Meeting Title: US x BF | Standup Date: 2025-06-03 Meeting participants: Caio Velasco, Amber Lin, Demilade Agboola, Uttam Kumaran, Emily Giant
WEBVTT
1 00:00:24.420 ⇒ 00:00:25.689 Caio Velasco: Hello, Emily!
2 00:00:26.120 ⇒ 00:00:27.680 Emily Giant: Hello! How are you?
3 00:00:27.820 ⇒ 00:00:29.020 Caio Velasco: Good! How are you?
4 00:00:29.320 ⇒ 00:00:30.050 Emily Giant: Good.
5 00:00:33.088 ⇒ 00:00:35.800 Caio Velasco: I see you outside. Is it cool weather.
6 00:00:36.250 ⇒ 00:00:39.449 Emily Giant: It’s so nice, so so nice.
7 00:00:40.590 ⇒ 00:00:42.469 Emily Giant: Yep! Hey! How are you?
8 00:00:42.470 ⇒ 00:00:44.210 Uttam Kumaran: Hey! Good!
9 00:00:46.750 ⇒ 00:00:48.959 Emily Giant: Haven’t seen you in a minute, but I know you’re busy.
10 00:00:49.698 ⇒ 00:00:54.220 Uttam Kumaran: Been a lot of meeting by phone.
11 00:00:54.220 ⇒ 00:00:54.920 Emily Giant: Yeah.
12 00:00:58.100 ⇒ 00:00:59.620 Emily Giant: How’s everything going.
13 00:00:59.800 ⇒ 00:01:17.190 Uttam Kumaran: Things are good. Yeah, I I’m actually very excited. We have some great, I think, work to share today. Some. We’ve basically audited almost a lot like a ton of the flows, and we have a bit of a process for the deprecation work for looker. So
14 00:01:17.970 ⇒ 00:01:21.569 Uttam Kumaran: yeah, it’s going well. So I’m glad.
15 00:01:24.930 ⇒ 00:01:25.620 Emily Giant: Hello!
16 00:01:25.620 ⇒ 00:01:26.699 Amber Lin: Oh, really.
17 00:01:31.230 ⇒ 00:01:39.909 Emily Giant: I know that Alex and Zack they’re interviewing. We had a team member, Amanda, who was amazing. She left for another position. So they’re like
18 00:01:40.840 ⇒ 00:01:52.010 Emily Giant: Super busy with interviews. Get started and I’ll fill them in. But I know that like they’re just now that Alex is back from vacation. He’s just like getting slammed with interviews.
19 00:01:53.790 ⇒ 00:01:58.030 Amber Lin: Okay, sounds good. I think Uton probably feels the same.
20 00:01:58.610 ⇒ 00:01:59.010 Emily Giant: Yeah.
21 00:01:59.010 ⇒ 00:02:00.349 Uttam Kumaran: Every day, every day.
22 00:02:01.790 ⇒ 00:02:06.450 Amber Lin: Alright. Let me pull that up and let me share my screen.
23 00:02:11.340 ⇒ 00:02:12.000 Amber Lin: Wow.
24 00:02:15.650 ⇒ 00:02:16.450 Amber Lin: so
25 00:02:18.949 ⇒ 00:02:28.180 Amber Lin: let’s just go through what we have in cycle first, st and then we can go look at the different projects and see where we’re at with them. So.
26 00:02:38.090 ⇒ 00:02:40.140 Amber Lin: yeah, a lot of any updates on this one.
27 00:02:40.350 ⇒ 00:02:41.800 Amber Lin: We’re still waiting right.
28 00:02:44.052 ⇒ 00:02:49.870 Emily Giant: I think that that polytomic is taking this, aren’t they?
29 00:02:50.160 ⇒ 00:02:50.840 Amber Lin: Hmm.
30 00:02:50.840 ⇒ 00:03:11.320 Demilade Agboola: Yeah, they’re trying to give us an update by Wednesday. That’s that’s what that’s what we put at the bottom. So they said to let us know the update by Wednesday. But right now the options we’re considering is like we’re not going to use polytomic, or we might want to get a new tool, but, like urban stamps have said, they don’t necessarily want to lean that way, for now
31 00:03:11.940 ⇒ 00:03:13.160 Demilade Agboola: avoid it. So.
32 00:03:13.430 ⇒ 00:03:14.920 Amber Lin: Okay, sounds good.
33 00:03:15.020 ⇒ 00:03:17.359 Amber Lin: So we’ll decide once we hear back from them.
34 00:03:21.810 ⇒ 00:03:25.980 Amber Lin: Next one, I think, okay, I just saw this one earlier.
35 00:03:27.910 ⇒ 00:03:32.389 Amber Lin: if this is, if this is what we discovered, what is our next steps.
36 00:03:33.886 ⇒ 00:03:40.410 Demilade Agboola: Yes, I’m just gonna like, look at the errors once more. Probably create a couple of Prs to test.
37 00:03:40.570 ⇒ 00:03:45.920 Demilade Agboola: I see why it keeps breaking and then figure out
38 00:03:46.220 ⇒ 00:03:58.699 Demilade Agboola: the commonality whether it’s a function of the user for Dbt staging meeting, I can update or just like, what is the minimum threshold it requires
39 00:03:58.970 ⇒ 00:04:05.260 Demilade Agboola: for it to be able to perform the duty. So maybe not necessarily being a super user. But what is the minimum threshold that Dbt staging.
40 00:04:06.376 ⇒ 00:04:08.360 Demilade Agboola: Environment requires to work.
41 00:04:09.463 ⇒ 00:04:10.610 Amber Lin: Sounds good.
42 00:04:11.990 ⇒ 00:04:20.049 Amber Lin: How long would like is this still gonna take 3 points, or is it? Is it gonna be less or more for you.
43 00:04:22.200 ⇒ 00:04:25.420 Amber Lin: I mean, it should be about the same.
44 00:04:25.420 ⇒ 00:04:26.140 Amber Lin: Okay.
45 00:04:34.580 ⇒ 00:04:35.830 Amber Lin: sounds good.
46 00:04:38.540 ⇒ 00:04:39.929 Amber Lin: So I should.
47 00:04:41.150 ⇒ 00:04:42.040 Amber Lin: No.
48 00:04:43.300 ⇒ 00:04:47.939 Amber Lin: Should I move this, I’ll move this back into some progress. I’ll work on that.
49 00:04:50.437 ⇒ 00:04:53.269 Amber Lin: Generally. Have you had a chance to look at this one?
50 00:04:54.050 ⇒ 00:04:56.749 Amber Lin: Oh, this can be closed. Okay.
51 00:04:56.750 ⇒ 00:05:04.059 Demilade Agboola: Yeah, I think we can close it. Nothing weird seems to have happened unless Emily has noticed something, but the revenue seems to be fine. Now.
52 00:05:04.490 ⇒ 00:05:05.350 Amber Lin: Okay, cool.
53 00:05:05.920 ⇒ 00:05:06.870 Amber Lin: Sounds good.
54 00:05:14.260 ⇒ 00:05:15.090 Amber Lin: Remember.
55 00:05:19.740 ⇒ 00:05:24.040 Uttam Kumaran: Can. Yeah, can we talk about the the one that’s on Kyle’s plate? The top 2.
56 00:05:24.310 ⇒ 00:05:25.720 Amber Lin: Oh, sure! The usage!
57 00:05:25.930 ⇒ 00:05:29.630 Uttam Kumaran: Sure I want to finish up the knowledge tickets. But yeah, let’s go hear that.
58 00:05:38.580 ⇒ 00:05:40.820 Amber Lin: Kyle, any updates on this one.
59 00:05:41.590 ⇒ 00:05:47.150 Caio Velasco: So that’s the next thing on my plate. Now that I finish the listing, the
60 00:05:47.720 ⇒ 00:05:57.430 Caio Velasco: the sources, tables and sources. So yeah, I would just log in and try to get the the list of dashboard, which, I think is should be simple.
61 00:05:57.895 ⇒ 00:06:00.649 Caio Velasco: And then we move forward with the other things. Yeah.
62 00:06:05.580 ⇒ 00:06:07.499 Amber Lin: You’re saying this one done right.
63 00:06:08.110 ⇒ 00:06:11.239 Caio Velasco: Yes, this one is done. I was able to
64 00:06:12.050 ⇒ 00:06:15.139 Caio Velasco: pull all tables and all sources from the 3.
65 00:06:15.630 ⇒ 00:06:17.260 Caio Velasco: Interesting tools which.
66 00:06:17.260 ⇒ 00:06:17.670 Amber Lin: Yeah.
67 00:06:17.670 ⇒ 00:06:20.430 Caio Velasco: Around 1,600 tables.
68 00:06:20.430 ⇒ 00:06:21.290 Amber Lin: Oh, goodness!
69 00:06:23.320 ⇒ 00:06:32.840 Caio Velasco: So at least we have them now. Now we have to somehow audit clean, understand? And this would be the next steps.
70 00:06:33.050 ⇒ 00:06:40.040 Amber Lin: Okay. Sounds good. Let me do you? Do we want anyone to pr review this? Or you think it’s fine
71 00:06:42.560 ⇒ 00:06:43.280 Amber Lin: in Vegas?
72 00:06:44.320 ⇒ 00:06:48.290 Uttam Kumaran: Well, I I thought we were gonna Kai. I thought we were gonna review this with Emily.
73 00:06:48.860 ⇒ 00:06:53.449 Amber Lin: Yeah, yeah, this is the. This is the 1st step. I just want to close out this ticket. So we.
74 00:06:53.450 ⇒ 00:06:54.610 Uttam Kumaran: Oh, I see. Okay.
75 00:06:54.610 ⇒ 00:06:56.409 Amber Lin: Going through. Yeah. Okay.
76 00:06:58.230 ⇒ 00:07:00.819 Amber Lin: So I’m gonna say, this is done. I’m gonna
77 00:07:02.520 ⇒ 00:07:05.480 Amber Lin: oh, I forgot to move, I think. Oh.
78 00:07:07.430 ⇒ 00:07:12.579 Amber Lin: we bought it for logic. I think I forgot to move one of them in cycle.
79 00:07:12.830 ⇒ 00:07:19.420 Amber Lin: But I think next step right here, Red Shift.
80 00:07:24.100 ⇒ 00:07:28.710 Amber Lin: Oh, this one! I forgot to move that.
81 00:07:30.440 ⇒ 00:07:35.729 Amber Lin: So I think we need to do both of these.
82 00:07:36.050 ⇒ 00:07:41.729 Amber Lin: So we should run a query to check and then also
83 00:07:41.990 ⇒ 00:07:46.720 Amber Lin: send. Probably after we’ve run a query, send a list to Emily and Tom.
84 00:07:47.620 ⇒ 00:07:49.510 Caio Velasco: Yes, yes, perfect.
85 00:07:49.510 ⇒ 00:07:50.070 Amber Lin: Awesome.
86 00:07:50.070 ⇒ 00:07:50.600 Caio Velasco: Cool.
87 00:07:51.180 ⇒ 00:07:59.370 Amber Lin: Okay, so put this okay as well and
88 00:08:00.020 ⇒ 00:08:04.159 Amber Lin: alright. So that seems to be due for tomorrow. That’s good
89 00:08:04.340 ⇒ 00:08:06.319 Amber Lin: going back to the current cycle.
90 00:08:07.370 ⇒ 00:08:14.069 Amber Lin: And then, yeah, this one is gonna be started today is what I recall.
91 00:08:14.360 ⇒ 00:08:18.260 Amber Lin: And so let’s that’s good.
92 00:08:18.600 ⇒ 00:08:26.620 Amber Lin: Let’s go into this one devil. You said this would be started yesterday, right.
93 00:08:27.851 ⇒ 00:08:36.400 Demilade Agboola: Yeah. So basically, I’ve just kind of been looking around trying to like notes. What changes have been made? I will.
94 00:08:36.780 ⇒ 00:08:45.670 Demilade Agboola: Link to the documents that I’m just like working on in terms of like what I’m understanding and just things that I feel we could change.
95 00:08:45.880 ⇒ 00:08:48.020 Amber Lin: But it’s.
96 00:08:48.380 ⇒ 00:08:51.979 Demilade Agboola: Work in progress. Basically, I should. I’ll finish it out today. Basically.
97 00:08:52.750 ⇒ 00:08:53.620 Amber Lin: Okay?
98 00:08:56.710 ⇒ 00:09:01.440 Amber Lin: And so I know this ticket. We said, we wanna document everything. And also.
99 00:09:02.870 ⇒ 00:09:09.649 Amber Lin: oh, never mind. It was not this ticket. Okay, document. And do you need to meet with Emily to talk about what happened.
100 00:09:10.750 ⇒ 00:09:27.460 Demilade Agboola: Oh, no, no, it’s more of like you. I mean, we could. It’s just basically like my observations and just things. I feel like we could put in a better spot. So obviously, this is just like an audit, the actual implementation we could obviously talk about it. And then we can have.
101 00:09:28.020 ⇒ 00:09:40.649 Demilade Agboola: Oh, like this. This is why this was done. Okay, like, can we move this here stuff like that? It’s not. It won’t necessarily like we’ll meet, but it won’t like be this. Like long conversation, the document, more structured.
102 00:09:40.900 ⇒ 00:09:47.510 Amber Lin: Awesome sounds good. Okay. We went over these. I think they’re all on good track to be finished.
103 00:09:49.630 ⇒ 00:09:56.259 Amber Lin: Oh, one last thing, I think. Is this still good for tomorrow, or should I move it back on the Grant automations.
104 00:09:56.620 ⇒ 00:09:59.199 Amber Lin: and probably should change the ticket name too?
105 00:10:03.390 ⇒ 00:10:06.720 Demilade Agboola: I will try and see if we’ll be ready for tomorrow. Can’t promise that to be honest.
106 00:10:07.234 ⇒ 00:10:14.430 Amber Lin: Then then don’t give me a more data. You’ll be more confident with.
107 00:10:15.748 ⇒ 00:10:20.149 Demilade Agboola: Let’s try. Let’s look at Friday. I think Friday will be decent. Yeah.
108 00:10:20.150 ⇒ 00:10:26.500 Uttam Kumaran: Yeah for this demo. I just get my help on anything you need like. If you have, you have a 1st pass of this.
109 00:10:27.173 ⇒ 00:10:29.900 Uttam Kumaran: I know grants redship grants can be tricky, so.
110 00:10:30.620 ⇒ 00:10:33.340 Demilade Agboola: Yeah, it’s just more of the thing of like
111 00:10:33.530 ⇒ 00:10:39.969 Demilade Agboola: just trying to figure out the general roots cause. And just like what the minimum threshold will be
112 00:10:41.620 ⇒ 00:10:47.550 Demilade Agboola: and how to apply that like automatically, consistently, so that we don’t have any staging issues.
113 00:10:47.780 ⇒ 00:10:49.853 Uttam Kumaran: Yeah. So this is where I think.
114 00:10:50.730 ⇒ 00:10:58.639 Uttam Kumaran: I don’t know. This is where I think the team may have to invest in either doing airflow or something, because we’re not going to be able to run.
115 00:10:58.930 ⇒ 00:11:01.850 Uttam Kumaran: I don’t know whether we’re gonna be able to run these post hooks
116 00:11:01.960 ⇒ 00:11:05.849 Uttam Kumaran: in Dbt. Cloud, and we’re not gonna be able to run it in polytomic.
117 00:11:06.160 ⇒ 00:11:07.310 Uttam Kumaran: So
118 00:11:09.180 ⇒ 00:11:14.909 Uttam Kumaran: I don’t know. I think I wanna I wanna understand whether there’s any opportunity for us to run something the other.
119 00:11:16.070 ⇒ 00:11:26.399 Uttam Kumaran: Yeah, if let’s just see how far we get to this, and we can have a conversation of if we like. I’m sure Alex has some solution for, and some orchestration tool that the team is using internally. So.
120 00:11:30.750 ⇒ 00:11:41.420 Emily Giant: I don’t know about that, but I know that Zack is like somewhat resistant to adding another tool to the stack and that’s why I went like directly to polytomic to see if
121 00:11:41.560 ⇒ 00:11:47.969 Emily Giant: they could assist. So I I agree with you, gentlemen, let’s see how far we can get. Also, we can probably, like.
122 00:11:48.100 ⇒ 00:11:52.320 Emily Giant: make the schedule somewhat less complex.
123 00:11:52.760 ⇒ 00:11:53.320 Uttam Kumaran: Yeah.
124 00:11:54.240 ⇒ 00:11:58.900 Uttam Kumaran: That was my solution, like either. We’re doing an hour every 2 h all time, and that’ll.
125 00:11:58.900 ⇒ 00:11:59.600 Emily Giant: Yeah.
126 00:11:59.600 ⇒ 00:12:01.250 Uttam Kumaran: Hit, most use cases.
127 00:12:01.540 ⇒ 00:12:01.960 Emily Giant: Yeah.
128 00:12:03.860 ⇒ 00:12:06.620 Uttam Kumaran: And then we can have a conversation about
129 00:12:07.060 ⇒ 00:12:10.379 Uttam Kumaran: if we wanna bring another tool, why and what it is.
130 00:12:10.380 ⇒ 00:12:17.390 Emily Giant: Yeah, I I think that like, if we did during business hours, every half hour, and then every 3 h
131 00:12:17.800 ⇒ 00:12:22.460 Emily Giant: after business hours that would hit most of the needs. There’s some like
132 00:12:22.820 ⇒ 00:12:28.949 Emily Giant: general confusion about what a Dbt. Refreshes versus like a source extraction with
133 00:12:29.370 ⇒ 00:12:31.470 Emily Giant: stakeholders, so I think that, like
134 00:12:31.870 ⇒ 00:12:38.119 Emily Giant: they probably wouldn’t. Not that I would like not tell them the difference, but like I don’t think
135 00:12:38.270 ⇒ 00:12:42.880 Emily Giant: they would recognize a significant difference with that cadence
136 00:12:43.740 ⇒ 00:12:48.129 Emily Giant: versus the proposed. The weekends are the thing, though, like
137 00:12:48.600 ⇒ 00:12:53.979 Emily Giant: we do not need that schedule on weekends, if that will like greatly reduce cost.
138 00:12:55.350 ⇒ 00:12:58.119 Emily Giant: So I think that that’s where we’re really looking for some like
139 00:12:59.246 ⇒ 00:13:03.279 Emily Giant: cost. Reduction is those Saturday Sunday extractions.
140 00:13:03.890 ⇒ 00:13:15.799 Uttam Kumaran: Yeah. The the only thing is like for example, we’re trying to move folks towards like we want to trigger polyatomic as soon as it’s done immediately trigger the downstream models that will actually solve this like
141 00:13:16.010 ⇒ 00:13:20.039 Uttam Kumaran: running polytomic and then running this like. And they sort of mismatch.
142 00:13:20.040 ⇒ 00:13:20.520 Emily Giant: Yeah.
143 00:13:20.520 ⇒ 00:13:23.280 Uttam Kumaran: Would. That’s the pitch, for like an for
144 00:13:23.520 ⇒ 00:13:27.270 Uttam Kumaran: like an orchestration tool, and these are these are like
145 00:13:28.090 ⇒ 00:13:31.969 Uttam Kumaran: pretty dirt cheap. But I think we’ll cross that bridge if we need to. When we get there.
146 00:13:31.970 ⇒ 00:13:34.980 Emily Giant: Okay, dirt cheap is good. We like that.
147 00:13:36.210 ⇒ 00:13:40.230 Emily Giant: If it’s saving us money with polytomic, then it’s like the.
148 00:13:40.230 ⇒ 00:13:41.002 Uttam Kumaran: That will be
149 00:13:41.260 ⇒ 00:13:45.089 Emily Giant: That is makes sense. Then, yeah.
150 00:13:51.620 ⇒ 00:14:01.140 Amber Lin: Great wrote that down just in case looking at these alright. So we’re, gonna
151 00:14:01.470 ⇒ 00:14:04.620 Amber Lin: I think, for Kyle’s tickets. We’re gonna run a query.
152 00:14:04.940 ⇒ 00:14:21.640 Amber Lin: and then send the list to Emily. So with the query prioritize cause there’s a lot of tables, and when, when, say, Emily and Utah looks at it. I want you guys to actually be able to know know what you should look at. I check. The table is massive.
153 00:14:21.780 ⇒ 00:14:27.070 Emily Giant: We probably use 90. I’m not kidding like, maybe.
154 00:14:30.030 ⇒ 00:14:36.979 Caio Velasco: Yeah, as in the in the. When I do the the script there is. It was very interesting to see that some sources have
155 00:14:37.510 ⇒ 00:14:46.339 Caio Velasco: of something being tracked or not tracked something with a button we can be on or off, so I try to put everything there, so that at least we see what is actually being.
156 00:14:47.180 ⇒ 00:14:55.429 Caio Velasco: Tracked. And yeah, it’s it should be all there. But then we would run the query and then work them to see what’s really happening in redshi.
157 00:14:56.270 ⇒ 00:14:56.620 Emily Giant: Yeah.
158 00:15:01.150 ⇒ 00:15:07.070 Amber Lin: this ticket? Demo at all. Let you take this so essentially, we’ll want to verify
159 00:15:07.330 ⇒ 00:15:24.070 Amber Lin: how we want to approach deprecating, dashboards and rich tables. So we want to use the usage and accuracy combined approach. But we want to confirm when you guys, before we before we start to make sure we cover everything.
160 00:15:24.610 ⇒ 00:15:26.800 Demilade Agboola: Yeah, it’s not. It’s not
161 00:15:28.910 ⇒ 00:15:48.319 Demilade Agboola: It’s just basically for the dashboards and models that we’re trying to like. Look into. We have. We’re going to create like a like a matrix of some sort where we have users on end and accuracy on the other axis. And the idea is, anything that isn’t accurate.
162 00:15:50.000 ⇒ 00:15:52.730 Demilade Agboola: And it’s not being used, but definitely get rid of it.
163 00:15:53.804 ⇒ 00:15:58.935 Demilade Agboola: And everything that is being used and is inaccurate. We’ll need to like
164 00:15:59.680 ⇒ 00:16:22.769 Demilade Agboola: rework it, and anything that is not being used and is accurate will actually just decide if that there’s any utility for it. And if we need to deprecate it. And anything that is using accurate can, you know, obviously still remain even if it will just have to redesign the flow or how it’s being used in total so basically, we have, like some definitions of how we want to look at
165 00:16:23.542 ⇒ 00:16:27.049 Demilade Agboola: accuracy because the usage is quite clear. Accuracy is a bit tricky.
166 00:16:27.310 ⇒ 00:16:47.760 Demilade Agboola: And so for accuracy, we’re just going to have to like, define, like Dbt models that we can trust as well as like looks and explores that we can trust as well, and then those would be like the basis or the foundation of like what we define as the accurate, like
167 00:16:50.550 ⇒ 00:17:01.300 Demilade Agboola: accurate dashboards. So that that way we we kind of have an easy idea of the flow, and how we can say what dashboards are accurate and what dashboards aren’t, or without the accuracy.
168 00:17:01.640 ⇒ 00:17:11.049 Demilade Agboola: So that’s basically the approach. And we wanted to let you know. So if you had any like thoughts. And you were like, actually, maybe consider this instead, or maybe factor this in
169 00:17:11.649 ⇒ 00:17:20.349 Demilade Agboola: we wouldn’t go through the entire process, go going through so many dashboards, and then come back to get feedback after. I think that would be a bit frustrating.
170 00:17:25.079 ⇒ 00:17:29.440 Demilade Agboola: So I don’t know, Emily, if you have any thoughts on the approach, or if you have any feedback on the approach.
171 00:17:29.960 ⇒ 00:17:36.669 Emily Giant: I I not off the top of my head? I think it’s gonna it’s starting here is good, and then
172 00:17:37.210 ⇒ 00:17:41.700 Emily Giant: we’ll see how it goes and amend from there. Yeah.
173 00:17:41.700 ⇒ 00:17:55.489 Amber Lin: Yeah, I remember. So for usage, we already have what direction from Zack of. We want to categorize them by business function. And look at the usage for the last 30, 60, and 90 days, I think, for accuracy.
174 00:17:55.850 ⇒ 00:18:04.339 Amber Lin: We’ll have something for you guys to see how it works. If we don’t have any feedback for now, I don’t think I think we’ll just go ahead and
175 00:18:04.898 ⇒ 00:18:08.330 Amber Lin: audit the different Dbt models and maybe audit
176 00:18:08.920 ⇒ 00:18:21.430 Amber Lin: the different explores, and maybe Zach or Alex will have something to add to that. But I know they’re not here today. So if you need, if you need us to write up something, so you can show them we can do that as well.
177 00:18:22.310 ⇒ 00:18:30.990 Emily Giant: Okay? So in terms of like, identify analyst owner. That’s not Emily, like, I should certainly take that step right like that would go much faster.
178 00:18:31.710 ⇒ 00:18:36.880 Emily Giant: Or are we gonna like, build this out into different tickets? These numbers.
179 00:18:38.210 ⇒ 00:18:38.880 Amber Lin: My
180 00:18:39.779 ⇒ 00:18:49.630 Amber Lin: what I imagine this would be best is that first, st we identify usage and accuracy. So we can mark what tables we actually want to deprecate or want to
181 00:18:50.380 ⇒ 00:18:51.160 Amber Lin: Say.
182 00:18:51.616 ⇒ 00:19:17.669 Amber Lin: wanna, because different tags will approach them differently. And I don’t want you to have to identify a hundred different dashboards. Analyst owners only for us to use 5. So, to save you some time, probably we should flag them first.st Maybe it will be great to let them know that we’re doing this. And if they have anything they want to raise in a process and say, Hey, we actually don’t use this, or we use this. That’ll be also really helpful as well.
183 00:19:19.500 ⇒ 00:19:26.000 Amber Lin: Okay, that I can do so going back to here.
184 00:19:27.590 ⇒ 00:19:46.929 Amber Lin: So essentially, this is sort of in the order that we want to do things. So we want. We’re currently verifying the usage. This will just pull in looker and then wanna make sure that we’re on the right approach. We’ll verify this with you, Zack and Alex. Then we’re gonna look at accuracy.
185 00:19:47.390 ⇒ 00:19:57.499 Amber Lin: flag the different dashboards present our findings to make sure that we’re categorizing everything correctly. And then for each of the flags we’ll have different
186 00:19:57.620 ⇒ 00:20:03.980 Amber Lin: different actions. Say, use and inaccurate will need to rebuild and unused and inaccurate will just deprecate.
187 00:20:04.520 ⇒ 00:20:07.840 Amber Lin: So this is sort of the rundown. How we want to approach this.
188 00:20:11.520 ⇒ 00:20:15.428 Amber Lin: Okay, let’s go back here.
189 00:20:16.490 ⇒ 00:20:22.799 Amber Lin: think this. I should say, I’ll send something check for it.
190 00:20:23.550 ⇒ 00:20:25.140 Amber Lin: One sec.
191 00:20:28.670 ⇒ 00:20:36.060 Amber Lin: And and one last bang should be this one.
192 00:20:44.040 ⇒ 00:20:49.470 Amber Lin: yeah, I think any updates on this on this one.
193 00:20:52.564 ⇒ 00:20:54.376 Demilade Agboola: No, not particularly
194 00:20:55.320 ⇒ 00:20:57.840 Uttam Kumaran: This one’s due on the 10th right? So we have a bunch more time.
195 00:20:57.840 ⇒ 00:20:58.200 Amber Lin: Yeah.
196 00:20:58.200 ⇒ 00:20:58.880 Demilade Agboola: Yeah, yeah.
197 00:20:59.700 ⇒ 00:21:05.180 Amber Lin: You probably should flush this out a little bit more, but
198 00:21:06.040 ⇒ 00:21:08.070 Amber Lin: I think this should give us some idea.
199 00:21:10.330 ⇒ 00:21:11.310 Demilade Agboola: We’ll do.
200 00:21:12.040 ⇒ 00:21:12.620 Amber Lin: Right.
201 00:21:13.470 ⇒ 00:21:20.019 Amber Lin: Any questions. Anything that pops up in your mind. We’ve ran through all everything here.
202 00:21:20.680 ⇒ 00:21:24.372 Uttam Kumaran: Yeah, so what are the meetings we have today? So we have, or this week, we have
203 00:21:24.590 ⇒ 00:21:27.230 Amber Lin: We have a grooming on Wednesday.
204 00:21:27.230 ⇒ 00:21:27.930 Uttam Kumaran: Tomorrow.
205 00:21:30.070 ⇒ 00:21:34.659 Amber Lin: And then this is so. The is this, the 1st week of the cycle
206 00:21:36.280 ⇒ 00:21:38.520 Amber Lin: our cycle started last tuesday.
207 00:21:38.520 ⇒ 00:21:39.310 Emily Giant: Hmm.
208 00:21:39.310 ⇒ 00:21:44.300 Amber Lin: So next Monday we’ll have our retros, and next Tuesday
209 00:21:44.500 ⇒ 00:21:47.360 Amber Lin: is our next sprint. Prep. Sprint.
210 00:21:47.360 ⇒ 00:21:52.659 Uttam Kumaran: So for this, for this 1 88. Are we? Are we in the Emily meeting about this, or is this Async.
211 00:21:53.756 ⇒ 00:21:55.539 Amber Lin: Give me a sec, and
212 00:21:55.940 ⇒ 00:22:00.419 Amber Lin: so I believe we need to run the query first, st and then we’ll send them to you.
213 00:22:00.690 ⇒ 00:22:04.709 Amber Lin: Wanna make sure that you put your time. Don’t waste your time on
214 00:22:05.230 ⇒ 00:22:08.260 Amber Lin: a massive list, so we’ll do this first.st
215 00:22:08.580 ⇒ 00:22:14.729 Uttam Kumaran: And then that’s gonna be like, so yeah, maybe ideally, we can look at that in parallel with us. 63,
216 00:22:14.870 ⇒ 00:22:16.630 Uttam Kumaran: the verified dashboards.
217 00:22:17.230 ⇒ 00:22:26.760 Uttam Kumaran: I’m not worried actually about this having, like a thousand tables like, I think we’ll quickly, pretty much be able to prioritize. But like, I also do think that, like we have to bite this bullet at some point so.
218 00:22:28.280 ⇒ 00:22:32.210 Uttam Kumaran: Us 63. Emily is like the list, looking at all dashboards, and looks.
219 00:22:33.450 ⇒ 00:22:37.909 Uttam Kumaran: So I don’t know. I feel like once those are ready.
220 00:22:38.510 ⇒ 00:22:44.739 Uttam Kumaran: I think it’d be good for for us to at least do like a 30 min meeting and like talk through. How we want to like, verify.
221 00:22:45.080 ⇒ 00:22:45.580 Amber Lin: Okay.
222 00:22:45.580 ⇒ 00:22:50.270 Uttam Kumaran: And then we could probably have some process, Async, to go through it.
223 00:22:50.270 ⇒ 00:22:50.910 Emily Giant: Yeah.
224 00:22:52.410 ⇒ 00:22:57.899 Uttam Kumaran: And even if even if these aren’t like both ready this week, I think it’d be nice to do this like Thursday, or something.
225 00:22:59.370 ⇒ 00:23:00.420 Emily Giant: That’s good
226 00:23:00.420 ⇒ 00:23:07.560 Emily Giant: usage. Things like it all exists in looker already so I think it’s just a matter of like pulling up the report and talking through them together.
227 00:23:07.940 ⇒ 00:23:08.900 Uttam Kumaran: Yeah.
228 00:23:12.490 ⇒ 00:23:16.500 Emily Giant: I think it’s like 17% of the existing dashboards are used.
229 00:23:16.500 ⇒ 00:23:26.620 Amber Lin: Yeah, yeah, it was 18% when we last looked at. It might have changed now. So I could be, I could book a meeting for you guys, or you guys can just find a time that works for you, too.
230 00:23:27.120 ⇒ 00:23:28.120 Uttam Kumaran: You want to book it.
231 00:23:28.610 ⇒ 00:23:33.149 Emily Giant: That’d be great if you could book it for us. I know that Utam is super busy, so.
232 00:23:33.420 ⇒ 00:23:35.880 Amber Lin: Emily, what time would work for you?
233 00:23:36.143 ⇒ 00:23:39.630 Amber Lin: Are we? What? What day are we thinking about? Thursday?
234 00:23:39.800 ⇒ 00:23:41.629 Emily Giant: Thursday. Let me pull up my calendar.
235 00:23:41.800 ⇒ 00:23:42.910 Emily Giant: Oh.
236 00:23:43.650 ⇒ 00:23:45.910 Uttam Kumaran: Or I mean I could do Friday also.
237 00:23:46.950 ⇒ 00:23:50.809 Emily Giant: Anytime after 2 o’clock Eastern
238 00:23:51.340 ⇒ 00:23:54.920 Emily Giant: anytime after one o’clock. Utam’s time.
239 00:23:54.920 ⇒ 00:23:55.610 Amber Lin: Okay?
240 00:23:57.140 ⇒ 00:24:05.039 Amber Lin: After 2 o’clock, I think when I’m between 2 and 3, you have the weekly sales retro. But you don’t go to those, so can I?
241 00:24:05.040 ⇒ 00:24:08.039 Uttam Kumaran: Yeah, we can. Yeah, we can just do 2 o’clock.
242 00:24:08.040 ⇒ 00:24:09.699 Amber Lin: Okay. Sounds good.
243 00:24:10.050 ⇒ 00:24:18.760 Amber Lin: 2 o’clock. Eastern booking. 30 min. Verify usage.
244 00:24:29.630 ⇒ 00:24:30.380 Amber Lin: Okay.
245 00:24:32.530 ⇒ 00:24:36.689 Amber Lin: Bent anything else on your mind.
246 00:24:37.240 ⇒ 00:24:49.839 Emily Giant: I have a quick, relatively unrelated question just to run by all of you, because you might be familiar. I found out I I met with our merch team yesterday and a lot of their product data is deprecated.
247 00:24:50.546 ⇒ 00:25:02.309 Emily Giant: Suddenly. So this was our shopify Hevo package. I realized that like, there’s a version 2 so I’m having to sub in some new tables. Does anyone know of like a Dbt
248 00:25:02.630 ⇒ 00:25:04.779 Emily Giant: shopify package.
249 00:25:05.080 ⇒ 00:25:21.359 Emily Giant: some kind of template, so that I’m not like rebuilding all of these from scratch. I just keep finding, like the 5 tran documentation or any resources. I don’t even need like the direct line to one of these packages. But if you have any recommendations of places that you look.
250 00:25:21.470 ⇒ 00:25:24.620 Emily Giant: I’m just trying to like, because.
251 00:25:24.620 ⇒ 00:25:31.440 Uttam Kumaran: We’ve modeled, shopify a bunch and like modeled off with a 5 trend one. I don’t know of a specific, Hevo
252 00:25:32.210 ⇒ 00:25:33.750 Uttam Kumaran: related one.
253 00:25:33.750 ⇒ 00:25:34.530 Emily Giant: Okay.
254 00:25:34.800 ⇒ 00:25:36.850 Uttam Kumaran: But I mean, we we’ve modeled.
255 00:25:36.980 ⇒ 00:25:37.710 Uttam Kumaran: Yeah, go ahead.
256 00:25:37.710 ⇒ 00:25:44.820 Demilade Agboola: The disparity in the data from Hevo or shopify, because ultimately, if the data foundation data is the same.
257 00:25:44.820 ⇒ 00:25:48.349 Uttam Kumaran: Should be the same. It’s just the raw tables are gonna maybe a little bit different.
258 00:25:48.700 ⇒ 00:25:49.170 Emily Giant: Yeah.
259 00:25:49.330 ⇒ 00:25:51.140 Demilade Agboola: Meeting will be a bit different. So.
260 00:25:51.140 ⇒ 00:26:02.079 Uttam Kumaran: I mean, honestly, what? Yeah, I think what you guys like if you can, just even even if you map the raw to whatever we typically expect we could use what our typical shopify models, too.
261 00:26:02.590 ⇒ 00:26:11.219 Uttam Kumaran: or maybe hand a version of that hand like a couple of base tables like order line order customers, products to Emily, and then she can, like
262 00:26:11.510 ⇒ 00:26:13.060 Uttam Kumaran: retrofit it in.
263 00:26:13.060 ⇒ 00:26:20.250 Emily Giant: Yeah, like anything. That’s because you’ve seen what we’ve done with our shopify stuff as we tried to shove it into our like, our old hevo oms.
264 00:26:20.250 ⇒ 00:26:21.010 Uttam Kumaran: Yeah, yeah.
265 00:26:21.010 ⇒ 00:26:28.770 Emily Giant: Just so bad. So I’m like I’m doing duct tape because I know that we’re gonna like refractor all of this stuff in a couple of months.
266 00:26:28.940 ⇒ 00:26:34.239 Emily Giant: But they need their data to be working yesterday. So I’m just trying to like at least
267 00:26:34.520 ⇒ 00:26:48.609 Emily Giant: deploy a couple of working product tables. So like the product variance. Meta fields, options and products are primarily the tables that I’m looking for, and like 5 trend has some great stuff. But the macros they use. They’re like very macro.
268 00:26:48.610 ⇒ 00:26:51.059 Uttam Kumaran: Yeah, yeah, they’re very specific. Yeah, yeah.
269 00:26:51.060 ⇒ 00:26:51.980 Emily Giant: I don’t know if.
270 00:26:52.510 ⇒ 00:26:57.999 Uttam Kumaran: No, we have some. I think we have some basic stuff. So yeah, maybe let’s create a ticket amber
271 00:26:58.120 ⇒ 00:27:04.590 Uttam Kumaran: on that. So we can say, just like, pass, shopify, template.
272 00:27:07.800 ⇒ 00:27:09.979 Uttam Kumaran: Like core objects to Emily.
273 00:27:10.190 ⇒ 00:27:10.720 Emily Giant: That would be.
274 00:27:10.720 ⇒ 00:27:12.767 Uttam Kumaran: And if you can,
275 00:27:13.720 ⇒ 00:27:24.490 Uttam Kumaran: sorry not past pass like past the ball. And if you can just write down these objects, order line, order, products, customer.
276 00:27:25.920 ⇒ 00:27:27.390 Uttam Kumaran: What else am I missing?
277 00:27:28.130 ⇒ 00:27:29.620 Uttam Kumaran: Online order.
278 00:27:29.850 ⇒ 00:27:31.520 Emily Giant: Transactions? Do you have transactions.
279 00:27:31.520 ⇒ 00:27:33.830 Uttam Kumaran: Yeah transactions.
280 00:27:35.091 ⇒ 00:27:37.198 Emily Giant: Let’s just start with that.
281 00:27:37.620 ⇒ 00:27:46.499 Emily Giant: Those are fine, like anything else I don’t want. I don’t want to get too in the weeds, since I know that we’re going to be working through this. But I just want to get their data like up and running Asap.
282 00:27:46.720 ⇒ 00:27:54.090 Emily Giant: The name started coming in blank like a week ago. And I’m pretty sure it’s just from like that deprecated table that we.
283 00:27:55.030 ⇒ 00:27:59.827 Emily Giant: the shopify thing is gonna you’ll yeah. You’ll see, you’ll see what we’ve done.
284 00:28:00.590 ⇒ 00:28:10.049 Uttam Kumaran: Amber. Can you ask? Can you actually ask? See if a wish on our team cause he was? We’re doing this for someone else as well like I think he he was thinking about this.
285 00:28:10.480 ⇒ 00:28:16.029 Uttam Kumaran: Maybe ask him, but like ideally, like maybe Demod or Kyle are the primary.
286 00:28:16.380 ⇒ 00:28:17.739 Amber Lin: Oh, okay, let me just.
287 00:28:17.740 ⇒ 00:28:23.390 Uttam Kumaran: Talking to me about this like a few days ago, like, Hey, we have some of these templated libraries we should share with clients so.
288 00:28:31.660 ⇒ 00:28:46.279 Amber Lin: Okay, let me copy this and as a wish, with that great anything else.
289 00:28:48.504 ⇒ 00:28:49.712 Emily Giant: One thing.
290 00:28:51.780 ⇒ 00:29:07.410 Emily Giant: for the inventory. Inventory, Mart. I we had a new ticket come in yesterday, and I’m just wondering, like, if new issues come in that weren’t known. Or do you want me to add those to the linear ticket.
291 00:29:09.270 ⇒ 00:29:13.310 Amber Lin: You mean, create a new ticket, or just add to the existing ticket.
292 00:29:13.310 ⇒ 00:29:20.549 Emily Giant: Add to the exist like, what do you prefer? Because this is gonna have to be addressed in the inventory mark? Finalization.
293 00:29:20.550 ⇒ 00:29:21.310 Amber Lin: But it’s.
294 00:29:21.310 ⇒ 00:29:22.640 Emily Giant: A new issue.
295 00:29:22.920 ⇒ 00:29:30.230 Amber Lin: Oh, if it’s a new issue, I think it will be best to create a separate ticket, so that we actually, we know how to complete a given ticket.
296 00:29:30.400 ⇒ 00:29:31.930 Emily Giant: Got it. Okay, thank you.
297 00:29:31.930 ⇒ 00:29:33.190 Amber Lin: Thank you for asking.
298 00:29:34.110 ⇒ 00:29:35.769 Emily Giant: I don’t want to mess up your board.
299 00:29:36.263 ⇒ 00:29:43.166 Amber Lin: I mean, this is our board. I I don’t feel like possession over this.
300 00:29:43.660 ⇒ 00:29:46.669 Emily Giant: I wish you did, because I do not want to feel possession.
301 00:29:46.920 ⇒ 00:29:50.484 Amber Lin: Okay, okay, fine. I I am very possessed.
302 00:29:50.930 ⇒ 00:29:51.910 Uttam Kumaran: I’m possessed.
303 00:29:51.910 ⇒ 00:29:56.189 Amber Lin: I’ll be very angry if anyone touched my board.
304 00:29:56.190 ⇒ 00:29:57.465 Emily Giant: Thank you. I like that.
305 00:29:57.720 ⇒ 00:29:58.270 Amber Lin: You know.
306 00:29:59.030 ⇒ 00:29:59.970 Amber Lin: All right.
307 00:30:00.490 ⇒ 00:30:01.770 Amber Lin: Thank you. Everyone.
308 00:30:01.770 ⇒ 00:30:02.659 Emily Giant: Alright. Thank you.
309 00:30:02.660 ⇒ 00:30:03.230 Uttam Kumaran: Thank you.
310 00:30:03.430 ⇒ 00:30:03.940 Emily Giant: Bye.
311 00:30:04.520 ⇒ 00:30:05.190 Amber Lin: Bye.