Meeting Title: UrbanStems | Interviewing Demilade! Date: 2025-04-25 Meeting participants: Luke Daque, Demilade Agboola, Amber Lin
WEBVTT
1 00:00:49.400 ⇒ 00:00:50.520 Amber Lin: Oh!
2 00:00:51.770 ⇒ 00:00:52.510 Demilade Agboola: Oh, boy!
3 00:00:53.910 ⇒ 00:00:56.929 Amber Lin: Let me let Luke know that we’re meeting
4 00:01:13.170 ⇒ 00:01:14.899 Amber Lin: books in the call, and I don’t know if you know.
5 00:01:14.900 ⇒ 00:01:15.410 Amber Lin: Oh.
6 00:01:15.720 ⇒ 00:01:16.500 Luke Daque: Hello!
7 00:01:16.500 ⇒ 00:01:17.070 Luke Daque: Hello!
8 00:01:17.340 ⇒ 00:01:18.210 Amber Lin: Nice.
9 00:01:18.870 ⇒ 00:01:26.850 Amber Lin: So let’s get ready for today’s interview. I did not prep a list of questions because I don’t even know where to start.
10 00:01:30.640 ⇒ 00:01:31.140 Amber Lin: It’s.
11 00:01:33.390 ⇒ 00:01:37.674 Demilade Agboola: So just a heads up, I mean, I’ll start. But like just a heads up
12 00:01:38.290 ⇒ 00:01:51.750 Demilade Agboola: even had some like pipeline issues, some kind of troubleshooting that as well. I’ve fixed an issue. But like I’m just running some jobs other job in the background. So sometimes I might sound like I’ve spaced off a little bit. That’s what’s going on.
13 00:01:51.930 ⇒ 00:01:53.682 Amber Lin: Okay. Okay. Sounds good.
14 00:01:54.460 ⇒ 00:01:57.219 Demilade Agboola: I can quickly share my screen, and we can start with.
15 00:01:57.220 ⇒ 00:01:58.369 Amber Lin: That would be great.
16 00:01:58.520 ⇒ 00:02:06.169 Amber Lin: and I can go find the. I’ll go find the urban standard architecture diagram, and then we can
17 00:02:06.480 ⇒ 00:02:08.080 Amber Lin: probably go for that.
18 00:02:09.949 ⇒ 00:02:11.399 Demilade Agboola: Sounds good.
19 00:02:11.929 ⇒ 00:02:13.279 Demilade Agboola: Second.
20 00:02:32.680 ⇒ 00:02:33.850 Demilade Agboola: hey?
21 00:02:36.654 ⇒ 00:02:38.059 Demilade Agboola: So can you see my screen.
22 00:02:40.336 ⇒ 00:02:41.710 Amber Lin: Yes, yes.
23 00:02:42.060 ⇒ 00:02:42.570 Demilade Agboola: All right.
24 00:02:42.570 ⇒ 00:02:50.379 Amber Lin: Let me share with you the 2 diagrams that we to diagram. Take my files so you can also look at them.
25 00:02:52.750 ⇒ 00:02:53.770 Amber Lin: Great.
26 00:02:58.190 ⇒ 00:03:00.150 Amber Lin: let’s get started.
27 00:03:07.280 ⇒ 00:03:19.220 Demilade Agboola: Okay, so the this is basically urban stems, entire flow.
28 00:03:20.198 ⇒ 00:03:24.790 Demilade Agboola: A lot. Most of their models, or like the important models, are found here.
29 00:03:26.665 ⇒ 00:03:27.540 Amber Lin: Okay.
30 00:03:29.260 ⇒ 00:03:34.800 Demilade Agboola: The real issues they’ve been having is like they load everything based on
31 00:03:35.150 ⇒ 00:03:39.409 Demilade Agboola: the source. And then they kind of do everything within that place.
32 00:03:39.530 ⇒ 00:03:40.680 Demilade Agboola: But then.
33 00:03:40.680 ⇒ 00:03:41.500 Amber Lin: Okay.
34 00:03:41.500 ⇒ 00:03:47.650 Demilade Agboola: So it just gives. Give me a second.
35 00:03:47.960 ⇒ 00:03:50.707 Demilade Agboola: Yes, we do everything within that place.
36 00:03:52.410 ⇒ 00:03:59.569 Demilade Agboola: and sometimes it’s just hard to figure out like what is feeding? What like? What are your math models? So maths, models allows us to know
37 00:03:59.680 ⇒ 00:04:01.760 Demilade Agboola: what models are being used for.
38 00:04:02.260 ⇒ 00:04:02.540 Amber Lin: Well.
39 00:04:02.540 ⇒ 00:04:10.229 Demilade Agboola: Dashboards or whatever. So it’s hard to determine this like, where does this feed? What does this like? Why do we have this here?
40 00:04:10.986 ⇒ 00:04:14.880 Demilade Agboola: It’s just. It’s just like a mess sometimes.
41 00:04:15.400 ⇒ 00:04:20.729 Demilade Agboola: and it’s kind of hard to always tell what is going on within the models.
42 00:04:21.457 ⇒ 00:04:26.319 Demilade Agboola: So they definitely don’t abide by like best practices of how to build models.
43 00:04:27.444 ⇒ 00:04:42.730 Demilade Agboola: And so to kind of mitigate that the new product we’re working on. We’ve implemented like how Dbt is generally structured. So we have our role models where we have the new models coming in.
44 00:04:44.070 ⇒ 00:04:46.559 Demilade Agboola: And then we have our
45 00:04:47.940 ⇒ 00:04:57.660 Demilade Agboola: yeah. So the new models are coming in, and then we have our staging models where we do slight like modifications on them. So like, for instance, some of these ones are like deduplications.
46 00:04:58.130 ⇒ 00:05:03.039 Demilade Agboola: So we just only selecting the latest data or like data that we need.
47 00:05:03.470 ⇒ 00:05:14.459 Demilade Agboola: And then we’ve started doing things in the intermediate model where we’re putting some of these things together and adding, like proper logic, to some of these things as well.
48 00:05:14.560 ⇒ 00:05:21.163 Demilade Agboola: doing the joins. And you know, defining certain features of case when statements and things like that.
49 00:05:21.910 ⇒ 00:05:28.130 Demilade Agboola: And then we finally have the march where we’re bringing a lot of these intermediate models together to be able to
50 00:05:29.063 ⇒ 00:05:34.716 Demilade Agboola: give answers to their dashboards, and so far, like I literally got off a call with
51 00:05:36.240 ⇒ 00:05:43.680 Demilade Agboola: I literally got off a call with Emily, like I was just.
52 00:05:44.010 ⇒ 00:05:47.819 Demilade Agboola: and we’re doing Qa. The Qa. Seems to be good.
53 00:05:48.627 ⇒ 00:05:56.660 Demilade Agboola: Some things are slightly off, but we’re trying to figure out like the cause of that that has to do with transaction lines for the most part.
54 00:05:57.080 ⇒ 00:05:59.560 Demilade Agboola: and how the transactions are structured.
55 00:06:00.300 ⇒ 00:06:00.850 Amber Lin: Oh!
56 00:06:01.864 ⇒ 00:06:10.359 Demilade Agboola: There’s just a lot of mess across the entire space. I know we were literally looking at. Give me one second. We’re looking at
57 00:06:10.710 ⇒ 00:06:16.280 Demilade Agboola: an inventory. I believe it’s inventory adjustment Xf.
58 00:06:16.580 ⇒ 00:06:18.520 Demilade Agboola: On revenue transactions, except
59 00:06:18.960 ⇒ 00:06:25.820 Demilade Agboola: but, like 1st off, the naming is not clear like there. There’s so many similar names like as you just type. As I typed inventory.
60 00:06:26.070 ⇒ 00:06:30.769 Demilade Agboola: you can see venture adjustment, snapshots, inventory exit, snapshots, inventory adjustment, snaps
61 00:06:31.310 ⇒ 00:06:41.549 Demilade Agboola: inventory adjustments excess snap like it’s kind of hard to figure out like what inventory adjustments like. It’s hard to figure out what each model is doing specifically.
62 00:06:42.030 ⇒ 00:06:50.410 Demilade Agboola: And so this is snapshots great. But how is inventory? Just my snapshots different from just in inventory adjustment, except snap.
63 00:06:52.130 ⇒ 00:06:56.000 Demilade Agboola: or inventory adjustment snap like? How are they? Different?
64 00:06:56.260 ⇒ 00:07:04.030 Demilade Agboola: Right? So it’s things like that, like the models, appear in multiple occasions in different names.
65 00:07:04.190 ⇒ 00:07:07.930 Demilade Agboola: Some of some logic is Dale
66 00:07:11.570 ⇒ 00:07:15.759 Demilade Agboola: and so this is cause they migrated their systems on the 11th of
67 00:07:16.590 ⇒ 00:07:24.019 Demilade Agboola: Sorry, the 6th of November. I I’m not used to 11 6 as a date format, but yeah, the 6th of November.
68 00:07:24.640 ⇒ 00:07:29.409 Demilade Agboola: And so since then some things are still, some things don’t work.
69 00:07:30.163 ⇒ 00:07:35.260 Demilade Agboola: Some of the models were built for the previous system and don’t like transfer to this new system.
70 00:07:38.540 ⇒ 00:07:43.220 Demilade Agboola: Yeah, I don’t know if you have any questions to be honest. But like basically.
71 00:07:43.790 ⇒ 00:07:45.170 Demilade Agboola: everything is a mess.
72 00:07:45.590 ⇒ 00:07:50.029 Demilade Agboola: there are constantly issues where Emily has to keep debugging and figuring out what went wrong.
73 00:07:50.669 ⇒ 00:07:56.539 Demilade Agboola: That also slows her down with some of the work we’re doing, because again, she’s trying to figure some stuff out.
74 00:07:57.060 ⇒ 00:08:00.060 Demilade Agboola: while also, like building out the new infrastructure.
75 00:08:04.790 ⇒ 00:08:08.160 Demilade Agboola: ideally, I I will think that
76 00:08:09.080 ⇒ 00:08:16.749 Demilade Agboola: once we go beyond mother’s day the important thing is being able to rearrange, reorganize, and then
77 00:08:16.980 ⇒ 00:08:19.749 Demilade Agboola: rebuild the entire system. So like
78 00:08:19.930 ⇒ 00:08:23.761 Demilade Agboola: inventory adjustments, let’s build that inventory.
79 00:08:24.870 ⇒ 00:08:37.739 Demilade Agboola: So we don’t have like multiple eventual adjustments. We don’t have event adjustments, snap event like we need snaps. Let’s take the snapshots. Let’s figure out the strategy. But like we have it such a way that it’s it’s clearly organized and tell you useful.
80 00:08:39.100 ⇒ 00:08:40.230 Amber Lin: Let’s see how.
81 00:08:40.230 ⇒ 00:08:48.670 Demilade Agboola: But the heavy must have some importance, some some way or some how, but I honestly don’t even know.
82 00:08:51.720 ⇒ 00:08:58.309 Amber Lin: I see? How are they? What do they think on migrating over to our new system?
83 00:08:59.970 ⇒ 00:09:01.340 Demilade Agboola: What do you mean by new system.
84 00:09:02.150 ⇒ 00:09:04.740 Amber Lin: Like we have a new model right?
85 00:09:05.230 ⇒ 00:09:11.680 Amber Lin: are they willing to change over to our new thing, or do they already.
86 00:09:14.480 ⇒ 00:09:15.220 Demilade Agboola: So
87 00:09:15.560 ⇒ 00:09:23.550 Demilade Agboola: in terms of like the new structure of building out the models, Emily is definitely on on board with that.
88 00:09:24.324 ⇒ 00:09:29.450 Demilade Agboola: I know she’s, she said, that even working within within this new system
89 00:09:29.710 ⇒ 00:09:37.530 Demilade Agboola: allowed her to be able to solve problems faster and just like kind of get used to the entire like proper structure, basically
90 00:09:39.410 ⇒ 00:09:40.550 Demilade Agboola: And so
91 00:09:41.360 ⇒ 00:09:48.620 Demilade Agboola: with that, I think she’s definitely on board. And that’s even like, I say, if we look at the models.
92 00:09:49.510 ⇒ 00:09:52.320 Demilade Agboola: Look at the models. I really did
93 00:09:52.880 ⇒ 00:10:05.789 Demilade Agboola: this in terms of inventory by herself. She’s been working with forecast on, like making it work that way within the same structure. So she does actually prefer this. So like, I invented what I worked on
94 00:10:06.220 ⇒ 00:10:11.160 Demilade Agboola: as I’m OP. And all that is what she’s working on herself, and then, you know.
95 00:10:11.850 ⇒ 00:10:15.910 Demilade Agboola: she’s she’s building in that structure. So she does prefer it.
96 00:10:17.240 ⇒ 00:10:19.630 Demilade Agboola: And it makes life easier for her. So.
97 00:10:20.300 ⇒ 00:10:27.140 Amber Lin: Okay, awesome. I’m just trying to recall what Autumn said last time. I think
98 00:10:27.510 ⇒ 00:10:30.887 Amber Lin: the purpose of this interview is to understand
99 00:10:32.470 ⇒ 00:10:40.959 Amber Lin: what mostly about the modeling like? What type of fixes? And I remember that he said, to run through from
100 00:10:41.180 ⇒ 00:10:58.019 Amber Lin: the the start to the end, so like from the ingestion to all the different layers. And to just point out all the technical difficulties or technical things we need to tackle for each step.
101 00:10:58.270 ⇒ 00:11:08.309 Amber Lin: Right. So do you think we should use the diagram that Hannah made? Or should we look at the original like
102 00:11:08.530 ⇒ 00:11:10.449 Amber Lin: fake jam document?
103 00:11:11.642 ⇒ 00:11:16.979 Demilade Agboola: Sure. Let’s look at the original like. So are we. Look, we’re looking at it from the perspective of what existed before.
104 00:11:18.330 ⇒ 00:11:19.130 Amber Lin: The.
105 00:11:21.110 ⇒ 00:11:28.440 Demilade Agboola: Are. Are we looking at it like the things that existed before, like where the the pro troublesome areas are, and where we will need to fix.
106 00:11:29.330 ⇒ 00:11:36.000 Amber Lin: Yeah, I I guess with the current state, too, of like, what? What needs to be fixed because
107 00:11:36.590 ⇒ 00:11:48.359 Amber Lin: wants to create a roadmap. And I think we just need very specific technical details of the roadmap, because what we have right now is very, very high level.
108 00:11:52.240 ⇒ 00:11:53.100 Amber Lin: Not sure if that.
109 00:11:53.230 ⇒ 00:11:54.480 Amber Lin: Yeah.
110 00:11:55.270 ⇒ 00:11:59.740 Demilade Agboola: Okay, so do you want to share your screen? And then we can like walk through. And I can talk about the different.
111 00:11:59.740 ⇒ 00:12:01.800 Amber Lin: Or, yeah, yeah, let me do that.
112 00:12:04.070 ⇒ 00:12:13.120 Amber Lin: I’m also a bit confused. So I’m just lying on you. Of, okay, where are we? Even? Where are we even starting? So this is the
113 00:12:13.590 ⇒ 00:12:15.949 Amber Lin: phase out that we have.
114 00:12:16.390 ⇒ 00:12:21.560 Amber Lin: I know this is kind of like our after talk.
115 00:12:29.270 ⇒ 00:12:33.129 Amber Lin: should we start from here, or start from the sources
116 00:12:33.770 ⇒ 00:12:38.120 Amber Lin: of like what dashboards go they have, and
117 00:12:39.970 ⇒ 00:12:43.630 Amber Lin: what sources go into each dashboard.
118 00:12:44.460 ⇒ 00:12:48.529 Amber Lin: Do you know a list of all their dashboards?
119 00:12:51.140 ⇒ 00:12:54.000 Demilade Agboola: I don’t have a list of all their dashboards. That’s.
120 00:12:54.000 ⇒ 00:12:54.389 Amber Lin: You know.
121 00:12:54.390 ⇒ 00:12:56.336 Demilade Agboola: Quite a number of dashboards to be honest.
122 00:12:56.580 ⇒ 00:12:57.930 Amber Lin: I see, I see.
123 00:13:00.450 ⇒ 00:13:02.929 Amber Lin: So maybe we should also get
124 00:13:04.360 ⇒ 00:13:08.259 Amber Lin: as a discovery ticket of like what dashboards they have.
125 00:13:12.280 ⇒ 00:13:12.970 Demilade Agboola: Brilliant,
126 00:13:21.140 ⇒ 00:13:24.299 Amber Lin: Okay, if we can’t do the.
127 00:13:24.300 ⇒ 00:13:26.830 Demilade Agboola: I’m sure you list other dashboards that.
128 00:13:27.000 ⇒ 00:13:27.450 Amber Lin: Sure.
129 00:13:27.450 ⇒ 00:13:28.820 Demilade Agboola: I don’t see.
130 00:13:28.820 ⇒ 00:13:31.530 Amber Lin: Yeah, let me stop sharing.
131 00:13:42.610 ⇒ 00:13:45.870 Demilade Agboola: yeah, dashboards.
132 00:13:48.110 ⇒ 00:13:50.159 Demilade Agboola: So they have.
133 00:13:53.710 ⇒ 00:13:59.849 Demilade Agboola: Yes, these are the ones I can see as dashboards. They do have a couple of like looks.
134 00:14:01.990 ⇒ 00:14:06.639 Demilade Agboola: You know, for the most part these these are the dashboards that they have.
135 00:14:07.800 ⇒ 00:14:08.520 Demilade Agboola: Nice.
136 00:14:08.520 ⇒ 00:14:12.600 Demilade Agboola: It’s blend. Let me see if I could just copy and paste.
137 00:14:17.880 ⇒ 00:14:18.930 Demilade Agboola: Okay? Sorry.
138 00:14:24.260 ⇒ 00:14:28.320 Demilade Agboola: Like, I said, like they have a bunch of things just going on. And I wasn’t sure.
139 00:14:29.740 ⇒ 00:14:34.539 Demilade Agboola: Yeah, like they have sales tax like it’s there’s a lot going on to be honest.
140 00:14:35.650 ⇒ 00:14:42.929 Demilade Agboola: I think they try to rely on data, but I don’t think that they like the best kind of the problem. In a way
141 00:14:43.170 ⇒ 00:14:50.930 Demilade Agboola: like there’s so much data needs. And I don’t think they’ve necessarily built out a strong data foundation and a data team.
142 00:14:51.240 ⇒ 00:15:02.600 Demilade Agboola: And as a result like, it’s just data going everywhere and anywhere and like any of these dashboards could potentially break on any day. And and we would have to be the one to like fix it
143 00:15:03.300 ⇒ 00:15:04.380 Demilade Agboola: so
144 00:15:10.760 ⇒ 00:15:12.669 Amber Lin: So go ahead.
145 00:15:12.670 ⇒ 00:15:21.879 Demilade Agboola: Another issue. Sometimes it’s just something to notice that like, because they have a lot of logic in looker as well, or some decent amount of logic in looker as well
146 00:15:22.000 ⇒ 00:15:29.330 Demilade Agboola: anytime like Emily has to troubleshoot. She has to go through Dvt. First.st Figure out if Dbt is fine, then hop onto looker.
147 00:15:29.450 ⇒ 00:15:31.840 Demilade Agboola: I forgot, if Luca is the problem as well.
148 00:15:32.718 ⇒ 00:15:39.879 Demilade Agboola: And then this is because they’re like manual sheets like Google sheets that people are ingesting. It could also be
149 00:15:39.880 ⇒ 00:15:40.210 Demilade Agboola: hmm
150 00:15:40.210 ⇒ 00:16:01.660 Demilade Agboola: like it could be this one like, for instance, one of the issues she had over the past week was that instead of someone putting 2025 as the date or something. They put 2024. And so the the joins were showing 0 like a revenue of 0 for this year. Obviously she doesn’t exist in the in the sheet.
151 00:16:03.010 ⇒ 00:16:04.430 Demilade Agboola: You know, things like that
152 00:16:04.780 ⇒ 00:16:11.820 Demilade Agboola: like, it’s just the same like, I don’t know if it’s when I say it’s an entire mess. But like, we basically, we need to ensure that like.
153 00:16:12.140 ⇒ 00:16:17.029 Demilade Agboola: we are able to give them systems that allow them to maybe impute data. But like.
154 00:16:17.550 ⇒ 00:16:26.479 Demilade Agboola: if it passes through Dbt, for instance, we can set up tests to be able to patch some issues, but because it doesn’t pass through Dbt, it goes directly into Looker.
155 00:16:29.270 ⇒ 00:16:35.290 Demilade Agboola: There was no test. He just he just the change was made, and it was imputed there.
156 00:16:38.600 ⇒ 00:16:41.491 Demilade Agboola: They also have issues with
157 00:16:44.680 ⇒ 00:16:47.719 Demilade Agboola: basically, there’s no data control. There’s no data quality control.
158 00:16:48.240 ⇒ 00:16:48.980 Amber Lin: Oh, okay.
159 00:16:49.482 ⇒ 00:16:50.990 Demilade Agboola: It can like
160 00:16:52.150 ⇒ 00:17:05.370 Demilade Agboola: bad data comes in sometimes, and we just no one knows. It’s just like someone says the dashboard looks off, and that’s what triggers an investigation. So, being able to create systems, allow them to be proactive will be very helpful as well.
161 00:17:11.180 ⇒ 00:17:16.680 Demilade Agboola: But yeah, the dashboards have. I’m not sure how like how many we want to put, because there are a lot
162 00:17:18.339 ⇒ 00:17:19.119 Amber Lin: I see.
163 00:17:20.359 ⇒ 00:17:22.174 Amber Lin: Okay, sounds good.
164 00:17:23.009 ⇒ 00:17:48.999 Amber Lin: I I think it clicked a little bit for me when we talk about okay, what did we want to build for them. We say we want to make them systems that allow them to be more proactive, like, what along those lines of like problem solution, things that we have in mind, like what other technical problems they have and what kind of solutions that you kind of thought of, we should give them.
165 00:17:51.694 ⇒ 00:17:53.749 Demilade Agboola: One is, I think
166 00:17:54.320 ⇒ 00:18:08.359 Demilade Agboola: I mean, I mentioned already, but one is basically restructuring the entire Dvt because the way it is, it’s kind, it’s it’s really difficult to troubleshoot and figure out what the issues are like. I know there are times, I mean, he has had issues because of that
167 00:18:08.740 ⇒ 00:18:12.080 Demilade Agboola: 2 will to be setting up tests.
168 00:18:12.868 ⇒ 00:18:14.760 Demilade Agboola: As many tests as possible.
169 00:18:18.190 ⇒ 00:18:18.930 Amber Lin: Hmm.
170 00:18:19.900 ⇒ 00:18:26.602 Demilade Agboola: Who catch these arrows. 3 will be figuring out which
171 00:18:28.320 ⇒ 00:18:33.649 Demilade Agboola: which, like sheets or dashboard like sheets, feed directly into like Luca.
172 00:18:34.720 ⇒ 00:18:35.420 Demilade Agboola: I didn’t.
173 00:18:35.610 ⇒ 00:18:40.600 Demilade Agboola: How we can find like, how we can ensure those sheets have good data quality.
174 00:18:40.730 ⇒ 00:18:44.930 Demilade Agboola: because the problem with sheets is obviously people can, you know, edit them.
175 00:18:45.240 ⇒ 00:18:50.050 Demilade Agboola: So how do we ensure that, like those tests like, we’re able to test the data.
176 00:18:50.500 ⇒ 00:18:57.030 Demilade Agboola: So we either have to ingest it into the Dbt like our warehouse and then test it, or.
177 00:18:57.720 ⇒ 00:19:03.959 Demilade Agboola: to be fair, I’m not used, looker that well to know. But like, figure out, if there’s any observability we can apply to looker.
178 00:19:04.240 ⇒ 00:19:11.550 Demilade Agboola: So like, are there any tests we can like tests or tools we can use to ensure that the data coming to look out into the dashboard
179 00:19:11.660 ⇒ 00:19:13.160 Demilade Agboola: is correct.
180 00:19:16.010 ⇒ 00:19:18.990 Demilade Agboola: And just general observability in their system.
181 00:19:19.390 ⇒ 00:19:23.690 Demilade Agboola: How can we be proactive with their data issues?
182 00:19:24.090 ⇒ 00:19:31.679 Demilade Agboola: So that, like, it’s not always a thing of like someone using dashboard, and all they are seeing is nulls or zeros. How can we be proactive with that
183 00:19:32.010 ⇒ 00:19:33.390 Demilade Agboola: on there, you know, for them.
184 00:19:36.172 ⇒ 00:19:43.430 Amber Lin: When we talk about the Dbt, what what areas do we have? Because I know we did inventory.
185 00:19:43.990 ⇒ 00:19:48.430 Amber Lin: What are the other same marks we want to build for them.
186 00:19:51.630 ⇒ 00:19:57.340 Demilade Agboola: So inventory is one I know. Revenue, like revenue attribution is another
187 00:19:57.680 ⇒ 00:20:09.110 Demilade Agboola: area. Those would be like too many areas we’ve talked about like where we’ve we’ve seen that like things could be better. But like, I think, just, generally speaking, things like forecast could be another area.
188 00:20:11.600 ⇒ 00:20:15.880 Demilade Agboola: I think there’s just, I think, because we’re trying to redo everything like
189 00:20:16.410 ⇒ 00:20:26.639 Demilade Agboola: I think we think of like, Hey, we know that, like your current system is a mess. I think that will be how we should approach it with them. What areas would you want to prioritize in terms of building them out.
190 00:20:26.980 ⇒ 00:20:29.130 Demilade Agboola: because if we’re trying to build everything.
191 00:20:29.510 ⇒ 00:20:36.300 Demilade Agboola: yes, that’s great. But like, there are certain areas that are obviously more important than others like. So if we can do
192 00:20:36.460 ⇒ 00:20:39.359 Demilade Agboola: a good revenue system, a good inventory system.
193 00:20:40.090 ⇒ 00:20:49.540 Demilade Agboola: and you know, figure out what what other things are the most important, then the other things that we may need to do can be lower priority.
194 00:20:50.050 ⇒ 00:21:03.889 Amber Lin: Okay, sounds good. That’s a that’s really good to know. I’m glad I asked that. So no, I’m not sure if this is from the original document or generated it. They they say, procurement, inventory, revenue. What else?
195 00:21:04.414 ⇒ 00:21:16.850 Amber Lin: There’s a subscriptions lifetime value churn cus, customer experience. Blah blah blah. So there’s a lot of different things. And I think you’re right inside, we should ask them to
196 00:21:16.960 ⇒ 00:21:18.070 Amber Lin: prioritize.
197 00:21:18.200 ⇒ 00:21:19.320 Amber Lin: Yeah.
198 00:21:19.590 ⇒ 00:21:25.389 Amber Lin: And for each of them, what does it look like when we do this
199 00:21:25.620 ⇒ 00:21:29.209 Amber Lin: remodeling? Is this something that we can sort of just
200 00:21:29.550 ⇒ 00:21:41.609 Amber Lin: apply copy and paste kind of the logic, or or what does the process even look like like what I I know. We talked about the main issues of them being by source, and then kind of just
201 00:21:41.780 ⇒ 00:21:49.259 Amber Lin: scattered all not by function. So what does the fixed look like in technical terms?
202 00:21:51.336 ⇒ 00:21:53.280 Demilade Agboola: The fix looks like.
203 00:21:55.480 ⇒ 00:21:59.489 Demilade Agboola: We will need to redefine figure out what the sources are.
204 00:21:59.780 ⇒ 00:22:07.724 Demilade Agboola: and then also define what the marks need to look like. So what dashboards are we trying to build? Who uses the dashboards?
205 00:22:08.540 ⇒ 00:22:12.019 Demilade Agboola: what do they need to see? Are they currently seeing everything they need to see.
206 00:22:12.230 ⇒ 00:22:14.870 Amber Lin: And then, if we need to like.
207 00:22:15.170 ⇒ 00:22:16.790 Demilade Agboola: Add new like
208 00:22:17.670 ⇒ 00:22:23.239 Demilade Agboola: filters or new like information to the to the final dashboard. We know that ahead of time.
209 00:22:24.470 ⇒ 00:22:29.739 Demilade Agboola: and then what we now need to do is we will need to basically reconstruct
210 00:22:30.180 ⇒ 00:22:39.259 Demilade Agboola: the entire flow going from each source. And the how different sources interact with each other up until we get to like what the Mods should look like.
211 00:22:39.500 ⇒ 00:22:40.069 Amber Lin: Oh no!
212 00:22:41.080 ⇒ 00:22:43.889 Demilade Agboola: I mean, obviously, we will look at what they already have.
213 00:22:44.000 ⇒ 00:22:49.229 Demilade Agboola: But, like, I said, some of the logic really just does not work in some cases, or in some cases, it’s such a
214 00:22:49.930 ⇒ 00:22:52.150 Demilade Agboola: badly written version.
215 00:22:52.590 ⇒ 00:23:00.419 Demilade Agboola: So we can’t like entirely rely on it. We need to. Basically, you know, basic rebuild these things ourselves.
216 00:23:03.830 ⇒ 00:23:05.997 Amber Lin: I see, I see,
217 00:23:07.550 ⇒ 00:23:15.680 Amber Lin: and right now, we’ve mostly looked at all the issues, mostly just related to inventory right, we haven’t really looked at the other
218 00:23:16.645 ⇒ 00:23:17.450 Amber Lin: sources.
219 00:23:21.440 ⇒ 00:23:27.890 Demilade Agboola: And yes, largely inventory. We basically had the
220 00:23:28.170 ⇒ 00:23:34.499 Demilade Agboola: because for mothers, that’s the real high priority. So we’ve just basically, you know, zoned in on that
221 00:23:39.620 ⇒ 00:23:45.580 Demilade Agboola: But yes, there are definitely other things that they need. We just haven’t, like, you know, necessarily focused on that.
222 00:23:46.360 ⇒ 00:24:03.420 Amber Lin: I see and when we sort of in work with inventory, we essentially touched all their different like sources, different types of, or applications of sources. Right? So we almost pretty much know how all their different sources work.
223 00:24:03.680 ⇒ 00:24:07.759 Amber Lin: Is that true? Or does different functions have different sources?
224 00:24:10.190 ⇒ 00:24:11.819 Amber Lin: Just gonna repeat that question. I
225 00:24:12.137 ⇒ 00:24:26.120 Amber Lin: so I I guess my to phrase it better. It was not very well phrased, so inventory has certain sources right? Like certain applications that deal with inventory and say, if we do marketing, then it has a lot of other different sources, right?
226 00:24:26.490 ⇒ 00:24:27.959 Demilade Agboola: Oh, yes, yes, yes, yes, so.
227 00:24:27.960 ⇒ 00:24:29.069 Amber Lin: Oh, okay.
228 00:24:29.070 ⇒ 00:24:34.590 Demilade Agboola: Inventory is cutting from like netsuite. The single source of truth.
229 00:24:35.161 ⇒ 00:24:42.409 Demilade Agboola: And yeah, the other. All the like teams and all that things. They need to have other sources.
230 00:24:42.882 ⇒ 00:24:50.310 Demilade Agboola: Haven’t completely interacted with every single one to be honest. And I think, like I said, the focus has been netsuite. So.
231 00:24:50.310 ⇒ 00:24:51.570 Amber Lin: Yeah. Don’t worry. Yeah.
232 00:24:51.570 ⇒ 00:24:54.190 Demilade Agboola: And that’s kind of where we’ve been playing a lot in.
233 00:24:54.490 ⇒ 00:24:57.110 Amber Lin: Yeah, sounds good. So.
234 00:24:57.110 ⇒ 00:25:05.070 Demilade Agboola: Another thing to like part of this call is that like
235 00:25:05.240 ⇒ 00:25:07.389 Demilade Agboola: we have. It is an idea of like
236 00:25:08.460 ⇒ 00:25:14.340 Demilade Agboola: what the problems are like. Just generally, however, I feel like we also need to set up a
237 00:25:14.780 ⇒ 00:25:18.259 Demilade Agboola: a discovery call with either Emily
238 00:25:18.490 ⇒ 00:25:26.220 Demilade Agboola: or Zack. I think Emily might be easiest to talk to, but like effectively, someone in the system who is
239 00:25:26.350 ⇒ 00:25:35.660 Demilade Agboola: aware of all the single problems that happening within the system. So like, if we can be like? What are what are the issues that come to you on a daily like, for instance, Emily?
240 00:25:36.051 ⇒ 00:25:39.679 Demilade Agboola: What are the issues that come to you on it like in an average week.
241 00:25:40.080 ⇒ 00:25:43.229 Demilade Agboola: What keeps breaking? What works, what doesn’t work?
242 00:25:43.728 ⇒ 00:25:52.519 Demilade Agboola: What are dashboards? You’ve had to retire? What dashboards you need to bring back like things like that, like basically gets into the heart of
243 00:25:52.740 ⇒ 00:25:56.990 Demilade Agboola: what she, as a data person, is seeing across the company
244 00:25:57.570 ⇒ 00:26:01.970 Demilade Agboola: what stakeholders have trust issues with your data. What stakeholders,
245 00:26:04.450 ⇒ 00:26:11.300 Demilade Agboola: or stakeholders regularly have feedback on issues with your data like things like that, like just basically gets into the heart
246 00:26:11.580 ⇒ 00:26:13.630 Demilade Agboola: of what it means to be the type of.
247 00:26:13.630 ⇒ 00:26:14.160 Amber Lin: When they come.
248 00:26:14.600 ⇒ 00:26:21.669 Demilade Agboola: Like, I mean cause. Like again, like I said, I have an idea of some of these things, but my focus has largely been on inventory data.
249 00:26:22.750 ⇒ 00:26:25.080 Demilade Agboola: And so what that means is that like
250 00:26:25.370 ⇒ 00:26:30.339 Demilade Agboola: because Number one, they’re very aware of the hours that we’re trying to work under.
251 00:26:30.680 ⇒ 00:26:34.730 Demilade Agboola: And they have something that they need.
252 00:26:35.360 ⇒ 00:26:36.200 Demilade Agboola: Thank you
253 00:26:36.820 ⇒ 00:26:45.459 Demilade Agboola: for. Mother’s day. So they have, like a lot of calls, are just like me with Emily, and we’re just like she’s like, I have this issue with this, and it’s specifically
254 00:26:46.260 ⇒ 00:26:52.349 Demilade Agboola: on inventory. But obviously over the course of the call, while maybe I’m trying to get put the logic together.
255 00:26:52.780 ⇒ 00:27:05.500 Demilade Agboola: You might just mention that. Oh, by the way, she’s kind of if if she’s like on a second screen is because she’s working on this, because, you know, someone reached out to her that this dashboard isn’t working, or something like, you know, things like that. So you can kind of hear that there’s a consistent pattern
256 00:27:05.890 ⇒ 00:27:11.169 Demilade Agboola: of dashboards breaking. But what dashboards are breaking I don’t necessarily always know.
257 00:27:12.434 ⇒ 00:27:19.790 Amber Lin: I see great. I don’t know if I, I don’t think I, I can talk to her just yet.
258 00:27:20.426 ⇒ 00:27:26.430 Amber Lin: But i’ll flesh out all the discovery tickets, and
259 00:27:26.560 ⇒ 00:27:29.560 Amber Lin: I’ll try to flesh out all the different
260 00:27:30.890 ⇒ 00:27:39.869 Amber Lin: march. Would I do? Do you know, if I have access to Dbt, because I do. Wanna when I asked Chat Gbt, I want like a list of.
261 00:27:40.050 ⇒ 00:27:48.429 Amber Lin: or at least their structure. I don’t know if it’s called schema or not but kind of like all the list of all their tables.
262 00:27:52.000 ⇒ 00:27:55.500 Amber Lin: Like kind of like a Github repository file.
263 00:27:56.726 ⇒ 00:28:04.230 Amber Lin: Because I want to feed that into Chat Gbt, and just let it let it explain to me.
264 00:28:08.820 ⇒ 00:28:14.639 Luke Daque: If you get. If we have access to the Github, I think cursor would be better
265 00:28:14.920 ⇒ 00:28:20.460 Luke Daque: to do that, because, like it, it gets like context, yeah.
266 00:28:20.460 ⇒ 00:28:29.799 Amber Lin: Wait. Is it sorry I’m good for for me? I don’t know. Is it in Dbt. Does it mean that it’s also in Github like? What does it? How does it work.
267 00:28:32.480 ⇒ 00:28:35.199 Luke Daque: Yeah, basically, all the models we have
268 00:28:35.750 ⇒ 00:28:38.409 Luke Daque: in Dbt would be like pushed to Github.
269 00:28:39.210 ⇒ 00:28:40.010 Amber Lin: Oh!
270 00:28:40.380 ⇒ 00:28:41.200 Luke Daque: Yeah.
271 00:28:41.580 ⇒ 00:28:53.740 Amber Lin: I see, so does our github. Donna. Does our Github also have their original models and our new models.
272 00:28:57.550 ⇒ 00:28:58.670 Demilade Agboola: No, they.
273 00:28:58.890 ⇒ 00:29:00.900 Demilade Agboola: I’m not sure I get that question.
274 00:29:01.359 ⇒ 00:29:08.710 Amber Lin: So I I know that since Luke said, the Github has all the Dbt models, right?
275 00:29:08.860 ⇒ 00:29:16.159 Amber Lin: So it has. It also has their original models. Right? So the one says, by source.
276 00:29:18.330 ⇒ 00:29:19.470 Demilade Agboola: Yes, yes, yes, yes, yes.
277 00:29:19.470 ⇒ 00:29:24.860 Amber Lin: Oh, okay. And it also has the new model that we developed like by function.
278 00:29:25.300 ⇒ 00:29:34.950 Amber Lin: Okay, do we do you? Can you use a repo. Mix to just get it in a single file, so I can feed it to chat.
279 00:29:36.614 ⇒ 00:29:41.020 Demilade Agboola: Sure I will like how, when she need up.
280 00:29:41.900 ⇒ 00:29:42.660 Amber Lin: Huh!
281 00:29:42.910 ⇒ 00:29:44.129 Demilade Agboola: When do you need that.
282 00:29:46.300 ⇒ 00:29:55.050 Amber Lin: I don’t know how long the repo mix takes. If you have it installed, it should be pretty quick like. I mean, I have a lot of meetings today. So.
283 00:29:55.560 ⇒ 00:29:57.119 Demilade Agboola: Yeah, that’s kind of why I ask.
284 00:29:57.310 ⇒ 00:29:58.189 Amber Lin: In. Like 4.
285 00:29:58.190 ⇒ 00:29:58.650 Demilade Agboola: Yes.
286 00:29:58.650 ⇒ 00:30:01.219 Amber Lin: 4 h, 4 or 5 h.
287 00:30:01.650 ⇒ 00:30:04.839 Demilade Agboola: So I have like 6 h of meetings today, and.
288 00:30:04.840 ⇒ 00:30:05.230 Amber Lin: Got it.
289 00:30:05.230 ⇒ 00:30:08.749 Demilade Agboola: I also have a flight by tomorrow morning at 7 Am. I.
290 00:30:08.750 ⇒ 00:30:09.080 Amber Lin: Goodness.
291 00:30:09.468 ⇒ 00:30:11.799 Demilade Agboola: Yeah. Open the Us. By.
292 00:30:11.800 ⇒ 00:30:14.900 Amber Lin: Okay, I see. Why don’t I? Why can’t.
293 00:30:14.900 ⇒ 00:30:17.710 Demilade Agboola: By Monday, I can. I can. I can actually, on Monday.
294 00:30:20.040 ⇒ 00:30:25.130 Amber Lin: Do you want to do it now? Cause I I don’t know like what questions are in my mind yet
295 00:30:25.840 ⇒ 00:30:28.780 Amber Lin: if we can use this time? Would it be possible.
296 00:30:28.990 ⇒ 00:30:30.860 Demilade Agboola: Okay, sure. Let’s just do. Now.
297 00:30:31.130 ⇒ 00:30:31.760 Amber Lin: Yeah.
298 00:30:32.600 ⇒ 00:30:33.330 Demilade Agboola: Oh!
299 00:30:33.687 ⇒ 00:30:40.119 Amber Lin: Luke. What’s what’s your take on this? Because you know much more about Dvt. Than I do
300 00:30:41.690 ⇒ 00:30:45.250 Amber Lin: any questions that come to your mind, any realization.
301 00:30:48.820 ⇒ 00:30:55.082 Luke Daque: Yeah, I don’t. I don’t really know yet, but I guess once I get
302 00:30:56.700 ⇒ 00:31:01.769 Luke Daque: yeah, once I get access to the Github and like I can check the models. I guess I can
303 00:31:02.910 ⇒ 00:31:05.670 Luke Daque: like figure out like what the
304 00:31:07.620 ⇒ 00:31:11.940 Luke Daque: what the old models that they created versus like what the Mla they created
305 00:31:12.230 ⇒ 00:31:18.600 Luke Daque: and like, I guess the the main thing that we would be doing is like migrate or like
306 00:31:19.070 ⇒ 00:31:21.860 Luke Daque: optimize, improve their models.
307 00:31:22.170 ⇒ 00:31:26.219 Luke Daque: Isn’t is what I understand is that correct dimalade.
308 00:31:26.220 ⇒ 00:31:29.091 Amber Lin: I think we might just making new ones.
309 00:31:29.450 ⇒ 00:31:35.829 Demilade Agboola: Oh, we’re we’re we’re making new ones. But like the old, because the old ones are like really badly done.
310 00:31:35.930 ⇒ 00:31:37.260 Demilade Agboola: So if we bye
311 00:31:37.260 ⇒ 00:31:48.799 Demilade Agboola: we fix those old ones like you make them work properly. We integrate the new logic. The problem is maintainability. It’s really hard with the currently structured.
312 00:31:49.370 ⇒ 00:31:51.189 Luke Daque: So I guess our our mindset.
313 00:31:52.510 ⇒ 00:31:53.499 Demilade Agboola: Here we go!
314 00:31:54.700 ⇒ 00:32:01.839 Luke Daque: I guess our mindset to this is basically start from scratch, right like, like, create everything from scratch, basically. So.
315 00:32:03.060 ⇒ 00:32:03.610 Demilade Agboola: Yeah.
316 00:32:03.610 ⇒ 00:32:11.660 Luke Daque: Which I think would, is also like better, because, like, it would be very difficult. I mean, it would take time to like, figure out what their models are
317 00:32:11.830 ⇒ 00:32:14.990 Luke Daque: compared to just creating from scratch.
318 00:32:15.540 ⇒ 00:32:16.130 Amber Lin: Hmm.
319 00:32:16.370 ⇒ 00:32:16.770 Luke Daque: So, yeah.
320 00:32:16.770 ⇒ 00:32:19.989 Demilade Agboola: Question, what do you have a preferred output format.
321 00:32:21.223 ⇒ 00:32:25.649 Amber Lin: I’m just now reading the repo mix thing.
322 00:32:25.790 ⇒ 00:32:28.909 Demilade Agboola: What do you think works better.
323 00:32:29.970 ⇒ 00:32:30.490 Amber Lin: I have.
324 00:32:33.600 ⇒ 00:32:35.499 Demilade Agboola: 1st see what Chat Gpt says.
325 00:32:38.710 ⇒ 00:32:44.510 Luke Daque: I don’t think it matters that much. As long as like Chat Gpt can read.
326 00:32:45.080 ⇒ 00:32:45.870 Amber Lin: That’s true.
327 00:32:45.870 ⇒ 00:32:46.820 Luke Daque: Yeah.
328 00:32:47.090 ⇒ 00:32:50.800 Amber Lin: Yeah, what’s the Xml file.
329 00:32:54.590 ⇒ 00:32:56.030 Amber Lin: That’s like that.
330 00:32:56.660 ⇒ 00:32:58.540 Amber Lin: It’s also my language.
331 00:32:58.540 ⇒ 00:33:04.979 Luke Daque: Similar to Jason, but different. So yeah, I get. I guess Markdown would be easier like you can.
332 00:33:06.110 ⇒ 00:33:08.210 Demilade Agboola: Yeah. It’s just wrapped on.
333 00:33:09.060 ⇒ 00:33:15.129 Amber Lin: Sounds good, and I guess, since we’re talking about what
334 00:33:15.240 ⇒ 00:33:28.030 Amber Lin: we said that it was super hard to maintain. Did we cover? Why, it was super hard to maintain. I kind of got a gist of it. But what was the main reasons.
335 00:33:35.450 ⇒ 00:33:45.190 Luke Daque: From what I understand, like what the milady mentioned earlier was that it’s just. It’s just like how they created their models. They weren’t like.
336 00:33:45.450 ⇒ 00:33:51.580 Luke Daque: They probably weren’t like using the best practices for creating models like how they created that.
337 00:33:51.580 ⇒ 00:33:52.650 Amber Lin: This is.
338 00:33:53.930 ⇒ 00:33:56.790 Luke Daque: Like, yeah, like having staging models, intermediate models.
339 00:33:56.790 ⇒ 00:33:57.420 Amber Lin: Stuff, like.
340 00:33:57.420 ⇒ 00:34:00.120 Luke Daque: That or like having tests. And
341 00:34:00.240 ⇒ 00:34:05.240 Luke Daque: and so, yeah, it. It’s probably in in those lines, like.
342 00:34:05.740 ⇒ 00:34:06.700 Amber Lin: I see.
343 00:34:06.920 ⇒ 00:34:15.400 Luke Daque: Probably just creating models for a specific report. And then there could be like, or some redundant
344 00:34:16.050 ⇒ 00:34:21.160 Luke Daque: business logic that’s being done in a certain model that’s also being
345 00:34:23.350 ⇒ 00:34:26.979 Luke Daque: used for a different model. So some some stuff like that.
346 00:34:26.989 ⇒ 00:34:35.499 Amber Lin: So I should also go look up. What best practices for wait, modeling, or like. What should I search.
347 00:34:36.699 ⇒ 00:34:39.259 Luke Daque: Yeah, you can search that. There’s there’s
348 00:34:39.479 ⇒ 00:34:42.619 Luke Daque: yeah, like, Dbt has good documentation on that.
349 00:34:42.620 ⇒ 00:34:42.980 Amber Lin: Okay.
350 00:34:42.989 ⇒ 00:34:43.529 Luke Daque: Accommodate.
351 00:34:43.530 ⇒ 00:34:44.440 Amber Lin: Modeling.
352 00:34:45.139 ⇒ 00:34:45.649 Demilade Agboola: Yes, it is.
353 00:34:45.650 ⇒ 00:34:46.540 Luke Daque: Yeah.
354 00:34:47.270 ⇒ 00:34:53.749 Demilade Agboola: Oh, I think it mix has worked. It worked as plain, though it couldn’t. It didn’t work as smackdown. So I was just saying.
355 00:34:53.750 ⇒ 00:34:57.323 Amber Lin: Okay. Whatever. Whatever works, I don’t care.
356 00:34:58.831 ⇒ 00:35:02.340 Demilade Agboola: But it’s okay. Client, that’s urban standards.
357 00:35:04.780 ⇒ 00:35:06.970 Demilade Agboola: It’s umbrella.
358 00:35:07.560 ⇒ 00:35:11.280 Amber Lin: Yeah, thank you. That’s very helpful.
359 00:35:13.564 ⇒ 00:35:22.927 Amber Lin: And yeah, okay, I’ll go figure out what the staging models and tests means.
360 00:35:24.400 ⇒ 00:35:29.630 Amber Lin: Do you guys have, like good websites or just documentations. You can point me to.
361 00:35:30.650 ⇒ 00:35:36.090 Demilade Agboola: I think DVD. Themselves have pretty good documents, find that pretty helpful. But, like.
362 00:35:36.600 ⇒ 00:35:37.110 Amber Lin: Okay.
363 00:35:38.320 ⇒ 00:35:39.709 Demilade Agboola: Dr. Anthony Sean.
364 00:35:39.870 ⇒ 00:35:42.160 Amber Lin: Okay, I will go. Take a look.
365 00:35:42.560 ⇒ 00:35:45.700 Demilade Agboola: And how we structure our projects. So they kind of explain.
366 00:35:45.700 ⇒ 00:35:47.429 Amber Lin: I see. Okay, okay.
367 00:35:49.770 ⇒ 00:35:53.710 Demilade Agboola: But yeah, effectively, this is how you’re supposed to kind of structure
368 00:35:54.100 ⇒ 00:35:57.599 Demilade Agboola: your projects. You’re supposed to have like sources.
369 00:35:57.600 ⇒ 00:35:58.080 Amber Lin: Oh!
370 00:35:58.080 ⇒ 00:36:01.179 Demilade Agboola: It’s not staging. You’re supposed to have your intermediate, and you’re marked.
371 00:36:01.740 ⇒ 00:36:03.580 Amber Lin: And wow.
372 00:36:03.580 ⇒ 00:36:08.559 Demilade Agboola: Like it helps like, it’s as you scale being able to go through
373 00:36:08.770 ⇒ 00:36:13.140 Demilade Agboola: like your different sources. Figure out what sources are like, what’s happening, your sources
374 00:36:13.450 ⇒ 00:36:16.510 Demilade Agboola: being able to. Then also go to
375 00:36:17.130 ⇒ 00:36:26.770 Demilade Agboola: your intermediate, like your staging your intermediate and say, Okay, what’s going on? Intermediate for, like, whatever concept we’re working towards and like, Oh.
376 00:36:27.050 ⇒ 00:36:31.450 Demilade Agboola: I am trying to build a new model that does this, or trying to build a new model that does that. And so now.
377 00:36:31.450 ⇒ 00:36:31.910 Amber Lin: On, mute.
378 00:36:31.910 ⇒ 00:36:37.440 Demilade Agboola: That middle model or that piece of code, you can keep maintaining it.
379 00:36:37.700 ⇒ 00:36:42.709 Demilade Agboola: So if I, if I have a model for in this case, like what we structured, we have a model for buffer counts.
380 00:36:42.980 ⇒ 00:36:46.580 Demilade Agboola: If the logic for buffer count changes right.
381 00:36:46.780 ⇒ 00:36:47.380 Amber Lin: Hmm.
382 00:36:47.660 ⇒ 00:36:54.130 Demilade Agboola: And just go to that model and make those changes. And now everything is fine across the entire system.
383 00:36:54.740 ⇒ 00:36:55.590 Amber Lin: But you can see.
384 00:36:56.000 ⇒ 00:37:25.509 Demilade Agboola: Yeah, so, and then you go to your march, and you can kind of also see that what is happening in your march as well like it allows you to build on top of each other. But when in the same folder, just based on sources, even though things will interact with other sources, or even potentially within that source. Because you don’t have one space where you’re looking for all your marks, it means you’re consistently hopping in between like different folders in such a way that it can be hard to keep track of what you’re doing.
385 00:37:26.150 ⇒ 00:37:36.070 Amber Lin: Oh, okay, so staging is kind of separate than the Mars. The staging is just different steps, that kind of like source. And then
386 00:37:36.744 ⇒ 00:37:46.970 Amber Lin: we post like different stages of the pipeline, and then the march is a combination of like different SQL. Files, and sequence right.
387 00:37:51.850 ⇒ 00:37:54.460 Amber Lin: I can go figure that out. It’s okay.
388 00:37:56.440 ⇒ 00:38:01.910 Demilade Agboola: Bye, bye, I’m trying to figure out the tableau. Job failed. So I’m trying to figure out.
389 00:38:01.910 ⇒ 00:38:16.119 Amber Lin: Yeah, don’t don’t worry, don’t worry. I will go figure things out. I I need to learn this anyways. Thank you guys for the meeting. I will keep this short, so you can have some time to your own. I will ask you if I have any more questions.
390 00:38:18.340 ⇒ 00:38:19.649 Demilade Agboola: Okay. Sounds good.
391 00:38:19.650 ⇒ 00:38:21.580 Amber Lin: Okay. Thank you guys.
392 00:38:22.520 ⇒ 00:38:23.340 Luke Daque: Thanks.
393 00:38:23.640 ⇒ 00:38:28.949 Amber Lin: Bye. Oh, Luke, I’ll hop on a call with you later about pool parts. I’ll call you in slack.
394 00:38:29.330 ⇒ 00:38:33.259 Luke Daque: Sure we can do it now if if you have time, because, like.
395 00:38:33.260 ⇒ 00:38:34.300 Amber Lin: Sure. Yeah.
396 00:38:34.730 ⇒ 00:38:41.269 Amber Lin: I just wanna use a different meeting room so that the recording in zoom doesn’t get confused.
397 00:38:41.840 ⇒ 00:38:42.719 Luke Daque: Sure sounds good.
398 00:38:42.720 ⇒ 00:38:45.209 Amber Lin: Okay, I’ll use the full parse link. Bye, bye.
399 00:38:45.980 ⇒ 00:38:46.335 Luke Daque: Bye.