Meeting Title: US x BF | Standup Date: 2025-06-02 Meeting participants: Demilade Agboola, Amber Lin, Caio Velasco, Uttam Kumaran
WEBVTT
1 00:00:14.230 ⇒ 00:00:15.220 Demilade Agboola: Hi amber.
2 00:00:35.160 ⇒ 00:00:36.400 Amber Lin: Hi, there!
3 00:00:37.680 ⇒ 00:00:38.789 Amber Lin: Yeah, I am.
4 00:00:39.430 ⇒ 00:00:46.658 Amber Lin: Hi! Give me a sec I think I should have my headphones. But let me pull up
5 00:00:48.420 ⇒ 00:00:50.329 Amber Lin: linear, real, quick.
6 00:00:58.610 ⇒ 00:01:04.950 Amber Lin: and then afterwards, you know, used to be the name.
7 00:01:20.590 ⇒ 00:01:21.250 Caio Velasco: Oh!
8 00:01:23.170 ⇒ 00:01:24.050 Demilade Agboola: Okay.
9 00:01:24.630 ⇒ 00:01:26.630 Caio Velasco: I think Landy amber.
10 00:01:39.190 ⇒ 00:01:40.310 Uttam Kumaran: Hello!
11 00:01:41.970 ⇒ 00:01:42.840 Demilade Agboola: Hi Utah!
12 00:01:42.840 ⇒ 00:01:43.340 Uttam Kumaran: Hey!
13 00:01:43.340 ⇒ 00:01:44.140 Caio Velasco: So.
14 00:01:44.640 ⇒ 00:01:45.310 Uttam Kumaran: I.
15 00:03:18.180 ⇒ 00:03:20.159 Amber Lin: Hello! This should be ready.
16 00:03:20.716 ⇒ 00:03:32.480 Amber Lin: Hi, are we? Okay? Let’s let’s run through linear. I know we have a planning a grooming session later, do we want to do that now, or do we want to do that later?
17 00:03:33.939 ⇒ 00:03:39.649 Uttam Kumaran: Let’s do that now. I mean, if if let’s just, we can just start on that now I have until the hour.
18 00:03:40.040 ⇒ 00:03:44.540 Amber Lin: Okay, sounds good. Let’s do that, then. Okay.
19 00:03:46.959 ⇒ 00:03:55.519 Amber Lin: before, as I pull it as I pull it up, any updates from from Kyle and Dunlotte.
20 00:03:56.250 ⇒ 00:03:58.599 Amber Lin: I know, Kyle, you’re meeting with Emily today.
21 00:03:59.380 ⇒ 00:04:00.830 Caio Velasco: I read it. Yes.
22 00:04:01.370 ⇒ 00:04:02.120 Amber Lin: Oh, you already did.
23 00:04:02.120 ⇒ 00:04:04.905 Caio Velasco: Yeah, yeah, I have an update.
24 00:04:05.820 ⇒ 00:04:06.180 Amber Lin: No.
25 00:04:06.633 ⇒ 00:04:12.099 Caio Velasco: So for this I have to take 2 tickets. This one is related to the dashboard deprecation.
26 00:04:13.610 ⇒ 00:04:21.319 Caio Velasco: And so she basically ran through all everything with her. And apparently she
27 00:04:21.630 ⇒ 00:04:25.500 Caio Velasco: that created a brain forged look on Looker with.
28 00:04:25.860 ⇒ 00:04:35.120 Caio Velasco: Dashboard, separated by function, like marketing sales and operations, etc, etc. And as I understood, those things are the ones
29 00:04:36.930 ⇒ 00:04:41.060 Caio Velasco: that are related to being deprecated. And the reason isn’t it
30 00:04:41.060 ⇒ 00:04:46.090 Caio Velasco: which in the beginning I didn’t have like an idea, why but
31 00:04:46.470 ⇒ 00:04:55.869 Caio Velasco: because they have so many problems in terms of the final tables they are connected to, or things that are like logic that is wrong, or
32 00:04:56.090 ⇒ 00:05:02.180 Caio Velasco: or maybe just because people are not really using them, or because they are super important to the sea level
33 00:05:02.300 ⇒ 00:05:20.429 Caio Velasco: that those have to be created because we’re gonna build those for them and building. Those means the building, all the Dbt logic, everything from source to dashboard. I just wanna run by you. And since your time is here as well. Is that? What are we doing? Is that where we are heading with this one.
34 00:05:23.970 ⇒ 00:05:34.420 Uttam Kumaran: Yeah, maybe I can talk about the the deprecation. So yes, there’s gonna so there’s gonna there’s almost like 2 categories. Right? There’s 1 category of stuff that’s like
35 00:05:35.190 ⇒ 00:05:36.638 Uttam Kumaran: it’s it’s almost a
36 00:05:37.920 ⇒ 00:05:43.520 Uttam Kumaran: 4 square block of used and accurate right? Is it being used? And is it accurate?
37 00:05:43.630 ⇒ 00:05:50.060 Uttam Kumaran: There’s gonna be a. The stuff that’s unused and not accurate is 1st to go right.
38 00:05:50.650 ⇒ 00:05:55.790 Uttam Kumaran: Does that make sense like if it’s not being used? And we’re not seeing usage, and it’s not accurate
39 00:05:56.050 ⇒ 00:06:01.949 Uttam Kumaran: we should just delete it. The second thing is, if it’s if it’s unused and accurate.
40 00:06:02.330 ⇒ 00:06:08.059 Uttam Kumaran: you should also consider removing it because it’s not being used right? So I would start with those 2
41 00:06:08.250 ⇒ 00:06:10.430 Uttam Kumaran: anything that’s being used.
42 00:06:11.336 ⇒ 00:06:15.699 Uttam Kumaran: Whether it’s accurate or not. This is, gonna be where it’s gonna be a little bit tougher.
43 00:06:15.880 ⇒ 00:06:20.039 Uttam Kumaran: right? If it’s used and inaccurate, we need to understand.
44 00:06:20.200 ⇒ 00:06:26.309 Uttam Kumaran: Do we? Can we just tweak it? Or do we need to completely redo it
45 00:06:26.740 ⇒ 00:06:29.099 Uttam Kumaran: right if it’s used and accurate.
46 00:06:29.480 ⇒ 00:06:32.379 Uttam Kumaran: Is there anything to do at all? Question Mark, you know.
47 00:06:34.230 ⇒ 00:06:34.990 Caio Velasco: It’s okay. No problem.
48 00:06:34.990 ⇒ 00:06:35.940 Uttam Kumaran: So for yeah.
49 00:06:35.940 ⇒ 00:06:37.480 Uttam Kumaran: So so I would say, the biggest.
50 00:06:37.650 ⇒ 00:06:40.280 Uttam Kumaran: probably area problem areas used and inaccurate.
51 00:06:40.280 ⇒ 00:06:41.110 Amber Lin: People.
52 00:06:41.110 ⇒ 00:06:48.380 Uttam Kumaran: Which means for the use and inaccurate. My suggestion is to rebuild the dashboard completely and then
53 00:06:48.980 ⇒ 00:06:57.550 Uttam Kumaran: have people switch to that, and that’s probably the, and then rebuild the dashboard side by side.
54 00:06:57.660 ⇒ 00:07:05.529 Uttam Kumaran: Verify all the numbers right? So so amber. If you just write some steps under the and inaccurate, this will be like the migration steps.
55 00:07:07.033 ⇒ 00:07:09.400 Amber Lin: So used and inaccurate right.
56 00:07:09.880 ⇒ 00:07:11.020 Uttam Kumaran: Yeah. So
57 00:07:11.640 ⇒ 00:07:21.560 Uttam Kumaran: like, for any, any, for any sort of deprecation. One, we need to have a replacement, right? So where are people going to find this information. Now
58 00:07:22.060 ⇒ 00:07:26.580 Uttam Kumaran: it may be one dashboard and maybe other dashboards. So there needs to be a replacement.
59 00:07:27.080 ⇒ 00:07:36.009 Uttam Kumaran: Second piece is there? There needs to be an indication of what the differences are between the replacement and the old one
60 00:07:36.270 ⇒ 00:07:41.339 Uttam Kumaran: is there like? If numbers are different, why are they different? Who signed off on them.
61 00:07:41.947 ⇒ 00:07:44.920 Uttam Kumaran: The 3rd thing is you, we need to put like a date.
62 00:07:45.120 ⇒ 00:07:48.629 Uttam Kumaran: So by X date, we’re gonna plan on deprecating
63 00:07:49.783 ⇒ 00:07:55.740 Uttam Kumaran: and the goal is going to be a source handoff by then.
64 00:07:56.785 ⇒ 00:08:03.360 Uttam Kumaran: I would say, for the deprecation. We need to get handoff from Zach and from probably leadership.
65 00:08:03.360 ⇒ 00:08:04.580 Amber Lin: It’s like.
66 00:08:07.780 ⇒ 00:08:12.490 Uttam Kumaran: So this needs to happen ideally. Probably in that data platform spreadsheet, we could just
67 00:08:12.750 ⇒ 00:08:15.130 Uttam Kumaran: create a dashboard migration plan.
68 00:08:17.470 ⇒ 00:08:22.670 Uttam Kumaran: and you could have. You can have Emily, probably work on this. But this is what this is what you probably needs to happen.
69 00:08:23.420 ⇒ 00:08:28.569 Uttam Kumaran: The cause, the con here’s the here’s here’s like, what happens if this doesn’t? If here’s the alternative reality
70 00:08:29.040 ⇒ 00:08:41.429 Uttam Kumaran: we create the new dashboards. We say, cool, go, use those dashboards. People go use them. It’s clear the numbers are different, right? But they’re right. But they’re different. People are. Gonna say, Oh, numbers aren’t accurate. Oh, like this doesn’t tie into my old stuff.
71 00:08:41.850 ⇒ 00:08:50.439 Uttam Kumaran: Oh, like I’m missing a metric, right? So the time between indicating the difference and deprecation we will find all those small things
72 00:08:51.060 ⇒ 00:08:53.579 Uttam Kumaran: one other can we add one other step here?
73 00:08:53.940 ⇒ 00:08:55.869 Uttam Kumaran: Dashboard needs an owner
74 00:08:56.240 ⇒ 00:09:01.780 Uttam Kumaran: like who is the business owner? That is our like, or who is the analyst owner.
75 00:09:03.710 ⇒ 00:09:05.560 Uttam Kumaran: Ideally, this is analyst owner.
76 00:09:06.620 ⇒ 00:09:10.740 Uttam Kumaran: So this is, they have several analysts, Perry, couple of other people
77 00:09:11.370 ⇒ 00:09:15.280 Uttam Kumaran: like, who is the person that we can go to for questions about this that is not
78 00:09:15.560 ⇒ 00:09:17.230 Uttam Kumaran: Emily or on our team.
79 00:09:21.370 ⇒ 00:09:27.589 Caio Velasco: Okay, okay, I I think I understand. And this is super good, because this is kind of an outcome that I wanted.
80 00:09:27.770 ⇒ 00:09:39.580 Caio Velasco: When I was talking to her, but she just went like one by one, and like talking about all the marks, the problem, the tables coming from different sources, like etc. and like, Hey, I don’t work for you guys. I don’t know anything about.
81 00:09:39.580 ⇒ 00:09:46.259 Uttam Kumaran: No, no, I agree. And that’s so. That’s yeah. So I I totally agree. So this process, I think, should help for for that.
82 00:09:46.980 ⇒ 00:09:47.620 Caio Velasco: Yeah.
83 00:09:47.800 ⇒ 00:09:48.410 Amber Lin: Great.
84 00:09:49.590 ⇒ 00:10:01.660 Amber Lin: and then these, I think this one, unused and inaccurate could be our 1st out to just remove. And then this one. We can just flag for consideration.
85 00:10:01.730 ⇒ 00:10:05.429 Demilade Agboola: My, my question is given, how many dashboards we have? How do we.
86 00:10:05.430 ⇒ 00:10:05.860 Amber Lin: Be defined.
87 00:10:05.970 ⇒ 00:10:07.490 Demilade Agboola: Greasy, so quickly.
88 00:10:12.680 ⇒ 00:10:14.150 Amber Lin: That Nope.
89 00:10:14.150 ⇒ 00:10:20.520 Uttam Kumaran: This is where it’s like, it’s gonna be based on. For example, yeah. So let’s let’s add a couple notes at the bottom here. So
90 00:10:21.070 ⇒ 00:10:23.239 Uttam Kumaran: how do, how do we determine accuracy?
91 00:10:23.240 ⇒ 00:10:23.650 Demilade Agboola: Take your time.
92 00:10:23.650 ⇒ 00:10:27.770 Uttam Kumaran: Yeah. The base level is that it’s pulling from tables that we could verify.
93 00:10:31.240 ⇒ 00:10:31.930 Amber Lin: A good night.
94 00:10:31.930 ⇒ 00:10:38.489 Uttam Kumaran: Right or the basically the looker version is, the dashboards are built from explorers that are verified.
95 00:10:43.610 ⇒ 00:10:44.310 Amber Lin: Stay.
96 00:10:46.580 ⇒ 00:10:49.824 Demilade Agboola: So quick. Question also that ties into this
97 00:10:50.600 ⇒ 00:10:56.050 Demilade Agboola: does that mean that we need to kind of push this back, verify, explores.
98 00:10:56.240 ⇒ 00:11:03.600 Demilade Agboola: or isn’t also kind of tied into us like auditing and building out like the dash, like the
99 00:11:04.480 ⇒ 00:11:12.610 Demilade Agboola: Dbt dashboards that we feel comfortable in or comfortable with, and that they then use, and then we can go. This is a verified
100 00:11:12.840 ⇒ 00:11:16.680 Demilade Agboola: source. We feel confidence in this, and this is accurate
101 00:11:19.170 ⇒ 00:11:22.100 Demilade Agboola: because they do have a ton of stuff. And I’m just trying to think of like.
102 00:11:24.060 ⇒ 00:11:30.159 Demilade Agboola: it seems like we need to verify the data sources. Then we do the dashboard in terms of accuracy.
103 00:11:33.760 ⇒ 00:11:35.459 Uttam Kumaran: Yeah, I mean, this is the thing it’s like we
104 00:11:35.800 ⇒ 00:11:44.309 Uttam Kumaran: you have to like. Accuracy can be. Oh, there’s like a dash. There’s like a missing metric could also be like, Oh, it’s pulling from like an old table.
105 00:11:45.950 ⇒ 00:11:53.429 Uttam Kumaran: So this is where I think, identifying the the analyst owner will be helpful to to identify whether this is still accurate or not.
106 00:11:54.300 ⇒ 00:11:54.960 Amber Lin: Pending.
107 00:11:56.675 ⇒ 00:12:02.340 Amber Lin: He’s a huge question.
108 00:12:06.180 ⇒ 00:12:06.990 Amber Lin: Delay.
109 00:12:08.470 ⇒ 00:12:23.340 Amber Lin: The problem that comes to mind right now is that there, it’s just a lot of dashboards, and we will need a certain level of prioritization to do that with. And I remember Zach brought up of, okay, we’re gonna categorize by this. I think
110 00:12:23.890 ⇒ 00:12:32.809 Amber Lin: if we have that as a 1st step, it’ll be really helpful to at least know the used and unused metrics, so we can at least have one level. One.
111 00:12:32.810 ⇒ 00:12:34.380 Uttam Kumaran: What is 1st meeting.
112 00:12:37.933 ⇒ 00:12:39.219 Amber Lin: Good question.
113 00:12:45.190 ⇒ 00:12:47.020 Uttam Kumaran: I think I see I don’t know.
114 00:12:47.020 ⇒ 00:12:47.360 Caio Velasco: Think that
115 00:12:47.740 ⇒ 00:12:51.679 Caio Velasco: that’s why we I think that that was the idea. For the 1st meeting I had with her.
116 00:12:51.680 ⇒ 00:12:53.930 Amber Lin: Oh, see! Ya!
117 00:12:53.930 ⇒ 00:13:00.150 Caio Velasco: But even though I brought that up in the beginning, she was just running through all the ones that she put in that.
118 00:13:00.150 ⇒ 00:13:01.170 Caio Velasco: yeah. So I think.
119 00:13:01.870 ⇒ 00:13:06.870 Uttam Kumaran: Yeah, I think, Amber, this may be helpful for you and Emily to do together.
120 00:13:09.640 ⇒ 00:13:10.730 Amber Lin: Thank you so much.
121 00:13:13.440 ⇒ 00:13:13.930 Amber Lin: Maybe.
122 00:13:17.182 ⇒ 00:13:25.600 Demilade Agboola: I’m thinking that it will be super helpful just even generally to have
123 00:13:28.610 ⇒ 00:13:34.239 Demilade Agboola: within Dvt as well. Potentially, we could just go through something.
124 00:13:35.020 ⇒ 00:13:40.889 Demilade Agboola: tables and kind of like which ones are being actively maintained, and we trust.
125 00:13:41.420 ⇒ 00:13:41.950 Uttam Kumaran: Yes.
126 00:13:42.190 ⇒ 00:13:56.339 Demilade Agboola: And so that that way that also helps us kind of look downstream of those tables and figure out like, Hey, what’s going on with the ones that we kind of already trust, or the Team Trust already.
127 00:13:58.220 ⇒ 00:14:04.150 Demilade Agboola: And then, if there’s stuff that they already like, you know, this table is stale. This table is not useful
128 00:14:04.584 ⇒ 00:14:09.600 Demilade Agboola: that allows us to quickly like knock off, maybe 5 or 10 explores, or whatever.
129 00:14:11.540 ⇒ 00:14:12.150 Amber Lin: Thank you.
130 00:14:14.685 ⇒ 00:14:18.919 Amber Lin: Great alright!
131 00:14:19.220 ⇒ 00:14:45.219 Amber Lin: So it seems like 1st step. I will meet with Emily on the usage which will help us categorize this on the accuracy side. I think. You guys will probably need to look at the Dbt models and also look at explores, and when we have both of them, we’ll be able to say, Hey, what’s in which category? And we should also share this process with them and see where we need their input.
132 00:14:48.780 ⇒ 00:14:51.555 Amber Lin: Okay.
133 00:14:55.530 ⇒ 00:15:04.620 Amber Lin: it we would be looking at the Dbt models for the explorers.
134 00:15:08.018 ⇒ 00:15:12.140 Uttam Kumaran: It probably is. Similarly Emily and demalade.
135 00:15:12.140 ⇒ 00:15:12.569 Amber Lin: Okay.
136 00:15:33.470 ⇒ 00:15:34.270 Amber Lin: okay.
137 00:15:37.980 ⇒ 00:15:46.260 Amber Lin: great. So I think we need a different. We need an alternate, the new ticket to just categorize
138 00:15:46.810 ⇒ 00:15:50.159 Amber Lin: and flag all these. This is flagging.
139 00:15:50.790 ⇒ 00:15:54.030 Amber Lin: Okay, to audit which one we trust in accuracy.
140 00:15:54.340 ⇒ 00:15:56.110 Uttam Kumaran: Yeah. So I would probably break this ticket up.
141 00:16:01.150 ⇒ 00:16:02.050 Amber Lin: Okay.
142 00:16:20.490 ⇒ 00:16:22.859 Amber Lin: I’ll just create it. I’ll deal with it later.
143 00:16:36.340 ⇒ 00:16:37.180 Amber Lin: Great!
144 00:16:39.700 ⇒ 00:16:44.980 Amber Lin: That will help us to stop this step.
145 00:16:45.430 ⇒ 00:16:48.910 Amber Lin: I think all dashboards has been listed at this point right.
146 00:16:52.830 ⇒ 00:16:54.050 Caio Velasco: That’s the thing.
147 00:16:54.050 ⇒ 00:16:55.680 Uttam Kumaran: I don’t know. Yeah.
148 00:16:56.448 ⇒ 00:17:00.599 Amber Lin: Kyle, is it? Is it listed? If not, well, I’ll just make another ticket. We’ll list all of them.
149 00:17:01.841 ⇒ 00:17:03.229 Amber Lin: No, no, no, no on this.
150 00:17:03.230 ⇒ 00:17:04.980 Amber Lin: Okay, okay, sounds good.
151 00:17:10.040 ⇒ 00:17:15.210 Amber Lin: Right? And then we’ll categorize Row 5 by each of these.
152 00:17:20.140 ⇒ 00:17:20.890 Amber Lin: Okay.
153 00:17:21.670 ⇒ 00:17:29.869 Amber Lin: And I think once we flag these, we’ll go identify the analyst owner because there’s a lot of dashboards, and I don’t want to overwhelm.
154 00:17:37.420 ⇒ 00:17:37.989 Caio Velasco: And then.
155 00:17:37.990 ⇒ 00:17:46.120 Caio Velasco: And as you mentioned, somehow, this is related to the data platform spreadsheet. So something that would guide us like at the dashboard level.
156 00:17:46.670 ⇒ 00:17:52.620 Caio Velasco: What is happening? I just wouldn’t see yet like what this would be. The final shape.
157 00:17:52.980 ⇒ 00:17:55.100 Amber Lin: But I have, like some ideas of.
158 00:17:57.070 ⇒ 00:17:59.260 Caio Velasco: Break down this this thing? Yeah.
159 00:17:59.430 ⇒ 00:18:12.880 Amber Lin: Okay, that’s awesome. Yes, that’s like, Wow, yeah, it does.
160 00:18:14.840 ⇒ 00:18:16.110 Amber Lin: How long? Sorry
161 00:18:25.320 ⇒ 00:18:27.120 Amber Lin: we can use that?
162 00:18:27.780 ⇒ 00:18:31.640 Amber Lin: So we’re gonna say.
163 00:18:37.102 ⇒ 00:18:42.429 Amber Lin: Oh, well, I’ll create it. We’ll deal with it later.
164 00:18:43.240 ⇒ 00:18:50.060 Amber Lin: Let’s go back to our current cycle.
165 00:18:50.910 ⇒ 00:18:54.670 Amber Lin: And how is this.
166 00:18:56.170 ⇒ 00:19:01.469 Caio Velasco: So yeah, this one. What I’m trying to do since I start like this Friday.
167 00:19:01.470 ⇒ 00:19:01.970 Amber Lin: Wonderful.
168 00:19:01.970 ⇒ 00:19:08.600 Caio Velasco: Or Thursday or Friday doing. Remember, it’s to list all tables, all sources that exist so.
169 00:19:09.200 ⇒ 00:19:14.020 Caio Velasco: Hevo and I was able to use Api.
170 00:19:14.020 ⇒ 00:19:14.590 Amber Lin: They can get.
171 00:19:14.590 ⇒ 00:19:17.310 Caio Velasco: From Hivo and then Polytomac, zd, but there was just one
172 00:19:17.860 ⇒ 00:19:23.349 Caio Velasco: to list at the table level. Everything that exists now for stitch. I was able.
173 00:19:23.810 ⇒ 00:19:28.320 Caio Velasco: All the sources, but not all the tables, from all the sources.
174 00:19:28.320 ⇒ 00:19:31.759 Caio Velasco: I’m still working on the scraper to to make it work.
175 00:19:32.563 ⇒ 00:19:37.570 Caio Velasco: But I I think I’m I’m almost there, because if I did the sources the tables should be all.
176 00:19:37.570 ⇒ 00:19:38.010 Amber Lin: Okay.
177 00:19:38.429 ⇒ 00:19:38.850 Caio Velasco: Together!
178 00:19:38.850 ⇒ 00:19:44.865 Amber Lin: Would you like someone? Maybe on the AI team to help you look at the scraper or
179 00:19:45.200 ⇒ 00:19:50.440 Caio Velasco: No, because I’m using Chat Gpt, and I have to read Debug step by step, and I mean.
180 00:19:50.440 ⇒ 00:19:51.580 Amber Lin: If I put the hours, it’s.
181 00:19:51.580 ⇒ 00:19:56.349 Caio Velasco: It was just a day. So I mean, okay, okay, it doesn’t feel a lot.
182 00:19:56.350 ⇒ 00:19:56.764 Amber Lin: Okay.
183 00:19:57.690 ⇒ 00:20:01.390 Amber Lin: How long would you estimate that? Would that would take.
184 00:20:02.590 ⇒ 00:20:09.979 Caio Velasco: To list. So I think today I would I would be able to list I was working on it. So it was just not working the last time I tried, but.
185 00:20:10.520 ⇒ 00:20:11.980 Caio Velasco: You have to debug a bit.
186 00:20:12.660 ⇒ 00:20:15.440 Amber Lin: Then we’ll have all the sources which is like.
187 00:20:15.790 ⇒ 00:20:19.409 Caio Velasco: Over 500 tables. If I’m not wrong, or much more.
188 00:20:19.410 ⇒ 00:20:32.440 Amber Lin: Hmm, okay, sounds good. And I know that’s just for the stitch ones. I know the next step is quite a big one that we need to understand what they mean, like, what units are using and what description there are about.
189 00:20:32.760 ⇒ 00:20:33.860 Amber Lin: I’m
190 00:20:34.580 ⇒ 00:20:40.399 Amber Lin: I know we probably it will be best if we have all tables, but I think we can get started
191 00:20:41.000 ⇒ 00:20:47.070 Amber Lin: to maybe talk with Emily about what each of the units mean.
192 00:20:47.920 ⇒ 00:20:49.229 Amber Lin: Do you think that.
193 00:20:49.230 ⇒ 00:20:54.869 Caio Velasco: That would be for the so yeah, for the source. One. Yeah, because we have 2 tabs, one for the source and one of the table.
194 00:20:54.870 ⇒ 00:20:55.739 Amber Lin: If you go.
195 00:20:56.040 ⇒ 00:20:56.340 Caio Velasco: But.
196 00:20:56.340 ⇒ 00:21:07.519 Amber Lin: Oh, okay. So I so when you say, start step so Sue.
197 00:21:08.190 ⇒ 00:21:13.470 Amber Lin: Oh, need to meet. Sorry.
198 00:21:19.260 ⇒ 00:21:19.970 Amber Lin: One second.
199 00:21:19.970 ⇒ 00:21:28.330 Caio Velasco: Yeah, because this was me just trying to understand, like, we are auditing sources. Okay? So they have like 500. I I don’t know a thousand tables.
200 00:21:29.350 ⇒ 00:21:36.029 Caio Velasco: and we have to somehow organize this. So what I did 1st was like, Okay, I need a list of everything.
201 00:21:36.750 ⇒ 00:21:40.959 Caio Velasco: Time. And then after that, it’s like, Okay, I need to understand. What are they? Because.
202 00:21:41.590 ⇒ 00:21:57.689 Caio Velasco: They? Each one is something different. So then, now I would need a bit of help to like what would be the next step, since there are so many things, and they can be super different. And how would you say that one is a duplicate for another table?
203 00:21:57.690 ⇒ 00:21:58.549 Amber Lin: From what?
204 00:21:58.550 ⇒ 00:22:03.340 Caio Velasco: One of those 500 like, is there a way to get there? But you guys have more experience.
205 00:22:03.720 ⇒ 00:22:07.420 Uttam Kumaran: Yeah. So one thing is, you should this, you should run a query to check
206 00:22:08.160 ⇒ 00:22:15.200 Uttam Kumaran: how often any all of these tables have been actually like varied against.
207 00:22:15.200 ⇒ 00:22:15.750 Amber Lin: Yeah.
208 00:22:16.882 ⇒ 00:22:20.310 Uttam Kumaran: You know, like second is, there’s
209 00:22:20.310 ⇒ 00:22:25.030 Uttam Kumaran: gonna be. I’ll automatically Emily will. You should have Emily take the 1st pass.
210 00:22:25.160 ⇒ 00:22:32.150 Uttam Kumaran: She’s she’s the most knowledgeable if she can just take a 1st pass and be like, Oh, we’re not using any of this. That’s easy.
211 00:22:33.010 ⇒ 00:22:37.050 Uttam Kumaran: you know the 3.rd The another thing is we just need to. We you do need to understand.
212 00:22:37.050 ⇒ 00:22:37.620 Amber Lin: 60.
213 00:22:37.620 ⇒ 00:22:46.370 Uttam Kumaran: Which ones are actually flowing into. Dbt, right now, cause there’s gonna be a lot of stuff that is not being touched by Dbt, if it’s not being touched by Dbt, it’s not used.
214 00:22:49.030 ⇒ 00:22:52.200 Uttam Kumaran: You know what I’m saying like, if it’s not in the Dbt repo, it’s not being used.
215 00:22:54.200 ⇒ 00:22:55.320 Amber Lin: Previously downloaded.
216 00:22:55.670 ⇒ 00:22:57.849 Uttam Kumaran: Like, if it’s not a source, basically.
217 00:22:58.500 ⇒ 00:23:00.129 Caio Velasco: Yeah, yeah, perfect.
218 00:23:00.640 ⇒ 00:23:06.350 Uttam Kumaran: So have Emily run a 1st pass. She will know a bunch of the tables that are really important.
219 00:23:06.830 ⇒ 00:23:12.599 Uttam Kumaran: And then we can talk about how to prioritize sort of like either, and then so I can. If maybe skip to the
220 00:23:13.090 ⇒ 00:23:17.440 Uttam Kumaran: last step, the decisions are, turn it off 1st
221 00:23:18.480 ⇒ 00:23:23.599 Uttam Kumaran: confirm, and then wait, probably a week or 2, see if anything happens, and then
222 00:23:24.490 ⇒ 00:23:26.490 Uttam Kumaran: deprecate like, drop the tables.
223 00:23:26.880 ⇒ 00:23:28.360 Amber Lin: At least. Very good luck.
224 00:23:29.950 ⇒ 00:23:31.280 Amber Lin: Next slide.
225 00:23:33.620 ⇒ 00:23:34.240 Amber Lin: Okay?
226 00:23:34.240 ⇒ 00:23:39.620 Uttam Kumaran: But I think in between. There, yeah, I think this is another one. Where have Emily take a 1st pass? Then.
227 00:23:39.620 ⇒ 00:23:40.279 Amber Lin: Talking about.
228 00:23:40.770 ⇒ 00:23:41.360 Amber Lin: Here’s the plan.
229 00:23:41.360 ⇒ 00:23:44.329 Uttam Kumaran: Then do. Then maybe I I’m probably helpful. Bring me in.
230 00:23:44.330 ⇒ 00:23:46.560 Uttam Kumaran: Just let me for a meeting, and we can go through.
231 00:23:47.820 ⇒ 00:23:49.220 Uttam Kumaran: But I want her to do the 1st pass.
232 00:23:49.220 ⇒ 00:23:58.659 Amber Lin: Do we do the 1st with her after we’ve ran a query because there’s a lot of tables, and she might not be able to read physically, read all of them.
233 00:23:58.660 ⇒ 00:24:04.549 Uttam Kumaran: Yeah, yeah, I would. I would run a query 1st to check if these tables have been queried against.
234 00:24:05.210 ⇒ 00:24:11.294 Uttam Kumaran: And you can ask AI how to do this within redshift, it’ll be pretty clear.
235 00:24:11.930 ⇒ 00:24:18.160 Uttam Kumaran: and what the basically the way you can do it? Is you just structure a query with all of these table names in the where clause.
236 00:24:18.610 ⇒ 00:24:21.679 Uttam Kumaran: and then, thank you me, all the last queries
237 00:24:22.320 ⇒ 00:24:27.680 Uttam Kumaran: structure a table with table name and most recent queried at right
238 00:24:28.160 ⇒ 00:24:33.579 Uttam Kumaran: or you can just actually get that for all tables and just put that and maybe like 2,000 tables. You just put that in the spreadsheet.
239 00:24:35.073 ⇒ 00:24:40.390 Uttam Kumaran: Maybe. Actually, maybe that’s cause we’re gonna need this, I think, anyway. So just get like all tables last query that
240 00:24:41.380 ⇒ 00:24:45.510 Uttam Kumaran: and and by who? By? Who was the who? Who last? Query? What was the username?
241 00:24:49.370 ⇒ 00:24:56.189 Caio Velasco: Yeah, I can get the. I can get the output of that and then join with the ones we have on the data platform and then flag them.
242 00:24:56.590 ⇒ 00:24:57.730 Caio Velasco: At least we are tracking.
243 00:25:00.150 ⇒ 00:25:05.149 Amber Lin: Which? Do you think it’s best to run by you? After we’ve done the Dvt audit.
244 00:25:06.940 ⇒ 00:25:10.343 Uttam Kumaran: So we’ll pass by Emily, after we do that.
245 00:25:11.100 ⇒ 00:25:12.239 Uttam Kumaran: Just send it to me like I can.
246 00:25:12.240 ⇒ 00:25:12.570 Amber Lin: Okay.
247 00:25:12.570 ⇒ 00:25:14.490 Uttam Kumaran: I think I do a pass myself.
248 00:25:14.490 ⇒ 00:25:15.460 Amber Lin: Okay. Okay.
249 00:25:15.640 ⇒ 00:25:16.380 Uttam Kumaran: Yeah.
250 00:25:16.630 ⇒ 00:25:17.420 Amber Lin: Sounds good.
251 00:25:17.420 ⇒ 00:25:19.639 Uttam Kumaran: And then that’ll we’ll at least have like
252 00:25:19.770 ⇒ 00:25:21.720 Uttam Kumaran: a good amount to start with. Like.
253 00:25:23.050 ⇒ 00:25:24.210 Amber Lin: Sounds, good.
254 00:25:24.210 ⇒ 00:25:26.230 Uttam Kumaran: And I’ll and I’ll share the priority.
255 00:25:26.700 ⇒ 00:25:32.760 Amber Lin: Wait. Should this dbt set be the last? Or should it be next to just right? Next to this query.
256 00:25:32.940 ⇒ 00:25:34.069 Amber Lin: it’s my question.
257 00:25:38.420 ⇒ 00:25:52.690 Amber Lin: Because in the last sorry in the last ticket we also said we need to check Dbt for all the dashboards. Should this just be a combined step that we check DVD. For what dashboard is being touched on. What sources
258 00:25:52.690 ⇒ 00:25:55.030 Amber Lin: he is not gonna have teams.
259 00:25:55.030 ⇒ 00:25:58.550 Uttam Kumaran: The dashboards gonna have the tables.
260 00:25:59.340 ⇒ 00:26:04.639 Uttam Kumaran: So you’re gonna have to get the tables that are being pushed by looker from Looker.
261 00:26:05.740 ⇒ 00:26:07.899 Uttam Kumaran: which is fine, that one you can get.
262 00:26:07.900 ⇒ 00:26:08.359 Amber Lin: Have you seen.
263 00:26:08.702 ⇒ 00:26:10.070 Uttam Kumaran: Pretty easily within, looker.
264 00:26:10.830 ⇒ 00:26:13.977 Uttam Kumaran: I think both of those are like step one and step 2.
265 00:26:14.410 ⇒ 00:26:14.980 Amber Lin: Cheaper.
266 00:26:15.760 ⇒ 00:26:17.069 Amber Lin: So this needs to go up.
267 00:26:17.070 ⇒ 00:26:19.310 Uttam Kumaran: Ideally step one should show you.
268 00:26:20.200 ⇒ 00:26:22.349 Uttam Kumaran: but it would be good to also do that step.
269 00:26:29.010 ⇒ 00:26:30.890 Amber Lin: Okay, sounds good.
270 00:26:32.140 ⇒ 00:26:34.379 Amber Lin: I think this is pretty one pretty good.
271 00:26:35.620 ⇒ 00:26:40.159 Amber Lin: We’ll need to make a ticket.
272 00:26:43.995 ⇒ 00:26:45.799 Amber Lin: Okay?
273 00:26:48.590 ⇒ 00:26:50.850 Amber Lin: Yes.
274 00:26:53.740 ⇒ 00:27:01.330 Demilade Agboola: Also, this is kind of the annoying part of them, not having, like Dvt. Mark, because ideally, it would just be easy to go to the Mods and get all tip.
275 00:27:01.928 ⇒ 00:27:12.210 Amber Lin: I see, I see what I call this is this source tables, or
276 00:27:16.750 ⇒ 00:27:18.840 Amber Lin: Okay, I’ll just share.
277 00:27:18.840 ⇒ 00:27:26.110 Caio Velasco: This is what we have almost in the the data platform. But then she would have to go. Yeah, through hundreds of things.
278 00:27:28.600 ⇒ 00:27:35.549 Uttam Kumaran: I mean, it’s look, it’s it’s it’s a hundreds. But it’s what we need to do. There’s no, there’s sometimes no other way. Really, you know.
279 00:27:38.330 ⇒ 00:27:39.700 Caio Velasco: No, exactly, exactly.
280 00:27:43.650 ⇒ 00:27:44.590 Amber Lin: Okay.
281 00:27:45.007 ⇒ 00:27:50.370 Amber Lin: I think we’re a lot more clear on that. I need to need to flesh out those tickets.
282 00:27:50.720 ⇒ 00:27:55.000 Amber Lin: But I think we know a good rundown, the different steps.
283 00:28:00.340 ⇒ 00:28:04.740 Amber Lin: Oh, computer there.
284 00:28:16.930 ⇒ 00:28:17.730 Amber Lin: Thank you.
285 00:28:17.970 ⇒ 00:28:24.070 Amber Lin: Okay, got it, thank you, alright
286 00:28:28.270 ⇒ 00:28:30.759 Uttam Kumaran: Yeah, I gotta jump to a marketing thing.
287 00:28:30.760 ⇒ 00:28:37.350 Amber Lin: Okay, yeah. Go ahead. Do you guys, Kyle, and don’t. Wanna do you want to continue later today
288 00:28:37.470 ⇒ 00:28:39.490 Amber Lin: as this time that we book for us.
289 00:28:40.000 ⇒ 00:28:41.860 Amber Lin: Now go! An hour later.
290 00:28:42.710 ⇒ 00:28:43.320 Amber Lin: It’s.
291 00:28:43.790 ⇒ 00:28:44.630 Caio Velasco: What time do we.
292 00:28:45.230 ⇒ 00:28:45.860 Amber Lin: Excellent.
293 00:28:46.190 ⇒ 00:28:52.824 Uttam Kumaran: If you guys have time now, I don’t know. I’ll probably stay on. I I’ll be I mean, I can hop back on later. But
294 00:28:53.510 ⇒ 00:28:56.100 Uttam Kumaran: I don’t know. It might be easier, since you’re already just like in the middle of it.
295 00:29:02.130 ⇒ 00:29:06.439 Demilade Agboola: I mean, I was supposed to have stand up with Eden, but that’s been pushed back by an hour.
296 00:29:07.420 ⇒ 00:29:09.230 Amber Lin: Hmm, okay.
297 00:29:11.243 ⇒ 00:29:12.889 Amber Lin: Let’s see.
298 00:29:14.044 ⇒ 00:29:19.215 Amber Lin: Well, we also have. Oh, Tom, you should just jump. I know you have a meeting. Yeah.
299 00:29:21.320 ⇒ 00:29:24.040 Amber Lin: I mean, we can’t continue.
300 00:29:24.661 ⇒ 00:29:32.399 Amber Lin: I I know this one. We probably would be starting today. There’s I guess there’s a lot of stuff floating around so.
301 00:29:33.470 ⇒ 00:29:35.889 Caio Velasco: Actually one question like, yes.
302 00:29:35.890 ⇒ 00:29:47.019 Caio Velasco: basic one. So we started on Thursday, for like a 1st time I met them. What would be a cycle. Then wouldn’t it be 2 weeks after the things need to be done?
303 00:29:47.320 ⇒ 00:29:51.040 Caio Velasco: It sounds like everything was for tomorrow. But I mean, we just started.
304 00:29:51.803 ⇒ 00:29:57.360 Amber Lin: We? Our cycle started on last Tuesday.
305 00:29:57.830 ⇒ 00:30:01.229 Amber Lin: So that’s when we 1st had the kickoff.
306 00:30:05.720 ⇒ 00:30:06.290 Caio Velasco: Okay.
307 00:30:06.290 ⇒ 00:30:11.079 Amber Lin: Appreciate it well, but at least usually.
308 00:30:11.080 ⇒ 00:30:15.249 Demilade Agboola: I think Kyle’s point is that the cycle started without us actually being.
309 00:30:15.787 ⇒ 00:30:19.010 Amber Lin: I see. That’s that’s very valid.
310 00:30:19.520 ⇒ 00:30:26.020 Amber Lin: I think on that. Do you? Do you guys think we should take out some some of these items
311 00:30:26.270 ⇒ 00:30:28.699 Amber Lin: for this cycle to get completed.
312 00:30:28.990 ⇒ 00:30:36.260 Amber Lin: I mean that one is due at the end of this. Wait, check what that date is.
313 00:30:39.482 ⇒ 00:30:45.520 Amber Lin: Check current cycle.
314 00:30:49.510 ⇒ 00:30:50.810 Amber Lin: You know, people.
315 00:30:54.365 ⇒ 00:30:59.480 Amber Lin: Okay. So our end date would be Tuesday.
316 00:31:01.890 ⇒ 00:31:06.300 Amber Lin: And so be good.
317 00:31:06.520 ⇒ 00:31:14.230 Amber Lin: Also, I think, yeah, I think this one. I meant to say that
318 00:31:14.878 ⇒ 00:31:19.919 Amber Lin: the flagging would. I didn’t know how long this would take.
319 00:31:20.330 ⇒ 00:31:27.940 Amber Lin: but it was more of like a 1st pass. Let’s adjust this ticket accordingly, and I do think we can push it back
320 00:31:28.270 ⇒ 00:31:30.560 Amber Lin: once we create these different tickets.
321 00:31:31.730 ⇒ 00:31:34.860 Amber Lin: But how long do you guys think it will take?
322 00:31:35.490 ⇒ 00:31:40.229 Amber Lin: We need still need. I still need to meet them on a dashboard usage. We need to look at
323 00:31:40.560 ⇒ 00:31:44.410 Amber Lin: the Dbt and explores like even those
324 00:31:44.520 ⇒ 00:31:48.009 Amber Lin: 1st few steps. Do you guys think we can do it in this cycle.
325 00:31:50.770 ⇒ 00:31:56.730 Caio Velasco: So for at least for the part of listing all sources from all ingestion tools. I’m almost done.
326 00:31:57.159 ⇒ 00:31:57.589 Amber Lin: Yeah.
327 00:31:57.590 ⇒ 00:32:00.859 Caio Velasco: Everything that comes after that which would be exactly this point.
328 00:32:02.260 ⇒ 00:32:06.950 Caio Velasco: Then I mean I honestly never know how much would take to be honest. But.
329 00:32:07.550 ⇒ 00:32:09.850 Caio Velasco: At least we are almost done with listing, all of them.
330 00:32:25.260 ⇒ 00:32:28.980 Amber Lin: Yeah, it’s like, it’s okay.
331 00:32:30.470 ⇒ 00:32:31.230 Amber Lin: Follows her.
332 00:32:31.700 ⇒ 00:32:40.570 Caio Velasco: Yeah. And then after we list them, then it’s exactly what you got today with the Witham. The 2 dimension thing used in.
333 00:32:41.080 ⇒ 00:32:41.590 Amber Lin: That’s neat.
334 00:32:41.590 ⇒ 00:32:43.209 Caio Velasco: It would be the second step.
335 00:32:44.140 ⇒ 00:32:45.870 Amber Lin: Okay, sounds good.
336 00:32:46.320 ⇒ 00:32:50.079 Amber Lin: You know, I’m reducing the scope of this ticket.
337 00:32:50.490 ⇒ 00:32:55.560 Amber Lin: And so these are not got it.
338 00:32:55.740 ⇒ 00:33:04.530 Amber Lin: You know what I’m gonna I think I’ll just delete that ticket and cool.
339 00:33:04.630 ⇒ 00:33:08.930 Amber Lin: I’ll say that this you say that the scraping can be done
340 00:33:09.440 ⇒ 00:33:12.220 Amber Lin: by tomorrow or by today, would.
341 00:33:13.080 ⇒ 00:33:17.670 Caio Velasco: Tomorrow, because then we have the time difference. So tomorrow, when you start should be done.
342 00:33:17.810 ⇒ 00:33:25.460 Amber Lin: Hmm! Sounds good, and then these would be a different 1. 2.
343 00:33:26.210 ⇒ 00:33:27.699 Amber Lin: That’s the big one.
344 00:33:31.690 ⇒ 00:33:34.779 Amber Lin: like the the owner of the city should be stolen.
345 00:33:35.900 ⇒ 00:33:48.550 Amber Lin: Oh, disconnected, that is it? A okay sounds good, sweet.
346 00:33:51.590 ⇒ 00:33:56.280 Amber Lin: So if that’s done by tomorrow, what’s next
347 00:33:57.410 ⇒ 00:34:01.810 Amber Lin: after that? Oh, this is, if this is done with tomorrow.
348 00:34:02.070 ⇒ 00:34:02.899 Amber Lin: I think this.
349 00:34:02.900 ⇒ 00:34:03.250 Caio Velasco: And then.
350 00:34:03.250 ⇒ 00:34:07.059 Amber Lin: Let me list all dashboards.
351 00:34:11.989 ⇒ 00:34:18.733 Caio Velasco: Yes, this this would be then me going to look her. I think I she sent. She’s
352 00:34:19.259 ⇒ 00:34:24.759 Caio Velasco: she showed me a part where you kind of have a a table that lists all dashboards. So I think.
353 00:34:24.760 ⇒ 00:34:25.479 Amber Lin: Yeah, yeah.
354 00:34:25.489 ⇒ 00:34:27.719 Caio Velasco: That’s true. Then it’s just copy and paste.
355 00:34:27.719 ⇒ 00:34:39.139 Amber Lin: Yeah, it is true, had was able to export all like it’s 3 different reports. And it did list. All of the dashboards in the in the strategy, so this wouldn’t be too hard. We do have all of them.
356 00:34:39.550 ⇒ 00:34:39.940 Caio Velasco: Okay.
357 00:34:39.969 ⇒ 00:34:48.929 Amber Lin: And I do believe they also have the usage data. So if you were able to take a look into that, I think that will be
358 00:34:49.190 ⇒ 00:34:51.761 Amber Lin: that will cover a lot of
359 00:34:55.480 ⇒ 00:34:58.480 Amber Lin: a lot of the current dashboards.
360 00:34:59.510 ⇒ 00:35:06.320 Amber Lin: I think that could be achievable. I don’t know if we want to say tomorrow or.
361 00:35:09.250 ⇒ 00:35:10.290 Caio Velasco: Well.
362 00:35:10.500 ⇒ 00:35:20.739 Caio Velasco: you can put one more day, but then, as soon as I end up the 1st one, as soon as I finish the 1st one. I would just jump into this so it could be that both get under the same day.
363 00:35:21.430 ⇒ 00:35:22.005 Amber Lin: Okay.
364 00:35:23.130 ⇒ 00:35:41.309 Amber Lin: I won’t. I would. I would like to help, because we have to say I can meet with Emily. I just don’t think I have time to meet with her today. Let’s push this back at least, like by Wednesday. I don’t think neither of either of us on the team have time to do this today, so it will not be done by tomorrow.
365 00:35:42.660 ⇒ 00:35:43.440 Caio Velasco: I agree.
366 00:35:43.810 ⇒ 00:35:47.160 Amber Lin: Yeah, let’s take this off.
367 00:35:54.680 ⇒ 00:35:55.893 Amber Lin: Good job.
368 00:35:58.480 ⇒ 00:36:02.509 Amber Lin: Okay, this is a later verification step.
369 00:36:02.970 ⇒ 00:36:07.020 Amber Lin: For, oh, where’s the decision?
370 00:36:16.550 ⇒ 00:36:17.620 Amber Lin: Anyways?
371 00:36:19.530 ⇒ 00:36:26.100 Amber Lin: Okay, that’s good. So we have list list solves.
372 00:36:30.350 ⇒ 00:36:31.530 Amber Lin: and
373 00:36:40.210 ⇒ 00:36:41.960 Amber Lin: oh, dear!
374 00:36:46.590 ⇒ 00:36:53.733 Amber Lin: Think we’ll let’s all dashboards you’ve got.
375 00:36:56.840 ⇒ 00:37:01.410 Amber Lin: I think, the next one after this.
376 00:37:02.850 ⇒ 00:37:10.179 Amber Lin: what would be once we list all dashboards with the usage stats. Do we go? Look at the
377 00:37:11.500 ⇒ 00:37:13.420 Amber Lin: it explores.
378 00:37:17.480 ⇒ 00:37:22.360 Caio Velasco: Rules remind me of what is the that one is to understand
379 00:37:23.380 ⇒ 00:37:30.310 Caio Velasco: what models are being used, or to to understand? If the dashboard is being used or not, or what is that about.
380 00:37:33.570 ⇒ 00:37:35.300 Demilade Agboola: What? Which are we? Which?
381 00:37:35.810 ⇒ 00:37:37.379 Demilade Agboola: What are we referring to right now?
382 00:37:37.690 ⇒ 00:37:42.660 Amber Lin: Yeah, Kyle, you sent something. Is that for the dashboards? Is that for the sources.
383 00:37:43.320 ⇒ 00:37:52.910 Caio Velasco: So yeah, I asked something different for the for this one. But what I sent in the chat. I was gonna mention that when I? Well, I took a lot of notes when she was in the
384 00:37:53.679 ⇒ 00:38:06.539 Caio Velasco: but when I was talking to her, and then I put on chat. This was like 3 pages of me writing basically during the meeting especially because we were not recording. And I think we should, because
385 00:38:07.420 ⇒ 00:38:10.100 Caio Velasco: the days like this, I can move really fast.
386 00:38:10.360 ⇒ 00:38:10.885 Amber Lin: Exactly.
387 00:38:11.410 ⇒ 00:38:12.340 Caio Velasco: Yeah.
388 00:38:12.672 ⇒ 00:38:23.300 Amber Lin: Ask I think if your meeting is transcribed you can be able to search the Zoom Meeting, and there will be a transcript that you can download, or you can use
389 00:38:23.808 ⇒ 00:38:30.090 Amber Lin: granola, which I use. But that needs to be paid for, or you can record with your phone.
390 00:38:30.210 ⇒ 00:38:34.770 Amber Lin: If you have an iphone, and then you can just press turn into transcript.
391 00:38:35.170 ⇒ 00:38:38.950 Amber Lin: So that would be really, really helpful. I record every single meeting.
392 00:38:39.910 ⇒ 00:38:44.319 Caio Velasco: Okay, yeah. Cause the one she sent was her gmail account.
393 00:38:44.320 ⇒ 00:38:44.860 Amber Lin: Hmm.
394 00:38:45.440 ⇒ 00:38:49.660 Caio Velasco: So then, when I tried to report, there was no, not permission for that as well.
395 00:38:49.660 ⇒ 00:38:50.770 Amber Lin: When was his first.st
396 00:38:50.770 ⇒ 00:38:51.970 Caio Velasco: Not too personal.
397 00:38:52.210 ⇒ 00:38:53.370 Demilade Agboola: Yeah, for sure.
398 00:38:53.370 ⇒ 00:38:58.499 Demilade Agboola: Ask Emily. I know Emily has like a note taking thing as well.
399 00:38:58.890 ⇒ 00:39:02.839 Demilade Agboola: So there is. I don’t know how detailed is, but I know she does
400 00:39:03.000 ⇒ 00:39:07.499 Demilade Agboola: have a way of track of like recording things that happen in meetings.
401 00:39:08.980 ⇒ 00:39:16.469 Caio Velasco: Okay, okay, I’ll talk. And when this layers were just like what whatever chat gave me in terms of like, what did we talk about in terms of the
402 00:39:17.040 ⇒ 00:39:18.380 Caio Velasco: at the end of the day, and so
403 00:39:19.668 ⇒ 00:39:26.661 Caio Velasco: more related to those things. You know, there are things that are because sources are super bad. There are things because of problems that they can not.
404 00:39:27.440 ⇒ 00:39:33.499 Caio Velasco: in terms of why an order is not breaking down by sub order or whatever right.
405 00:39:33.960 ⇒ 00:39:34.370 Amber Lin: I see.
406 00:39:34.370 ⇒ 00:39:36.720 Caio Velasco: All those things would point to deprecation.
407 00:39:37.184 ⇒ 00:39:59.379 Amber Lin: So do you think this makes sense of the I, if you can see my screen here so the layer one, you said technical usage audit. That will essentially be this right where we list all the dashboards and list all their usage. The second one is, we’re gonna look at the reliability which is, gonna be okay. We’re gonna look at. I don’t know if we’re gonna look at explores or Dvt. But ultimately we flag them by accuracy. So if they’re reliable.
408 00:39:59.784 ⇒ 00:40:05.335 Amber Lin: and then I think you bring on a really good point of these. I think we just need more tickets to
409 00:40:06.350 ⇒ 00:40:14.019 Amber Lin: like assign. I guess that’s like a assign dashboard owners or get business
410 00:40:14.320 ⇒ 00:40:19.859 Amber Lin: logic. Let me see if this is based on it
411 00:40:23.050 ⇒ 00:40:24.980 Amber Lin: inside dashboard. No,
412 00:40:28.640 ⇒ 00:40:33.040 Amber Lin: I think we identify.
413 00:40:35.650 ⇒ 00:40:39.069 Caio Velasco: I think we. Still we need a layer. We need to flag them.
414 00:40:39.240 ⇒ 00:40:44.430 Amber Lin: Before we do this right cause. There’s just too many for us to do.
415 00:40:44.970 ⇒ 00:40:45.490 Caio Velasco: Exactly.
416 00:40:45.550 ⇒ 00:40:48.459 Amber Lin: Yeah, let me say, flag.
417 00:40:49.681 ⇒ 00:40:52.740 Amber Lin: Great, we can do that.
418 00:40:55.750 ⇒ 00:41:00.140 Amber Lin: Hmm, there’s no problem.
419 00:41:02.380 ⇒ 00:41:03.240 Amber Lin: Alright. Hello.
420 00:41:04.140 ⇒ 00:41:09.886 Amber Lin: Okay, great. So we will flag that see you.
421 00:41:10.540 ⇒ 00:41:16.600 Amber Lin: and then we’ll be ready to interview. I guess we enter.
422 00:41:17.800 ⇒ 00:41:21.799 Amber Lin: What do we do here? Do we meet with them? Do we interview them?
423 00:41:22.750 ⇒ 00:41:25.370 Amber Lin: Deprioritized flat.
424 00:41:25.550 ⇒ 00:41:28.289 Caio Velasco: Bob, this this is a sorry go ahead.
425 00:41:29.070 ⇒ 00:41:33.940 Demilade Agboola: I was gonna say, once we once we flag that I think it would just present our findings to them.
426 00:41:35.630 ⇒ 00:41:38.570 Demilade Agboola: These are like what we’ve noticed, and then they can give.
427 00:41:38.570 ⇒ 00:41:39.120 Amber Lin: But.
428 00:41:39.120 ⇒ 00:41:40.300 Demilade Agboola: You know, whatever feedback.
429 00:41:41.150 ⇒ 00:41:46.190 Demilade Agboola: And if they have any, you know.
430 00:41:47.420 ⇒ 00:41:51.179 Demilade Agboola: Yeah, I think, yeah, we should just give them feedback on that. Also, I also think that.
431 00:41:52.171 ⇒ 00:41:53.829 Demilade Agboola: Just so that we don’t
432 00:41:53.980 ⇒ 00:42:03.420 Demilade Agboola: potentially have to redo certain things. I think we can also, even though we can talk about our methodology to the open stems team.
433 00:42:04.250 ⇒ 00:42:09.639 Demilade Agboola: They have any other ways in which they would consider like data access or not, we can just get back from them.
434 00:42:10.380 ⇒ 00:42:12.399 Demilade Agboola: We don’t go through a process.
435 00:42:12.560 ⇒ 00:42:18.280 Demilade Agboola: present our findings, and then they feel like, Hey, but did you consider this as well.
436 00:42:18.280 ⇒ 00:42:24.869 Amber Lin: Oh, so before we do this, or and as a step of this, we need to, I guess, verify
437 00:42:25.210 ⇒ 00:42:30.060 Amber Lin: verified logic before we start.
438 00:42:30.820 ⇒ 00:42:33.430 Demilade Agboola: Yeah, it’s like, maybe between tomorrow stand up, for instance, or.
439 00:42:34.300 ⇒ 00:42:43.908 Demilade Agboola: Like we just, it’s literally just a key question like, Hey, we want to look at our data this way and want to look at your dashboards. This way.
440 00:42:44.240 ⇒ 00:42:51.779 Amber Lin: So we should, I think. Yeah, based on what you said. We should list out our logic first, st
441 00:42:52.410 ⇒ 00:42:57.969 Amber Lin: like I don’t. I don’t think we know exactly what we’re doing or know how we’re gonna say it yet.
442 00:42:58.200 ⇒ 00:43:02.640 Demilade Agboola: Not necessarily like logic and sense of like these are the but just in the sense of
443 00:43:03.090 ⇒ 00:43:05.120 Demilade Agboola: we are very dashboard. Yeah.
444 00:43:05.510 ⇒ 00:43:05.979 Demilade Agboola: Is, that.
445 00:43:07.220 ⇒ 00:43:18.249 Demilade Agboola: Your Dbt models and look at the outputs and see how often they’re being used. And who’s querying them and all that stuff? And we’re gonna on my last word. Query that show all of that to you.
446 00:43:18.610 ⇒ 00:43:19.330 Amber Lin: Yeah.
447 00:43:19.510 ⇒ 00:43:23.029 Demilade Agboola: Write our like everything into like used.
448 00:43:23.510 ⇒ 00:43:26.109 Demilade Agboola: We’re going to like, create a matrix phase of.
449 00:43:26.110 ⇒ 00:43:26.570 Amber Lin: Yeah.
450 00:43:26.570 ⇒ 00:43:27.100 Demilade Agboola: Connect
451 00:43:27.240 ⇒ 00:43:35.769 Demilade Agboola: right? And so they hear that and go. Oh, potentially that sounds good, or it sounds good. But have you considered this as well, or
452 00:43:36.710 ⇒ 00:43:38.920 Demilade Agboola: we don’t. Don’t just go by task.
453 00:43:38.920 ⇒ 00:43:47.279 Amber Lin: You’re you’re really right. Okay, let me put this usage accuracy approach.
454 00:43:47.450 ⇒ 00:43:57.339 Amber Lin: Let me say, this is to do. We’re gonna do this tomorrow, I think, and then we’re gonna do 2 of these. And then after that, we’ll present our findings to them.
455 00:43:58.620 ⇒ 00:44:07.590 Amber Lin: and then for their findings and get sign off advanced verification. Oh, well.
456 00:44:08.080 ⇒ 00:44:17.690 Amber Lin: and then I guess we flag it after we’ve already, I guess. Flag, you know, present findings.
457 00:44:18.440 ⇒ 00:44:21.099 Amber Lin: and then I guess this is one of them.
458 00:44:25.440 ⇒ 00:44:26.230 Amber Lin: Gosh!
459 00:44:39.880 ⇒ 00:44:42.549 Amber Lin: And then for these we need to.
460 00:44:43.830 ⇒ 00:44:44.690 Amber Lin: And
461 00:44:50.880 ⇒ 00:44:52.749 Amber Lin: and then this is like a
462 00:44:54.630 ⇒ 00:44:58.330 Amber Lin: next step for the use and inaccurate ones.
463 00:44:58.930 ⇒ 00:45:09.849 Amber Lin: And then we have unused, inaccurate that we can also deprecate.
464 00:45:15.000 ⇒ 00:45:20.240 Amber Lin: I guess we have each one we have like things we are going to do.
465 00:45:23.077 ⇒ 00:45:28.950 Amber Lin: There was this that was, I think that was identified last time. Is this for, Looker?
466 00:45:29.850 ⇒ 00:45:33.269 Amber Lin: I think this was, for I don’t know what this was for.
467 00:45:53.890 ⇒ 00:45:54.730 Amber Lin: Okay.
468 00:46:03.070 ⇒ 00:46:04.260 Amber Lin: sounds good.
469 00:46:05.551 ⇒ 00:46:15.050 Amber Lin: I think that’s for Looker. I think that’s for that will be tomorrow.
470 00:46:15.430 ⇒ 00:46:16.240 Amber Lin: No.
471 00:46:23.890 ⇒ 00:46:35.109 Amber Lin: and I think these 2 we can do in this. Do you think we can do these in the site at least start these in the cycle, we technically have another. We have another week essentially.
472 00:46:39.320 ⇒ 00:46:42.150 Caio Velasco: Yeah, right? I think so. Let’s see.
473 00:46:42.650 ⇒ 00:46:47.160 Caio Velasco: at least from what I understood, the 1st one would be the miller, the second one.
474 00:46:47.330 ⇒ 00:46:49.510 Caio Velasco: I could be involved with that brand.
475 00:46:50.780 ⇒ 00:46:51.490 Amber Lin: Hmm.
476 00:46:51.770 ⇒ 00:46:57.100 Demilade Agboola: Yeah, so we can always start. I don’t know about finishing, but we can always start in the cycle.
477 00:46:59.848 ⇒ 00:47:02.920 Amber Lin: ideally, we should have a finish date.
478 00:47:03.510 ⇒ 00:47:13.750 Amber Lin: We can break them up further, if we want an exact oh, wrong person, wrong thing if we want. If we like. If we want to finish day, we can break them up further.
479 00:47:25.040 ⇒ 00:47:27.360 Amber Lin: This is for feel like, maybe
480 00:47:27.700 ⇒ 00:47:35.189 Amber Lin: ideally, we can achieve this by the end of the cycle, because that wouldn’t take too long. If we have the verify accuracy and very like
481 00:47:35.730 ⇒ 00:47:37.280 Amber Lin: we’ll get usage.
482 00:47:46.085 ⇒ 00:47:49.859 Amber Lin: Okay, we’ll do those later
483 00:47:50.610 ⇒ 00:47:53.990 Amber Lin: and alright, that’s that’s for looker,
484 00:47:56.800 ⇒ 00:47:58.779 Amber Lin: Now for redshift.
485 00:48:00.240 ⇒ 00:48:06.020 Amber Lin: How is this one? We’re still waiting for polyatomic right?
486 00:48:10.292 ⇒ 00:48:13.107 Demilade Agboola: Yes, still kind of waiting for polytomic
487 00:48:14.340 ⇒ 00:48:16.240 Demilade Agboola: To be honest, I’ll be honest
488 00:48:16.880 ⇒ 00:48:25.609 Demilade Agboola: my most productive day. I had a lot of I moved houses last week. So yeah, a lot of things around that. But I am.
489 00:48:25.610 ⇒ 00:48:26.430 Amber Lin: Absolutely.
490 00:48:27.070 ⇒ 00:48:28.439 Demilade Agboola: This week, my my wife.
491 00:48:28.440 ⇒ 00:48:28.879 Amber Lin: Oh, my! Gosh!
492 00:48:28.880 ⇒ 00:48:32.850 Demilade Agboola: So like, yeah, I should make some progress with, of course.
493 00:48:32.850 ⇒ 00:48:37.780 Amber Lin: Wait down a lot that you say your wife did you get married? Did you just get married?
494 00:48:38.490 ⇒ 00:48:45.889 Demilade Agboola: Well, the very like niche joke, but actually wi-fi
495 00:48:46.830 ⇒ 00:48:48.262 Amber Lin: Oh, my God!
496 00:48:49.140 ⇒ 00:48:56.240 Demilade Agboola: Joke I was going to make was I’m from a tribe in Nigeria, called the Yoruba tribe, and then of
497 00:48:56.480 ⇒ 00:49:10.109 Demilade Agboola: jokes about how Yoruba men are like heartbreakers, and like they would have a wife. And you have no idea about it. That was and say, Oh, I’m Yoruba man, after all so, but
498 00:49:10.290 ⇒ 00:49:14.450 Demilade Agboola: it was, too. It was too niche. I I realized I would have to explain the joke so.
499 00:49:14.450 ⇒ 00:49:17.729 Amber Lin: Oh, that’s so funny.
500 00:49:19.320 ⇒ 00:49:32.799 Amber Lin: Okay, okay? Anyways, yeah, I’m also like, last week I was traveling a lot. Wednesday. I was on the plane for 6 h. So I’m also like all over the place. But I think this week we’ll get it together, and we’re we’re good.
501 00:49:33.070 ⇒ 00:49:40.010 Amber Lin: So, and this one is stuck on polyatomic. If if it will be great, if we can ask them how the progress is. I just remind them.
502 00:49:40.240 ⇒ 00:49:41.759 Amber Lin: do you think we can do that?
503 00:49:43.741 ⇒ 00:49:53.070 Demilade Agboola: Yes, actually, I just checked, and I discovered that I was the last person to. I was the person I needed to respond. I just responded, now, actually.
504 00:49:53.070 ⇒ 00:49:57.839 Amber Lin: Okay, sounds good. So this is like this is this is going.
505 00:49:58.010 ⇒ 00:50:05.879 Amber Lin: Let’s go back to redshift. We need to list all the tables moving that to development.
506 00:50:07.270 ⇒ 00:50:15.140 Amber Lin: And okay, enlist all of it. We run a query, right? That’s the next step.
507 00:50:24.460 ⇒ 00:50:31.179 Amber Lin: and then we send, oh dear, where is it.
508 00:50:34.410 ⇒ 00:50:36.219 Amber Lin: And then we send.
509 00:51:02.610 ⇒ 00:51:12.160 Amber Lin: okay? And then we blog for decisions.
510 00:51:16.900 ⇒ 00:51:20.899 Amber Lin: Yeah, this is there anything else that
511 00:51:22.990 ⇒ 00:51:26.710 Amber Lin: you guys come to your mind when we’re talking about Redshift.
512 00:51:28.070 ⇒ 00:51:30.659 Amber Lin: We have all these steps, I believe.
513 00:51:31.380 ⇒ 00:51:34.020 Amber Lin: Oh, not that one, this one!
514 00:51:40.720 ⇒ 00:51:48.750 Amber Lin: Oh, that this one we need to understand. What they mean
515 00:51:50.380 ⇒ 00:51:53.790 Amber Lin: is that before we run the query, or after we run the query.
516 00:51:55.825 ⇒ 00:51:59.660 Caio Velasco: So this was just me. At the end of the day I was just trying to. Well.
517 00:52:00.040 ⇒ 00:52:01.330 Caio Velasco: well, let me restart
518 00:52:01.740 ⇒ 00:52:10.920 Caio Velasco: from everything that we’re doing today. It’s clear that it’s as if we were building a framework to how to deprecate dashboard with brain force. Right.
519 00:52:11.040 ⇒ 00:52:11.430 Amber Lin: Yeah.
520 00:52:11.430 ⇒ 00:52:17.319 Caio Velasco: Basically what we were doing. So then, when I was starting with this last week, I was like, Okay, I need to list off tables, I need to kind
521 00:52:17.710 ⇒ 00:52:21.299 Caio Velasco: and what they mean. So this was just part of the process of doing the job.
522 00:52:22.250 ⇒ 00:52:27.189 Caio Velasco: I don’t know it’s, if it’s needed or not. But I mean, how come? How can you deprecate a source or.
523 00:52:27.190 ⇒ 00:52:28.130 Amber Lin: Without knowing what it is.
524 00:52:28.130 ⇒ 00:52:29.999 Caio Velasco: Don’t even exactly.
525 00:52:30.923 ⇒ 00:52:32.490 Caio Velasco: But yeah.
526 00:52:33.277 ⇒ 00:52:46.630 Amber Lin: Do you think it’ll be helpful because there’s just so many we need to do? Do you think it’s helpful we do it after we run the query at least to see, okay, let’s start from the most used ones to list to at least understand what they mean. Okay.
527 00:52:46.630 ⇒ 00:52:47.000 Caio Velasco: Yeah.
528 00:52:47.000 ⇒ 00:52:48.910 Amber Lin: Let let me.
529 00:52:50.440 ⇒ 00:52:51.120 Amber Lin: Okay.
530 00:52:51.720 ⇒ 00:52:52.950 Amber Lin: Alright.
531 00:52:53.050 ⇒ 00:52:55.189 Amber Lin: Ready for development.
532 00:52:55.650 ⇒ 00:52:56.810 Amber Lin: Great?
533 00:52:58.970 ⇒ 00:52:59.880 Amber Lin: Yeah.
534 00:53:01.220 ⇒ 00:53:02.680 Amber Lin: Oh, great.
535 00:53:03.740 ⇒ 00:53:11.640 Amber Lin: I feel like this that move to this.
536 00:53:11.980 ⇒ 00:53:13.369 Amber Lin: So I think we can
537 00:53:15.040 ⇒ 00:53:23.689 Amber Lin: like these 3 is already. I feel like that’s plenty for the cycle. But I don’t know how long they would take.
538 00:53:24.390 ⇒ 00:53:29.419 Amber Lin: I need you guys, my amazing engineers to tell me how long they will take us. I have no clue.
539 00:53:31.040 ⇒ 00:53:31.590 Caio Velasco: So
540 00:53:32.980 ⇒ 00:53:46.959 Caio Velasco: list of active ingestion tools tomorrow. Hopefully. Yes, I think if I go into redshift as and run a query, I think that should be easy.
541 00:53:47.910 ⇒ 00:53:48.530 Amber Lin: Hmm!
542 00:53:48.880 ⇒ 00:53:56.329 Amber Lin: So that you say would be after tomorrow. Do you think that would be like 2 2 days? Do you think it’ll be
543 00:53:56.570 ⇒ 00:54:01.020 Amber Lin: like? When would that be available?
544 00:54:01.470 ⇒ 00:54:07.430 Caio Velasco: Tomorrow end of the day, because then I I finish one thing, and then I start the other and the other should be finished. I think.
545 00:54:07.960 ⇒ 00:54:13.710 Amber Lin: Oh, dear! Let me let me give you another day. Tomorrow we’ll do that.
546 00:54:13.710 ⇒ 00:54:15.410 Amber Lin: It’s probably us or.
547 00:54:15.410 ⇒ 00:54:16.745 Caio Velasco: I’m always optimistic.
548 00:54:17.190 ⇒ 00:54:22.355 Amber Lin: like.
549 00:54:26.780 ⇒ 00:54:43.310 Amber Lin: like you say, Wednesday, yeah, sure, please. Yeah, these 2 can be done concurrent concurrently.
550 00:54:44.420 ⇒ 00:54:49.259 Amber Lin: because we just need to send it to them. And that’s kinda like on them. Once we send it.
551 00:54:50.110 ⇒ 00:54:58.251 Amber Lin: So once we have, yeah, so we can send, I’ll just say, this is for end of cycle
552 00:54:59.520 ⇒ 00:55:08.920 Amber Lin: end of this cycle that one probably also like.
553 00:55:10.000 ⇒ 00:55:13.890 Amber Lin: maybe I’ll say, this is for Monday, anyways.
554 00:55:14.940 ⇒ 00:55:24.699 Amber Lin: Yeah, old yeah, I think we have good enough. Plenty for this cycle
555 00:55:26.310 ⇒ 00:55:37.759 Amber Lin: probably will have to extend beyond this cycle, but I don’t know how long these would take. So that’s we have redshift and looker down pretty well, I think, for inventory. We have
556 00:55:38.562 ⇒ 00:55:39.950 Amber Lin: a few things.
557 00:55:41.420 ⇒ 00:55:44.260 Amber Lin: Do you guys still have time? We can come back later.
558 00:55:45.550 ⇒ 00:55:48.379 Caio Velasco: I I can say a bit more if I needed.
559 00:55:48.660 ⇒ 00:55:55.649 Amber Lin: Okay, I’m free. I think. I I technically have a climbing, but don’t know if she’s joining.
560 00:56:00.730 ⇒ 00:56:05.540 Amber Lin: Devon. Is this something that we are going to start today for the.
561 00:56:08.340 ⇒ 00:56:08.910 Demilade Agboola: Yes.
562 00:56:09.120 ⇒ 00:56:10.340 Amber Lin: Oh, okay.
563 00:56:10.750 ⇒ 00:56:13.460 Amber Lin: And we said this was, gonna take
564 00:56:17.020 ⇒ 00:56:24.219 Amber Lin: like, until Wednesday. You said 2 days here, okay, Wednesday.
565 00:56:25.297 ⇒ 00:56:36.679 Amber Lin: We need to flush out that ticket. I really don’t know what’s what this, what needs to go into this. So if you can just work with Gpt to make a ticket that will be awesome.
566 00:56:39.120 ⇒ 00:56:48.130 Amber Lin: Is that, oh, okay, are we doing this one? Or is that.
567 00:56:48.320 ⇒ 00:56:49.599 Demilade Agboola: Buy a ticket, though.
568 00:56:49.980 ⇒ 00:56:50.590 Amber Lin: Huh!
569 00:56:50.970 ⇒ 00:56:53.880 Demilade Agboola: That already I’m like, do you mean a description, or do you mean.
570 00:56:53.880 ⇒ 00:57:00.970 Amber Lin: Oh, yeah, just goal and acceptance criteria, essentially like what? What defines done?
571 00:57:01.180 ⇒ 00:57:06.700 Amber Lin: Cause, I don’t want us to have to like do this. And then we add a little bit more and add a little bit more.
572 00:57:06.870 ⇒ 00:57:08.830 Amber Lin: just like just a quick blurb.
573 00:57:09.010 ⇒ 00:57:17.679 Amber Lin: Cause you’re doing this. So it’s technically it’s for you. But if you want to write down what makes this ticket done that would be awesome.
574 00:57:18.070 ⇒ 00:57:18.910 Demilade Agboola: Okay. Sure.
575 00:57:18.910 ⇒ 00:57:19.410 Amber Lin: Yeah.
576 00:57:21.023 ⇒ 00:57:26.089 Amber Lin: I’ll just send another 2 flesh out tickets.
577 00:57:27.020 ⇒ 00:57:28.040 Amber Lin: All right.
578 00:57:29.100 ⇒ 00:57:35.740 Amber Lin: I don’t remember if we’re still doing this, I know that we want. That
579 00:57:38.360 ⇒ 00:57:46.190 Amber Lin: is this covered incremental logic freshness test. Is this covered by any of these? Or should we move it later.
580 00:57:48.240 ⇒ 00:57:58.830 Demilade Agboola: Oh, no, that’s different. I mean, we’ll take well, technically, when we’re doing the redesign. We can like the incremental logic and freshness test for that.
581 00:57:58.830 ⇒ 00:57:59.229 Amber Lin: Doesn’t mine.
582 00:58:00.583 ⇒ 00:58:10.780 Demilade Agboola: So I guess it can be a subset of the model model and logic redesign, or Ms redeliveries. So us 79 basically.
583 00:58:11.190 ⇒ 00:58:19.619 Amber Lin: Hmm! But would it? I I guess that’s why we also want the to define the ticket. Is that something you want to add in that ticket, because it adds some work
584 00:58:19.910 ⇒ 00:58:21.749 Amber Lin: to what you already have.
585 00:58:22.600 ⇒ 00:58:28.210 Demilade Agboola: Yeah, I, if I remember correctly, I believe this was pre mother’s day.
586 00:58:28.600 ⇒ 00:58:29.910 Amber Lin: Oh!
587 00:58:30.160 ⇒ 00:58:34.920 Demilade Agboola: And so this is like, so post mother’s day, as we’re rebuilding out the infrastructure.
588 00:58:35.830 ⇒ 00:58:45.279 Demilade Agboola: It’s no longer like. If if fixed, or a patch to certain things kind of like, we’re rebuilding things, and as we’re rebuilding them. We can then add.
589 00:58:46.191 ⇒ 00:58:49.650 Demilade Agboola: the things that we’re missing in the 1st place, which is kind of why, that’s it.
590 00:58:49.950 ⇒ 00:58:50.930 Demilade Agboola: Lots.
591 00:58:51.130 ⇒ 00:58:52.510 Amber Lin: Yeah, I see.
592 00:58:53.850 ⇒ 00:58:57.329 Amber Lin: So we’re adding it. So should I. Just
593 00:59:01.070 ⇒ 00:59:06.379 Amber Lin: keep this as a different one, or put it here.
594 00:59:06.630 ⇒ 00:59:07.300 Amber Lin: Yeah.
595 00:59:08.290 ⇒ 00:59:16.140 Demilade Agboola: I’m not exactly sure how like linear works. But I know, like with Jared is a way you could make it like a sub task or something.
596 00:59:16.140 ⇒ 00:59:23.570 Amber Lin: I I know. I think a long time ago we decided not to do subtest, because it just gets really like it’s a lot of layers.
597 00:59:24.795 ⇒ 00:59:31.320 Amber Lin: But I’ll I’ll I understand, like this is just relate like these 3 are kind of related essentially.
598 00:59:35.752 ⇒ 00:59:39.370 Demilade Agboola: Sorry. What? 3, 79, 48, and.
599 00:59:39.760 ⇒ 00:59:48.160 Amber Lin: 40. Yeah, Us. 48. Is that just part of like this ticket? And this ticket.
600 00:59:49.200 ⇒ 00:59:49.600 Demilade Agboola: Hello!
601 00:59:49.600 ⇒ 00:59:50.270 Amber Lin: That’s right.
602 00:59:50.960 ⇒ 00:59:54.590 Demilade Agboola: So 80 is is different, though it is.
603 00:59:54.590 ⇒ 00:59:56.499 Amber Lin: And 80 is just an audit.
604 00:59:56.920 ⇒ 00:59:57.360 Demilade Agboola: Yeah.
605 00:59:57.360 ⇒ 00:59:58.989 Amber Lin: I see, I see.
606 00:59:58.990 ⇒ 01:00:03.410 Demilade Agboola: Even in in terms of like the logic of it, Haiti is still different, because
607 01:00:04.322 ⇒ 01:00:08.120 Demilade Agboola: I know, since, like mother’s day.
608 01:00:08.120 ⇒ 01:00:08.559 Amber Lin: Of the month.
609 01:00:08.560 ⇒ 01:00:12.410 Demilade Agboola: Emily has been pushing some updates to certain things.
610 01:00:12.550 ⇒ 01:00:18.019 Demilade Agboola: and even when I, when I had a call with her last week and I had. I did a quick perusal of stuff.
611 01:00:18.430 ⇒ 01:00:23.189 Demilade Agboola: I really she has made some changes that don’t fit into like Dbt. Best practices.
612 01:00:24.480 ⇒ 01:00:26.779 Demilade Agboola: Mine like part of the work we’ve done.
613 01:00:26.980 ⇒ 01:00:32.620 Demilade Agboola: So being able to like audit that, and just being able to like, Hey, we need to move into this way, change that that way.
614 01:00:32.620 ⇒ 01:00:33.120 Amber Lin: Yeah.
615 01:00:33.530 ⇒ 01:00:36.950 Demilade Agboola: This that way. That’s kind of. So it’s not even like
616 01:00:37.430 ⇒ 01:00:43.940 Demilade Agboola: the lower levels, which is what 79 is about 80 is just talking about
617 01:00:44.170 ⇒ 01:00:47.539 Demilade Agboola: the like. Just the what we did basically.
618 01:00:47.850 ⇒ 01:00:50.960 Amber Lin: I see, I see. So that’s a like a retro.
619 01:00:51.320 ⇒ 01:00:54.929 Demilade Agboola: Yes. Well, yeah, er, Rachel, but we from like a technical perspective.
620 01:00:55.800 ⇒ 01:01:04.550 Amber Lin: Okay, in terms of these, I think, I remember the inventory supply chain folks, last.
621 01:01:05.536 ⇒ 01:01:08.900 Amber Lin: after that, I guess we’re are we
622 01:01:09.350 ⇒ 01:01:15.180 Amber Lin: doing like, say, for example, in this cycle, what would we be doing in these tickets?
623 01:01:20.125 ⇒ 01:01:25.560 Demilade Agboola: So in this cycle, we will be
624 01:01:27.450 ⇒ 01:01:32.480 Demilade Agboola: trying to redefine the flow of inventory data, and like how the models run.
625 01:01:34.080 ⇒ 01:01:36.549 Demilade Agboola: And so we will start from the ground up.
626 01:01:37.260 ⇒ 01:01:42.570 Demilade Agboola: and oms redelivery and oms sub orders are 2 very heavy models.
627 01:01:42.570 ⇒ 01:01:43.449 Amber Lin: Where’s the problem?
628 01:01:43.450 ⇒ 01:01:47.230 Demilade Agboola: They come, they are foundational models for how we calculate inventory.
629 01:01:49.120 ⇒ 01:01:54.740 Demilade Agboola: So being able to redefine them and just be redesign them, and just be able to understand
630 01:01:55.416 ⇒ 01:02:01.290 Demilade Agboola: how they are built. Some of the logic is really still kind of.
631 01:02:01.290 ⇒ 01:02:02.609 Amber Lin: See here. So yeah.
632 01:02:02.610 ⇒ 01:02:06.099 Demilade Agboola: Been doing a patch job on it rather than just like breaking it apart, and just.
633 01:02:07.690 ⇒ 01:02:09.370 Amber Lin: I, see, I, see.
634 01:02:09.420 ⇒ 01:02:11.779 Demilade Agboola: What what I’m going to be doing.
635 01:02:11.780 ⇒ 01:02:22.140 Amber Lin: I see. So this feels like quite a big quite a bit ticket that we probably need to break up. We’ll just do some issues here, and then
636 01:02:26.940 ⇒ 01:02:30.459 Amber Lin: I don’t know what you guys think of what should go in there.
637 01:02:32.930 ⇒ 01:02:38.820 Amber Lin: This is the webinar try is not here.
638 01:02:47.090 ⇒ 01:02:47.860 Amber Lin: It’s
639 01:02:55.040 ⇒ 01:02:59.750 Amber Lin: so I guess we need to audit we need to.
640 01:03:00.500 ⇒ 01:03:06.409 Amber Lin: It’s it’s but build new models.
641 01:03:06.810 ⇒ 01:03:07.650 Amber Lin: Got it.
642 01:03:12.100 ⇒ 01:03:18.880 Amber Lin: Yeah, yeah, I guess that’s like, I don’t know where this would go into.
643 01:03:21.750 ⇒ 01:03:27.210 Amber Lin: Okay, do you think this this should be broken up like freshness tests would be a different thing
644 01:03:27.800 ⇒ 01:03:30.480 Amber Lin: like, do you think freshness test should just be here.
645 01:03:32.970 ⇒ 01:03:34.745 Demilade Agboola: But.
646 01:03:37.030 ⇒ 01:03:43.290 Amber Lin: Oh, they’re different. Okay, so I’ll just say, freshness test.
647 01:03:49.280 ⇒ 01:03:50.630 Amber Lin: That would be better
648 01:04:02.040 ⇒ 01:04:04.510 Amber Lin: should not be later or should not be.
649 01:04:04.700 ⇒ 01:04:07.209 Amber Lin: It did not end these.
650 01:04:07.850 ⇒ 01:04:08.380 Amber Lin: Yeah.
651 01:04:08.380 ⇒ 01:04:13.989 Demilade Agboola: So I mean, we could. We could relate so technically, it would come while I’m building your model.
652 01:04:14.520 ⇒ 01:04:21.689 Amber Lin: So I would just say, Yeah, I’m gonna take this.
653 01:04:22.330 ⇒ 01:04:29.010 Amber Lin: And for the in here it’s
654 01:04:29.120 ⇒ 01:04:31.149 Amber Lin: is that how it should be.
655 01:04:32.541 ⇒ 01:04:33.870 Demilade Agboola: Yeah, sure, that’s fine. That’s good.
656 01:04:33.870 ⇒ 01:04:35.450 Amber Lin: Sounds good.
657 01:04:35.680 ⇒ 01:04:37.220 Amber Lin: I’m just late.
658 01:04:37.750 ⇒ 01:04:42.159 Amber Lin: Okay, so I guess auditing this probably comes earlier.
659 01:04:42.310 ⇒ 01:04:44.589 Amber Lin: This probably we can do in cycle.
660 01:04:48.870 ⇒ 01:04:54.380 Amber Lin: I don’t know if this goes before that like. Is this the better?
661 01:04:55.690 ⇒ 01:04:58.660 Amber Lin: Or is this also part of the build new models.
662 01:04:59.210 ⇒ 01:05:00.040 Demilade Agboola: Oh no!
663 01:05:00.040 ⇒ 01:05:00.870 Amber Lin: Okay.
664 01:05:00.870 ⇒ 01:05:04.870 Demilade Agboola: It. It kind of depends on the flow, but like right, the way I want to do it now.
665 01:05:05.090 ⇒ 01:05:09.880 Amber Lin: Hmm, okay. Sounds good. I feel like building models. Gonna take a long time.
666 01:05:10.350 ⇒ 01:05:13.050 Amber Lin: If you want to break that down. That would be great.
667 01:05:13.440 ⇒ 01:05:19.909 Demilade Agboola: Yeah. Can you? Can you flip it around like how it was before so spoilage? And she was looking for.
668 01:05:20.110 ⇒ 01:05:20.970 Amber Lin: Of course.
669 01:05:23.580 ⇒ 01:05:29.649 Amber Lin: So that will be so. That will also be like, I’ll say, ready for development.
670 01:05:31.780 ⇒ 01:05:33.160 Amber Lin: And
671 01:05:54.470 ⇒ 01:05:56.990 Amber Lin: yeah, okay,
672 01:06:01.650 ⇒ 01:06:11.869 Amber Lin: let me go really quick and check on my client meeting. I will be back. If that’s not happening, give me 2 min. I’ll text you guys, if I need to need to hop.
673 01:06:13.270 ⇒ 01:06:14.020 Amber Lin: Okay.