Meeting Title: [Javvy] Daily Standup Date: 2025-04-03 Meeting participants: Annie Yu, Robert Tseng, Awaish Kumar, Caio Velasco
WEBVTT
1 00:00:57.550 ⇒ 00:00:58.600 Robert Tseng: Hey, everyone.
2 00:01:04.260 ⇒ 00:01:05.050 Caio Velasco: Go ahead!
3 00:01:14.500 ⇒ 00:01:18.893 Robert Tseng: Hey? I think it’s just this group today. So
4 00:01:19.920 ⇒ 00:01:21.599 Robert Tseng: give me a second. Pull it up.
5 00:01:40.710 ⇒ 00:01:46.652 Robert Tseng: Okay, I think. Let me just start 1st with the cautions updates here, because I already have it here.
6 00:01:47.490 ⇒ 00:01:52.260 Robert Tseng: So I think.
7 00:01:53.390 ⇒ 00:02:07.430 Robert Tseng: yeah, I mean the recap we met with Aman and flawed yesterday, and we did like the 1st training Annie, as a heads up. I feel like there’s probably like a couple more. You probably won’t be looped into all of them, I think, especially if we do like a
8 00:02:08.870 ⇒ 00:02:14.510 Robert Tseng: data warehouse deep dive, I’ll probably pull either Kyle oration. Probably. Kyle.
9 00:02:15.264 ⇒ 00:02:17.569 Robert Tseng: But yeah, I think that’s a
10 00:02:18.240 ⇒ 00:02:23.649 Robert Tseng: it’s create more opportunities for the team to get facetime with the client.
11 00:02:25.480 ⇒ 00:02:26.830 Robert Tseng: And then.
12 00:02:27.280 ⇒ 00:02:36.230 Robert Tseng: yeah, I think we were trying to get the white dash Demo over to Aman. So I think that’s that’s just on a caution. We got to finish.
13 00:02:38.440 ⇒ 00:02:42.190 Robert Tseng: And yeah, so I think that’s it for that.
14 00:02:43.270 ⇒ 00:02:47.329 Robert Tseng: Let me just jump to kind of what we have in cycle. Right now.
15 00:02:47.540 ⇒ 00:02:50.450 Robert Tseng: I’ll go through everything that’s in progress. So
16 00:02:50.720 ⇒ 00:03:01.190 Robert Tseng: even light dash, I think this is yeah talked about. So s, 2 back
17 00:03:01.970 ⇒ 00:03:09.600 Robert Tseng: north beam into snowflake. Yeah. Way, she want to connect progress. Update how we’re, how are you doing on this
18 00:03:11.150 ⇒ 00:03:13.215 Awaish Kumar: Yeah, I logged into it. And
19 00:03:13.750 ⇒ 00:03:39.100 Awaish Kumar: we have actually, now we have the portable connector set up for north beam. But similar to attentive in the portable. The North beam connector is also dummy one. So it basically is not ingesting any data right now. So I and I. Now I have to put more research on this to find if there is any other tool which we can use, or
20 00:03:39.260 ⇒ 00:03:42.779 Awaish Kumar: we might have to find some other way to do it.
21 00:03:44.900 ⇒ 00:03:51.249 Robert Tseng: Got it. I mean, I saw you sent the attentive data to Aman. So you were able to work around that right
22 00:03:51.250 ⇒ 00:03:55.239 Awaish Kumar: For for attentive, we move to 5 trend. Basically.
23 00:03:55.460 ⇒ 00:04:05.229 Awaish Kumar: So 5 trend had the attentive connector. So 1st of all, we use portable. But it was not working. Then we use 5 trend. But there was no data.
24 00:04:05.330 ⇒ 00:04:07.270 Awaish Kumar: But yesterday I
25 00:04:07.560 ⇒ 00:04:14.890 Awaish Kumar: I saw there’s after some. After some time we have synced some data. So I shared that with Aman
26 00:04:15.070 ⇒ 00:04:18.070 Awaish Kumar: for north beam there’s no connector in
27 00:04:18.529 ⇒ 00:04:22.619 Awaish Kumar: in 5 trend, and also the in the one in portable is not like
28 00:04:22.960 ⇒ 00:04:25.420 Awaish Kumar: not the one which we can use.
29 00:04:26.020 ⇒ 00:04:28.859 Awaish Kumar: So we have to find some alternative for that
30 00:04:35.380 ⇒ 00:04:36.350 Robert Tseng: Okay.
31 00:04:42.700 ⇒ 00:04:45.369 Awaish Kumar: Like, because, like in Eden, also, we
32 00:04:45.770 ⇒ 00:04:52.299 Awaish Kumar: are using an like a custom script from them. So we don’t have any
33 00:04:52.540 ⇒ 00:04:55.470 Awaish Kumar: any tool right now set up for North beam data.
34 00:05:00.750 ⇒ 00:05:07.549 Awaish Kumar: if I will see if I can find any tool which I can suggest to them. But yeah, we are
35 00:05:08.730 ⇒ 00:05:11.539 Awaish Kumar: blocked on that one for for finding a tool.
36 00:05:12.230 ⇒ 00:05:13.660 Robert Tseng: Yeah, I mean.
37 00:05:14.100 ⇒ 00:05:19.960 Robert Tseng: what if we also explored doing the custom script that Eden does like? I feel like, that’s not that hard to set up either.
38 00:05:21.385 ⇒ 00:05:29.330 Awaish Kumar: It’s not. But we have to maintain like right now for Eden. They have set up the cloud functions in Gcp. And the
39 00:05:29.520 ⇒ 00:05:33.560 Awaish Kumar: they have set up the cloud scheduler. So like
40 00:05:33.800 ⇒ 00:05:37.319 Awaish Kumar: they are doing like all handling. All of this is
41 00:05:37.710 ⇒ 00:05:42.080 Awaish Kumar: like all of this this, like running on a regular schedule and
42 00:05:42.180 ⇒ 00:05:53.610 Awaish Kumar: deployment on cloud function is being handled in their platform. For if I like, maybe I can copy that script or something like that, but how we want to
43 00:05:53.790 ⇒ 00:06:00.870 Awaish Kumar: schedule that, how we want to run it right now. We don’t have any kind of platform to run
44 00:06:01.670 ⇒ 00:06:07.729 Awaish Kumar: a custom script for a client. So we we have to set up the full flow like kind of a different.
45 00:06:08.070 ⇒ 00:06:15.770 Awaish Kumar: a production environment, where we go in, set up the environment and run the script which loads the data into the snowflake
46 00:06:16.690 ⇒ 00:06:19.170 Robert Tseng: I see? Yeah, I mean, right now.
47 00:06:19.670 ⇒ 00:06:25.670 Robert Tseng: the cloud function for Eden pulls that directly, and they can host it on Gcp, they don’t use the.
48 00:06:25.670 ⇒ 00:06:26.870 Robert Tseng: So yeah, segment
49 00:06:26.870 ⇒ 00:06:27.720 Awaish Kumar: This this?
50 00:06:27.990 ⇒ 00:06:34.980 Awaish Kumar: No, they like I can. I can see in the Gcp. There’s some cloud functions
51 00:06:35.610 ⇒ 00:06:44.299 Awaish Kumar: from where there is a script loaded there and then there is a cloud scheduler job which basically triggers that function
52 00:06:45.190 ⇒ 00:06:45.880 Robert Tseng: Okay.
53 00:06:46.960 ⇒ 00:06:52.060 Robert Tseng: we wouldn’t be able to host that in Snowflake and run it from there orchestrated. So I don’t know
54 00:06:52.060 ⇒ 00:07:00.120 Awaish Kumar: Yeah, I don’t think like Gcp is a full cloud platform provides functions and all. I don’t think Snowflake
55 00:07:00.120 ⇒ 00:07:01.469 Robert Tseng: Yeah. Snowflakes, not good
56 00:07:01.470 ⇒ 00:07:07.750 Awaish Kumar: Anything like that. So we have to find a cloud provider like a Gcp. Or Aws. Something like that
57 00:07:07.750 ⇒ 00:07:09.180 Robert Tseng: Well to them, or like
58 00:07:10.400 ⇒ 00:07:26.029 Robert Tseng: so address, matching python script like we post that in Snowflake now, I mean, I guess you still have to run it. But I think you can run it like on stuff. I don’t think we’re running it locally anymore. The one that pi is kind of built out. And then I guess we host
59 00:07:27.088 ⇒ 00:07:32.000 Robert Tseng: I mean, I haven’t. I haven’t looked into his script, and how it how it runs. But
60 00:07:32.180 ⇒ 00:07:37.420 Robert Tseng: like, I I wonder if definitely can support this? And yeah.
61 00:07:38.400 ⇒ 00:07:43.379 Awaish Kumar: Okay we we can. I can confirm that with pass, how he does it
62 00:07:43.910 ⇒ 00:07:48.429 Robert Tseng: Okay, yeah, can you like, just investigate a bit more like, okay? So
63 00:07:49.190 ⇒ 00:07:53.130 Robert Tseng: alright. So I wish to investigate. Alright, I mean
64 00:07:54.008 ⇒ 00:07:58.930 Robert Tseng: Provider. That’s that’s Utah decision. But like, maybe you kind of figure out.
65 00:07:59.590 ⇒ 00:08:03.280 Robert Tseng: figure out if we can, host and Snowflake
66 00:08:27.320 ⇒ 00:08:30.460 Robert Tseng: Okay, yeah, I know this one is.
67 00:08:31.280 ⇒ 00:08:39.420 Robert Tseng: So I’m gonna but that tomorrow we’re gonna call this
68 00:08:47.540 ⇒ 00:08:48.290 Robert Tseng: okay.
69 00:08:51.500 ⇒ 00:08:52.230 Awaish Kumar: Yep.
70 00:08:55.350 ⇒ 00:08:59.440 Awaish Kumar: So some of the tickets I moved them to actually done.
71 00:08:59.560 ⇒ 00:09:06.239 Awaish Kumar: basically because they they were done. I cannot see it here.
72 00:09:12.530 ⇒ 00:09:14.889 Robert Tseng: Yeah, things that you moved into that. Okay, yeah, no.
73 00:09:17.400 ⇒ 00:09:18.190 Robert Tseng: Right?
74 00:09:20.790 ⇒ 00:09:26.483 Robert Tseng: Alright. I guess I could. I could just filter by you. But
75 00:09:28.010 ⇒ 00:09:33.109 Robert Tseng: I don’t think it’ll show me done here. Oh, I have to do it from here. Okay, got it?
76 00:09:39.900 ⇒ 00:09:41.990 Robert Tseng: yeah. Which ones do you want to talk about?
77 00:09:44.470 ⇒ 00:09:48.599 Awaish Kumar: Like the the anise Pr that’s reviewed and merged.
78 00:09:49.720 ⇒ 00:09:50.400 Awaish Kumar: Oh.
79 00:09:51.930 ⇒ 00:10:00.220 Awaish Kumar: it was about the Jess, right? And then, yeah, then one was for attentive. I I moved it towards or turn
80 00:10:00.390 ⇒ 00:10:02.660 Awaish Kumar: I because I shared the data with amund
81 00:10:03.090 ⇒ 00:10:04.410 Robert Tseng: Yeah, and
82 00:10:04.910 ⇒ 00:10:14.180 Awaish Kumar: Yeah, that’s I think that. And then this one monthly quote summary table, I have worked on it. I’m halfway through. So I will finish
83 00:10:14.410 ⇒ 00:10:15.629 Awaish Kumar: finish it today.
84 00:10:18.700 ⇒ 00:10:24.000 Robert Tseng: Okay, yeah, this is just a like kind of Annie’s work. Right?
85 00:10:25.880 ⇒ 00:10:29.240 Awaish Kumar: Yeah, it’s not like model. I have to build for any. So it will be.
86 00:10:35.130 ⇒ 00:10:37.550 Awaish Kumar: I, I hope to finish it right? Okay.
87 00:10:38.590 ⇒ 00:10:39.590 Robert Tseng: Yeah, okay.
88 00:10:44.880 ⇒ 00:10:45.550 Awaish Kumar: Okay.
89 00:10:45.550 ⇒ 00:10:46.300 Robert Tseng: Well.
90 00:10:46.703 ⇒ 00:10:53.570 Awaish Kumar: Yeah, I I think you have seen Autam’s message that polyatomic is working on shop Tiktok shop
91 00:10:54.110 ⇒ 00:10:54.780 Robert Tseng: Yep.
92 00:10:57.800 ⇒ 00:11:01.970 Robert Tseng: okay. I think that’s it for you. On that stuff which is all good. There.
93 00:11:02.170 ⇒ 00:11:06.999 Robert Tseng: I guess we’ll jump to. I mean, we’re talking about annual go to Annie.
94 00:11:08.810 ⇒ 00:11:11.230 Robert Tseng: Yeah, I guess. Where do you want to?
95 00:11:12.390 ⇒ 00:11:13.200 Robert Tseng: Alright
96 00:11:14.161 ⇒ 00:11:21.599 Annie Yu: Yeah, we can start with the in progress. So I think the training one is done. And then this
97 00:11:22.640 ⇒ 00:11:29.650 Annie Yu: this report. If you click into it, I kind of mock the similar thing
98 00:11:30.520 ⇒ 00:11:30.950 Robert Tseng: Yep.
99 00:11:31.301 ⇒ 00:11:41.840 Annie Yu: Pivot table. One question, though, is that some of price? So currently, I only use the sum of price. I’m not so sure what they’re using in their pivot table
100 00:11:43.650 ⇒ 00:11:45.650 Robert Tseng: Yeah, they’re probably using some of price.
101 00:11:46.600 ⇒ 00:11:47.600 Annie Yu: Okay. So I
102 00:11:47.600 ⇒ 00:11:51.620 Robert Tseng: Did you? Did you check? I don’t know. I mean, I know I don’t do you
103 00:11:53.210 ⇒ 00:11:59.099 Robert Tseng: just like I don’t know how close we are to them like I wouldn’t trust what they have there. But, like, I wonder if it’s
104 00:11:59.690 ⇒ 00:12:02.860 Annie Yu: I I think honestly, they have more
105 00:12:04.710 ⇒ 00:12:08.000 Annie Yu: They have higher numbers than I do.
106 00:12:09.250 ⇒ 00:12:10.170 Robert Tseng: Okay?
107 00:12:10.950 ⇒ 00:12:16.568 Robert Tseng: Yeah. Well, I’m not sure what data source you’re pulling from one of these sheets.
108 00:12:16.920 ⇒ 00:12:19.952 Annie Yu: And for my pivot table I actually use the
109 00:12:20.440 ⇒ 00:12:24.490 Annie Yu: What’s that order? Line table
110 00:12:24.870 ⇒ 00:12:25.720 Robert Tseng: Borderline
111 00:12:25.870 ⇒ 00:12:28.929 Annie Yu: Yeah, so we have the product type
112 00:12:31.250 ⇒ 00:12:35.676 Robert Tseng: Oh, I see, I think,
113 00:12:40.480 ⇒ 00:12:47.350 Robert Tseng: yeah, I mean the, we can keep the product type one. Can you make another one using fact orders and just using the funnel type.
114 00:12:48.560 ⇒ 00:12:51.099 Robert Tseng: because when they talk about product type
115 00:12:51.390 ⇒ 00:12:51.900 Annie Yu: Hmm.
116 00:12:51.900 ⇒ 00:13:00.530 Robert Tseng: They’re not actually look, because what we have in order line is more granular than they’ve ever seen, because, like, I think, we have more than coffee and protein like we have all these other things in there, right?
117 00:13:01.592 ⇒ 00:13:03.360 Robert Tseng: Maybe I should just
118 00:13:03.640 ⇒ 00:13:14.040 Annie Yu: Yeah, I I tried to do that yesterday was fact order, too, because I want to compare them side by side, and for some reason, the unique, the distinct order. Id
119 00:13:14.310 ⇒ 00:13:18.950 Annie Yu: is like, fairly low. So the Aov is really high.
120 00:13:19.150 ⇒ 00:13:23.870 Annie Yu: I’m not sure if that’s accurate, but I can. I can do that again.
121 00:13:24.451 ⇒ 00:13:27.439 Annie Yu: I’m just gonna be one with packed order, because it’s quick.
122 00:13:28.130 ⇒ 00:13:30.630 Annie Yu: and we can see we can go from there
123 00:13:31.580 ⇒ 00:13:34.781 Robert Tseng: Okay. I’m not sure where your report is. I didn’t see a link, but
124 00:13:35.010 ⇒ 00:13:35.420 Annie Yu: Basically
125 00:13:35.420 ⇒ 00:13:41.970 Robert Tseng: Like this was built with back order line, or we have 4 line. Obviously, you have product type.
126 00:13:44.440 ⇒ 00:13:45.659 Robert Tseng: And then.
127 00:13:46.070 ⇒ 00:13:52.512 Robert Tseng: yeah, this is built with fact orders. So like funnel type is just protein versus concentrate pretty much
128 00:13:53.350 ⇒ 00:13:56.420 Annie Yu: And then there’s 1 called both right
129 00:13:56.420 ⇒ 00:13:58.039 Robert Tseng: Yeah. And then there’s both. Yeah.
130 00:13:59.370 ⇒ 00:14:04.480 Robert Tseng: But that’s a very small percentage. It’s only like or like a hundred, 60
131 00:14:04.930 ⇒ 00:14:05.260 Annie Yu: All right.
132 00:14:05.260 ⇒ 00:14:05.670 Robert Tseng: Sorry.
133 00:14:05.670 ⇒ 00:14:12.290 Annie Yu: I’ll just do a a similar view with fact order and then funnel type, and then we can compare
134 00:14:12.860 ⇒ 00:14:13.540 Robert Tseng: Yeah.
135 00:14:14.423 ⇒ 00:14:18.650 Robert Tseng: If you just use total price from
136 00:14:18.980 ⇒ 00:14:28.309 Robert Tseng: order line, it will be higher than total price for back orders, because that will be before discounts
137 00:14:29.398 ⇒ 00:14:36.211 Robert Tseng: Yeah, that’s the main. That’s the main thing. It’s also before discount before shipping and before tax. So
138 00:14:36.840 ⇒ 00:14:42.210 Robert Tseng: anyway, the the discount is the main thing that will inflate the the numbers. If you use order, line
139 00:14:42.930 ⇒ 00:14:50.590 Annie Yu: Okay, so for order, do I also just use sum of total price for now
140 00:14:50.880 ⇒ 00:14:57.020 Robert Tseng: Yeah, for for the total price. That’s pretty much all they were showing. And there’s
141 00:14:57.430 ⇒ 00:14:57.780 Annie Yu: Yep.
142 00:14:59.250 ⇒ 00:15:00.090 Robert Tseng: Yeah.
143 00:15:03.823 ⇒ 00:15:08.650 Annie Yu: Okay, at myself, just for some note
144 00:15:09.050 ⇒ 00:15:18.290 Robert Tseng: Okay, great cool. So you’re still blocked on the north theme. You’re still blocked on Amazon. And but
145 00:15:18.670 ⇒ 00:15:25.550 Annie Yu: For the North being one. I saw that Utam made a comment. He said, there is a table
146 00:15:26.150 ⇒ 00:15:27.460 Annie Yu: in raw.
147 00:15:29.880 ⇒ 00:15:34.130 Annie Yu: If you click into that ticket is that it?
148 00:15:37.700 ⇒ 00:15:38.450 Annie Yu: Yeah.
149 00:15:40.140 ⇒ 00:15:45.330 Annie Yu: So he did share a table in raw. But I look into it. There is.
150 00:15:45.680 ⇒ 00:15:48.729 Annie Yu: These are the columns that we have, and then
151 00:15:49.543 ⇒ 00:15:50.329 Robert Tseng: Data right
152 00:15:51.100 ⇒ 00:16:00.629 Annie Yu: Yeah, I’m I’m not. I’m not sure I can’t. I can’t verify, because above that these are the long list of kind of what the dashboard needs.
153 00:16:01.360 ⇒ 00:16:10.140 Annie Yu: So we need to be able to slice all these by like product, customer country time.
154 00:16:10.771 ⇒ 00:16:14.300 Annie Yu: And I don’t really know what that table is.
155 00:16:15.995 ⇒ 00:16:16.490 Annie Yu: Call
156 00:16:18.010 ⇒ 00:16:33.539 Robert Tseng: No, I mean, this is good. This is how I would approach it. Yeah, you outline what you need to replicate. And then you kind of assess like, what do you? What do we actually have? What can we actually do in a v 1? And then, if anything, we need to pull in more like we could just make that like a v 2. So if you want, I can
157 00:16:34.130 ⇒ 00:16:39.280 Robert Tseng: and help you figure out what’s what you can actually do here, I mean.
158 00:16:40.120 ⇒ 00:16:47.870 Annie Yu: Yeah, I think so far with that raw table. I I don’t really know what I can do honestly cause
159 00:16:47.870 ⇒ 00:16:54.609 Awaish Kumar: Okay? Actually, okay. So he, he says that data is in data export results. Table, right?
160 00:16:54.830 ⇒ 00:16:58.119 Robert Tseng: Yeah, I’ve been checking other tables, and they were empty.
161 00:16:58.250 ⇒ 00:17:04.960 Awaish Kumar: So I will look at this table and see if I can make some modeling work here for any tool
162 00:17:05.250 ⇒ 00:17:07.009 Awaish Kumar: to build something on top of it.
163 00:17:07.680 ⇒ 00:17:12.930 Robert Tseng: Okay, I guess so. Which ones were you looking at? Wish
164 00:17:12.930 ⇒ 00:17:16.359 Awaish Kumar: I will look. I would look. I was looking at like the spend table
165 00:17:16.690 ⇒ 00:17:23.369 Awaish Kumar: there is like there are 3 4 tables like orders, spend sources, spend
166 00:17:23.888 ⇒ 00:17:25.979 Awaish Kumar: and they were all empty.
167 00:17:26.260 ⇒ 00:17:29.140 Awaish Kumar: so I haven’t looked at the one which
168 00:17:29.760 ⇒ 00:17:33.520 Awaish Kumar: the bottom shared, so I will see
169 00:17:36.270 ⇒ 00:17:39.060 Robert Tseng: I mean, I guess we could just look at it.
170 00:17:45.900 ⇒ 00:17:48.680 Annie Yu: I’ll I’ll add a wish for
171 00:17:51.090 ⇒ 00:17:53.660 Annie Yu: on this ticket, so you can see
172 00:17:56.800 ⇒ 00:17:58.250 Robert Tseng: Portal.
173 00:17:59.460 ⇒ 00:18:01.099 Awaish Kumar: Yeah. It’s under Clavio
174 00:18:02.410 ⇒ 00:18:03.480 Robert Tseng: Under Clayview
175 00:18:04.460 ⇒ 00:18:05.290 Awaish Kumar: Portable
176 00:18:05.290 ⇒ 00:18:06.110 Robert Tseng: Oh, oh, God!
177 00:18:08.480 ⇒ 00:18:08.960 Robert Tseng: Yeah!
178 00:18:08.960 ⇒ 00:18:09.340 Awaish Kumar: Okay.
179 00:18:10.800 ⇒ 00:18:13.430 Awaish Kumar: Data export results. You know this one.
180 00:18:14.010 ⇒ 00:18:15.659 Awaish Kumar: I missed this one
181 00:18:24.350 ⇒ 00:18:25.145 Robert Tseng: Okay.
182 00:18:26.310 ⇒ 00:18:33.139 Robert Tseng: yeah. Maybe it was. Just look at this 1st before we kind of. But yeah, it looks like it has daily spend here, but kind of I see.
183 00:18:34.440 ⇒ 00:18:40.969 Robert Tseng: which I think is enough to answer all the things that Annie has. We just have to obviously model it into a way where she can.
184 00:18:43.170 ⇒ 00:18:44.900 Robert Tseng: At least the product names.
185 00:18:45.140 ⇒ 00:18:47.400 Robert Tseng: It’ll be similar to
186 00:18:47.670 ⇒ 00:18:57.400 Robert Tseng: to Eden, where a wish like they’re just gonna have the campaign product filters. I don’t even know if they have campaign name here. And we have to use this to map
187 00:18:57.760 ⇒ 00:19:01.369 Robert Tseng: to what we already have in the fact, order, fact, order, lines
188 00:19:03.800 ⇒ 00:19:05.760 Awaish Kumar: So, yeah, we have this
189 00:19:08.200 ⇒ 00:19:14.039 Robert Tseng: Yeah. So like, what would be need to be in the model is like, you know, for her to be able to.
190 00:19:14.620 ⇒ 00:19:18.020 Robert Tseng: Sweet.
191 00:19:18.900 ⇒ 00:19:28.600 Robert Tseng: should we do it at both order and order line level? If we do at the order level, then yeah, obviously, coffee concentrate versus protein like that’s easy, like we can do. The we can
192 00:19:28.780 ⇒ 00:19:35.540 Robert Tseng: group goes. I mean, everything else seems pretty standard. Anyway. I’ll I’ll let you kind of you can take a 1st pass at it, but it seems like we’ll have to map
193 00:19:35.710 ⇒ 00:19:41.439 Robert Tseng: this column to the products that we already have in order and order line
194 00:19:43.000 ⇒ 00:19:45.389 Awaish Kumar: Okay, do we want to go to product
195 00:19:45.660 ⇒ 00:19:53.619 Awaish Kumar: product type level, like in the quarter line, or want to keep it as a like protein versus concentrate versus
196 00:19:53.810 ⇒ 00:19:56.760 Awaish Kumar: both, like the funnel type thing
197 00:19:57.931 ⇒ 00:20:25.120 Robert Tseng: I mean, I haven’t looked at this entire data set, but it looks like it’s only just protein, instant latte, coffee and marketplace. So I don’t even know if they have that many to do of an order line breakdown. If it’s just this, then we should just do it at the order level or like we should, we shouldn’t. Even we don’t. We can’t even do category. That’s what I’m saying. I’m not sure like. Maybe there’s maybe there’s more specific campaign data. But yeah, this is my 1st time. Looking at this as well, we have to. Kinda
198 00:20:25.990 ⇒ 00:20:27.709 Robert Tseng: we have to. We have to look at it
199 00:20:28.830 ⇒ 00:20:29.540 Awaish Kumar: Okay.
200 00:20:32.990 ⇒ 00:20:33.690 Robert Tseng: Okay.
201 00:20:35.500 ⇒ 00:20:43.789 Robert Tseng: yeah. I mean, you let me know. You just show me like what you find on the product side. And I can. I can help you make that call on like, whether we do it at the category level or just the
202 00:20:44.490 ⇒ 00:20:45.609 Awaish Kumar: Okay. Yeah. Sure.
203 00:20:46.820 ⇒ 00:20:51.519 Robert Tseng: Okay. Alright. Let’s go to
204 00:20:51.520 ⇒ 00:21:01.510 Annie Yu: Oh, and no! Just one more thing that subscribe and save I I think I’ll find some time to do it today. No, not that one. I think it’s in the to do
205 00:21:02.630 ⇒ 00:21:03.240 Robert Tseng: Okay.
206 00:21:03.360 ⇒ 00:21:04.240 Annie Yu: Yeah.
207 00:21:04.660 ⇒ 00:21:04.980 Annie Yu: Oh.
208 00:21:04.980 ⇒ 00:21:05.810 Robert Tseng: Yeah, this one.
209 00:21:06.050 ⇒ 00:21:11.950 Annie Yu: Yeah, Tom and I discuss couple of approaches. I think my approach would take
210 00:21:12.340 ⇒ 00:21:16.619 Annie Yu: definitely longer than an hour. So I’m gonna just try his approach
211 00:21:17.560 ⇒ 00:21:21.270 Robert Tseng: Yeah. Or I guess if you could summarize for me, what were you? What were you thinking
212 00:21:23.176 ⇒ 00:21:23.863 Annie Yu: No,
213 00:21:25.390 ⇒ 00:21:32.639 Annie Yu: Okay, wait, no, can I? I’m just gonna stick to his approach cause I
214 00:21:32.640 ⇒ 00:21:33.220 Robert Tseng: Okay.
215 00:21:33.220 ⇒ 00:21:40.960 Annie Yu: I was trying to find, using, like repeating order interval to kind of flag the most likely
216 00:21:41.911 ⇒ 00:21:46.249 Annie Yu: orders for subscribe and save, and then start from there. But that’s
217 00:21:46.250 ⇒ 00:21:47.620 Robert Tseng: Oh, interesting. Yeah.
218 00:21:47.620 ⇒ 00:21:49.820 Annie Yu: Or more? Yeah.
219 00:21:49.820 ⇒ 00:21:50.490 Robert Tseng: Yeah.
220 00:21:52.750 ⇒ 00:21:55.279 Robert Tseng: Yeah, I think, really, the the ask here is like.
221 00:21:55.480 ⇒ 00:22:01.229 Robert Tseng: can we even identify that these are, is there any? Are there any like fields for these
222 00:22:01.940 ⇒ 00:22:07.390 Robert Tseng: like, can we identify, subscribe, and save in any other way, like ideally like
223 00:22:07.570 ⇒ 00:22:09.320 Robert Tseng: they would have had like a
224 00:22:09.960 ⇒ 00:22:16.390 Robert Tseng: Ss. Or something, or like in one of the other attributes. That shows us that. Okay, we could actually just use that as a filter.
225 00:22:16.744 ⇒ 00:22:18.879 Robert Tseng: I think that’s all we really need to to figure out
226 00:22:19.110 ⇒ 00:22:23.540 Annie Yu: Okay, so I’ll look up to
227 00:22:23.670 ⇒ 00:22:27.010 Annie Yu: into like raw database and then check out. What’s there
228 00:22:27.390 ⇒ 00:22:45.800 Robert Tseng: Yeah. But if you think your approach, it works. And you’re basically like my understanding of what you described is okay. You’re gonna look at orders for a particular customer if they’re ordering consistently on a monthly basis, then on the same day that you would, you would infer that they are subscribed same customer. That’s basically what you’re saying, right?
229 00:22:46.400 ⇒ 00:22:56.919 Annie Yu: Yes, but then I look into these 4 examples. If I use my approach, these 4 wouldn’t be categorized. Not all of them will be categorized like
230 00:22:57.380 ⇒ 00:23:05.620 Annie Yu: subscribe and save just because I think some of them only have, like 2 orders or so
231 00:23:06.310 ⇒ 00:23:07.219 Robert Tseng: I see?
232 00:23:09.380 ⇒ 00:23:18.156 Robert Tseng: Yeah, I mean, maybe like that will that would work. So I mean, but yeah, anyway, you you do this first.st I’m just gonna make a note here.
233 00:23:41.120 ⇒ 00:23:50.570 Annie Yu: Yeah. But also how accurate would this be? I don’t know. Like I I have subscribe and save over there, and I skip them all the time, and I if I want them earlier, I I move them
234 00:23:50.860 ⇒ 00:23:51.500 Annie Yu: bored
235 00:23:52.550 ⇒ 00:23:59.300 Robert Tseng: That’s true. You’re not. I mean, I at least, I’m pretty consistent with my subscriber. So I guess
236 00:23:59.590 ⇒ 00:24:02.500 Robert Tseng: yeah, it would be a very
237 00:24:02.880 ⇒ 00:24:09.059 Robert Tseng: inaccurate. But anyway, okay, cool. Let’s jump to Kyle.
238 00:24:12.240 ⇒ 00:24:13.120 Robert Tseng: Hello, we’re new.
239 00:24:13.705 ⇒ 00:24:14.290 Robert Tseng: Yeah.
240 00:24:16.718 ⇒ 00:24:19.670 Caio Velasco: So I saw aaliyah
241 00:24:19.920 ⇒ 00:24:36.074 Caio Velasco: message, and I well, from from what I felt, it seems that she’s gonna take today. Maybe tomorrow. So I would expect something more concrete on Monday, I guess, at least for a 1st version of whatever she’s gonna put into that spreadsheet.
242 00:24:36.780 ⇒ 00:24:47.300 Caio Velasco: And so this would be one part from for one of those tickets and for the team product yesterday I did a 1st version.
243 00:24:48.551 ⇒ 00:24:55.989 Caio Velasco: Well, the way, and I also push the Pr. And I tag wish, if we can review it
244 00:24:56.543 ⇒ 00:25:12.900 Caio Velasco: and also when I was doing that, since I had to, you know. Think about all the important fields that should be in the in the zoom product. I was always targeting whatever is, in fact, orders or in the other fact order tables.
245 00:25:13.020 ⇒ 00:25:15.069 Caio Velasco: or also the thing that should be there.
246 00:25:15.300 ⇒ 00:25:30.630 Caio Velasco: And I got to a point that I was wondering if I think the answer yes, but still I’m gonna ask the if we need the price calculation and the cogs things in them products. And if so.
247 00:25:32.170 ⇒ 00:25:45.907 Caio Velasco: which one is the last one, I assume is the one from factors. But since I see other stuff in the Amazon models, I’m not sure if those were updated when I wish updated the the cogs pricing structure.
248 00:25:46.668 ⇒ 00:26:07.911 Caio Velasco: Then this would be even another ticket to go into all the data models from Amazon, like the raw ones, the int ones, and maybe one or 2 different ones in in March, and then update them. Because I see a lot of like platform fees, peak fees, cogs, total those things that we we dealt. In the last weeks.
249 00:26:08.773 ⇒ 00:26:13.020 Caio Velasco: So yeah, so this would be some questions for a wish. And
250 00:26:13.700 ⇒ 00:26:21.490 Caio Velasco: then, yeah, that that I think that covers almost all of the tickets
251 00:26:22.870 ⇒ 00:26:25.699 Robert Tseng: The dim products was in staging. That’s the one that you worked on
252 00:26:26.560 ⇒ 00:26:34.879 Caio Velasco: Yeah, I didn’t push. I didn’t do a dbt run for that. I just did a Dbt build just to locally see if it was working. Then I pushed the Pr.
253 00:26:35.190 ⇒ 00:26:38.730 Caio Velasco: And I saw that there was no conflict
254 00:26:38.730 ⇒ 00:26:40.710 Robert Tseng: What the products am I looking at right now
255 00:26:41.720 ⇒ 00:26:43.220 Caio Velasco: Stage, one
256 00:26:44.050 ⇒ 00:26:46.729 Robert Tseng: Is this just a shopify one, or like, what? What am I looking at?
257 00:26:49.620 ⇒ 00:26:51.630 Robert Tseng: Isn’t there like I see it in products here
258 00:26:55.480 ⇒ 00:27:00.629 Caio Velasco: Cause, I know, since I didn’t do a DVD. Run. I think you. I assume you wouldn’t go into the Snowflake right
259 00:27:02.570 ⇒ 00:27:09.870 Robert Tseng: Okay, well, there is a dim products in Snowflake. So I thought you told me that there was no dim products and snowflake. And you’re kind of rebuilding this from scratch or whatever
260 00:27:10.260 ⇒ 00:27:12.299 Robert Tseng: here, and I see one so
261 00:27:12.760 ⇒ 00:27:17.809 Caio Velasco: Yeah, I don’t. I don’t know if this one is the one I did, because I don’t know if it was gonna be pushed to stage
262 00:27:19.480 ⇒ 00:27:31.749 Caio Velasco: because I’m not like completely versed in Cicd. So I assume that I was just gonna have anything against the database. If I had run a Dbt. Run, but I didn’t. So maybe in the Ci CD, something happens
263 00:27:35.390 ⇒ 00:27:36.200 Caio Velasco: at least
264 00:27:36.200 ⇒ 00:27:37.660 Robert Tseng: I’m just like I feel like
265 00:27:37.660 ⇒ 00:27:42.669 Robert Tseng: I’m just not sure how to review what you did. Feel like. We kind of had a couple of days to
266 00:27:43.000 ⇒ 00:27:50.470 Robert Tseng: put out the version, and I don’t. I don’t really know how I would. I would like, see what you did. So if I can’t view it and stuff like
267 00:27:52.230 ⇒ 00:27:52.800 Caio Velasco: Yeah.
268 00:27:53.348 ⇒ 00:27:57.859 Caio Velasco: Aish, do you know if if but then you
269 00:27:57.860 ⇒ 00:27:58.340 Robert Tseng: So you would.
270 00:27:58.340 ⇒ 00:28:00.419 Robert Tseng: Yes, engaging. That’s what we said yesterday.
271 00:28:00.990 ⇒ 00:28:12.119 Caio Velasco: Yeah, no, I didn’t do that part yet, because I thought it was. It was easier to just do the commit and check if it was correct, and then I would push it, just to avoid having to push it twice, since we are everyone at Sync.
272 00:28:12.500 ⇒ 00:28:16.609 Caio Velasco: But maybe it’s it’s automatic already, but I I don’t really know.
273 00:28:16.900 ⇒ 00:28:18.089 Caio Velasco: I can check
274 00:28:28.400 ⇒ 00:28:32.320 Caio Velasco: Well, there was in March. So I’m assuming that’s the one I did.
275 00:28:32.510 ⇒ 00:28:33.820 Caio Velasco: That’s yeah.
276 00:28:34.640 ⇒ 00:28:35.330 Robert Tseng: Okay?
277 00:28:36.460 ⇒ 00:28:49.267 Robert Tseng: Well, yeah, I mean, can you just make it clear? For like, what like, what do I need to review it? Like, yeah, okay, is it? I think I’m confused, like, why, we don’t know like what I’m looking at here.
278 00:28:49.840 ⇒ 00:28:55.049 Robert Tseng: like, I yeah, I feel you should be able to tell me if this is this is the one that you worked on or not.
279 00:28:56.020 ⇒ 00:28:57.410 Caio Velasco: Okay, I’ll I’ll check
280 00:28:58.430 ⇒ 00:28:59.080 Robert Tseng: Okay?
281 00:29:03.649 ⇒ 00:29:10.350 Robert Tseng: Yeah. Obviously, I know you’re still waiting for the cogs sheet to be updated so you can pull in cogs there. But assuming
282 00:29:10.900 ⇒ 00:29:15.220 Robert Tseng: me test, assuming this, is it?
283 00:29:16.450 ⇒ 00:29:20.079 Robert Tseng: I mean, these are all shopify. I don’t see anything in Amazon here. So
284 00:29:26.140 ⇒ 00:29:32.939 Robert Tseng: anyway, I alright, I think that’s I’m just like use of that piece there?
285 00:29:35.720 ⇒ 00:29:42.069 Robert Tseng: yeah, I mean, I think we will need to. I mean, I’m gonna be with cost later today. So we’re gonna
286 00:29:43.190 ⇒ 00:29:49.920 Robert Tseng: we’ll figure out what’s I mean, ideally, I want you can close this out soon so that we can actually add something else onto your plate. But
287 00:29:50.480 ⇒ 00:29:50.990 Caio Velasco: Do you mind?
288 00:29:50.990 ⇒ 00:29:51.460 Robert Tseng: Feel, like.
289 00:29:51.460 ⇒ 00:29:56.999 Caio Velasco: Do. Do you mind opening again the database? Just so that I see the name of the columns because I have it here
290 00:29:57.370 ⇒ 00:29:58.170 Robert Tseng: Yeah.
291 00:29:58.170 ⇒ 00:30:01.070 Caio Velasco: For the name for the integrity.
292 00:30:04.580 ⇒ 00:30:11.760 Caio Velasco: Yeah, that’s the one that’s the one I didn’t know there was gonna be pushed, even though I didn’t do. Dbt, run. But okay, so so it’s pushed. Yeah.
293 00:30:11.760 ⇒ 00:30:24.569 Robert Tseng: Okay, yeah, I mean, I don’t. I don’t mind it being in staging. That’s the point of it is, it’s easier for like either Annie and I to use it, and kind of give you feedback on, like how like this is, have everything we need, or whatever. So
294 00:30:25.310 ⇒ 00:30:26.230 Caio Velasco: Perfect, perfect.
295 00:30:26.970 ⇒ 00:30:27.570 Robert Tseng: Yeah.
296 00:30:30.440 ⇒ 00:30:35.809 Robert Tseng: okay, well, I’ve got a nudge. Hopefully, you can get a Leon call today, like, I wanna try to close that out.
297 00:30:37.174 ⇒ 00:30:42.410 Robert Tseng: But yeah, okay, I think that’s that. Seems to be it for this client.
298 00:30:43.240 ⇒ 00:30:46.440 Robert Tseng: I guess I didn’t about myself, but that’s
299 00:30:47.100 ⇒ 00:30:49.040 Robert Tseng: I know we’re a bit over so
300 00:30:53.540 ⇒ 00:30:54.260 Robert Tseng: cool.
301 00:30:54.450 ⇒ 00:30:59.029 Robert Tseng: Alright! If there are any other questions, just let me know in slack. Otherwise I’ll talk to you guys later
302 00:30:59.390 ⇒ 00:31:00.420 Annie Yu: Thank you.
303 00:31:00.420 ⇒ 00:31:01.290 Caio Velasco: Perfect. Thank you.