us this shopify recent data.
59 00:06:14.440 ⇒ 00:06:19.109 Luke Daque: So from March 31 to April 6, th which was like Friday last week.
60 00:06:20.810 ⇒ 00:06:23.830 Luke Daque: So that’s basically what I did. I tried to compare
61 00:06:24.230 ⇒ 00:06:31.539 Luke Daque: what she has from, like the report, the manual report that she’s doing and what we have from
62 00:06:31.910 ⇒ 00:06:35.180 Luke Daque: the data model that we we currently have.
63 00:06:35.450 ⇒ 00:06:41.900 Luke Daque: And so so far, it looks like just the gross sales it’s pretty like similar.
64 00:06:42.760 ⇒ 00:06:54.790 Luke Daque: It’s just like a couple of dollars difference per day, and I don’t know if this is like time zone related, or something. But I think we we it’s like safe to say that we’re good with like gross sales, which is
65 00:06:55.340 ⇒ 00:06:58.710 Luke Daque: total sales minus discounts and returns.
66 00:07:00.660 ⇒ 00:07:01.180 Luke Daque: This is.
67 00:07:01.180 ⇒ 00:07:08.599 Caio Velasco: What is it? What is it? Okay? What is the difference between the one in the top and this one in the bottom.
68 00:07:09.190 ⇒ 00:07:14.290 Luke Daque: This one is a report that Kim is like exporting manually
69 00:07:14.550 ⇒ 00:07:20.100 Luke Daque: from I I don’t know, like, from different sources like shopify and stuff like that.
70 00:07:20.310 ⇒ 00:07:25.350 Luke Daque: And this one at the bottom is coming from the data model. So basically, I just ran it in
71 00:07:25.820 ⇒ 00:07:27.090 Luke Daque: in here.
72 00:07:27.750 ⇒ 00:07:32.810 Luke Daque: And then basically just copy, copy the output, and then put it here.
73 00:07:35.280 ⇒ 00:07:40.700 Caio Velasco: Okay. So maybe even like less than 10% business. Yeah, I see right.
74 00:07:40.700 ⇒ 00:07:43.759 Luke Daque: Like 1 1% difference, which is pretty fine.
75 00:07:44.150 ⇒ 00:07:52.379 Luke Daque: And the problem, so Google cost is also like almost exactly the same. So we’re good with that.
76 00:07:54.510 ⇒ 00:08:04.489 Luke Daque: Facebook. We’re also like exactly the same. We’re good with that. The thing we don’t have is impact fee and Cj, like all of these other fees.
77 00:08:06.980 ⇒ 00:08:07.660 Luke Daque: please. I’ve.
78 00:08:07.660 ⇒ 00:08:08.430 Caio Velasco: 4.
79 00:08:09.520 ⇒ 00:08:13.370 Luke Daque: These are like marketing costs. By the way, so
80 00:08:13.810 ⇒ 00:08:18.792 Luke Daque: yeah, I don’t think we have impact. And Cj fees,
81 00:08:19.880 ⇒ 00:08:25.290 Luke Daque: it looks like it’s being pulled manually. So we don’t have actual data in it
82 00:08:25.780 ⇒ 00:08:27.940 Luke Daque: in our snowflake for this one.
83 00:08:28.400 ⇒ 00:08:31.580 Luke Daque: But yeah, we’ll have to.
84 00:08:31.580 ⇒ 00:08:32.070 Caio Velasco: Okay.
85 00:08:32.070 ⇒ 00:08:35.439 Luke Daque: Check with Kim. Like how we can pull these.
86 00:08:36.159 ⇒ 00:08:37.679 Luke Daque: So anyway.
87 00:08:38.990 ⇒ 00:08:39.530 Caio Velasco: Can you.
88 00:08:39.539 ⇒ 00:08:40.039 Luke Daque: Do, have, a.
89 00:08:40.039 ⇒ 00:08:40.469 Caio Velasco: Shit.
90 00:08:40.470 ⇒ 00:08:42.469 Luke Daque: Oh, yeah. Yeah. Go ahead.
91 00:08:43.392 ⇒ 00:08:54.707 Caio Velasco: You can finish. But I was just gonna ask if you can then show me, for example, the query that view this one, the number that or this table, so that I can check
92 00:08:56.160 ⇒ 00:08:56.960 Luke Daque: Yeah, sure.
93 00:08:57.650 ⇒ 00:08:58.340 Caio Velasco: But yeah.
94 00:09:00.050 ⇒ 00:09:06.270 Luke Daque: yeah, the the yeah. Let me like finish all this first, st and then I can show you like, how the query looks like, maybe.
95 00:09:07.280 ⇒ 00:09:11.900 Caio Velasco: And I would also be interested to understand, like the total calls this was.
96 00:09:12.280 ⇒ 00:09:16.089 Caio Velasco: see like what is cost for you in this. In this case.
97 00:09:16.340 ⇒ 00:09:21.989 Luke Daque: Yeah, that’s a good point. Because that’s also why it’s also different. Like, if you can see here, it’s like 40
98 00:09:22.160 ⇒ 00:09:25.680 Luke Daque: percent higher than what she has.
99 00:09:25.930 ⇒ 00:09:35.119 Luke Daque: So we we have higher cogs. We have higher marketing costs. But the reason why we have marketing cost higher is because we are adding, we are
100 00:09:35.220 ⇒ 00:09:40.850 Luke Daque: including Amazon ads. And Kim wasn’t basically.
101 00:09:41.400 ⇒ 00:09:45.409 Luke Daque: So maybe we are actually more accurate in this regard.
102 00:09:46.182 ⇒ 00:09:50.309 Luke Daque: For cogs, we are come. These are coming from unleashed.
103 00:09:50.550 ⇒ 00:09:57.519 Luke Daque: And for it looks like from Kim. It’s coming from shopify. So maybe that’s also one thing, because we are like
104 00:09:58.819 ⇒ 00:10:01.519 Luke Daque: getting it from different sources.
105 00:10:02.420 ⇒ 00:10:03.740 Caio Velasco: What is.
106 00:10:04.130 ⇒ 00:10:08.060 Luke Daque: Unleash is another source that we have. So
107 00:10:09.710 ⇒ 00:10:13.250 Luke Daque: this yeah, this one. It looks like.
108 00:10:14.350 ⇒ 00:10:18.699 Caio Velasco: It’s say something where they also sell stuff.
109 00:10:20.000 ⇒ 00:10:21.959 Luke Daque: Looks like it.
110 00:10:23.240 ⇒ 00:10:25.250 Luke Daque: Let’s try it. Let’s see.
111 00:10:29.020 ⇒ 00:10:31.660 Luke Daque: yeah, it’s like a software management.
112 00:10:34.010 ⇒ 00:10:35.460 Luke Daque: Let’s check here.
113 00:10:35.710 ⇒ 00:10:38.139 Luke Daque: Stock inventory management team.
114 00:10:41.940 ⇒ 00:10:42.800 Luke Daque: But yeah.
115 00:10:43.250 ⇒ 00:10:50.180 Luke Daque: so this is where they’re get that. That’s where we are getting our product cost or the cost of goods sold.
116 00:10:52.605 ⇒ 00:10:53.370 Luke Daque: Yeah.
117 00:10:53.770 ⇒ 00:11:01.970 Luke Daque: And then for shipment, it’s pretty different. It’s a lot different, actually, and it looks like
118 00:11:02.140 ⇒ 00:11:05.749 Luke Daque: she’s getting it from Chuck, who’s another stakeholder?
119 00:11:06.120 ⇒ 00:11:14.929 Luke Daque: And then we are getting it from a lot of things like from ship station, Ltl unispremus. And then we are also incorporating, like some
120 00:11:15.240 ⇒ 00:11:20.649 Luke Daque: logic, to get already the shipping cost. So maybe this is also something we need to
121 00:11:20.930 ⇒ 00:11:26.130 Luke Daque: verify or confirm with with Kim. To basically all of these.
122 00:11:27.210 ⇒ 00:11:37.649 Luke Daque: from marketing to calls to shipping cost is like where we need very fit a lot of like verification confirmation where to actually get the numbers from.
123 00:11:39.720 ⇒ 00:11:40.230 Caio Velasco: Okay.
124 00:11:40.230 ⇒ 00:11:48.010 Luke Daque: Yeah, so yeah, there’s there’s a lot difference here, more than 10%, or like up to 40 and negative 24
125 00:11:48.150 ⇒ 00:11:49.120 Luke Daque: shipment.
126 00:11:51.200 ⇒ 00:11:54.429 Luke Daque: So I can maybe walk you through. How I did that.
127 00:11:55.570 ⇒ 00:11:57.458 Luke Daque: So 1st of all,
128 00:11:59.190 ⇒ 00:12:07.100 Luke Daque: we are using the real dashboard right the Daily Kpi dashboard. And so if if I look into
129 00:12:07.440 ⇒ 00:12:17.409 Luke Daque: that specific real dashboard, the Daily Kpi, I’ll go to the real folder and then dashboards.
130 00:12:17.580 ⇒ 00:12:20.630 Luke Daque: There’s daily Kpi dashboard
131 00:12:22.280 ⇒ 00:12:28.280 Luke Daque: like this is just like a reverse engineering, right? Like to be able to know where the source is coming from.
132 00:12:28.960 ⇒ 00:12:34.400 Luke Daque: It’s coming from the Daily Kpi model, which is coming from
133 00:12:35.320 ⇒ 00:12:40.999 Luke Daque: like Daily Kpi. If you look at the sources, the Daily Kpi, it’s coming from
134 00:12:42.550 ⇒ 00:12:49.280 Luke Daque: a daily Kpr Kpi ad, basically from our snowflake database.
135 00:12:49.660 ⇒ 00:12:56.519 Luke Daque: So that means it’s using this daily Kpi ad model that we have from Dbt.
136 00:13:00.540 ⇒ 00:13:03.320 Caio Velasco: What, and or.
137 00:13:03.460 ⇒ 00:13:05.149 Luke Daque: A aggregate basis.
138 00:13:05.636 ⇒ 00:13:07.579 Caio Velasco: From aggregate. Okay? Okay?
139 00:13:08.070 ⇒ 00:13:12.770 Luke Daque: Yeah, so, yeah, and.
140 00:13:12.770 ⇒ 00:13:18.109 Caio Velasco: So just so that I understand like, what is that ginger like Arg’s dot snowflake?
141 00:13:20.380 ⇒ 00:13:21.264 Caio Velasco: Oh, wait and.
142 00:13:21.930 ⇒ 00:13:22.600 Luke Daque: And.
143 00:13:22.600 ⇒ 00:13:23.259 Caio Velasco: Yes, in this order.
144 00:13:25.254 ⇒ 00:13:28.295 Luke Daque: That’s basically just the the variable. So,
145 00:13:30.090 ⇒ 00:13:33.000 Luke Daque: where was that going to this one? Can you.
146 00:13:33.410 ⇒ 00:13:35.319 Caio Velasco: Yes, thank you.
147 00:13:35.810 ⇒ 00:13:40.793 Luke Daque: Yeah. So if we look at the really ammo, we, the
148 00:13:41.940 ⇒ 00:13:45.790 Luke Daque: we added a variable here called Snowflake database.
149 00:13:46.090 ⇒ 00:13:51.590 Luke Daque: That way, we can easily just change this variable, and it will reflect on all
150 00:13:52.000 ⇒ 00:13:54.140 Luke Daque: models that are using it.
151 00:13:54.760 ⇒ 00:13:58.460 Luke Daque: So if if we needed to change, because, like.
152 00:13:58.660 ⇒ 00:14:04.420 Luke Daque: I did some tech depths on full parts.
153 00:14:04.650 ⇒ 00:14:11.909 Luke Daque: Guess like, if you remember, like pool parts wasn’t really was like pool parts, was the very 1st
154 00:14:12.070 ⇒ 00:14:18.880 Luke Daque: customer that Utam had for Brainforge and it we weren’t like we didn’t have standard
155 00:14:19.693 ⇒ 00:14:27.590 Luke Daque: or like best practices yet. So we did. We didn’t put everything to Broad March. It was all in the the analytics database.
156 00:14:28.275 ⇒ 00:14:38.399 Luke Daque: That’s why, like we needed to change it to from analytics to broad marts, or in Broad, in March, or Dev marts or stage marts.
157 00:14:39.270 ⇒ 00:14:41.110 Luke Daque: And stuff like that.
158 00:14:41.460 ⇒ 00:14:44.709 Luke Daque: So yeah, in order for us to
159 00:14:45.300 ⇒ 00:14:51.119 Luke Daque: change that really easily instead of like manually changing each source.
160 00:14:51.550 ⇒ 00:15:00.730 Luke Daque: Right? We can just have one. We can. We can have every source as variables and then create that variable in in the really unknown
161 00:15:01.100 ⇒ 00:15:05.970 Luke Daque: that way. We only have to change this, and not the the others.
162 00:15:08.070 ⇒ 00:15:14.440 Caio Velasco: Okay, okay. I see. I didn’t know that it would do the select directly in the Ml. In the source. That’s that’s nice.
163 00:15:14.440 ⇒ 00:15:17.770 Luke Daque: Yeah, but yeah, that’s cool. Right?
164 00:15:18.240 ⇒ 00:15:22.619 Caio Velasco: Yeah, okay, and the date. Okay?
165 00:15:23.400 ⇒ 00:15:33.380 Luke Daque: So yeah. So going back to the Daily Kpi aggregate model, it’s pretty huge and like, like,
166 00:15:34.570 ⇒ 00:15:41.070 Luke Daque: if you look at it, it’s like. There’s order, Kpis, for all the measures here, and broken down by
167 00:15:41.680 ⇒ 00:15:42.960 Luke Daque: a date.
168 00:15:43.310 ⇒ 00:15:51.470 Luke Daque: And then this is where I added the selling platform and fulfillment channel which I I need to remove this fulfillment channel without using it.
169 00:15:51.470 ⇒ 00:16:08.300 Caio Velasco: This this sorry? Just a question. So because I think I was confused then. So that source cm, file, it’s for the the real dashboard is not like the source for like for the DVD model that would start in raw and then teams and then marks.
170 00:16:10.040 ⇒ 00:16:11.700 Luke Daque: This one.
171 00:16:11.700 ⇒ 00:16:12.260 Caio Velasco: Yeah.
172 00:16:12.260 ⇒ 00:16:17.250 Luke Daque: Yeah, cause we are. This, this sources is for the rail.
173 00:16:18.355 ⇒ 00:16:19.310 Luke Daque: Dashboard.
174 00:16:19.810 ⇒ 00:16:25.269 Luke Daque: So we’ll it’s it’s not the source for the Dbt models.
175 00:16:25.550 ⇒ 00:16:32.219 Luke Daque: because if you go, if you remember, if you go back to the pigma board that you just shared
176 00:16:34.720 ⇒ 00:16:36.440 Luke Daque: workable parts.
177 00:16:41.990 ⇒ 00:16:42.730 Luke Daque: right?
178 00:16:45.870 ⇒ 00:16:51.660 Luke Daque: So these are the actual raw data sources.
179 00:16:52.110 ⇒ 00:16:52.830 Caio Velasco: Yes.
180 00:16:53.250 ⇒ 00:16:56.189 Luke Daque: Shop what they call this snowflake.
181 00:16:56.570 ⇒ 00:17:05.410 Luke Daque: And then, oh, here, actually, so these are actually raw data sources that go to 5 tran for the ingestion, and then they are all in Snowflake.
182 00:17:05.829 ⇒ 00:17:07.540 Caio Velasco: And then we create.
183 00:17:08.257 ⇒ 00:17:14.220 Luke Daque: Data models out of them. And one of that is the Daily Kpi aggregate model.
184 00:17:14.490 ⇒ 00:17:15.060 Caio Velasco: Yes.
185 00:17:15.069 ⇒ 00:17:26.049 Luke Daque: And which is being used as the source for our real dashboard. So this one really kpi aggregate is the source, for this one is the model
186 00:17:26.429 ⇒ 00:17:29.289 Luke Daque: that we created from DVD.
187 00:17:30.418 ⇒ 00:17:40.280 Caio Velasco: In the repo, not in like Meta Base. I think everything is inside Meta base in this one. It’s a it’s a source.
188 00:17:40.550 ⇒ 00:17:41.740 Luke Daque: You know that’s correct.
189 00:17:41.740 ⇒ 00:17:44.770 Caio Velasco: Okay, okay, that’s nice. I never.
190 00:17:44.770 ⇒ 00:17:45.290 Luke Daque: So that’s.
191 00:17:45.290 ⇒ 00:17:46.000 Caio Velasco: Okay.
192 00:17:46.000 ⇒ 00:17:52.632 Luke Daque: Yeah, yeah, that’s that’s why like we come likes real better. Because, like, it’s
193 00:17:53.560 ⇒ 00:17:58.209 Luke Daque: it’s also like code based. And it’s not like Meta Base, where you just like.
194 00:17:58.210 ⇒ 00:17:58.720 Caio Velasco: Yes.
195 00:17:58.720 ⇒ 00:18:03.179 Luke Daque: It’s all in in Meta base. And this one you can like create in in local.
196 00:18:03.180 ⇒ 00:18:03.640 Caio Velasco: Okay.
197 00:18:03.640 ⇒ 00:18:04.250 Luke Daque: So something.
198 00:18:04.250 ⇒ 00:18:04.770 Caio Velasco: Okay, this one.
199 00:18:04.770 ⇒ 00:18:10.449 Luke Daque: This is, yeah, this is the source coming from the date Dbt data model that we have.
200 00:18:11.190 ⇒ 00:18:11.810 Caio Velasco: Okay.
201 00:18:14.720 ⇒ 00:18:26.780 Luke Daque: So now that we know that this is that that source is the Daily Kpi aggregate Dbt model, then we can look into the Dbt Kpi aggregate data model itself.
202 00:18:28.660 ⇒ 00:18:30.889 Luke Daque: To see what’s going on.
203 00:18:32.010 ⇒ 00:18:33.160 Luke Daque: So
204 00:18:34.320 ⇒ 00:18:41.229 Luke Daque: this is where I added the selling platform as like one of the dimensions, and also tried to add fulfillment channel
205 00:18:41.951 ⇒ 00:18:50.110 Luke Daque: and product class initially, but like these did not work. I’ll have to remove this but
206 00:18:51.500 ⇒ 00:19:01.879 Luke Daque: but yeah, I’ll do that later. But I’ll have to remove this. But yeah, the reason why this didn’t work is because, like it needed to use all order items
207 00:19:02.420 ⇒ 00:19:11.469 Luke Daque: which would be like, potentially duplicating or like, yeah, duplicating stuff.
208 00:19:12.210 ⇒ 00:19:18.099 Luke Daque: Yes, are not. Yeah, using older items. Basically. So I had to remove that for now,
209 00:19:20.740 ⇒ 00:19:27.399 Luke Daque: so yeah, we have metrics coming from all orders. We have metrics from refunds, a full refunds.
210 00:19:27.640 ⇒ 00:19:29.190 Luke Daque: like refund amount.
211 00:19:29.690 ⇒ 00:19:36.659 Luke Daque: We have shipment Kpi, which is coming from shipments, which is a different data model.
212 00:19:37.300 ⇒ 00:19:38.320 Luke Daque: And
213 00:19:42.410 ⇒ 00:19:46.100 Luke Daque: marketing is also coming from a different data model.
214 00:19:47.840 ⇒ 00:19:54.440 Luke Daque: And the basically the final Cp is just like adding everything.
215 00:19:55.370 ⇒ 00:20:00.320 Luke Daque: All the measures and and dimensions in here.
216 00:20:00.630 ⇒ 00:20:05.930 Luke Daque: and be able to calculate that the the profit which is
217 00:20:07.340 ⇒ 00:20:16.270 Luke Daque: the total gross sales, minus all the costs incurred like cogs discount refunds, marketing shipment fees.
218 00:20:17.360 ⇒ 00:20:18.190 Luke Daque: Yeah.
219 00:20:18.890 ⇒ 00:20:22.449 Caio Velasco: And you basically joined on the selling platform.
220 00:20:24.860 ⇒ 00:20:28.310 Luke Daque: Yeah, yeah, basically joined on the date.
221 00:20:28.610 ⇒ 00:20:32.290 Luke Daque: because, like marketing is like broken down by date.
222 00:20:34.040 ⇒ 00:20:42.530 Luke Daque: And also shipments are also coming from they? They have dates and like 7 platforms. Well, so, yeah.
223 00:20:44.670 ⇒ 00:20:46.729 Caio Velasco: Okay, shoot me. Let me check.
224 00:20:47.930 ⇒ 00:20:53.049 Caio Velasco: because I am the snowflake here on group that I can have a view of it.
225 00:20:57.440 ⇒ 00:20:58.130 Caio Velasco: True.
226 00:21:02.770 ⇒ 00:21:07.660 Caio Velasco: in snowflake. What is the source?
227 00:21:09.560 ⇒ 00:21:11.789 Caio Velasco: It’s 5 to one thing.
228 00:21:14.880 ⇒ 00:21:17.370 Luke Daque: What what’s like here?
229 00:21:20.050 ⇒ 00:21:21.690 Luke Daque: Other databasing.
230 00:21:21.998 ⇒ 00:21:25.700 Caio Velasco: Yeah, they run data source. Yeah, I’m checking our system over here.
231 00:21:27.840 ⇒ 00:21:28.340 Caio Velasco: Okay.
232 00:21:28.420 ⇒ 00:21:32.120 Luke Daque: I got logged out.
233 00:21:53.790 ⇒ 00:22:01.609 Luke Daque: So yeah, this one, the 5 trend database is where we have all the sources from 5 K,
234 00:22:01.850 ⇒ 00:22:02.520 Luke Daque: and then.
235 00:22:02.520 ⇒ 00:22:04.570 Caio Velasco: I don’t have access to that one.
236 00:22:05.660 ⇒ 00:22:06.620 Luke Daque: Oh! This one!
237 00:22:07.110 ⇒ 00:22:07.740 Caio Velasco: Yeah.
238 00:22:11.090 ⇒ 00:22:18.620 Caio Velasco: I just have the project marks the dev ones, the intermediate ones and raw.
239 00:22:22.540 ⇒ 00:22:23.635 Luke Daque: One all right.
240 00:22:26.940 ⇒ 00:22:28.360 Luke Daque: and you’ve got to.
241 00:22:28.360 ⇒ 00:22:31.870 Caio Velasco: My my role, my role is rope, transform.
242 00:22:35.130 ⇒ 00:22:37.350 Luke Daque: They should be able to see that
243 00:22:40.000 ⇒ 00:22:45.910 Luke Daque: I don’t also see you here. There’s Bull. There’s Nicholas by us, me
244 00:22:46.670 ⇒ 00:22:50.370 Luke Daque: in Utah, but I don’t see you. Here are you in like 4 parts.
245 00:22:52.880 ⇒ 00:22:54.300 Caio Velasco: I’m checking them.
246 00:23:02.150 ⇒ 00:23:07.630 Caio Velasco: Yeah, well, I have the role transform. And that’s it.
247 00:23:13.320 ⇒ 00:23:15.690 Luke Daque: Yeah, let me try to add you then.
248 00:23:26.000 ⇒ 00:23:30.450 Caio Velasco: I usually sky over the the one that I see.
249 00:23:31.390 ⇒ 00:23:32.360 Luke Daque: Oh! Which one.
250 00:23:38.970 ⇒ 00:23:40.140 Caio Velasco: Looks like this.
251 00:23:44.730 ⇒ 00:23:45.429 Caio Velasco: Just me.
252 00:23:59.760 ⇒ 00:24:00.520 Luke Daque: None of it.
253 00:24:01.830 ⇒ 00:24:04.360 Luke Daque: Let’s just do Green Forge.
254 00:24:05.450 ⇒ 00:24:06.979 Luke Daque: Oh, wait! It’s in.
255 00:24:09.030 ⇒ 00:24:10.960 Caio Velasco: And then email sheet.
256 00:24:34.440 ⇒ 00:24:37.379 Luke Daque: But I don’t see you in the users for some reason.
257 00:24:38.600 ⇒ 00:24:40.939 Luke Daque: so I’ll try. I’ll just add you.
258 00:24:43.330 ⇒ 00:24:47.940 Caio Velasco: Oh, that’s interesting, because I should be able to see
259 00:24:50.058 ⇒ 00:24:53.899 Caio Velasco: any kind of database over there right.
260 00:24:53.900 ⇒ 00:24:54.710 Luke Daque: Yeah.
261 00:25:10.470 ⇒ 00:25:15.390 Luke Daque: Or maybe it’s my wrong language. Please change.
262 00:25:19.830 ⇒ 00:25:23.319 Luke Daque: but maybe we can do that. I’ll I’ll do that after maybe.
263 00:25:23.710 ⇒ 00:25:25.340 Caio Velasco: Yeah. Yeah. Don’t. Okay.
264 00:25:25.650 ⇒ 00:25:32.540 Luke Daque: But yeah, this is what you’re supposed to see it, unless can you try refreshing?
265 00:25:33.610 ⇒ 00:25:35.190 Luke Daque: Let’s see if that works.
266 00:25:39.686 ⇒ 00:25:40.283 Caio Velasco: No.
267 00:25:42.520 ⇒ 00:25:45.840 Luke Daque: Yeah, okay, so basically, it’s supposed to.
268 00:25:46.180 ⇒ 00:25:52.559 Luke Daque: you’re supposed to have all of the databases here, including Fiveran, which is the role
269 00:25:53.310 ⇒ 00:25:57.230 Luke Daque: databases, that of data sources that we have.
270 00:25:58.140 ⇒ 00:26:02.140 Luke Daque: Yeah, this is supposed to be like, yeah.
271 00:26:02.140 ⇒ 00:26:06.889 Caio Velasco: Yeah, we’ll see you usually. Who’s the response? Visit Utah, Utah?
272 00:26:07.740 ⇒ 00:26:13.960 Luke Daque: Yeah, but I believe Utam already gave me access to be able to.
273 00:26:17.070 ⇒ 00:26:17.960 Luke Daque: Yeah.
274 00:26:17.960 ⇒ 00:26:20.240 Caio Velasco: So so that you can add people, okay.
275 00:26:20.240 ⇒ 00:26:21.810 Luke Daque: Yeah, I should be able to add.
276 00:26:27.590 ⇒ 00:26:28.430 Luke Daque: Yeah.
277 00:26:29.160 ⇒ 00:26:29.570 Caio Velasco: Don’t worry.
278 00:26:29.570 ⇒ 00:26:30.730 Luke Daque: To add people.
279 00:26:31.380 ⇒ 00:26:34.009 Luke Daque: But yeah, I’ll add you after the call.
280 00:26:34.860 ⇒ 00:26:35.840 Luke Daque: So
281 00:26:39.180 ⇒ 00:26:41.680 Luke Daque: yeah, it’s it’s here.
282 00:26:43.990 ⇒ 00:26:52.580 Luke Daque: 5. And then the Daily Kpi ag. Initially it was. Everything was here in analytics, but we did move this to
283 00:26:52.880 ⇒ 00:26:57.999 Luke Daque: like the standard format that you have. So it should be in the under prod march for all them
284 00:26:58.540 ⇒ 00:27:00.050 Luke Daque: marks models.
285 00:27:01.680 ⇒ 00:27:02.739 Luke Daque: That’s this one.
286 00:27:03.040 ⇒ 00:27:08.200 Luke Daque: It’s the case that I have to fix as well.
287 00:27:08.630 ⇒ 00:27:11.439 Luke Daque: But yeah, that’s like good with it. But
288 00:27:11.750 ⇒ 00:27:19.309 Luke Daque: we have daily kpi aggregate here already, and this is where we have stuff.
289 00:27:20.010 ⇒ 00:27:23.654 Caio Velasco: And they all orders one that you were referencing.
290 00:27:24.330 ⇒ 00:27:31.650 Caio Velasco: in in almost all cities. Is it? That is like just raw completely. Is that raw table, or.
291 00:27:31.880 ⇒ 00:27:35.329 Luke Daque: No, it’s another. It’s another model that we have.
292 00:27:35.820 ⇒ 00:27:47.930 Luke Daque: That’s like, basically adding all the orders from shopify. So this one, under reporting all orders.
293 00:27:48.090 ⇒ 00:27:52.300 Luke Daque: It has orders from Amazon. It has orders from shopify.
294 00:27:52.540 ⇒ 00:27:54.870 Luke Daque: and it has orders from Walmart.
295 00:27:55.030 ⇒ 00:27:58.439 Luke Daque: And that’s basically like we’re just combining them.
296 00:27:58.850 ⇒ 00:28:01.860 Luke Daque: So we have all orders in one table.
297 00:28:03.260 ⇒ 00:28:04.260 Luke Daque: Okay?
298 00:28:04.260 ⇒ 00:28:04.920 Caio Velasco: So it.
299 00:28:06.330 ⇒ 00:28:06.710 Luke Daque: They!
300 00:28:06.710 ⇒ 00:28:10.279 Caio Velasco: So at the end, at the end you have all orders
301 00:28:10.550 ⇒ 00:28:17.350 Caio Velasco: or the level. So with the date. And then you’re just adding, you’re joining
302 00:28:17.730 ⇒ 00:28:21.229 Caio Velasco: on the date and and aggregating whatever you have to aggregate.
303 00:28:21.760 ⇒ 00:28:22.950 Luke Daque: Yes, that’s correct.
304 00:28:24.660 ⇒ 00:28:27.849 Luke Daque: So if you look at the lineage of the Daily Kpi.
305 00:28:28.320 ⇒ 00:28:37.030 Luke Daque: it’s yeah. It’s coming from all orders combined marketing. It’s also some from all the items good shipping.
306 00:28:40.110 ⇒ 00:28:46.850 Caio Velasco: And when when you, when you do this, then, is a question that I have. When you do this in reference, all orders.
307 00:28:49.060 ⇒ 00:28:52.680 Caio Velasco: Which is like, Okay, shopify orders, Amazon orders, etc.
308 00:28:53.606 ⇒ 00:28:58.719 Caio Velasco: Do you know what kind of events are those orders, is it?
309 00:28:58.930 ⇒ 00:29:06.060 Caio Velasco: But, for example, let’s see if someone buy something, then return, or has a refund or has a charge back, whatever happens.
310 00:29:07.008 ⇒ 00:29:09.320 Caio Velasco: Are those things captured.
311 00:29:12.790 ⇒ 00:29:13.820 Luke Daque: Well.
312 00:29:14.900 ⇒ 00:29:27.170 Luke Daque: yeah. So if it depends on like the source, right? So like for shopify, you’ll be able to see. Like, if you go here, you can actually see what that looks like in shopify.
313 00:29:28.200 ⇒ 00:29:32.380 Luke Daque: If you have access to the actual source.
314 00:29:36.900 ⇒ 00:29:45.810 Luke Daque: And if you go to orders you should have. There’s like this fulfillment status.
315 00:29:46.490 ⇒ 00:29:53.870 Luke Daque: You’ll be able to see whether the order was shipped or complete, or cancel or refund.
316 00:29:54.870 ⇒ 00:29:58.669 Luke Daque: So these are all orders basically coming from shopify.
317 00:29:59.240 ⇒ 00:30:00.909 Caio Velasco: Yeah, anytime? Yeah.
318 00:30:01.870 ⇒ 00:30:08.590 Luke Daque: And yeah, you’ll be able to see all the other stuff like refund. This order has a discount.
319 00:30:09.010 ⇒ 00:30:14.349 Luke Daque: I believe, by joining it here from the discount.
320 00:30:15.180 ⇒ 00:30:21.510 Luke Daque: Yeah, cause this has order, id and code to and amount. So you’ll be able to know, like
321 00:30:22.920 ⇒ 00:30:33.019 Luke Daque: this specific order has a discount by joining this in there, which is what we did. If you look at the shopify order stable that we have
322 00:30:34.170 ⇒ 00:30:39.940 Luke Daque: smart data model, so
323 00:30:40.450 ⇒ 00:30:46.269 Luke Daque: we are joining it from refunds. We are joining refunds. So we’ll have refund information.
324 00:30:46.820 ⇒ 00:31:00.969 Luke Daque: And yeah, it’s it’s mostly coming from order. Item order. Item, there’s also like data coming from customers
325 00:31:01.660 ⇒ 00:31:04.770 Luke Daque: looking for their name. 1st name, last name.
326 00:31:05.743 ⇒ 00:31:11.079 Luke Daque: data coming from shipments. So we get the shipping amount.
327 00:31:14.111 ⇒ 00:31:20.290 Luke Daque: So yeah, it’s it’s pretty a bit complicated already. But yeah, there’s refunds
328 00:31:20.680 ⇒ 00:31:25.590 Luke Daque: this Ltl shipments, which is another shipment source
329 00:31:25.790 ⇒ 00:31:31.070 Luke Daque: discount code, which is the the one that I showed you earlier from a snowflake.
330 00:31:31.710 ⇒ 00:31:33.620 Luke Daque: We get the discounts.
331 00:31:36.150 ⇒ 00:31:40.139 Caio Velasco: Okay, and the all orders were was based on all of these.
332 00:31:40.960 ⇒ 00:31:49.800 Luke Daque: Yeah. All orders was based on shopify orders for the shopify piece and then shopify orders is coming from shopify order, line
333 00:31:50.120 ⇒ 00:31:54.340 Luke Daque: order items such as, like all of these other different stuff.
334 00:31:58.390 ⇒ 00:31:59.270 Caio Velasco: Beauty.
335 00:32:00.165 ⇒ 00:32:11.539 Caio Velasco: I have to obviously trust you on that, because it’s just I couldn’t. You know, when I, when I saw that I had to review is like, should I go like to
336 00:32:11.690 ⇒ 00:32:12.709 Caio Velasco: step number one?
337 00:32:13.560 ⇒ 00:32:14.619 Caio Velasco: All of this.
338 00:32:15.270 ⇒ 00:32:20.300 Luke Daque: I guess if you if we look at the things that I changed in
339 00:32:20.490 ⇒ 00:32:23.549 Luke Daque: in the review, I mean the Tr.
340 00:32:23.650 ⇒ 00:32:33.040 Luke Daque: it’s basically just removing the fulfillment channel, adding the selling platform dimension.
341 00:32:33.550 ⇒ 00:32:36.649 Luke Daque: do all the like, refunds the shipments.
342 00:32:38.050 ⇒ 00:32:41.009 Luke Daque: and then adding that to the join
343 00:32:41.680 ⇒ 00:32:50.130 Luke Daque: this before it was just joining, using the date. But now it’s now joining, including the selling platform that way. We are not like
344 00:32:51.520 ⇒ 00:32:53.190 Luke Daque: duplicating stuff.
345 00:32:54.420 ⇒ 00:32:55.030 Caio Velasco: Okay.
346 00:32:56.130 ⇒ 00:33:04.889 Luke Daque: That way. We can like categorize the shipment by selling platform, and also the refund marketing by selling platform as well
347 00:33:05.100 ⇒ 00:33:06.530 Luke Daque: and refunds.
348 00:33:08.500 ⇒ 00:33:10.860 Luke Daque: And then the other thing that I
349 00:33:11.120 ⇒ 00:33:21.289 Luke Daque: changed here was for the shipments. Initially, there was no shipping or selling platform for the shipments model. So I added, selling platform.
350 00:33:21.550 ⇒ 00:33:25.180 Luke Daque: So yeah, and it’s based on the source as well. Like, if
351 00:33:25.310 ⇒ 00:33:30.020 Luke Daque: the source was Amazon, the selling platform. I just added that on his own.
352 00:33:30.380 ⇒ 00:33:36.309 Luke Daque: And then for any source that was shopify like this one, I added, shopify as the source.
353 00:33:38.080 ⇒ 00:33:41.330 Luke Daque: That’s basically what I added for the Pr.
354 00:33:42.150 ⇒ 00:33:45.480 Luke Daque: and that’s that’s why, that helped in like
355 00:33:46.410 ⇒ 00:33:52.789 Luke Daque: creating the real dashboard. So we’ll have this dimension, and you’ll be able to like filter them.
356 00:33:54.830 ⇒ 00:33:58.160 Luke Daque: And that way we can compare like
357 00:33:58.290 ⇒ 00:34:05.830 Luke Daque: Amazon, for example, for the month of March there’s a hundred, 75,000 total sales compared to the
358 00:34:06.960 ⇒ 00:34:10.429 Luke Daque: what they have here, which is 157.
359 00:34:10.550 ⇒ 00:34:11.710 Luke Daque: So we are.
360 00:34:12.730 ⇒ 00:34:18.419 Luke Daque: We’re we have more data like sales is like higher.
361 00:34:20.710 ⇒ 00:34:25.620 Luke Daque: But it this could be, I guess we can.
362 00:34:26.030 ⇒ 00:34:31.719 Luke Daque: We’ll have to remove discounts and refunds, for from here.
363 00:34:33.650 ⇒ 00:34:34.230 Caio Velasco: Okay.
364 00:34:34.860 ⇒ 00:34:43.460 Luke Daque: Think if we do that, let’s try 1 75,000 minus
365 00:34:43.600 ⇒ 00:34:49.809 Luke Daque: really fine. So if he’s good, yeah, we’re getting pretty close
366 00:34:54.130 ⇒ 00:34:55.920 Luke Daque: where she says tonight
367 00:34:56.150 ⇒ 00:35:04.090 Luke Daque: think the discounts might need to be. We need to look into this like, it’s showing 0. So it’s like, I don’t know. Maybe
368 00:35:04.550 ⇒ 00:35:07.420 Luke Daque: this is, yeah, this is something I’ll have to look into as well.
369 00:35:12.400 ⇒ 00:35:17.309 Caio Velasco: Yeah, I was gonna ask that, like, we are assuming that the joins were done
370 00:35:18.690 ⇒ 00:35:23.369 Caio Velasco: well correctly done in those 2 columns, date and platform.
371 00:35:23.540 ⇒ 00:35:31.329 Caio Velasco: and then here, what is missing maybe would be the discount. Then you would have maybe like a closer number. That’s the.
372 00:35:32.330 ⇒ 00:35:33.170 Luke Daque: Yeah, yeah, that’s my.
373 00:35:33.170 ⇒ 00:35:33.710 Caio Velasco: Assumption.
374 00:35:34.150 ⇒ 00:35:35.769 Luke Daque: Assumption. This is correct.
375 00:35:36.200 ⇒ 00:35:36.750 Caio Velasco: Okay.
376 00:35:39.480 ⇒ 00:35:40.090 Luke Daque: So, yeah.
377 00:35:40.960 ⇒ 00:35:44.100 Caio Velasco: Okay, cool. Okay, that looks good. Man.
378 00:35:45.030 ⇒ 00:35:45.610 Luke Daque: Cool.
379 00:35:46.840 ⇒ 00:35:49.960 Luke Daque: Yeah. We actually have a
380 00:35:50.070 ⇒ 00:35:53.569 Luke Daque: meeting with Kim later. I don’t know if you want to join.
381 00:35:54.210 ⇒ 00:35:57.270 Luke Daque: I’ll also like, Ask amber or luton, if, like.
382 00:35:58.240 ⇒ 00:36:02.540 Luke Daque: we need to join that. But basically, it’s just the yeah, I’ll just be asking
383 00:36:02.970 ⇒ 00:36:07.370 Luke Daque: or like showing her the updates that we had since last Friday.
384 00:36:08.475 ⇒ 00:36:14.149 Luke Daque: That way, like, yeah, we can discuss. I can ask her the things that I I noticed here.
385 00:36:14.450 ⇒ 00:36:20.970 Luke Daque: right? The Amazon ads is not included in her marketing.
386 00:36:21.130 ⇒ 00:36:27.630 Luke Daque: which we are. And then like for logs, I want to
387 00:36:28.040 ⇒ 00:36:31.830 Luke Daque: check, like what? Where she’s looking in
388 00:36:32.240 ⇒ 00:36:36.350 Luke Daque: phone calls like, where is specifically in shopify
389 00:36:36.690 ⇒ 00:36:40.180 Luke Daque: to looking at cogs which we are using unleashed.
390 00:36:40.560 ⇒ 00:36:44.260 Luke Daque: And like, yeah, that’s probably why you’re higher.
391 00:36:45.430 ⇒ 00:36:47.820 Luke Daque: So yeah, and stuff like that.
392 00:36:50.600 ⇒ 00:36:52.830 Caio Velasco: Yeah, because well, I assume that
393 00:36:53.680 ⇒ 00:37:03.650 Caio Velasco: calls. Part of the calls also come from Amazon. Or you know, they’re not using Amazon selling this. This ads.
394 00:37:04.180 ⇒ 00:37:07.440 Caio Velasco: okay, they okay, right or not.
395 00:37:07.440 ⇒ 00:37:08.100 Luke Daque: Yeah.
396 00:37:09.284 ⇒ 00:37:13.320 Luke Daque: no, I’m I’m not sure as well. Yeah, that would be a good question to ask as well.
397 00:37:15.840 ⇒ 00:37:21.280 Caio Velasco: Okay. So then, for the for the pi, I’ll I’ll I’ll just approve the only thing that it would be even
398 00:37:22.313 ⇒ 00:37:30.540 Caio Velasco: good for me in the future would be in those joins. Just if you can make any kind of comments.
399 00:37:31.070 ⇒ 00:37:31.980 Luke Daque: And.
400 00:37:31.980 ⇒ 00:37:36.360 Caio Velasco: Just to say, like, Well, joining on date. And this because of Xyz.
401 00:37:37.280 ⇒ 00:37:37.840 Luke Daque: Yeah, that.
402 00:37:37.840 ⇒ 00:37:43.460 Caio Velasco: And that seems the most important part. For the rest, I think it’s and that would be saying.
403 00:37:44.280 ⇒ 00:37:52.250 Luke Daque: Yeah, I think it’s also best practice to put to add, like a description of the Pr, because, like, I’ve never been adding the descriptions.
404 00:37:53.970 ⇒ 00:38:06.040 Luke Daque: but yes, that way. It’s easier for, like you like, whoever is reviewing to know what the context of this Pr is like, I started doing that for, like one of the stack clips prs, I’ll show you.
405 00:38:06.640 ⇒ 00:38:08.060 Luke Daque: It looks like
406 00:38:10.330 ⇒ 00:38:12.500 Luke Daque: Let’s muted postings
407 00:38:17.750 ⇒ 00:38:24.160 Luke Daque: so like this one. This Pr. Was just adding a documenting DVD models with schema.
408 00:38:24.280 ⇒ 00:38:27.620 Luke Daque: But, I added, like context here.
409 00:38:28.570 ⇒ 00:38:34.810 Luke Daque: what the Pr. Is doing, and may I, would this help? Would this be like, be helpful
410 00:38:35.450 ⇒ 00:38:36.330 Luke Daque: in the future?
411 00:38:36.330 ⇒ 00:38:49.340 Caio Velasco: Yeah, yeah, it would, it would, for sure. Then it depends on how much you think it’s important to have there when someone gets it. Because, for example, when I if I were to review that one without doing this call.
412 00:38:49.570 ⇒ 00:38:56.119 Caio Velasco: the important thing for me would be like, Okay, I created this model coming from these sources.
413 00:38:56.620 ⇒ 00:39:09.239 Caio Velasco: It’s like adding stuff from I don’t know. Shopify Amazon, because it’s like an overview of the of the client itself, because then would be the 1st Pr. Of course. Not for all the pr
414 00:39:11.210 ⇒ 00:39:16.440 Caio Velasco: and then the the join was made in this in this way, because I’m assuming this.
415 00:39:17.050 ⇒ 00:39:21.520 Caio Velasco: So yeah, for me, it would be enough. Then it’s more like up to you.
416 00:39:22.940 ⇒ 00:39:23.910 Luke Daque: Sounds good.
417 00:39:25.213 ⇒ 00:39:31.280 Caio Velasco: Cool so, but I’ll do the Pr. Here and make the comments, and then we’re we’re good.
418 00:39:32.030 ⇒ 00:39:33.459 Luke Daque: Sounds good nice.
419 00:39:34.120 ⇒ 00:39:38.529 Caio Velasco: That’s cool, perfect. Thank you, man. I appreciate, for for the overview.
420 00:39:39.190 ⇒ 00:39:45.079 Luke Daque: No problem. Thanks as well. If you have any other like questions, or if you want to do any.
421 00:39:45.470 ⇒ 00:39:48.919 Luke Daque: discuss anything out, you can just slack me anytime as well.
422 00:39:50.920 ⇒ 00:39:51.860 Caio Velasco: Perfect.
423 00:39:52.440 ⇒ 00:39:53.300 Caio Velasco: Thank you.
424 00:39:53.790 ⇒ 00:39:56.979 Luke Daque: Cool. Thanks, thanks, Kyle. Have a nice rest of your day.
425 00:39:57.350 ⇒ 00:39:57.940 Caio Velasco: You too.