Meeting Title: US x BF | Standup Date: 2025-06-05 Meeting participants: Emily Giant, Caio Velasco, Amber Lin, Demilade Agboola
WEBVTT
1 00:00:48.690 ⇒ 00:00:49.490 Caio Velasco: So.
2 00:00:50.210 ⇒ 00:00:51.380 Emily Giant: Hi! How are you?
3 00:00:51.380 ⇒ 00:00:52.999 Caio Velasco: Hi! Good! How are you?
4 00:00:53.880 ⇒ 00:00:55.850 Emily Giant: Good, busy.
5 00:00:56.120 ⇒ 00:00:57.030 Caio Velasco: This is me!
6 00:00:57.030 ⇒ 00:01:02.459 Emily Giant: So, you know, as long as you’re getting done I don’t mind being busy.
7 00:01:03.432 ⇒ 00:01:04.377 Caio Velasco: True, true.
8 00:01:05.140 ⇒ 00:01:07.940 Emily Giant: Being productive is always the 1st choice.
9 00:01:08.320 ⇒ 00:01:08.790 Emily Giant: Yeah.
10 00:01:08.790 ⇒ 00:01:09.300 Caio Velasco: Yes.
11 00:01:09.830 ⇒ 00:01:19.560 Emily Giant: I feel like. Also I had 50 meetings on my calendar today, and, like 5 of them, have disappeared. And that’s good. I like that
12 00:01:19.780 ⇒ 00:01:27.950 Emily Giant: that gives me more time. I’m not kidding like I think 3 of them are gone. So I’ve just had such a meeting heavy week
13 00:01:28.380 ⇒ 00:01:46.129 Emily Giant: that it’s been really difficult to like, get things done during standard operating hours. And now things are looking up and nobody from the other teams works on Friday during the summer. They all like take their summer Friday. So the Dev team is like, yes, we can get it.
14 00:01:47.760 ⇒ 00:01:50.160 Caio Velasco: No, that’s good. I’m also definitely not like a
15 00:01:50.670 ⇒ 00:01:55.259 Caio Velasco: meeting person like if I, if I can reduce always the best to get work done.
16 00:01:55.760 ⇒ 00:01:56.080 Emily Giant: Yeah.
17 00:01:56.080 ⇒ 00:01:57.650 Caio Velasco: But yeah, yeah, I understand.
18 00:01:58.700 ⇒ 00:02:14.065 Emily Giant: Yeah, like, I like meeting with y’all and doing like work together, like when we’re doing modeling and whatnot. But most of the time the meetings are about broken data. And I’m like, Yeah, I know.
19 00:02:14.810 ⇒ 00:02:15.150 Caio Velasco: Like.
20 00:02:15.150 ⇒ 00:02:18.833 Emily Giant: We hired consultants. It’s not because it’s.
21 00:02:19.850 ⇒ 00:02:20.580 Caio Velasco: True, true.
22 00:02:20.920 ⇒ 00:02:21.850 Emily Giant: Hey! Amber.
23 00:02:23.120 ⇒ 00:02:24.110 Caio Velasco: Hello! Good morning!
24 00:02:27.980 ⇒ 00:02:32.348 Emily Giant: I meant to have the cats here for you today, but they’re doing their thing.
25 00:02:32.940 ⇒ 00:02:34.500 Amber Lin: Okay, they will show up.
26 00:02:34.500 ⇒ 00:02:35.060 Amber Lin: Okay.
27 00:02:35.500 ⇒ 00:02:36.649 Emily Giant: Don’t worry.
28 00:02:39.870 ⇒ 00:02:41.030 Amber Lin: Let me.
29 00:02:41.900 ⇒ 00:02:44.310 Amber Lin: Sorry I am in the hotel.
30 00:02:45.700 ⇒ 00:02:46.750 Amber Lin: Hallway.
31 00:02:47.430 ⇒ 00:02:50.020 Emily Giant: I was. Gonna say, you are somewhere new every day.
32 00:02:51.590 ⇒ 00:02:57.660 Amber Lin: This is cool. This is a few weeks that I am traveling. I haven’t travel for like 2 years.
33 00:02:59.030 ⇒ 00:03:01.549 Emily Giant: Have or not been traveling for 2 years.
34 00:03:01.550 ⇒ 00:03:02.519 Amber Lin: I have not.
35 00:03:02.770 ⇒ 00:03:08.559 Amber Lin: Oh, so I I wish but I am in Chicago, so.
36 00:03:08.560 ⇒ 00:03:10.880 Emily Giant: Oh, nice! You’re close to me now.
37 00:03:10.880 ⇒ 00:03:12.419 Amber Lin: Oh, really! Where are you at?
38 00:03:12.590 ⇒ 00:03:16.830 Emily Giant: I’m in Indiana. I’m like 2 and a half, 3 h from Chicago.
39 00:03:16.830 ⇒ 00:03:19.149 Amber Lin: Oh, let me see!
40 00:03:19.330 ⇒ 00:03:25.869 Emily Giant: Yeah, if you feel like coming to a farm in Indiana, we have extra bedrooms. Come on over.
41 00:03:26.390 ⇒ 00:03:31.259 Emily Giant: How crazy would that be if we got on this call one day, and we were both in the same room.
42 00:03:31.260 ⇒ 00:03:36.140 Amber Lin: I know that’s what that’s what happened last time where I was in awe.
43 00:03:36.977 ⇒ 00:03:43.809 Amber Lin: I went to Austin, and Utam is based in Austin. And so one day we were just on a call together, and then.
44 00:03:43.810 ⇒ 00:03:44.270 Emily Giant: Great.
45 00:03:44.270 ⇒ 00:03:45.800 Amber Lin: And people are like, Huh.
46 00:03:46.780 ⇒ 00:03:48.270 Emily Giant: Where is your home? Base.
47 00:03:49.202 ⇒ 00:03:50.929 Amber Lin: I’m usually in la.
48 00:03:50.930 ⇒ 00:03:51.590 Emily Giant: Okay.
49 00:03:51.770 ⇒ 00:03:58.250 Amber Lin: But my lease is ending, so might not be in La for long.
50 00:03:58.470 ⇒ 00:03:59.180 Emily Giant: Yeah.
51 00:03:59.400 ⇒ 00:04:00.340 Amber Lin: Oh, yeah.
52 00:04:00.340 ⇒ 00:04:07.600 Emily Giant: La’s got its environmental stuff that I can see. Why, it’s a good time to think about relocating.
53 00:04:08.609 ⇒ 00:04:09.949 Amber Lin: That’s true.
54 00:04:10.710 ⇒ 00:04:16.839 Amber Lin: Yeah, last time we were I was in downtown. La, so we had to evacuate.
55 00:04:17.230 ⇒ 00:04:17.910 Emily Giant: Oh, my! Gosh!
56 00:04:17.910 ⇒ 00:04:19.870 Amber Lin: But it was fun.
57 00:04:20.279 ⇒ 00:04:20.879 Emily Giant: Yeah. I’m glad.
58 00:04:20.880 ⇒ 00:04:23.700 Amber Lin: Because we were renting, and it wasn’t
59 00:04:24.420 ⇒ 00:04:28.659 Amber Lin: wasn’t a humongous issue. It was just a lot of smoke.
60 00:04:28.660 ⇒ 00:04:29.560 Emily Giant: Yeah.
61 00:04:29.560 ⇒ 00:04:32.150 Amber Lin: Hmm, anyways.
62 00:04:32.150 ⇒ 00:04:34.346 Emily Giant: Where you go. There are like issues.
63 00:04:35.050 ⇒ 00:04:37.699 Amber Lin: I know. I know it’s just you pick and choose.
64 00:04:37.700 ⇒ 00:04:42.130 Emily Giant: Exactly which issues are you okay with cause? You’re gonna have them.
65 00:04:45.438 ⇒ 00:04:54.020 Amber Lin: I was looking at our cycle. This seems pretty healthy. I’m, just looking at our progression and
66 00:05:00.830 ⇒ 00:05:01.660 Amber Lin: oh.
67 00:05:02.440 ⇒ 00:05:04.840 Emily Giant: You can move the one in testing
68 00:05:05.240 ⇒ 00:05:13.529 Emily Giant: the redshift grants. I’m pretty sure that’s complete. I was testing those all morning and any permissions issues
69 00:05:14.026 ⇒ 00:05:14.480 Emily Giant: go ahead.
70 00:05:14.480 ⇒ 00:05:14.829 Amber Lin: You know.
71 00:05:14.830 ⇒ 00:05:22.069 Emily Giant: But they were real, so the permission stuff seems to be resolved with the work he did.
72 00:05:22.590 ⇒ 00:05:24.490 Amber Lin: Okay, that is awesome.
73 00:05:24.490 ⇒ 00:05:24.990 Emily Giant: Yeah.
74 00:05:24.990 ⇒ 00:05:31.570 Amber Lin: So I’m gonna move that still waiting? Oh, wait! I thought they were gonna
75 00:05:32.640 ⇒ 00:05:34.939 Amber Lin: by Wednesday. Did it give an update.
76 00:05:34.940 ⇒ 00:05:41.930 Emily Giant: They may have. I’m so have like a hundred unread things.
77 00:05:41.930 ⇒ 00:05:47.600 Amber Lin: I know it’s okay. The point of these tickets are so we remember, or else I’m not gonna remember anything.
78 00:05:50.780 ⇒ 00:05:54.959 Amber Lin: Did they respond? If not, we can just ping them again.
79 00:05:54.960 ⇒ 00:06:00.730 Emily Giant: Yeah, okay, okay, earliest would be Friday, June 6.th
80 00:06:05.170 ⇒ 00:06:08.369 Emily Giant: Hello, no updates, he hasn’t written any. I’m just gonna ping him.
81 00:06:26.590 ⇒ 00:06:32.790 Emily Giant: Okay, I just pinged him and I can add any of his updates to that ticket.
82 00:06:33.160 ⇒ 00:06:39.581 Amber Lin: Okay. Awesome. I checked up here. Kyle said. This is in pr review.
83 00:06:40.750 ⇒ 00:06:49.170 Amber Lin: Did you need so it’s I guess this is for Emily to review or demo, to review.
84 00:06:51.520 ⇒ 00:06:55.720 Caio Velasco: So yeah, this I was able to build the query.
85 00:06:56.460 ⇒ 00:07:24.150 Caio Velasco: Red ship to query. All tables have been used, and I put some other use it statistics over there as well. It’s already in the spreadsheet as a new tab, as usual. And yeah, I think now we just have to decide more or less how to meet in the middle, because I think we’re starting with auditing dashboards on this side and then auditing sources. And now also red ship stuff. And now, somehow, we have to like bring them together, and maybe
86 00:07:24.830 ⇒ 00:07:36.389 Caio Velasco: a little bit of a a flavor of business as well. So that and we can like show also, like from the business point of view, like what are the most important things, and I think the intersection of all this would be
87 00:07:36.861 ⇒ 00:07:42.829 Caio Velasco: like a good outcome to where to start, and what what should be deprecated or not. That’s how I’m seeing things now.
88 00:07:43.580 ⇒ 00:07:47.869 Amber Lin: Okay, so this one, this ticket helps it. Look at usage.
89 00:07:49.021 ⇒ 00:07:56.690 Amber Lin: I guess my question is, is ready to send to Emily, and for just a quick 1st pass.
90 00:07:57.696 ⇒ 00:08:03.509 Caio Velasco: Yes, yes. And over there, since there is some interesting column, they can decide like okay, let’s look at the 10
91 00:08:03.780 ⇒ 00:08:06.290 Caio Velasco: ones, or, okay, 30 days or something.
92 00:08:06.860 ⇒ 00:08:07.530 Amber Lin: Okay.
93 00:08:08.200 ⇒ 00:08:16.930 Emily Giant: Would you be able to add the query that you used into the notes? Just so I can keep it on file for like future audits.
94 00:08:17.600 ⇒ 00:08:20.330 Emily Giant: I’m so that one over there
95 00:08:20.330 ⇒ 00:08:30.070 Emily Giant: new at Redshift, like I don’t know how to use it. Very well. So I’m just curious, like what the actual query was that you use so that I can have that knowledge.
96 00:08:30.070 ⇒ 00:08:31.609 Caio Velasco: Share as a comment.
97 00:08:31.610 ⇒ 00:08:32.659 Emily Giant: Hey! Yoo! Hoo!
98 00:08:32.669 ⇒ 00:08:36.940 Amber Lin: Oh, one better this one.
99 00:08:37.059 ⇒ 00:08:38.529 Emily Giant: Oh, thank you. Okay.
100 00:08:38.530 ⇒ 00:08:40.080 Caio Velasco: And I and I I did.
101 00:08:40.080 ⇒ 00:08:45.750 Caio Velasco: I did in a dodgetic way, because I was also teaching myself for the 1st time. So I think that can be helpful to you as well.
102 00:08:46.030 ⇒ 00:08:48.860 Amber Lin: Okay, thank you. Guys are awesome.
103 00:08:48.860 ⇒ 00:08:54.770 Emily Giant: One of my goals with our whole engagement is to understand better how to use redshift.
104 00:08:55.266 ⇒ 00:09:00.029 Emily Giant: It’s a very clunky tool, in my opinion, and so I’ve like
105 00:09:00.290 ⇒ 00:09:05.740 Emily Giant: stayed away from it. But I I don’t have really a choice anymore. I need to learn it.
106 00:09:06.660 ⇒ 00:09:17.560 Caio Velasco: No problem, no problem at the end. It’s just sequel queries. But since we are you, we are looking at usage. Then we are not querying a table from Redshift itself, but redshift itself
107 00:09:17.830 ⇒ 00:09:27.669 Caio Velasco: in terms of internal usage. Things so over there you can at least see a little bit if you have any questions, and I also, of course, using always AI to expedite the work.
108 00:09:28.187 ⇒ 00:09:30.689 Caio Velasco: Which has been amazing to be honest.
109 00:09:31.540 ⇒ 00:09:32.810 Emily Giant: But feel free. Yeah.
110 00:09:33.500 ⇒ 00:09:35.280 Emily Giant: Which AI do you use?
111 00:09:36.120 ⇒ 00:09:37.469 Caio Velasco: Share the beauty, easy.
112 00:09:37.470 ⇒ 00:09:44.414 Emily Giant: Yeah, yeah, it’s the only one that I’m like, yeah, this looks correct.
113 00:09:45.400 ⇒ 00:09:59.559 Caio Velasco: And what I learned with chat is that you have to have patience. So you go step by step, teaching to start from small to. I don’t get like a huge prompt, and that never works for me. So that’s a tip. Yeah.
114 00:10:03.290 ⇒ 00:10:04.849 Amber Lin: Yay, okay?
115 00:10:05.240 ⇒ 00:10:08.450 Amber Lin: And let’s see.
116 00:10:09.840 ⇒ 00:10:15.630 Amber Lin: And would you say, this is done? Or, we still need someone to review the pr.
117 00:10:16.590 ⇒ 00:10:34.729 Caio Velasco: No, I think it’s done. I already put in the channel, then took a look, at least in the 1st version, and then, if we need. And, for example, for this one, I just all the query was done looking at the last 30 days. So usage in the last 30 days, if we need some more, we just change aware clause, and that’s it. Super easy.
118 00:10:36.270 ⇒ 00:10:42.400 Caio Velasco: But it’s also, if you go into the spreadsheet I put like a very big orange saying, only last 30 days.
119 00:10:44.360 ⇒ 00:10:52.910 Amber Lin: I see. Let me check if okay.
120 00:10:56.410 ⇒ 00:10:59.090 Amber Lin: try to find a spreadsheet. I think this is the one.
121 00:11:02.530 ⇒ 00:11:07.389 Caio Velasco: And that’s a note I think I can. I can send you one sec.
122 00:11:10.880 ⇒ 00:11:18.239 Amber Lin: Hmm, anyways, and I’ll look at that later.
123 00:11:18.350 ⇒ 00:11:21.650 Amber Lin: So I think what we can do now is
124 00:11:27.340 ⇒ 00:11:35.530 Amber Lin: We can send this to Emily and uten for a quick 1st pass, and then
125 00:11:36.350 ⇒ 00:11:41.500 Amber Lin: we maybe we can add a column to just
126 00:11:43.030 ⇒ 00:11:48.370 Amber Lin: so that they can put down. Okay, this is probably used or not used.
127 00:11:54.590 ⇒ 00:11:58.350 Amber Lin: Yeah, hmm.
128 00:12:05.940 ⇒ 00:12:09.560 Amber Lin: there’s a lot of ones that we say it’s used here.
129 00:12:11.170 ⇒ 00:12:16.610 Amber Lin: So what what will they be looking at is, are they? Gonna look at the scan, count.
130 00:12:18.400 ⇒ 00:12:26.050 Caio Velasco: Then it’s a it’s a matter of like deciding what are the most important metrics, or the scan count, or when was last queried.
131 00:12:26.200 ⇒ 00:12:28.639 Caio Velasco: I also had one to count.
132 00:12:29.425 ⇒ 00:12:35.039 Caio Velasco: How many time was used. But I mean this, yeah, that’s the discount that I was talking about something.
133 00:12:35.420 ⇒ 00:12:36.220 Caio Velasco: Let’s see
134 00:12:36.220 ⇒ 00:12:45.389 Caio Velasco: and counts. Yeah, basically. And this one, the false one, is because they are also table that were not queried. So I had to build another query only for that part.
135 00:12:46.445 ⇒ 00:12:51.060 Amber Lin: So the true and false false just means it was never really queried.
136 00:12:51.470 ⇒ 00:12:53.790 Caio Velasco: In 30 days, or at all.
137 00:12:54.210 ⇒ 00:12:55.459 Amber Lin: Yes, turkey date.
138 00:12:55.460 ⇒ 00:12:56.893 Amber Lin: False means.
139 00:13:00.430 ⇒ 00:13:11.280 Caio Velasco: And maybe I mean, if I’m not wrong if you go to the column name there is a note. I hope that I have put that note over there as well. If you go over the column.
140 00:13:12.743 ⇒ 00:13:19.120 Amber Lin: I see awesome. So if it’s not
141 00:13:19.570 ⇒ 00:13:27.559 Amber Lin: checking. Oh, okay, so if false here, we can take a further look. Great. I think this is for a good place to start.
142 00:13:28.770 ⇒ 00:13:34.520 Amber Lin: Probably we’ll sort by these, and then I’ll add a column for Emily, and add a column for.
143 00:13:34.910 ⇒ 00:13:35.480 Emily Giant: Great.
144 00:13:36.050 ⇒ 00:13:37.309 Caio Velasco: Yes, feel free.
145 00:13:37.580 ⇒ 00:13:42.219 Amber Lin: And then we can probably do a dropdown. I don’t know how you guys want to do it, but we’ll
146 00:13:42.960 ⇒ 00:13:48.540 Amber Lin: let’s see great going back to here.
147 00:13:48.540 ⇒ 00:14:00.199 Demilade Agboola: Like when looking at this like looking at that and trying to filter on like comment. I’d be helpful to put a filter on the schema as well like the Schema name.
148 00:14:00.200 ⇒ 00:14:00.940 Amber Lin: I mean.
149 00:14:00.960 ⇒ 00:14:08.900 Demilade Agboola: So that, like it might just be helpful to just some schemas can be removed all at once, especially like maybe schemas
150 00:14:09.985 ⇒ 00:14:18.279 Demilade Agboola: like those are just for specific prs, so we could remove entire schemas for that. So that might make it a bit faster.
151 00:14:22.190 ⇒ 00:14:27.519 Amber Lin: Okay, awesome. So you guys have this list.
152 00:14:27.840 ⇒ 00:14:35.989 Amber Lin: And I, I think we can get started on looking at at least the ones that we know that we’re definitely not using, and we can go 5 of them.
153 00:14:36.910 ⇒ 00:14:38.140 Amber Lin: So
154 00:14:42.310 ⇒ 00:14:44.619 Amber Lin: we’ll repeat Utah as well.
155 00:14:48.490 ⇒ 00:14:59.679 Caio Velasco: Which should? Yeah, that’s what I was. Gonna say. If we just ping them, or should we turn for send them? Well, how do you think we should do, too, because it’s already here? People know it just so that we don’t forget.
156 00:14:59.680 ⇒ 00:15:00.494 Amber Lin: Alright!
157 00:15:02.720 ⇒ 00:15:05.733 Amber Lin: Both of you have access to that ticket.
158 00:15:06.280 ⇒ 00:15:06.980 Caio Velasco: Interesting.
159 00:15:08.450 ⇒ 00:15:13.739 Amber Lin: And just checking. Here were we able to pass this to Emily Amade.
160 00:15:13.920 ⇒ 00:15:24.038 Emily Giant: I wanted to chat about that. Well, we don’t really need to chat. I was going to add the tick, the tables that I’m looking forward to
161 00:15:25.280 ⇒ 00:15:36.720 Emily Giant: to the ticket. But essentially it’s the ones up top. The order line, order, product, customer and transactions. Customer is the least important
162 00:15:39.870 ⇒ 00:15:48.509 Emily Giant: transactions and transactions. Order and order line are important
163 00:15:49.030 ⇒ 00:15:52.069 Emily Giant: product. I’ve got pretty much figured out
164 00:15:55.290 ⇒ 00:16:03.380 Emily Giant: those I built, and they look accurate. But I’m having issues
165 00:16:03.800 ⇒ 00:16:10.815 Emily Giant: with inflated revenue. Our old model, like has completely deprecated. And
166 00:16:11.710 ⇒ 00:16:18.945 Emily Giant: I’m looking for a way to like just legacy that as soon as possible, so that I can get the teams
167 00:16:20.010 ⇒ 00:16:24.929 Emily Giant: revenue directly from shopify instead of what is being used now, it.
168 00:16:27.470 ⇒ 00:16:28.410 Demilade Agboola: Monica.
169 00:16:30.960 ⇒ 00:16:37.029 Emily Giant: So that’s like order, order, line, and transactions. But there are like.
170 00:16:37.140 ⇒ 00:16:38.890 Demilade Agboola: Meta fields.
171 00:16:39.360 ⇒ 00:16:43.909 Emily Giant: In order that are probably going to be important as well.
172 00:16:47.547 ⇒ 00:16:48.259 Amber Lin: Don’t log in.
173 00:16:48.260 ⇒ 00:16:49.100 Emily Giant: Did you.
174 00:16:49.100 ⇒ 00:16:49.670 Amber Lin: You think.
175 00:16:49.670 ⇒ 00:16:56.319 Emily Giant: Devo table names, or just like in the shopify Schema. That’s general what they call them.
176 00:16:56.930 ⇒ 00:17:00.010 Demilade Agboola: Yeah, like the Shopify Schema table names.
177 00:17:00.670 ⇒ 00:17:07.569 Emily Giant: I will put those in, because they, of course, have their like specific names. But I’ll list to this ticket.
178 00:17:08.764 ⇒ 00:17:16.500 Amber Lin: Okay, sounds good in progress. Great. I’m checking these.
179 00:17:25.760 ⇒ 00:17:27.780 Amber Lin: yeah, for these 2.
180 00:17:28.440 ⇒ 00:17:30.919 Amber Lin: Were we able to start them? So
181 00:17:31.180 ⇒ 00:17:34.870 Amber Lin: DVD models for local DVD. Models for redshift.
182 00:17:38.744 ⇒ 00:17:44.859 Demilade Agboola: Yeah, for the look. I have put the list of all like, look and expose. I put a list of all the Dbt models.
183 00:17:46.945 ⇒ 00:17:47.660 Demilade Agboola: So.
184 00:17:48.090 ⇒ 00:17:48.690 Amber Lin: Yay!
185 00:17:48.860 ⇒ 00:17:51.309 Demilade Agboola: That is there.
186 00:17:51.490 ⇒ 00:17:54.090 Demilade Agboola: They’re about 3 50 of them.
187 00:17:55.970 ⇒ 00:18:00.750 Demilade Agboola: and I put them as well like if you click on it. You can see I put the schemas as well.
188 00:18:03.640 ⇒ 00:18:08.050 Demilade Agboola: so you can just kind of go through and just see
189 00:18:08.470 ⇒ 00:18:12.789 Demilade Agboola: what I like. So I rating them based on like accuracy.
190 00:18:14.509 ⇒ 00:18:18.920 Demilade Agboola: So we can just quick like go through and knock them out.
191 00:18:18.920 ⇒ 00:18:30.260 Amber Lin: Okay, so sounds good. So seems like we’re ready to rank by accuracy is what I hear.
192 00:18:34.550 ⇒ 00:18:38.730 Amber Lin: Who do you need to do the accuracy is that Emily? Is that gonna be you.
193 00:18:40.020 ⇒ 00:18:43.760 Demilade Agboola: Yeah. So Emi was to like, create an odd comment.
194 00:18:44.290 ⇒ 00:18:50.109 Amber Lin: Okay, okay, so great this is now gonna be.
195 00:19:04.900 ⇒ 00:19:10.480 Amber Lin: I mean, I can list. I can probably make another ticket. I don’t know.
196 00:19:11.650 ⇒ 00:19:16.969 Amber Lin: Emily, would you? Would you know that this is this ticket is yours if it’s still under dun Laude.
197 00:19:19.810 ⇒ 00:19:21.040 Amber Lin: Make another ticket.
198 00:19:21.040 ⇒ 00:19:24.089 Emily Giant: Maybe we could add like a label or something like that.
199 00:19:25.320 ⇒ 00:19:27.449 Amber Lin: And how do I.
200 00:19:27.450 ⇒ 00:19:33.710 Emily Giant: There’s some kind of indicator. We could add that it’s being passed to me temporarily.
201 00:19:34.320 ⇒ 00:19:35.230 Amber Lin: Hmm!
202 00:19:40.320 ⇒ 00:19:41.540 Amber Lin: Don’t think so.
203 00:19:41.540 ⇒ 00:19:42.800 Emily Giant: Sub tab, is there.
204 00:19:42.800 ⇒ 00:19:43.540 Amber Lin: That’s not just.
205 00:19:43.540 ⇒ 00:19:44.310 Emily Giant: Capability.
206 00:19:44.310 ⇒ 00:19:45.770 Amber Lin: Make a sub issue.
207 00:19:45.770 ⇒ 00:19:47.110 Emily Giant: Yeah, perfect.
208 00:19:54.810 ⇒ 00:19:57.321 Emily Giant: I’m like, don’t make another ticket.
209 00:20:02.380 ⇒ 00:20:03.350 Amber Lin: Great.
210 00:20:03.570 ⇒ 00:20:05.060 Amber Lin: So this is
211 00:20:15.240 ⇒ 00:20:21.860 Amber Lin: alright. I’m just gonna say this one is done.
212 00:20:22.220 ⇒ 00:20:25.210 Amber Lin: Skip sub issue great.
213 00:20:44.170 ⇒ 00:20:47.600 Amber Lin: Oh, checking.
214 00:20:48.080 ⇒ 00:20:49.770 Amber Lin: So these are done
215 00:20:52.800 ⇒ 00:20:58.010 Amber Lin: anything anything here. This is still in progress, right?
216 00:21:04.180 ⇒ 00:21:11.558 Demilade Agboola: Yes, but like part of it is the output from Kyle’s
217 00:21:12.320 ⇒ 00:21:24.870 Demilade Agboola: like part of, I think part of it is the outputs generated. So it will just be important to like, go through that and then kind of see which ones match anything within. Dbt, and then I can like.
218 00:21:25.450 ⇒ 00:21:30.969 Demilade Agboola: So basically, it’s the output of what I’ve done all diving models with what Kyle has done.
219 00:21:31.100 ⇒ 00:21:37.839 Demilade Agboola: And then I can sort of compare them and kind of say, Hey, these are the ones that Dbt interacts with.
220 00:21:39.780 ⇒ 00:21:46.530 Amber Lin: Oh, great, so essentially this spreadsheet and this spreadsheet together.
221 00:21:47.910 ⇒ 00:21:52.099 Demilade Agboola: Yeah, just kind of see which where the overlaps lie.
222 00:21:52.330 ⇒ 00:21:56.500 Demilade Agboola: And then anyone that isn’t overlapped is kind of on its own.
223 00:21:56.830 ⇒ 00:22:00.710 Amber Lin: I see great.
224 00:22:03.820 ⇒ 00:22:06.399 Amber Lin: Then if I overlash.
225 00:22:11.410 ⇒ 00:22:14.999 Caio Velasco: A question, maybe the middle of the knows or the enemy.
226 00:22:15.760 ⇒ 00:22:16.440 Caio Velasco: Our
227 00:22:17.890 ⇒ 00:22:26.850 Caio Velasco: all tables in redshift were built by Dbt. Or not necessarily some things before before. Right? Okay. Very mixed.
228 00:22:26.850 ⇒ 00:22:28.920 Emily Giant: Not even like not.
229 00:22:28.920 ⇒ 00:22:30.960 Demilade Agboola: Yeah close.
230 00:22:30.960 ⇒ 00:22:46.820 Demilade Agboola: It’s like only 10 of all tables that seem to be like just using the eye gaze. They’re like 3 50 Dbt. Models in, and all of that. But they’re about 3,500 redshift tables, or some.
231 00:22:47.080 ⇒ 00:22:54.260 Emily Giant: Yeah, we have. We have all of the staging models from like 2 bi directors ago.
232 00:22:55.520 ⇒ 00:23:01.400 Emily Giant: So this can go but yeah, there’s a lot of junk in redshift.
233 00:23:03.970 ⇒ 00:23:05.870 Amber Lin: Goodness. Okay.
234 00:23:06.427 ⇒ 00:23:10.960 Amber Lin: Seems like we pass all of it. I still I should. Still.
235 00:23:11.380 ⇒ 00:23:14.079 Amber Lin: I don’t know. We’ll check with Alex and Zach.
236 00:23:16.660 ⇒ 00:23:23.570 Amber Lin: Anything? Any updates here? Emily, these 2 data issues.
237 00:23:24.893 ⇒ 00:23:30.709 Emily Giant: Yes, so I did issue a Pr. And tagged in Demo Latte to check the code.
238 00:23:30.710 ⇒ 00:23:34.129 Amber Lin: Oh, okay, sounds good. Oh, oops!
239 00:23:35.170 ⇒ 00:23:36.080 Amber Lin: Great!
240 00:23:37.573 ⇒ 00:23:38.940 Amber Lin: Pr review!
241 00:23:39.990 ⇒ 00:23:42.090 Amber Lin: Think they’re not able to go check that.
242 00:23:43.530 ⇒ 00:23:48.020 Demilade Agboola: It appears it appears it timed out, though, like the check.
243 00:23:48.470 ⇒ 00:23:54.380 Emily Giant: I know so when I ran it in my instance, it was okay.
244 00:23:54.560 ⇒ 00:24:04.929 Emily Giant: But I would like to check to go all the way through. It was like, maybe I saved something, and it when I click, commit in that branch it will cut it off
245 00:24:05.510 ⇒ 00:24:07.890 Emily Giant: and start again, which makes sense.
246 00:24:09.310 ⇒ 00:24:14.200 Emily Giant: So I can try running it again. But it’s hitting the same 5 errors which
247 00:24:14.420 ⇒ 00:24:20.430 Emily Giant: I’m reluctant to fix because they’re de I mean, I can fix them, but they’re old models that are being sunset.
248 00:24:22.220 ⇒ 00:24:28.172 Emily Giant: They’re not crucial to fix but I will.
249 00:24:29.510 ⇒ 00:24:32.450 Emily Giant: No, I just I was. I was just wondering, like.
250 00:24:32.600 ⇒ 00:24:38.359 Demilade Agboola: How long it took on your local branch, and why it’s taking like 30 plus minutes.
251 00:24:38.570 ⇒ 00:24:45.700 Emily Giant: It doesn’t make sense to me. Took about 5 min on my branch.
252 00:24:46.390 ⇒ 00:24:52.930 Demilade Agboola: Yeah. So I’m would have to also just figure that out in terms of like, why, like, that’s taking so long on the
253 00:24:52.930 ⇒ 00:24:53.370 Demilade Agboola: oh.
254 00:24:53.657 ⇒ 00:25:01.999 Demilade Agboola: staging, because the idea of staging is at least we should be able to. It should run, you know. We shouldn’t have any issues for 20 seconds so long.
255 00:25:02.360 ⇒ 00:25:03.060 Emily Giant: Yeah.
256 00:25:03.260 ⇒ 00:25:12.751 Emily Giant: well, I’ll pop in after this and fix those errors because some of them are tests which I won’t be able to fix like the revenue issue.
257 00:25:13.270 ⇒ 00:25:18.721 Emily Giant: but other ones are just like fact tables that
258 00:25:19.790 ⇒ 00:25:28.560 Emily Giant: They are saying that the test in the fact table is looking for a string or like a varchar.
259 00:25:28.690 ⇒ 00:25:36.670 Emily Giant: and the actual field is an integer. I’ve not gotten those before. I don’t know why, like
260 00:25:36.890 ⇒ 00:25:42.879 Emily Giant: suddenly we’d be getting that error. But would those be in the configuration or in a yaml file?
261 00:25:43.830 ⇒ 00:25:55.249 Emily Giant: The you know what, demo a you have 5 min that you could stay on after. So I could show you those errors, because I think that, like you would be able to fix them in like 20 seconds, or know where to look.
262 00:25:56.272 ⇒ 00:26:00.860 Demilade Agboola: Well, not. I have a call right after this, but I believe.
263 00:26:00.860 ⇒ 00:26:03.979 Amber Lin: Done. You guys can just use this room to just do that.
264 00:26:03.980 ⇒ 00:26:05.760 Demilade Agboola: Okay. Alright. Then I know.
265 00:26:05.760 ⇒ 00:26:07.440 Emily Giant: Oh, yeah, we have 4 min. And I think.
266 00:26:07.440 ⇒ 00:26:08.090 Amber Lin: Okay.
267 00:26:08.090 ⇒ 00:26:09.136 Emily Giant: Do so.
268 00:26:09.660 ⇒ 00:26:12.169 Amber Lin: Okay, I’ll hop on alright. Have a great day, guys.
269 00:26:12.170 ⇒ 00:26:12.870 Emily Giant: Alright. Thank you.
270 00:26:12.870 ⇒ 00:26:18.459 Emily Giant: Thank you. I okay, so I will be super. Quick. Let me share my screen.
271 00:26:22.390 ⇒ 00:26:26.040 Emily Giant: This one share.
272 00:26:27.430 ⇒ 00:26:33.519 Emily Giant: Okay, so the Dbt error that I’m getting. Pull up a new window here.
273 00:26:38.110 ⇒ 00:26:38.880 Emily Giant: Hmm.
274 00:26:49.490 ⇒ 00:26:59.160 Emily Giant: it is okay. So if I go to, they’ve changed the side of this. So I think, orchestration. Yeah jobs if I go to
275 00:27:05.720 ⇒ 00:27:06.830 Emily Giant: staging.
276 00:27:24.280 ⇒ 00:27:30.550 Emily Giant: So it’s saying, like, this isn’t a good one. Let me go to the run prior to this.
277 00:27:43.670 ⇒ 00:27:48.870 Emily Giant: So these are the ones that are not going to get resolved until I fix what I’m working on now. But.
278 00:27:48.870 ⇒ 00:27:49.630 Demilade Agboola: Okay.
279 00:27:49.630 ⇒ 00:27:55.869 Emily Giant: The subscription Id is type, big int. But expression is type, character varying, and this is in
280 00:27:56.000 ⇒ 00:28:03.120 Emily Giant: the subscriptions Xf. Historical, not historical, but historical. Fact.
281 00:28:03.350 ⇒ 00:28:10.920 Emily Giant: My God! So I haven’t touched this subscription. Id situation.
282 00:28:11.740 ⇒ 00:28:16.650 Emily Giant: That’s not necessarily true. I have. Is this
283 00:28:17.090 ⇒ 00:28:23.719 Emily Giant: okay? If I open the historical, sorry fact table.
284 00:28:26.380 ⇒ 00:28:30.980 Emily Giant: There’s no like typecasting or anything like that, but
285 00:28:31.940 ⇒ 00:28:36.609 Emily Giant: I don’t know where it wants me to fix it. It must just be like an upstream table.
286 00:28:36.900 ⇒ 00:28:41.629 Emily Giant: But I do not know for sure.
287 00:28:50.310 ⇒ 00:28:56.960 Emily Giant: Okay, so, and it’s the same error in the other one.
288 00:29:03.760 ⇒ 00:29:08.780 Demilade Agboola: Can we? You could just go to the previous space and copy and paste right.
289 00:29:08.780 ⇒ 00:29:10.559 Emily Giant: True chat. Oh.
290 00:29:24.820 ⇒ 00:29:25.600 Emily Giant: okay.
291 00:29:27.620 ⇒ 00:29:33.350 Emily Giant: So it’s saying that subscription Id is
292 00:29:34.950 ⇒ 00:29:39.710 Emily Giant: supposed to be an integer. But it’s text.
293 00:29:40.870 ⇒ 00:29:44.549 Demilade Agboola: Yeah. So it appears that maybe
294 00:29:45.170 ⇒ 00:29:50.540 Demilade Agboola: in the table. So pictures. Sf, Xf, can we go there?
295 00:29:52.870 ⇒ 00:29:57.660 Demilade Agboola: Let’s see, can we find the subscriptions? Id.
296 00:30:03.650 ⇒ 00:30:07.280 Emily Giant: So it must be upstream of this, because there’s no.
297 00:30:21.370 ⇒ 00:30:26.020 Demilade Agboola: Yeah, so it’s the backfill is, is text.
298 00:30:26.949 ⇒ 00:30:27.529 Emily Giant: Okay.
299 00:30:27.530 ⇒ 00:30:33.910 Demilade Agboola: So I’m good. Oh, my guess is you could just keep looking at. My guess is what happened was initially was a begins call.
300 00:30:34.980 ⇒ 00:30:41.506 Demilade Agboola: And over time someone switched it to text or like Varchar. So that varying characters just means text, basically.
301 00:30:42.593 ⇒ 00:30:49.849 Demilade Agboola: so I’m guessing as the screenshots. The snapshots have been happening over time. It’s just noticed that disparity.
302 00:30:51.440 ⇒ 00:30:52.470 Demilade Agboola: So
303 00:30:53.510 ⇒ 00:31:01.810 Demilade Agboola: I would need to look at it and just kind of like, do we even need to switch it? Because I mean, obviously there’s a reason why it was cast as text. In the 1st place.
304 00:31:03.791 ⇒ 00:31:07.549 Demilade Agboola: But if if not potentially
305 00:31:09.165 ⇒ 00:31:13.899 Demilade Agboola: we’ll just need to figure out like how to progress with that. To be honest.
306 00:31:14.060 ⇒ 00:31:14.650 Emily Giant: Okay.
307 00:31:14.964 ⇒ 00:31:21.249 Demilade Agboola: But yeah, that’s like, this isn’t like icky, unless you thing, it’s just probably something that’s happened over time.
308 00:31:21.490 ⇒ 00:31:37.369 Emily Giant: Yeah. And the reason that I cast it as text is because, one of the tables we used to use became deprecated, and the new one. Had like variable characters in it, so it can’t be a big int like it’s got letters in it.
309 00:31:37.800 ⇒ 00:31:45.329 Demilade Agboola: Yeah, yeah, I I think I think it should just be. This will be in minor effects, like, relatively, just to get rid of the the problem.
310 00:31:45.790 ⇒ 00:31:54.869 Emily Giant: Okay. So if if I updated if I made sure things were cast as text and did a full refresh on the fact table. Would that fix it.
311 00:31:55.550 ⇒ 00:32:07.989 Demilade Agboola: Not particularly because snapshots just keep taking snapshots of the outputs. They don’t, necessarily, but I’ll have to look into that, probably, and probably I haven’t had to like refresh, quote unquote snapshots, but I’ll look into that.
312 00:32:07.990 ⇒ 00:32:11.530 Emily Giant: Okay, do you want me to make a ticket? Or just add a comment to that.
313 00:32:11.940 ⇒ 00:32:14.960 Demilade Agboola: You just add a comment and tag me in it so it’ll pop up.
314 00:32:15.620 ⇒ 00:32:20.469 Emily Giant: Okey Dokey. Well, thanks for hanging on. I know you have to hop because you have another meeting, but I will talk to you soon.
315 00:32:20.470 ⇒ 00:32:22.359 Demilade Agboola: Alright, thanks, bye.