Meeting Title: US x BF | Standup Date: 2025-11-07 Meeting participants: Awaish Kumar, Amber Lin, Emily Giant, Demilade Agboola
WEBVTT
1 00:00:30.210 ⇒ 00:00:31.420 Amber Lin: Hello!
2 00:00:34.240 ⇒ 00:00:35.430 Amber Lin: Hi, Emily.
3 00:00:43.310 ⇒ 00:00:44.020 Amber Lin: Great.
4 00:00:47.520 ⇒ 00:00:52.429 Amber Lin: Let’s take a quick look at linear. I don’t know if tickets are…
5 00:00:52.750 ⇒ 00:00:57.110 Amber Lin: The most up-to-date, or if they capture all the work that you guys have been doing.
6 00:00:57.400 ⇒ 00:00:59.189 Amber Lin: Let’s see…
7 00:00:59.530 ⇒ 00:01:08.530 Amber Lin: Are we doing any of these? Can I move some to next week? Feels like we were focusing on, like, performance, and then…
8 00:01:09.240 ⇒ 00:01:10.249 Amber Lin: Oh my god, I…
9 00:01:10.250 ⇒ 00:01:16.739 Awaish Kumar: All of it is kind of done… done, like, the 449 is… is… is just, like.
10 00:01:17.130 ⇒ 00:01:22.219 Amber Lin: Like, we can move it to next cycle, it’s just sitting there, I don’t have any requirements.
11 00:01:22.220 ⇒ 00:01:24.240 Awaish Kumar: It needs grooming.
12 00:01:24.260 ⇒ 00:01:25.280 Amber Lin: Yeah, makes sense.
13 00:01:25.280 ⇒ 00:01:36.719 Awaish Kumar: 48-4, we already started working, like, we have a plan. Now, it’s kind of… so we have a plan now, like, we are just working on next action items.
14 00:01:36.990 ⇒ 00:01:43.429 Awaish Kumar: So… So we can… I don’t know, if we just mark it done, and then move on to the…
15 00:01:43.540 ⇒ 00:01:46.569 Awaish Kumar: Creating new tickets for new work.
16 00:01:46.870 ⇒ 00:01:48.010 Amber Lin: Sure.
17 00:01:49.490 ⇒ 00:01:56.960 Awaish Kumar: So, like, And then for DVD job failures, like, for example, I have made some changes just
18 00:01:57.260 ⇒ 00:02:04.409 Awaish Kumar: yesterday, regarding configurations and doing analysis on dbt logs.
19 00:02:04.540 ⇒ 00:02:09.120 Awaish Kumar: I have Notion doc for that, and our urban stamps documents on Notion.
20 00:02:09.490 ⇒ 00:02:14.859 Awaish Kumar: But that’s done, basically, also. Like, but the thing is, it’s…
21 00:02:15.400 ⇒ 00:02:31.240 Awaish Kumar: the few things which needed to be done at first are done. Now we need monitoring, like, ongoing monitoring of that, so we can move it to done, or maybe add new ticket for, like, monitoring, so you can monitor job failures, and
22 00:02:31.840 ⇒ 00:02:34.080 Awaish Kumar: Keep improving the…
23 00:02:34.080 ⇒ 00:02:35.030 Amber Lin: Gotcha. Okay.
24 00:02:36.090 ⇒ 00:02:38.660 Amber Lin: Who needs to review the…
25 00:02:38.660 ⇒ 00:02:40.240 Awaish Kumar: That’s done quickly.
26 00:02:40.400 ⇒ 00:02:45.090 Awaish Kumar: And, like, No one, basically. I shared it with Utam, and it’s…
27 00:02:45.090 ⇒ 00:02:45.750 Amber Lin: Okay.
28 00:02:46.180 ⇒ 00:02:47.390 Awaish Kumar: Most.
29 00:02:47.390 ⇒ 00:02:54.140 Amber Lin: Let me, monitor… TV show failures. Okay.
30 00:02:55.390 ⇒ 00:03:01.890 Awaish Kumar: It’s an ongoing thing, and then Looker was also an ongoing thing, like, we might have few
31 00:03:01.990 ⇒ 00:03:10.219 Awaish Kumar: additional things, so, like, Emily has already marked the dashboards, I can look into it and…
32 00:03:10.530 ⇒ 00:03:18.569 Awaish Kumar: take, the subset of explorers, which needs… needs… need for migration, and then, yeah, start from there.
33 00:03:19.890 ⇒ 00:03:20.490 Amber Lin: Cool.
34 00:03:21.150 ⇒ 00:03:28.399 Awaish Kumar: Law 3 Touchpoints is, like, model is ready, that’s… that ticket was for integration, that is done.
35 00:03:28.400 ⇒ 00:03:29.130 Amber Lin: Yeah.
36 00:03:29.610 ⇒ 00:03:31.929 Awaish Kumar: Modeling is,
37 00:03:32.320 ⇒ 00:03:40.350 Awaish Kumar: is ready also, I’ve worked on that, although there’s no ticket for this, because it was a high priority to bring it in the.
38 00:03:40.350 ⇒ 00:03:41.430 Amber Lin: I see.
39 00:03:41.430 ⇒ 00:03:47.469 Awaish Kumar: So, but I’ll need to run a few redshift commands, which I’m stuck at right now.
40 00:03:48.860 ⇒ 00:03:58.269 Awaish Kumar: I’m, like, trying to run it with dbt, if it runs, okay, otherwise, maybe I will bring Alex again. So, yeah, start putting…
41 00:03:58.650 ⇒ 00:04:02.039 Awaish Kumar: Okay, so I… Between S3 and Redshift.
42 00:04:02.410 ⇒ 00:04:05.859 Amber Lin: Sorry, is this this ticket? The modeling? Is it this one?
43 00:04:08.880 ⇒ 00:04:11.010 Awaish Kumar: Yeah, this worked.
44 00:04:11.010 ⇒ 00:04:11.870 Amber Lin: Oh, okay.
45 00:04:12.250 ⇒ 00:04:17.420 Amber Lin: Cool. Modeling… I’ll just cancel that ticket.
46 00:04:18.160 ⇒ 00:04:19.010 Amber Lin: So, awesome.
47 00:04:19.010 ⇒ 00:04:23.780 Awaish Kumar: It was not canceled, like, the other one was the integration, and it’s done.
48 00:04:23.880 ⇒ 00:04:26.480 Amber Lin: Oh, sorry, I made another one.
49 00:04:27.990 ⇒ 00:04:31.490 Amber Lin: Okay, so I’ll say this is in progress. Cool.
50 00:04:32.600 ⇒ 00:04:36.160 Amber Lin: This is… Ongoing…
51 00:04:38.520 ⇒ 00:04:42.129 Awaish Kumar: We needed… we need one for Looker as well, so…
52 00:04:43.260 ⇒ 00:04:51.899 Amber Lin: Looker… what’s for Looker? Because I know we made other… I made other tickets based on the Looker migration call. Should I just move those in?
53 00:04:53.560 ⇒ 00:04:55.350 Awaish Kumar: Like, we…
54 00:04:58.130 ⇒ 00:05:11.580 Awaish Kumar: Right, look at migration, like, we made a different… we took a different approach here, so Emily worked… I don’t know how… how… how much she spent on it, but she worked on it a little bit, and I will start working on it right now, so…
55 00:05:12.720 ⇒ 00:05:13.329 Amber Lin: Okay.
56 00:05:13.330 ⇒ 00:05:14.190 Awaish Kumar: Yeah, so…
57 00:05:14.190 ⇒ 00:05:16.440 Amber Lin: If there’s a ticket, what should I call it?
58 00:05:18.680 ⇒ 00:05:31.260 Awaish Kumar: Yeah, like, we can call it, like, for example, like, Audit dashboard and… and assign… Priorities?
59 00:05:31.700 ⇒ 00:05:35.569 Amber Lin: Oh, I see, so this is, like, the usage, and then what we deprecate first.
60 00:05:36.160 ⇒ 00:05:36.880 Amber Lin: Ticket.
61 00:05:37.420 ⇒ 00:05:42.669 Awaish Kumar: Yeah, and that is, like, that’s… that was for… Or,
62 00:05:43.310 ⇒ 00:05:46.339 Awaish Kumar: Emily and she has already done it, so…
63 00:05:46.990 ⇒ 00:05:47.780 Amber Lin: Okay.
64 00:05:48.730 ⇒ 00:05:54.289 Awaish Kumar: For me, it’s, like, now to explore the… Explore the changes, what…
65 00:05:54.410 ⇒ 00:05:58.450 Awaish Kumar: on Emily Maine, and then from there, get subset of
66 00:05:58.870 ⇒ 00:06:05.699 Awaish Kumar: Explorers get, old columns and then map it to the new columns, so…
67 00:06:12.280 ⇒ 00:06:17.619 Awaish Kumar: Yeah, that will be also an ongoing thing, like, it’s an incremental thing, so I have to…
68 00:06:18.440 ⇒ 00:06:22.990 Awaish Kumar: Do it go by model by model, or explore by explore?
69 00:06:23.440 ⇒ 00:06:29.660 Awaish Kumar: Things like that. So, it’s going to be the, ongoing ticket, so I don’t know the…
70 00:06:29.780 ⇒ 00:06:32.660 Awaish Kumar: We don’t have any exact estimation for that.
71 00:06:32.660 ⇒ 00:06:34.610 Amber Lin: I see. Okay.
72 00:06:34.770 ⇒ 00:06:44.960 Amber Lin: This is… To-do… Okay, might move that. Okay, and then on Demolize tickets.
73 00:06:45.220 ⇒ 00:06:48.680 Amber Lin: Are any of these ones in review? Can they be closed?
74 00:06:50.130 ⇒ 00:06:53.790 Emily Giant: Mmm… Oh, you’re here! Sorry, go ahead.
75 00:06:54.530 ⇒ 00:07:00.380 Demilade Agboola: Yeah, I think… so I have done the merge of the historical revenue, so I think that’s basically done.
76 00:07:01.300 ⇒ 00:07:06.219 Demilade Agboola: So right now, we’re just kind of going to look at it in Looker…
77 00:07:06.740 ⇒ 00:07:09.610 Demilade Agboola: And 479 has also been done.
78 00:07:09.740 ⇒ 00:07:10.829 Demilade Agboola: as well.
79 00:07:13.130 ⇒ 00:07:22.050 Demilade Agboola: Yeah, so right now, we’re just trying to get it as an explore in Looker, and then we’ll, from that point, be able to figure out what the next steps will be.
80 00:07:22.360 ⇒ 00:07:23.439 Amber Lin: Okay. If we need.
81 00:07:24.120 ⇒ 00:07:27.380 Amber Lin: Yeah, so I should make a ticket for…
82 00:07:28.080 ⇒ 00:07:32.009 Amber Lin: correct orders, or forced upgrades into locations?
83 00:07:32.010 ⇒ 00:07:38.560 Demilade Agboola: That’s… yeah. I mean, that’s for Emily, but, like, that would just be, like, Looker Explore.
84 00:07:39.770 ⇒ 00:07:41.579 Demilade Agboola: or facts for those.
85 00:07:48.700 ⇒ 00:07:58.760 Amber Lin: I know this is from a while back in stand-up. I remember you and Emily said you were working on legacy data fixes. Is that still valid? Should I keep this ticket?
86 00:08:00.050 ⇒ 00:08:01.540 Emily Giant: No.
87 00:08:02.240 ⇒ 00:08:03.720 Emily Giant: He fixed it.
88 00:08:04.330 ⇒ 00:08:04.890 Amber Lin: Oh.
89 00:08:04.990 ⇒ 00:08:06.770 Amber Lin: Okay, I’ll say 10.
90 00:08:07.310 ⇒ 00:08:11.449 Amber Lin: Cool, does PK still need the promo codes?
91 00:08:12.280 ⇒ 00:08:16.360 Demilade Agboola: Yes, but he’s been a bit on the back burner, but I could always look at that, like…
92 00:08:16.680 ⇒ 00:08:19.959 Demilade Agboola: No, that I have a bit more capacity.
93 00:08:20.190 ⇒ 00:08:20.780 Amber Lin: Okay.
94 00:08:20.880 ⇒ 00:08:22.479 Amber Lin: What about this one?
95 00:08:26.810 ⇒ 00:08:28.410 Emily Giant: That’s definitely needed.
96 00:08:28.900 ⇒ 00:08:29.400 Demilade Agboola: Yeah, yeah.
97 00:08:30.660 ⇒ 00:08:33.279 Demilade Agboola: That’s fine, I could do that.
98 00:08:34.230 ⇒ 00:08:48.779 Emily Giant: I deployed the staging table for shipping from Hivo, because it doesn’t look like Shopify has a lot of that functionality. But it’s just the staging table. I didn’t work it in, but we had chatted yesterday about,
99 00:08:49.470 ⇒ 00:08:55.409 Emily Giant: Starting to pull that delivery information to connect the orders, so that model does exist.
100 00:08:56.530 ⇒ 00:08:58.520 Demilade Agboola: Oh, okay, alright, that sounds good.
101 00:08:59.190 ⇒ 00:09:03.450 Demilade Agboola: So, that’s shipping for the heat flow data, okay.
102 00:09:03.860 ⇒ 00:09:04.540 Emily Giant: Yeah.
103 00:09:04.680 ⇒ 00:09:12.190 Emily Giant: I’d love to not have it out of Pivo, but it… I just can’t find equivalencies in, Shopify.
104 00:09:13.740 ⇒ 00:09:14.060 Amber Lin: Huh.
105 00:09:14.990 ⇒ 00:09:18.069 Demilade Agboola: Like, you mean the shipping cost, or just, like, the shipping info?
106 00:09:18.430 ⇒ 00:09:23.270 Emily Giant: Shipping info, like the carrier, the tracking number.
107 00:09:23.570 ⇒ 00:09:24.620 Demilade Agboola: Gotcha, gotcha.
108 00:09:25.360 ⇒ 00:09:27.010 Emily Giant: I can look again.
109 00:09:27.470 ⇒ 00:09:37.600 Emily Giant: we’re still gonna need the staging shipping for historicals, so it’s not a total bust or waste of time. It might be in the fulfillment Shopify models, but,
110 00:09:38.520 ⇒ 00:09:44.609 Emily Giant: Yeah, one way or the other, we’re gonna need that staging shipping table for the final revenue mart, but I’ll look again.
111 00:09:44.610 ⇒ 00:09:46.129 Amber Lin: Is it related to this?
112 00:09:47.850 ⇒ 00:09:50.599 Emily Giant: No, that is… our dev team has to do that.
113 00:09:50.600 ⇒ 00:09:51.470 Amber Lin: Okay.
114 00:09:51.470 ⇒ 00:09:52.779 Emily Giant: Let’s do that.
115 00:09:53.070 ⇒ 00:09:53.680 Amber Lin: Sure.
116 00:09:57.210 ⇒ 00:10:03.799 Amber Lin: Cool. Let’s… oh, is this still a ticket?
117 00:10:03.920 ⇒ 00:10:05.479 Amber Lin: Is this still valid?
118 00:10:12.680 ⇒ 00:10:15.420 Amber Lin: I guess for, like, demo.
119 00:10:15.420 ⇒ 00:10:17.589 Awaish Kumar: I think it’s just, like…
120 00:10:18.160 ⇒ 00:10:23.899 Demilade Agboola: Yeah, it’s an ongoing thing, but, like, generally it’s fine, but, like, I know the recent runs have failed.
121 00:10:24.380 ⇒ 00:10:26.280 Demilade Agboola: So it’s something to look… Yeah.
122 00:10:26.280 ⇒ 00:10:26.820 Emily Giant: Yeah.
123 00:10:26.820 ⇒ 00:10:28.830 Amber Lin: Okay, sh- away should I?
124 00:10:28.830 ⇒ 00:10:30.220 Awaish Kumar: I mean, one of the…
125 00:10:30.800 ⇒ 00:10:38.510 Awaish Kumar: Yeah, you can. One of the, recent runs which… which were… which is, like, which the…
126 00:10:38.740 ⇒ 00:10:45.999 Awaish Kumar: the recent notification, it says one of the model, like, it’s for… the question is maybe for Amelia WRA.
127 00:10:46.120 ⇒ 00:10:48.449 Awaish Kumar: It says the model already exists.
128 00:10:48.960 ⇒ 00:11:00.210 Awaish Kumar: I haven’t looked further into it, like, do you know if… have you seen this error before? Like, in dbt, like, most of our models, like, just exist there.
129 00:11:01.610 ⇒ 00:11:05.630 Emily Giant: Yeah. When that happens, it’s usually that the table, like, wasn’t dropped.
130 00:11:06.980 ⇒ 00:11:14.649 Emily Giant: And so it’s… right, Demolade? Like, that’s the one that, like, when it says this already exists, it’s because, like, somehow the run didn’t…
131 00:11:15.630 ⇒ 00:11:17.659 Emily Giant: Drop it and fill it.
132 00:11:18.230 ⇒ 00:11:30.299 Demilade Agboola: So normally, that’s usually the dbt backup already exists. Like, the same model, the model name.dbt backdoc backup exists. Usually, it’s when there’s a concurrent run, so two things are running at the same time.
133 00:11:30.430 ⇒ 00:11:34.270 Demilade Agboola: And they’re using the same table, so that causes a conflict.
134 00:11:34.380 ⇒ 00:11:43.180 Demilade Agboola: But this one is a slightly different error. I haven’t started looking into… I just saw the error, but I haven’t really looked into it, because I was… it was right before this meeting.
135 00:11:43.380 ⇒ 00:11:44.100 Emily Giant: Yeah.
136 00:11:45.500 ⇒ 00:11:50.519 Awaish Kumar: Yeah, but… Like, this run… like, the ticket was, like, 8 days ago, so…
137 00:11:50.750 ⇒ 00:12:00.349 Awaish Kumar: I don’t know, it is still valid. I’m just talking about the recent runs, which are failing, and one of them was having this error, maybe I can rerun it and it will just…
138 00:12:01.100 ⇒ 00:12:02.900 Awaish Kumar: run fine.
139 00:12:03.080 ⇒ 00:12:07.410 Awaish Kumar: But yeah, I’m… I’m on it at trying to…
140 00:12:08.170 ⇒ 00:12:16.289 Awaish Kumar: Like, collect the logs and figure out where all the errors… what all the errors are, and then we can basically fix them.
141 00:12:16.730 ⇒ 00:12:23.299 Amber Lin: Cool. Yeah, I think this was from, like, last week, and it might already be looked at.
142 00:12:23.460 ⇒ 00:12:28.139 Amber Lin: Cool. Emily, for these tickets, are we still, like…
143 00:12:29.020 ⇒ 00:12:33.820 Amber Lin: What’s our plan here? Because I know you have a lot, and as far as.
144 00:12:33.820 ⇒ 00:12:34.520 Emily Giant: Friday and Friday.
145 00:12:34.520 ⇒ 00:12:36.919 Amber Lin: So probably some of them are for next week.
146 00:12:38.140 ⇒ 00:12:56.430 Emily Giant: No, I can do that today, for sure. The Looker Explorer for fact orders. I mean, there is one, I just need to do the new one. It will take 5 minutes, so I’m definitely doing that today, I’m definitely doing 490 today. Excuse me, not 490. That’s already done, right? The audit looker dashboards?
147 00:12:58.190 ⇒ 00:13:03.610 Amber Lin: I’m just putting it there so that Wish remembers that… but I guess we’ve already made a ticket.
148 00:13:04.000 ⇒ 00:13:04.370 Emily Giant: Yeah.
149 00:13:04.370 ⇒ 00:13:05.330 Amber Lin: Okay, close that.
150 00:13:05.330 ⇒ 00:13:12.530 Emily Giant: And then, for… I was gonna do the prepaid logic today. The, subscriptions, 426.
151 00:13:12.710 ⇒ 00:13:18.929 Emily Giant: I should be able to finish that today. It’s not… I don’t think it’s very hard. So,
152 00:13:19.060 ⇒ 00:13:23.899 Emily Giant: Yeah, those are all going to be done, and then the, refund mapping should move to next week.
153 00:13:24.190 ⇒ 00:13:29.659 Amber Lin: And then also, I guess, for the paid media, then B2B sales.
154 00:13:30.140 ⇒ 00:13:32.499 Emily Giant: Yeah, next week. Those, those are…
155 00:13:33.140 ⇒ 00:13:34.150 Amber Lin: Cool. Yeah.
156 00:13:34.460 ⇒ 00:13:35.110 Amber Lin: Alright.
157 00:13:36.390 ⇒ 00:13:38.850 Amber Lin: Alright, sounds good.
158 00:13:41.150 ⇒ 00:13:48.430 Amber Lin: Okay, could you guys send me this week’s wins? I need to write it, but I am… I am…
159 00:13:48.630 ⇒ 00:13:53.749 Amber Lin: Lost as to what significant thing we’ve done this week.
160 00:13:54.010 ⇒ 00:14:00.939 Emily Giant: I would say fixing the data problems in the revenue mark for historicals is the biggest win.
161 00:14:01.360 ⇒ 00:14:03.220 Emily Giant: That was large.
162 00:14:04.320 ⇒ 00:14:10.970 Awaish Kumar: Yeah, also, like, fixing the… the… like, dbt,
163 00:14:11.320 ⇒ 00:14:28.619 Awaish Kumar: like, dynamic schema creation, like, so now for staging, and we… and for dev, we have stable schemas, and that is going to improve our execution time for our models, and reduce the… and hopefully it will reduce the errors we are getting.
164 00:14:38.090 ⇒ 00:14:43.630 Amber Lin: Okay… Let’s see, what else did I…
165 00:14:43.910 ⇒ 00:14:50.640 Amber Lin: Oh, and then the performance PR that Utam sent, how do I talk about that?
166 00:14:51.290 ⇒ 00:14:52.010 Awaish Kumar: twice.
167 00:14:52.140 ⇒ 00:15:00.109 Awaish Kumar: Sorry, also… I wanted to add about North Beam thing, so we already ingested North Beam data.
168 00:15:00.830 ⇒ 00:15:06.110 Awaish Kumar: So, that’s also been… for Utam’s PR, like, we… that’s, like.
169 00:15:06.820 ⇒ 00:15:13.590 Awaish Kumar: Performance optimization for existing redshift tables.
170 00:15:14.740 ⇒ 00:15:21.999 Awaish Kumar: So, using, the… tech… redshift, like, optimization techniques.
171 00:15:22.560 ⇒ 00:15:32.100 Awaish Kumar: So, basically, and that will also help us improve the, the execution time for our… Models.
172 00:15:33.480 ⇒ 00:15:34.210 Amber Lin: Cool.
173 00:15:35.010 ⇒ 00:15:40.100 Demilade Agboola: Also, there’s data governance information about how to…
174 00:15:40.540 ⇒ 00:15:45.839 Demilade Agboola: properly use certain optimization techniques, like sort keys and… yeah.
175 00:15:46.740 ⇒ 00:15:50.229 Amber Lin: Is this… what’s the… what’s that… who’s that for?
176 00:15:51.410 ⇒ 00:15:59.630 Demilade Agboola: just anybody that can… will work in DBT. So it’s something for the quarantine, but also for the future.
177 00:15:59.630 ⇒ 00:16:00.290 Amber Lin: Cool.
178 00:16:03.720 ⇒ 00:16:04.690 Amber Lin: Awesome.
179 00:16:06.560 ⇒ 00:16:10.049 Amber Lin: And then next week, we’re doing the Looker stuff.
180 00:16:11.900 ⇒ 00:16:12.660 Amber Lin: Okay.
181 00:16:14.650 ⇒ 00:16:24.350 Amber Lin: Okay, we can talk more about what we do and planning. That’s good enough. I’ll send it in the channel, and then I might need you guys to help with the wording, because I’m not sure if I got it all.
182 00:16:26.720 ⇒ 00:16:27.450 Amber Lin: Right?
183 00:16:27.450 ⇒ 00:16:28.210 Emily Giant: Cool. Thanks, all.
184 00:16:29.000 ⇒ 00:16:29.970 Emily Giant: Thank you!
185 00:16:30.450 ⇒ 00:16:31.520 Amber Lin: Alright, bye.
186 00:16:31.890 ⇒ 00:16:32.520 Emily Giant: Bye.