Meeting Title: US x BF | Grooming Date: 2025-07-16 Meeting participants: Emily Giant, Caio Velasco, Uttam Kumaran, Amber Lin, Demilade Agboola
WEBVTT
1 00:00:49.430 ⇒ 00:00:51.140 Caio Velasco: Hello! Again.
2 00:00:52.360 ⇒ 00:00:53.709 Emily Giant: Hello! Again.
3 00:00:54.830 ⇒ 00:01:04.883 Emily Giant: I have a crazy. I didn’t realize this when we logged on this morning. My entire day is like meetings. I only have like 30 min between every meeting.
4 00:01:05.960 ⇒ 00:01:09.650 Caio Velasco: So difficult to switch contests all the time.
5 00:01:09.930 ⇒ 00:01:13.549 Caio Velasco: I have to go back to work and meeting work and meeting. I cannot do that.
6 00:01:13.720 ⇒ 00:01:17.800 Emily Giant: Me neither. Well, it’s just our job. You can’t get anything done.
7 00:01:18.050 ⇒ 00:01:22.209 Caio Velasco: Yeah, exactly, was mentioned. The same thing like last week.
8 00:01:22.440 ⇒ 00:01:33.809 Emily Giant: Yeah, it’s fine when it’s like what we were doing where you like are blocked on your work unless you have the meeting. That’s fine. I don’t mind those. It’s when you’re not actually like
9 00:01:34.940 ⇒ 00:01:43.649 Emily Giant: actioning things in the meeting. They’re like touch bases and all hands meetings that I’m like, oh, my God!
10 00:01:44.360 ⇒ 00:01:45.759 Emily Giant: Let us be free!
11 00:01:46.460 ⇒ 00:01:56.499 Caio Velasco: True. True. No, I totally get it. It’s frustrating for me, too, although I don’t know how days I have the last meeting that I used to have before. But yeah, I remember.
12 00:01:57.090 ⇒ 00:01:59.440 Emily Giant: Yeah, today is definitely like
13 00:02:00.080 ⇒ 00:02:09.030 Emily Giant: I was just complaining. Believe it or not. To Kyle, about my whole day is like meetings, and usually like I have.
14 00:02:09.389 ⇒ 00:02:27.909 Emily Giant: I like block off my afternoon so that I can just work because this kind of work you like cannot have meetings and get things done so it just one of those days where none of them are like productive meetings. Outside of this, and then the working session. The rest are like all hands, meetings like.
15 00:02:27.910 ⇒ 00:02:28.550 Amber Lin: Okay.
16 00:02:28.550 ⇒ 00:02:31.970 Emily Giant: Oh, my God, it’s okay, though.
17 00:02:32.470 ⇒ 00:02:35.589 Emily Giant: I’ll just drink lots of coffee and get like real crazy.
18 00:02:36.940 ⇒ 00:02:39.359 Uttam Kumaran: Yeah, that’s me, too. Coffee day today.
19 00:02:39.360 ⇒ 00:02:39.830 Emily Giant: Yeah.
20 00:02:40.600 ⇒ 00:02:41.690 Uttam Kumaran: Coffee day.
21 00:02:41.690 ⇒ 00:02:42.120 Uttam Kumaran: If.
22 00:02:45.560 ⇒ 00:02:57.359 Amber Lin: Hi! Everyone is still stuck in the Ada meeting, and he also told me that he can only join for 15 min, so I’m glad we talked to you.
23 00:02:57.360 ⇒ 00:02:58.840 Uttam Kumaran: Cover, yeah.
24 00:02:59.120 ⇒ 00:03:14.669 Amber Lin: Awesome. So the main purpose of this grooming last time we went through a lot of inventory. But right now we have some new tickets from Felipe that we that I want us to address and look at.
25 00:03:14.850 ⇒ 00:03:22.960 Amber Lin: Let me see if oh, so
26 00:03:23.370 ⇒ 00:03:35.240 Amber Lin: let me share screen. We can go through them, and then, share screens, urban stems, projects.
27 00:03:38.720 ⇒ 00:03:44.409 Amber Lin: Sorry today, 16th and I let it
28 00:03:45.830 ⇒ 00:03:49.290 Amber Lin: generate tickets as we. As we look here.
29 00:03:50.090 ⇒ 00:03:57.059 Amber Lin: So here’s the ones at the bottom are the ones that already created. Based on.
30 00:03:58.790 ⇒ 00:04:06.039 Amber Lin: based on Felipe’s requests. Emily, can you help me see which ones are needed, and which ones are not.
31 00:04:06.040 ⇒ 00:04:06.870 Emily Giant: Yes.
32 00:04:07.780 ⇒ 00:04:11.500 Emily Giant: Oh, sorry. I just had to lift a cat. Yes.
33 00:04:12.350 ⇒ 00:04:18.370 Emily Giant: he’s not even supposed to be in here. He’s our outdoor cat. Okay? So
34 00:04:18.690 ⇒ 00:04:24.200 Emily Giant: netsuite dash records mismatch. That’s like an older ticket that’s probably
35 00:04:24.380 ⇒ 00:04:28.254 Emily Giant: able to be closed down into
36 00:04:30.370 ⇒ 00:04:33.559 Emily Giant: one of the other inventory tickets.
37 00:04:35.950 ⇒ 00:04:39.540 Emily Giant: Okay, so clean up historical data tests
38 00:04:40.060 ⇒ 00:04:45.209 Emily Giant: that one update looker to includes. Okay, that one is
39 00:04:45.590 ⇒ 00:04:50.689 Emily Giant: 2 0, 7 can be me. And that’s like already in pr review.
40 00:04:50.970 ⇒ 00:04:53.280 Amber Lin: Oh, so that’s like a duplicate.
41 00:04:56.960 ⇒ 00:04:58.010 Emily Giant: So
42 00:04:59.610 ⇒ 00:05:06.995 Emily Giant: to put an umbrella over all these new tickets, when I built the hard good mart instead of just building a hardgood mart. I built out
43 00:05:08.290 ⇒ 00:05:13.824 Emily Giant: like an inventory adjustment table and an inventory lot table. And it’s going to
44 00:05:17.190 ⇒ 00:05:20.430 Emily Giant: It’s gonna kill most of these tickets when we deploy it
45 00:05:21.028 ⇒ 00:05:25.709 Emily Giant: strategy, so that one is definitely needed, 2 on one.
46 00:05:26.870 ⇒ 00:05:32.640 Amber Lin: So 2, 1 1. So the other 2 Hi!
47 00:05:32.640 ⇒ 00:05:32.990 Emily Giant: Hey!
48 00:05:32.990 ⇒ 00:05:40.770 Amber Lin: The other 2. We already kind of have in essentially. Npr. Review.
49 00:05:42.376 ⇒ 00:05:43.309 Amber Lin: Anything with.
50 00:05:43.310 ⇒ 00:05:50.870 Emily Giant: Discrepancies is going to be in that review, or will be fixed before I deploy it.
51 00:05:50.870 ⇒ 00:06:02.049 Amber Lin: I see. So I will just say that both of these are duplicates and then
52 00:06:07.310 ⇒ 00:06:15.760 Amber Lin: and the so we need that one. What about 2, 12.
53 00:06:17.340 ⇒ 00:06:17.680 Emily Giant: Every.
54 00:06:17.680 ⇒ 00:06:24.379 Demilade Agboola: So I mean, yeah, I guess. Like, that’s part of what we’re doing with the Qa.
55 00:06:24.960 ⇒ 00:06:29.160 Demilade Agboola: I mean review and just do it feels it’s fine.
56 00:06:29.450 ⇒ 00:06:36.609 Demilade Agboola: 2, 1, 3. I think we’ve done that. Isn’t that like? Logically added to the market venture.
57 00:06:37.160 ⇒ 00:06:42.139 Emily Giant: Yep, yeah, that’s all part of I’m very, or the Pr review.
58 00:06:42.140 ⇒ 00:06:42.860 Amber Lin: Oh!
59 00:06:44.410 ⇒ 00:06:48.209 Demilade Agboola: 2, 1, 4. I think that’s still active, because it appears the numbers.
60 00:06:48.210 ⇒ 00:06:48.770 Emily Giant: Yeah.
61 00:06:48.770 ⇒ 00:06:53.889 Demilade Agboola: Don’t necessarily match what we’re getting out of our Dvt model. So there is a disparity
62 00:06:54.020 ⇒ 00:06:55.660 Demilade Agboola: in that. So yes.
63 00:06:55.660 ⇒ 00:06:56.160 Amber Lin: Okay.
64 00:06:56.160 ⇒ 00:07:01.290 Demilade Agboola: I think that should be maybe high priority. That’s like we just want to be able to figure that out right now.
65 00:07:01.670 ⇒ 00:07:02.150 Amber Lin: Okay.
66 00:07:02.150 ⇒ 00:07:02.700 Demilade Agboola: So.
67 00:07:02.700 ⇒ 00:07:05.720 Amber Lin: This need, this actually needs to be in cycle.
68 00:07:06.615 ⇒ 00:07:07.510 Demilade Agboola: Yes.
69 00:07:07.510 ⇒ 00:07:16.340 Amber Lin: Okay, so let me add that there, I know we have this one as low priority and cycle. Do we want to take that out.
70 00:07:18.010 ⇒ 00:07:18.780 Demilade Agboola: Yeah, sure.
71 00:07:19.080 ⇒ 00:07:19.910 Amber Lin: Okay.
72 00:07:20.130 ⇒ 00:07:20.860 Emily Giant: Done a lot of.
73 00:07:20.860 ⇒ 00:07:21.280 Amber Lin: Cool.
74 00:07:21.280 ⇒ 00:07:31.609 Emily Giant: Can I? I haven’t deployed this fixes yet, because I’m just doing a little bit of cleanup before can we wait to do the one that
75 00:07:31.950 ⇒ 00:07:40.410 Emily Giant: we just cited as moving to the cycle to like midday today. So that I can deploy this, I just want to make sure that, like
76 00:07:40.980 ⇒ 00:07:47.240 Emily Giant: once the changes are deployed, the problem still exists because it may go away.
77 00:07:49.570 ⇒ 00:07:51.080 Emily Giant: I doubt it, but.
78 00:07:51.570 ⇒ 00:07:54.260 Demilade Agboola: We want you to do a post investigation, and then, like.
79 00:07:54.450 ⇒ 00:07:57.640 Emily Giant: Have it all just change when we deploy the updates.
80 00:07:58.880 ⇒ 00:08:05.569 Demilade Agboola: Okay, yeah, we can have a test cycle. And then tomorrow, we can have, like during stand up, we can decide if we want to go ahead with it or not.
81 00:08:06.780 ⇒ 00:08:08.260 Emily Giant: Okay, that sounds good.
82 00:08:08.870 ⇒ 00:08:12.929 Amber Lin: Sorry as some one of it disappeared where?
83 00:08:12.930 ⇒ 00:08:13.390 Emily Giant: Was.
84 00:08:14.268 ⇒ 00:08:16.410 Amber Lin: This one, it was too.
85 00:08:16.920 ⇒ 00:08:17.850 Amber Lin: Okay.
86 00:08:17.990 ⇒ 00:08:19.989 Amber Lin: Okay, I’m gonna move that out.
87 00:08:22.170 ⇒ 00:08:24.239 Amber Lin: How many points is this.
88 00:08:28.550 ⇒ 00:08:31.319 Emily Giant: 2 or 3, I’d say, what do you think? Demalade.
89 00:08:34.820 ⇒ 00:08:37.500 Demilade Agboola: Yeah, we’ll take 2, 3, just because I would say.
90 00:08:38.179 ⇒ 00:08:38.929 Amber Lin: Okay.
91 00:08:40.199 ⇒ 00:08:44.369 Amber Lin: Sorry, Emily, you said. This one’s a duplicate of something else.
92 00:08:46.950 ⇒ 00:08:51.650 Emily Giant: Yeah, I think it’s the same thing as the aggregations problem.
93 00:08:52.710 ⇒ 00:08:56.399 Amber Lin: Oh, okay, as long as it’s recorded in there.
94 00:08:57.123 ⇒ 00:09:05.739 Amber Lin: As long as you guys know, that’s fine. I just made a few more issues. Gonna add that
95 00:09:07.062 ⇒ 00:09:10.650 Amber Lin: all of these are assumed are inventory.
96 00:09:11.570 ⇒ 00:09:14.330 Amber Lin: So let’s go there.
97 00:09:16.650 ⇒ 00:09:19.620 Amber Lin: Okay, just a few ones to look at.
98 00:09:21.990 ⇒ 00:09:22.830 Amber Lin: Hmm.
99 00:09:32.190 ⇒ 00:09:35.640 Amber Lin: This one incremental runs for models.
100 00:09:36.700 ⇒ 00:09:38.290 Amber Lin: Oh, I think it’s this.
101 00:09:39.770 ⇒ 00:09:42.670 Amber Lin: This is from the meeting today.
102 00:09:49.455 ⇒ 00:10:00.970 Demilade Agboola: Yeah, basically, we need to be able to figure out part of why, there’s a disparity and uncommitted order. And I’ll sync with Emily on that. But I I kind of have an idea. So sometimes, when like
103 00:10:01.740 ⇒ 00:10:04.790 Demilade Agboola: orders of her, and there are
104 00:10:05.790 ⇒ 00:10:11.429 Demilade Agboola: non-floor goods, it’s also counted unconfeit, even though it should not end to that
105 00:10:12.054 ⇒ 00:10:18.035 Demilade Agboola: because the the team doesn’t need to know that. So it will be. It will be fixing that
106 00:10:18.560 ⇒ 00:10:20.520 Demilade Agboola: I’m sorry to feel that that
107 00:10:21.020 ⇒ 00:10:28.849 Demilade Agboola: the uncommitted number reflects the times when people wanted to purchase a certain order, but got a different order instead.
108 00:10:29.490 ⇒ 00:10:35.900 Amber Lin: Okay is this, are all these 3 high priority? Do we need to do them in this
109 00:10:36.070 ⇒ 00:10:38.039 Amber Lin: cycle or next cycle?
110 00:10:38.600 ⇒ 00:10:40.910 Demilade Agboola: This cycle because we’re trying to get the number.
111 00:10:41.650 ⇒ 00:10:42.640 Amber Lin: Oh, okay.
112 00:10:42.640 ⇒ 00:10:43.470 Demilade Agboola: As possible.
113 00:10:43.740 ⇒ 00:10:47.989 Amber Lin: Okay, so all these 3 are high priority.
114 00:10:48.930 ⇒ 00:10:50.049 Emily Giant: I would say, I would say.
115 00:10:50.050 ⇒ 00:10:52.180 Demilade Agboola: It is also high priority.
116 00:10:52.360 ⇒ 00:10:54.660 Emily Giant: Maybe the highest out of those.
117 00:10:54.880 ⇒ 00:11:02.369 Amber Lin: Okay, so that’s Hi, what is this? Also? Also high.
118 00:11:02.370 ⇒ 00:11:03.050 Emily Giant: Hmm.
119 00:11:03.830 ⇒ 00:11:04.600 Amber Lin: Or medium.
120 00:11:04.600 ⇒ 00:11:11.989 Demilade Agboola: Okay. Well, 2, 1, 6, 2, 1, 6 could be medium, because it’s I mean, it’s not really breaking anything right now. Obviously, we would want to. It’s a nice to have.
121 00:11:12.230 ⇒ 00:11:16.440 Demilade Agboola: It’s not really something, something that you know is affecting our data quality.
122 00:11:16.600 ⇒ 00:11:17.150 Demilade Agboola: Okay.
123 00:11:17.150 ⇒ 00:11:17.680 Amber Lin: Okay.
124 00:11:18.110 ⇒ 00:11:22.070 Emily Giant: And the rest is high priority, or can any of them be medium.
125 00:11:23.856 ⇒ 00:11:38.159 Demilade Agboola: I I think 2, 1, 8. If I take stick 2, 1, 8, 2, 1, 8 out, because 2, 1, 8 is kind of just 2, 1, 7, but just in a different way, because Felipe was expressing himself and trying to think of how we should remove the UN
126 00:11:38.370 ⇒ 00:11:43.189 Demilade Agboola: on the non floral goods. So that’s why you said product skew.
127 00:11:44.690 ⇒ 00:11:48.420 Emily Giant: Yeah, someone’s gonna be that can
128 00:11:50.100 ⇒ 00:11:56.399 Emily Giant: leave it for now. But I’m relatively sure that that will be taken care of with the new deployment.
129 00:11:57.170 ⇒ 00:12:00.310 Amber Lin: Okay, so I’m gonna put that lower.
130 00:12:00.906 ⇒ 00:12:04.240 Amber Lin: So these 3 gotta go in cycle.
131 00:12:04.370 ⇒ 00:12:05.810 Amber Lin: Is that correct?
132 00:12:08.020 ⇒ 00:12:10.619 Emily Giant: That looks good to me. Demalade! What do you think.
133 00:12:13.080 ⇒ 00:12:17.729 Amber Lin: There’s a lot of extra work that we’re adding onto you onto yourself. Don’t water.
134 00:12:17.730 ⇒ 00:12:18.110 Emily Giant: But.
135 00:12:18.110 ⇒ 00:12:18.939 Demilade Agboola: Yeah, that’s good.
136 00:12:18.940 ⇒ 00:12:23.449 Emily Giant: To take 2, 1, 7, Demo Ade, and then we can work together on that
137 00:12:25.740 ⇒ 00:12:33.880 Emily Giant: because you’ve already for that. And I think that the 2, 1, 7. It’s gonna take a little bit of understanding like
138 00:12:34.640 ⇒ 00:12:44.980 Emily Giant: really securely forced upgrades versus uncommitted orders, and it might be good for me to take like step, one of that investigation, so that you don’t have to like
139 00:12:45.230 ⇒ 00:12:47.090 Emily Giant: acquire all those learnings.
140 00:12:51.950 ⇒ 00:13:00.790 Demilade Agboola: That’s fine. I think we can. We can take on that in our working session tomorrow. I do know. I have an idea of what to do. I will make it sweet, and I’ll see how it fits with you.
141 00:13:01.250 ⇒ 00:13:02.529 Emily Giant: Okay, that sounds good.
142 00:13:05.090 ⇒ 00:13:08.079 Amber Lin: Okay, estimate for this is.
143 00:13:12.740 ⇒ 00:13:13.560 Demilade Agboola: 2 points.
144 00:13:13.560 ⇒ 00:13:20.240 Emily Giant: Yeah, I was, gonna say, is it just the investigating? Or does this also include fixing it in the table?
145 00:13:20.700 ⇒ 00:13:25.069 Amber Lin: Do we have anything that records fixing it, or should we?
146 00:13:25.070 ⇒ 00:13:26.490 Amber Lin: I know to the ticket.
147 00:13:27.000 ⇒ 00:13:29.140 Demilade Agboola: I will do both to be honest. So.
148 00:13:29.140 ⇒ 00:13:34.629 Amber Lin: Okay, investigate and fix uncommitted orders and system.
149 00:13:34.630 ⇒ 00:13:37.869 Emily Giant: And I would change it to 3, just in case.
150 00:13:38.230 ⇒ 00:13:38.970 Amber Lin: Okay.
151 00:13:41.030 ⇒ 00:13:44.939 Amber Lin: Create report for uncommitted orders. How many points is this.
152 00:13:45.920 ⇒ 00:13:46.710 Emily Giant: One.
153 00:13:47.110 ⇒ 00:13:47.720 Amber Lin: Okay.
154 00:13:47.720 ⇒ 00:13:54.150 Uttam Kumaran: Can we also, can we separate the investigation? Because I think the investigation is gonna be harder?
155 00:13:54.320 ⇒ 00:13:56.879 Uttam Kumaran: I mean, sometimes it’s harder than fixing.
156 00:13:56.880 ⇒ 00:13:57.850 Uttam Kumaran: And then.
157 00:13:57.850 ⇒ 00:13:58.410 Amber Lin: Valid.
158 00:13:58.410 ⇒ 00:14:03.939 Uttam Kumaran: I think it’s worth separating them out and then keeping one is like basically blocked by the other.
159 00:14:03.940 ⇒ 00:14:05.430 Amber Lin: Okay, that’s valid.
160 00:14:05.630 ⇒ 00:14:06.280 Uttam Kumaran: Yeah.
161 00:14:06.650 ⇒ 00:14:10.559 Amber Lin: Do we want to do the fixing in this cycle as well.
162 00:14:11.080 ⇒ 00:14:12.910 Emily Giant: Yes, I I think so.
163 00:14:13.466 ⇒ 00:14:14.120 Amber Lin: Oh, okay.
164 00:14:14.120 ⇒ 00:14:17.550 Emily Giant: Once we know, once we do the investigation, the fix won’t be hard.
165 00:14:18.202 ⇒ 00:14:25.649 Amber Lin: Okay, fixing is gonna be like 1 point. I’m gonna change the change. The other one back to 2 points.
166 00:14:27.620 ⇒ 00:14:35.250 Amber Lin: Say, Cinna laude here, I’ll say investigations 2 points.
167 00:14:38.060 ⇒ 00:14:42.010 Emily Giant: But the fix and the report, 2, 1, 9, are blocked.
168 00:14:42.160 ⇒ 00:14:48.220 Amber Lin: Until the investigation is hmm, I see logged by.
169 00:14:48.490 ⇒ 00:14:53.410 Amber Lin: So investigate is gonna be higher priority.
170 00:14:53.740 ⇒ 00:14:55.729 Amber Lin: What about this one
171 00:14:59.400 ⇒ 00:15:01.139 Amber Lin: in terms of estimates?
172 00:15:01.140 ⇒ 00:15:06.300 Emily Giant: That was the one that melody. I guess we could move that out of the cycle, since it like
173 00:15:07.430 ⇒ 00:15:08.150 Emily Giant: it’s 1 of.
174 00:15:08.150 ⇒ 00:15:08.720 Demilade Agboola: I think.
175 00:15:08.720 ⇒ 00:15:09.640 Emily Giant: Be optimized.
176 00:15:09.640 ⇒ 00:15:10.220 Emily Giant: So yeah.
177 00:15:10.990 ⇒ 00:15:12.100 Amber Lin: No, thank you. Great.
178 00:15:12.100 ⇒ 00:15:13.689 Demilade Agboola: It’s a nice to have. Yeah.
179 00:15:14.430 ⇒ 00:15:20.529 Amber Lin: Yeah, I’m gonna move it out. We don’t have enough time. The cycle to do that much. How many points is this.
180 00:15:23.670 ⇒ 00:15:24.960 Demilade Agboola: Okay, about 2.
181 00:15:25.410 ⇒ 00:15:31.980 Amber Lin: Okay, so let’s see, we moved.
182 00:15:32.860 ⇒ 00:15:42.630 Amber Lin: I think we moved a 3 pointer out. And then I guess we added, like 6
183 00:15:43.130 ⇒ 00:15:46.529 Amber Lin: or no. Actually, I think we added 4 points.
184 00:15:46.940 ⇒ 00:15:51.099 Amber Lin: Think we’re still pretty close to before
185 00:15:51.622 ⇒ 00:15:54.520 Amber Lin: can you help me give estimates to these?
186 00:15:56.340 ⇒ 00:16:01.250 Amber Lin: So just I don’t know if we’re going to do this.
187 00:16:01.620 ⇒ 00:16:07.600 Amber Lin: But just just in case is it gonna be like a 1.1 pointer.
188 00:16:09.710 ⇒ 00:16:14.870 Demilade Agboola: It’s time to go investigate around age, so I’ll take. That’s why I said we should. We should take you to our question.
189 00:16:15.050 ⇒ 00:16:19.739 Demilade Agboola: The product skew is to identify when unafformative products are sold
190 00:16:20.100 ⇒ 00:16:22.210 Demilade Agboola: story. It’s all kind of together.
191 00:16:26.600 ⇒ 00:16:31.540 Amber Lin: Okay, so I’ll put 2 points. I’ll just put 2 points for now.
192 00:16:31.540 ⇒ 00:16:32.610 Uttam Kumaran: I would put 2.
193 00:16:33.010 ⇒ 00:16:39.680 Amber Lin: Yeah. Okay, incremental strategy to transaction transaction line models.
194 00:16:39.680 ⇒ 00:16:44.830 Uttam Kumaran: I’d probably put 2, or if yeah, if it’s not done, is this done.
195 00:16:45.110 ⇒ 00:16:46.150 Amber Lin: Is this done?
196 00:16:46.750 ⇒ 00:16:48.360 Emily Giant: Yeah, I think so.
197 00:16:48.360 ⇒ 00:16:50.980 Uttam Kumaran: Do this? Didn’t we do this like.
198 00:16:50.980 ⇒ 00:16:52.530 Emily Giant: During mother’s Day. Yeah.
199 00:16:52.530 ⇒ 00:16:57.249 Uttam Kumaran: Well, maybe just maybe just put it in re, put it in review, and then we can double check.
200 00:16:57.530 ⇒ 00:17:05.469 Uttam Kumaran: and at least link the the sequel where the yeah, incremental is happening.
201 00:17:05.470 ⇒ 00:17:11.500 Emily Giant: Okay, yeah. Cause as far as I know, that’s been working.
202 00:17:11.500 ⇒ 00:17:13.490 Uttam Kumaran: Yeah, this was big.
203 00:17:13.890 ⇒ 00:17:22.989 Demilade Agboola: Yeah, but I think the last couple of days it’s actually gotten worse. Well, not worse. But, like the numbers, have been able to optimize it.
204 00:17:23.450 ⇒ 00:17:25.420 Uttam Kumaran: Oh, I see. Okay.
205 00:17:26.050 ⇒ 00:17:27.470 Amber Lin: What does that mean?
206 00:17:28.580 ⇒ 00:17:30.397 Uttam Kumaran: Well, let’s just yeah. Let’s so
207 00:17:31.160 ⇒ 00:17:32.930 Amber Lin: Do I take it out.
208 00:17:32.930 ⇒ 00:17:35.520 Uttam Kumaran: Hold on one sec. Wait, wait one second, one second.
209 00:17:35.930 ⇒ 00:17:37.790 Uttam Kumaran: So.
210 00:17:38.540 ⇒ 00:17:45.059 Uttam Kumaran: demo on this one, is it just? We need to add more. We need to like add more primary keys. Basically.
211 00:17:47.592 ⇒ 00:18:01.470 Demilade Agboola: And those tables are like the primary blockers. So how do we strike? You?
212 00:18:01.710 ⇒ 00:18:12.839 Demilade Agboola: Work on our strategy to maybe update about less time. But it’s a 14 day window. Maybe we’ll use less time. Like, just basically, it’s, how do we need to think our strategy to make you run faster.
213 00:18:13.140 ⇒ 00:18:20.159 Uttam Kumaran: Okay, so let’s for the acceptance. Let’s put one I just want to sort of investigate like what the what the current run times are
214 00:18:20.280 ⇒ 00:18:22.470 Uttam Kumaran: for these 2 models.
215 00:18:24.460 ⇒ 00:18:31.130 Uttam Kumaran: The second goal is to basically like, propose a change
216 00:18:31.270 ⇒ 00:18:35.539 Uttam Kumaran: that like would ideally move this to sub 5 min.
217 00:18:39.364 ⇒ 00:18:44.490 Uttam Kumaran: I I don’t. I would maybe like, let’s not put 5 min. Let’s just say 20 to 50% less time.
218 00:18:47.010 ⇒ 00:18:51.769 Uttam Kumaran: and then create the Pr like basically create the Pr to make the change for approval.
219 00:18:56.230 ⇒ 00:19:08.469 Uttam Kumaran: Basically, incremental strategy is like when you refresh a table, there’s no need. Because records already happened. There’s no need to refresh those records. Instead, you just refresh the records that are need to get updated.
220 00:19:09.171 ⇒ 00:19:16.490 Uttam Kumaran: So what we want to find out is like, are we refreshing records that have not been updated? Can we make this a little bit tighter? The strategy.
221 00:19:18.670 ⇒ 00:19:22.229 Amber Lin: Okay, is that something we want to do this cycle.
222 00:19:22.600 ⇒ 00:19:28.390 Amber Lin: It’s another few hours might not complete that on time.
223 00:19:28.390 ⇒ 00:19:32.730 Uttam Kumaran: I don’t know if this is like the highest priority. What.
224 00:19:32.730 ⇒ 00:19:41.849 Demilade Agboola: Yeah, that’s why I say, it’s a it’s a low priority task. We can still keep it recycle. But if we don’t, if we don’t reach it like it’s nothing is going to
225 00:19:41.980 ⇒ 00:19:45.440 Demilade Agboola: value. It’s just lovely. It’s just a nice to have.
226 00:19:45.990 ⇒ 00:19:52.219 Uttam Kumaran: Yeah. So I guess, amber, it depends on like, if if we have capacity, if we don’t, then this is the 1st this is like can get moved out.
227 00:19:52.410 ⇒ 00:19:59.999 Amber Lin: Okay, yeah, we’ll we’ll do one last check when we finish. I just wanna add
228 00:20:00.880 ⇒ 00:20:05.420 Amber Lin: something to these 2, and then we’ll look at the in cycle ones.
229 00:20:06.390 ⇒ 00:20:07.600 Amber Lin: Is this?
230 00:20:07.770 ⇒ 00:20:12.550 Amber Lin: What priority is this? And what estimate should I put.
231 00:20:18.020 ⇒ 00:20:22.699 Demilade Agboola: I mean, it’s I think it’s actually ongoing, which is kind of probably.
232 00:20:23.437 ⇒ 00:20:28.160 Demilade Agboola: We just want to be sure that, like the numbers and look at are
233 00:20:28.770 ⇒ 00:20:33.589 Demilade Agboola: good numbers are much. Ppc numbers will be cool. I think it also tries to go fast from the
234 00:20:34.292 ⇒ 00:20:36.660 Demilade Agboola: investigators as far into.
235 00:20:37.650 ⇒ 00:20:39.309 Amber Lin: Is it part of this?
236 00:20:41.891 ⇒ 00:20:48.850 Amber Lin: Sort of it’s a bike. So we’ve updated this. But the numbers don’t match, which is kind of what we got.
237 00:20:48.850 ⇒ 00:20:49.740 Amber Lin: Okay.
238 00:20:49.740 ⇒ 00:20:53.660 Uttam Kumaran: Yeah, I would mark this as like related to that one. And then.
239 00:20:54.010 ⇒ 00:21:00.389 Uttam Kumaran: if can we? If we could also link the I guess the view here?
240 00:21:01.285 ⇒ 00:21:05.120 Uttam Kumaran: I guess. Like, yeah, I don’t know exactly like, how are you? Gonna Qa, this
241 00:21:05.230 ⇒ 00:21:09.329 Uttam Kumaran: Emily? Is it mainly going to be like comparing this to the old one.
242 00:21:10.550 ⇒ 00:21:15.960 Emily Giant: No, it’s comparing until netsuite and.
243 00:21:15.960 ⇒ 00:21:16.660 Demilade Agboola: Lawsuits.
244 00:21:17.090 ⇒ 00:21:18.400 Uttam Kumaran: Hmm, okay.
245 00:21:28.400 ⇒ 00:21:30.720 Amber Lin: Yeah, I didn’t hear the other one.
246 00:21:34.190 ⇒ 00:21:37.270 Amber Lin: Priority is medium low.
247 00:21:38.000 ⇒ 00:21:39.990 Uttam Kumaran: I don’t know. I thought maybe high.
248 00:21:40.170 ⇒ 00:21:40.980 Amber Lin: Hi.
249 00:21:42.570 ⇒ 00:21:46.179 Uttam Kumaran: Cause. This is the this is sort of our team. Sign off on this right.
250 00:21:46.750 ⇒ 00:21:49.810 Demilade Agboola: Yeah, it’s a 5. Okay?
251 00:21:52.540 ⇒ 00:21:56.210 Amber Lin: So estimate is 1.2 point.
252 00:21:59.630 ⇒ 00:22:01.070 Emily Giant: Definitely, not one.
253 00:22:01.530 ⇒ 00:22:02.439 Demilade Agboola: Yeah, thank, you.
254 00:22:02.440 ⇒ 00:22:04.469 Amber Lin: No, no all right.
255 00:22:04.470 ⇒ 00:22:05.370 Uttam Kumaran: 3, yeah.
256 00:22:05.370 ⇒ 00:22:14.180 Amber Lin: Okay? If this is high priority, are we going to do this this cycle? Or probably we have to wait until next cycle. When this one’s done.
257 00:22:14.550 ⇒ 00:22:16.989 Amber Lin: how far is this from done otherwise?
258 00:22:21.360 ⇒ 00:22:23.896 Emily Giant: That one nearly done
259 00:22:25.620 ⇒ 00:22:28.539 Emily Giant: But the investigation of like the uncommitted
260 00:22:29.245 ⇒ 00:22:33.690 Emily Giant: And ethics, needs to be done before doing.
261 00:22:34.070 ⇒ 00:22:39.730 Uttam Kumaran: So for that one amber, the looker one. The acceptance criteria is is just a looker. Pr, basically.
262 00:22:39.910 ⇒ 00:22:40.570 Amber Lin: Okay.
263 00:22:42.230 ⇒ 00:22:44.441 Uttam Kumaran: Not for this one for the
264 00:22:44.990 ⇒ 00:22:46.160 Amber Lin: The other one, that.
265 00:22:46.160 ⇒ 00:22:48.040 Uttam Kumaran: It is one. Yeah, yes.
266 00:22:48.040 ⇒ 00:22:48.540 Amber Lin: Okay.
267 00:22:48.540 ⇒ 00:22:58.101 Uttam Kumaran: The acceptance here would be a looker. Pr. I think also it’d be great. And I’m second telling us a couple of teams just to use the the branch in
268 00:22:59.240 ⇒ 00:23:00.969 Uttam Kumaran: and linear if possible.
269 00:23:01.130 ⇒ 00:23:02.969 Uttam Kumaran: Like. I don’t know, Emily, are you? If you’re
270 00:23:03.200 ⇒ 00:23:07.053 Uttam Kumaran: if you’re familiar with this, it’s it’s there in Jira as well, but
271 00:23:07.840 ⇒ 00:23:08.580 Emily Giant: Oh, yeah. Yeah.
272 00:23:08.580 ⇒ 00:23:15.360 Uttam Kumaran: If you create your branch with, if you just click on this thing, it’ll literally give you a branch name, and then it’ll just link to the ticket. So.
273 00:23:15.360 ⇒ 00:23:19.059 Emily Giant: Nice. Okay, yeah. I haven’t used it in linear, but I do it in Jira all the time.
274 00:23:19.060 ⇒ 00:23:20.100 Uttam Kumaran: Okay, cool.
275 00:23:20.210 ⇒ 00:23:21.199 Uttam Kumaran: So same thing.
276 00:23:21.360 ⇒ 00:23:21.860 Emily Giant: Sweet.
277 00:23:21.860 ⇒ 00:23:26.460 Amber Lin: Okay, okay, so this, probably.
278 00:23:26.730 ⇒ 00:23:28.110 Amber Lin: Oh, okay.
279 00:23:29.090 ⇒ 00:23:38.419 Amber Lin: not ready, but closer up that we need to do it. So last one in inventory deliverables, I don’t know.
280 00:23:39.410 ⇒ 00:23:41.180 Amber Lin: Oh, so you created this
281 00:23:44.690 ⇒ 00:23:45.420 Amber Lin: 4.
282 00:23:46.920 ⇒ 00:23:48.639 Uttam Kumaran: I don’t know what this is.
283 00:23:48.910 ⇒ 00:23:54.109 Amber Lin: Okay, never mind, we’ll we’ll. I think this is more about making sure that we
284 00:23:54.540 ⇒ 00:23:59.598 Amber Lin: have clear deliverables and we hand off correctly. I’m just gonna say, this is.
285 00:23:59.960 ⇒ 00:24:00.440 Uttam Kumaran: Yeah, yeah.
286 00:24:00.440 ⇒ 00:24:08.490 Amber Lin: I do have other stuff for that great. I just wanna look at the current cycle that. You tell me if this is too much.
287 00:24:09.582 ⇒ 00:24:14.660 Uttam Kumaran: Demo, I just hopped, but I can give you the I’ll give you the jurisdiction. Yeah.
288 00:24:14.660 ⇒ 00:24:17.319 Amber Lin: Okay. So everything is added to cycle.
289 00:24:20.600 ⇒ 00:24:23.140 Uttam Kumaran: And this cycle is like the next 2 weeks.
290 00:24:23.360 ⇒ 00:24:29.960 Amber Lin: No, this cycle, this cycle is gonna end. That’s why I’m like, I don’t know if we can complete all of this this cycle.
291 00:24:29.960 ⇒ 00:24:32.420 Uttam Kumaran: Yeah. So let’s go through. So can we sort by.
292 00:24:32.420 ⇒ 00:24:32.930 Amber Lin: Them allotted.
293 00:24:32.930 ⇒ 00:24:36.000 Uttam Kumaran: Can we sort by prior? Can we sort by priority on this.
294 00:24:36.360 ⇒ 00:24:37.060 Amber Lin: Yes,
295 00:24:41.790 ⇒ 00:24:42.340 Uttam Kumaran: Okay.
296 00:24:44.550 ⇒ 00:24:46.040 Uttam Kumaran: So
297 00:24:51.450 ⇒ 00:24:53.100 Amber Lin: That’s just what needs to go.
298 00:24:55.370 ⇒ 00:24:59.530 Uttam Kumaran: So let’s move the incremental strategy out the next cycle.
299 00:25:03.990 ⇒ 00:25:06.459 Uttam Kumaran: And then, yeah, I guess, like I would.
300 00:25:07.310 ⇒ 00:25:09.860 Uttam Kumaran: There’s 5 in progress.
301 00:25:11.160 ⇒ 00:25:14.690 Uttam Kumaran: So I think that’s probably. But like 2 of these are kind of like.
302 00:25:14.690 ⇒ 00:25:18.910 Amber Lin: Like this is not really, he already said, sent. He already sent the.
303 00:25:19.430 ⇒ 00:25:23.750 Uttam Kumaran: So this one can you just put as like, can we put as like client review.
304 00:25:23.750 ⇒ 00:25:28.369 Amber Lin: Or whatever. Yeah, he sent a comment. So if Alibi is gonna get back.
305 00:25:28.370 ⇒ 00:25:33.040 Uttam Kumaran: And then investigate moving. More model.
306 00:25:33.200 ⇒ 00:25:35.490 Uttam Kumaran: I’ll send a unique identifier to, you know.
307 00:25:35.790 ⇒ 00:25:38.819 Uttam Kumaran: Rename folders. Can you click on the last one.
308 00:25:39.930 ⇒ 00:25:44.389 Amber Lin: This is an ongoing one. I’m not really sure.
309 00:25:44.390 ⇒ 00:25:47.370 Uttam Kumaran: Let’s let’s kick this out. Because this is.
310 00:25:47.730 ⇒ 00:25:50.580 Uttam Kumaran: let’s put this as a as a like, a low priority.
311 00:25:52.160 ⇒ 00:25:54.759 Uttam Kumaran: This is just like renaming. It’s just like housekeeping.
312 00:25:58.430 ⇒ 00:26:01.250 Amber Lin: Same with the okay. Never mind.
313 00:26:01.250 ⇒ 00:26:04.165 Uttam Kumaran: That one seems like a little bit beefier.
314 00:26:06.090 ⇒ 00:26:10.780 Uttam Kumaran: yeah, I guess like and then for the top 2 are blocked right.
315 00:26:12.080 ⇒ 00:26:14.249 Amber Lin: So can do anything, anyways.
316 00:26:14.810 ⇒ 00:26:17.289 Uttam Kumaran: So does this. This looks a little bit healthier.
317 00:26:17.770 ⇒ 00:26:18.540 Amber Lin: Okay.
318 00:26:19.550 ⇒ 00:26:26.590 Amber Lin: okay, yeah. And this is ongoing. It’s not really 3 points for this cycle. I think a lot of it has been done before. So.
319 00:26:26.590 ⇒ 00:26:34.269 Uttam Kumaran: Yeah, like for any of those that are like ongoing, moving mart model logic into intermediate models. I would suggest, we have
320 00:26:34.440 ⇒ 00:26:37.999 Uttam Kumaran: like, if you click into that.
321 00:26:38.230 ⇒ 00:26:46.630 Uttam Kumaran: Ideally, we should like, Okay, cool, great. This is what kind of what I was looking for, which is like, what logic are we looking to move.
322 00:26:47.370 ⇒ 00:26:52.990 Uttam Kumaran: And alright, yeah, this is it. I was like expecting this not to have details. Okay, cool.
323 00:26:53.170 ⇒ 00:26:56.579 Amber Lin: Okay. So yeah, he or I think he just had.
324 00:26:56.580 ⇒ 00:27:01.450 Uttam Kumaran: I mean, the best thing you can do is split. You could split that into. You could split that into 3, and then.
325 00:27:02.348 ⇒ 00:27:04.790 Amber Lin: But it’s 3. It’s 3 points total.
326 00:27:06.340 ⇒ 00:27:08.510 Uttam Kumaran: Yeah, but I guess I don’t know what is ongoing.
327 00:27:09.634 ⇒ 00:27:12.370 Amber Lin: I think what Demo I told me is that
328 00:27:12.500 ⇒ 00:27:20.590 Amber Lin: he is moving them to intermediate models as he as he builds. So this is not like a separate task from building.
329 00:27:21.760 ⇒ 00:27:22.740 Uttam Kumaran: Hmm!
330 00:27:23.630 ⇒ 00:27:25.700 Uttam Kumaran: Is there a task for building.
331 00:27:27.870 ⇒ 00:27:33.170 Amber Lin: I think the 1st is like
332 00:27:34.800 ⇒ 00:27:50.560 Amber Lin: they’re building. I think that’s mostly why I asked for Felipe’s tickets, because I think that’s the only building that’s left, because in my impression, most of the building has been done for the for last cycle. And this cycle most of building is for Felipe.
333 00:27:51.300 ⇒ 00:27:52.160 Uttam Kumaran: Hmm.
334 00:27:52.310 ⇒ 00:27:55.289 Amber Lin: The additional requests that came up as we met with him.
335 00:28:02.450 ⇒ 00:28:03.200 Uttam Kumaran: Okay.
336 00:28:03.710 ⇒ 00:28:05.509 Amber Lin: Yeah. So I guess what’s left, they’re gonna.
337 00:28:05.510 ⇒ 00:28:11.959 Uttam Kumaran: I guess like that is a low priority, so like if I would kick that out if the the one right below this.
338 00:28:12.510 ⇒ 00:28:13.210 Amber Lin: Okay.
339 00:28:13.610 ⇒ 00:28:20.070 Uttam Kumaran: And it like it looks like it’s in prog. But like I would, I would Mark to be kicked out if you need to kick another thing out.
340 00:28:20.300 ⇒ 00:28:26.039 Amber Lin: Okay, okay. Great. What about that?
341 00:28:30.650 ⇒ 00:28:32.170 Uttam Kumaran: Hmm!
342 00:28:34.507 ⇒ 00:28:40.149 Uttam Kumaran: I guess, Emily, I guess. How. How do you think this is like a super high priority? I guess I’m I don’t have all the context.
343 00:28:40.150 ⇒ 00:28:46.600 Emily Giant: Yeah, I mean they won’t work if there are dupes. So.
344 00:28:46.890 ⇒ 00:28:51.920 Uttam Kumaran: Yeah, that’s I would I would put it as a higher priority, and then this should stick in there.
345 00:28:53.300 ⇒ 00:28:57.339 Uttam Kumaran: But like, this is more important than moving stuff to end. So.
346 00:28:58.710 ⇒ 00:29:00.760 Amber Lin: Okay, I’m gonna boot this.
347 00:29:00.760 ⇒ 00:29:01.670 Uttam Kumaran: To kick it out. Yeah.
348 00:29:01.670 ⇒ 00:29:11.460 Amber Lin: Progress, we can complete it next cycle great. This is better there. Emily and Demolan is going to do this tomorrow, and then these 2 hopefully get unblocked. And then we can.
349 00:29:11.700 ⇒ 00:29:14.470 Amber Lin: We have, like, today’s Wednesday
350 00:29:14.660 ⇒ 00:29:25.252 Amber Lin: and Thursday kind of Friday, Monday off. Okay, there are 2 there, one pointers. So hopefully that gets done. Okay, I feel good about it.
351 00:29:26.040 ⇒ 00:29:35.119 Amber Lin: I wanted to. I know revenue is still a bit early to groom the tickets. I know
352 00:29:35.750 ⇒ 00:29:57.729 Amber Lin: Kyle had a question about what really deliverables are we looking for for the auditing phase? I I really want to set that in stone because I didn’t really have the ability. I can’t define what auditing should deliver. I need a tech lead to help me with that, so we can define it now, or we can discuss it tomorrow.
353 00:29:57.730 ⇒ 00:29:58.970 Uttam Kumaran: Yeah, let’s discuss.
354 00:30:00.110 ⇒ 00:30:02.629 Amber Lin: Okay, today, right? Now.
355 00:30:02.630 ⇒ 00:30:03.025 Emily Giant: Sure.
356 00:30:03.420 ⇒ 00:30:07.970 Amber Lin: Great Kyle, can you quickly
357 00:30:08.160 ⇒ 00:30:10.989 Amber Lin: say your question again about auditing.
358 00:30:12.180 ⇒ 00:30:13.650 Caio Velasco: Yeah, sure. Sure.
359 00:30:14.246 ⇒ 00:30:30.089 Caio Velasco: So yeah, let’s say that I spend the last 4 days. I mean, not full days, but working on this tickets, and well, I haven’t touched anything regarding models, because I was just doing the deprecation work.
360 00:30:30.742 ⇒ 00:30:50.739 Caio Velasco: So then I started with orders. Then there was the transactions, and then I ended up hitting refund and other things, and I was understanding them along the way, right like I was opening like a very upstream model source from Hevo and understanding, like trying to basically build the stage model that comes after it. So that I understand the logic
361 00:30:51.229 ⇒ 00:30:58.169 Caio Velasco: and I understand what is happening right? And then I had the meeting today with Emily, and she explained me a lot of things.
362 00:30:58.813 ⇒ 00:31:02.650 Caio Velasco: And since those things are new to me as well.
363 00:31:05.530 ⇒ 00:31:14.789 Caio Velasco: What I’m doing is discovery and and learning about like logic, and and even the models themselves in this in the tables and sources.
364 00:31:14.920 ⇒ 00:31:35.670 Caio Velasco: So I didn’t have like a clear idea of what would be a deliverer for this if there is one, and then on the next phase, modeling would also be very important for me to in this time have more concrete, even if it’s like small steps deliverable. So that I can understand. Okay, if we’re going to build like effect orders, what do you want
365 00:31:35.810 ⇒ 00:31:39.680 Caio Velasco: in the fact orders, or in the next version, what should I add.
366 00:31:39.680 ⇒ 00:31:40.250 Uttam Kumaran: Yeah.
367 00:31:40.250 ⇒ 00:31:46.439 Caio Velasco: Probably, you know, based on complexity as well, which is something that I don’t know, because I didn’t build them myself.
368 00:31:46.440 ⇒ 00:31:51.650 Uttam Kumaran: I mean, I would like to see. I would like to see like a technical design document for this model
369 00:31:51.880 ⇒ 00:31:54.059 Uttam Kumaran: for the inventory model. Basically,
370 00:31:54.870 ⇒ 00:31:57.929 Amber Lin: This is what we have for the
371 00:31:58.060 ⇒ 00:32:02.860 Amber Lin: this is what John and Kyle made for the revenue. One.
372 00:32:03.220 ⇒ 00:32:07.570 Uttam Kumaran: So does this have information about the logic in it. Kyle.
373 00:32:09.001 ⇒ 00:32:15.509 Caio Velasco: That was actually the military. We discussed a bit, but then took it and and finished it.
374 00:32:16.060 ⇒ 00:32:16.520 Uttam Kumaran: So I guess my.
375 00:32:16.520 ⇒ 00:32:21.619 Caio Velasco: So it was more like an overview, like a coordinate, overview kind of thing like high level. This one.
376 00:32:21.880 ⇒ 00:32:27.750 Uttam Kumaran: So I guess my question is like, for example, let’s say you find, hey, there’s like 5 tables that model the same
377 00:32:27.860 ⇒ 00:32:35.850 Uttam Kumaran: piece of logic around inventory. I want to consolidate that into Inventory Table X, because
378 00:32:36.170 ⇒ 00:32:38.599 Uttam Kumaran: there’s duplicated 5 times right
379 00:32:39.030 ⇒ 00:32:44.669 Uttam Kumaran: that we all agree. That’s probably a common finding that we may come across that sort of like
380 00:32:45.220 ⇒ 00:32:53.140 Uttam Kumaran: proposal, or like that sort of insight, needs to get codified into a design document for the inventory.
381 00:32:53.420 ⇒ 00:33:15.609 Uttam Kumaran: I think. One thing we could work on is we could we could work on sort of what that template is. But basically, I think for all of us to go through and approve ideally for for an inventory, for a new data model. Right? We want to know what the tables are, gonna be like, what the core. What the use cases per table are gonna be, and how we move like, what tables?
382 00:33:15.760 ⇒ 00:33:19.880 Uttam Kumaran: What do the new tables replace from the old models?
383 00:33:20.060 ⇒ 00:33:21.630 Uttam Kumaran: Right, at least at minimum.
384 00:33:22.770 ⇒ 00:33:23.870 Uttam Kumaran: Does that make sense
385 00:33:25.400 ⇒ 00:33:32.100 Uttam Kumaran: so like as part of your audit? I think, one. It’s helpful to go through and say, look, I went through like these 30 tables.
386 00:33:32.250 ⇒ 00:33:36.580 Uttam Kumaran: and here are my findings per table. At least it gives you somewhere to write those down.
387 00:33:36.700 ⇒ 00:33:41.389 Uttam Kumaran: I think that’s perfect background work as part of this design document.
388 00:33:41.810 ⇒ 00:33:48.300 Uttam Kumaran: Second is, I would love to see what a proposal for the new data model is. It doesn’t need to be like an Erd.
389 00:33:48.550 ⇒ 00:33:59.119 Uttam Kumaran: It doesn’t even need to. You don’t even. I don’t really care much about like the column names and like stuff like that. I care about like, what is the goal of that table like? What are the relationships between other tables?
390 00:34:00.450 ⇒ 00:34:03.220 Uttam Kumaran: And what tables does it replace from the old model.
391 00:34:06.060 ⇒ 00:34:07.610 Caio Velasco: Okay. Okay.
392 00:34:07.610 ⇒ 00:34:13.980 Uttam Kumaran: So I think, as an outcome of all the audit tasks. I think a design document that we as a group can.
393 00:34:14.920 ⇒ 00:34:21.099 Uttam Kumaran: and you know you could put up for Rfc. Like request for comments, and we can go comment it up would be great.
394 00:34:22.500 ⇒ 00:34:24.819 Uttam Kumaran: We’re not. Of course we’re not gonna know.
395 00:34:24.949 ⇒ 00:34:35.220 Uttam Kumaran: like, but I I think we should be able to get 80% there like. And you know, again, I don’t. I don’t. Wanna. I don’t really necessarily care about getting the column names accurate in this document like, we don’t need to
396 00:34:35.400 ⇒ 00:34:45.489 Uttam Kumaran: sort of be like like a huge org here. But I do want somewhere where we can write down all of the things you’re finding as part of the audit process.
397 00:34:45.860 ⇒ 00:34:51.670 Uttam Kumaran: And ideally, you can do that on a per table basis since that’s probably the way you’re you’re going at it now.
398 00:34:52.469 ⇒ 00:34:56.365 Amber Lin: And feel like that’s kind of what Kyle is already doing.
399 00:34:56.719 ⇒ 00:34:57.409 Caio Velasco: It’s not always.
400 00:34:57.410 ⇒ 00:34:59.810 Amber Lin: Very thorough. Where is it?
401 00:35:00.290 ⇒ 00:35:02.589 Caio Velasco: Maybe at the end on the Q. And a.
402 00:35:02.750 ⇒ 00:35:24.389 Caio Velasco: Because every time I open a model and trying to sorry, not at the very end. Yes, perfect. Every time I go and try to build like Column B, like both of them trying to build if I’m trying to build that one, but I have to understand what comes before. And then I ended up understanding new things and noticing that some things are there. Some things are not, and this takes time. I won’t lie.
403 00:35:24.620 ⇒ 00:35:31.330 Uttam Kumaran: No, no, this takes time, but I think exactly this should go into that document, because then we can put it up for comments on some cycle.
404 00:35:31.530 ⇒ 00:35:31.980 Amber Lin: Oh!
405 00:35:32.292 ⇒ 00:35:38.719 Uttam Kumaran: I do think it’s like you could do it here as well. But this is all relate. If it’s related to inventory.
406 00:35:38.830 ⇒ 00:35:44.689 Uttam Kumaran: it would be great for this commentary to end up, or if it’s related to revenue, then it would be great for this to end up
407 00:35:45.110 ⇒ 00:35:49.379 Uttam Kumaran: into that document, because it’ll show what all the problems are.
408 00:35:50.082 ⇒ 00:35:56.299 Uttam Kumaran: And maybe you have enough context, Kyle, in parallel to start working on what the next stuff is. Right? So that way.
409 00:35:56.540 ⇒ 00:36:04.079 Uttam Kumaran: if you’re like, okay, I just want to note this down somewhere like cool, we definitely need this table. We definitely need this table. You can start to build that out.
410 00:36:04.801 ⇒ 00:36:06.969 Uttam Kumaran: So I think probably one thing
411 00:36:07.500 ⇒ 00:36:15.110 Uttam Kumaran: like we can work on internally. Amber is like, maybe like a data model Tdd format.
412 00:36:15.787 ⇒ 00:36:17.010 Uttam Kumaran: Like I can.
413 00:36:17.240 ⇒ 00:36:21.030 Uttam Kumaran: We can work on that. I have some opinions on that. And then
414 00:36:21.230 ⇒ 00:36:23.609 Uttam Kumaran: that’s what I would like to set a date on
415 00:36:23.990 ⇒ 00:36:27.909 Uttam Kumaran: basically getting that to approval. So we can sign off on building there
416 00:36:29.000 ⇒ 00:36:35.860 Uttam Kumaran: like the Tdd. I just saw was is more about like how we do Dbt stuff.
417 00:36:37.410 ⇒ 00:36:37.920 Uttam Kumaran: Which?
418 00:36:38.150 ⇒ 00:36:39.909 Uttam Kumaran: Yes, there, that is like
419 00:36:40.260 ⇒ 00:36:45.019 Uttam Kumaran: that is helpful. But I want to see the model logic right? That’s the real meat of the whole thing.
420 00:36:47.230 ⇒ 00:36:49.910 Amber Lin: Oh, we’re meeting with Zach tomorrow.
421 00:36:55.710 ⇒ 00:36:59.060 Amber Lin: is. I don’t think this is ready to show him.
422 00:37:01.170 ⇒ 00:37:02.059 Uttam Kumaran: I mean, we don’t.
423 00:37:02.200 ⇒ 00:37:09.460 Uttam Kumaran: Yeah, but we don’t have to do that. We don’t have to do this in this meeting. I think. Let’s just keep going with grooming, and then let’s just finish up whatever we need to do.
424 00:37:10.144 ⇒ 00:37:14.850 Amber Lin: Okay, what is, do we need a meeting to talk about this? I can book that.
425 00:37:16.052 ⇒ 00:37:23.560 Uttam Kumaran: No, there’s a task on my plate to review this right? So I don’t think there’s anything I’ll just put. I’ll put comments, I think I wish put some comments too.
426 00:37:23.990 ⇒ 00:37:27.479 Amber Lin: Okay. Great internal review.
427 00:37:28.240 ⇒ 00:37:33.150 Amber Lin: Kyle, does that help you get a better understanding of what auditing you need to do.
428 00:37:34.130 ⇒ 00:37:50.730 Caio Velasco: Yeah, yeah. So I think I just have to continue that and try to be like faster at least. Because sometimes I spend a lot of time like really understanding, like tracing like 2, 3, 4, different orders. I can just maybe just get a
429 00:37:50.970 ⇒ 00:37:52.719 Caio Velasco: an overview of each one.
430 00:37:53.260 ⇒ 00:37:58.310 Caio Velasco: And then we at least can have, like a 1st layer of of all the tenants.
431 00:37:58.310 ⇒ 00:38:03.389 Amber Lin: Okay, okay, due date.
432 00:38:04.588 ⇒ 00:38:19.719 Amber Lin: I think these these few, Emily, I wanted to ask you. This was originally placed an inventory. We moved it over. Are! Do they still make sense to? Are they duplicates? Do we need to cancel them.
433 00:38:21.190 ⇒ 00:38:26.879 Emily Giant: Staging transactions and refunds is not part of inventory that’s revenue.
434 00:38:26.880 ⇒ 00:38:30.340 Amber Lin: Oh, sorry we moved it to revenue. We’re in revenue right now.
435 00:38:30.340 ⇒ 00:38:31.760 Emily Giant: Oh, yes, okay.
436 00:38:32.840 ⇒ 00:38:35.689 Emily Giant: And what’s what’s the question? Sorry now I’m in.
437 00:38:35.690 ⇒ 00:38:38.049 Amber Lin: We still do. We still need them?
438 00:38:39.550 ⇒ 00:38:42.720 Amber Lin: I mean, we haven’t built them. We haven’t built them yet.
439 00:38:48.580 ⇒ 00:38:51.020 Emily Giant: I don’t. What is intermediate orders.
440 00:38:51.930 ⇒ 00:38:53.290 Amber Lin: I don’t know.
441 00:39:02.850 ⇒ 00:39:05.670 Emily Giant: I think we need to ask them a lot, because I know he’s done work.
442 00:39:06.270 ⇒ 00:39:08.411 Emily Giant: As far as needing them.
443 00:39:09.180 ⇒ 00:39:12.789 Emily Giant: I I think that’s a conversation with
444 00:39:13.050 ⇒ 00:39:19.990 Emily Giant: Dum, a lot of Kyle and myself, based on what we decide is the ultimate strategy for revenue.
445 00:39:19.990 ⇒ 00:39:30.669 Amber Lin: Okay? Sounds good. I’m gonna skip grooming this. We’re now. We’re still in audits, and we’ll do the next cycle. I wanted us to look at what’s left for redshift and looker.
446 00:39:31.460 ⇒ 00:39:39.359 Amber Lin: So, Redshift, we’re currently I think we’re pretty much done with this. We’re already here.
447 00:39:39.730 ⇒ 00:39:47.959 Amber Lin: And then I think, layer 2 and 3 are relatively lower priority. I think I duplicated this ticket.
448 00:39:48.780 ⇒ 00:39:58.130 Amber Lin: and I want us to go through, especially these to see if we still need them. And how are we gonna go about doing that?
449 00:40:01.550 ⇒ 00:40:05.790 Amber Lin: So the 1st one is to build a cost dashboard with
450 00:40:06.070 ⇒ 00:40:09.049 Amber Lin: sdl, query locks. Do we still want this.
451 00:40:18.940 ⇒ 00:40:21.959 Uttam Kumaran: Probably not like. It’s low priority.
452 00:40:27.210 ⇒ 00:40:28.519 Uttam Kumaran: I would leave it.
453 00:40:29.400 ⇒ 00:40:29.900 Amber Lin: Okay.
454 00:40:29.900 ⇒ 00:40:31.470 Uttam Kumaran: But I just put in the backlog. Yeah.
455 00:40:31.470 ⇒ 00:40:32.160 Amber Lin: Okay.
456 00:40:32.400 ⇒ 00:40:33.120 Amber Lin: Sounds. Great.
457 00:40:33.120 ⇒ 00:40:34.160 Uttam Kumaran: Nice to have.
458 00:40:35.894 ⇒ 00:40:38.589 Amber Lin: What about this one?
459 00:40:39.120 ⇒ 00:40:43.990 Amber Lin: Define? Wlm. Queries. I don’t really know what Wlm means.
460 00:40:45.330 ⇒ 00:40:46.250 Emily Giant: I don’t either.
461 00:40:50.350 ⇒ 00:40:55.100 Uttam Kumaran: This is for basically like
462 00:40:55.220 ⇒ 00:41:01.960 Uttam Kumaran: it’s think of it like highways for each type of task. You just want to make sure that there’s enough lanes per task.
463 00:41:02.840 ⇒ 00:41:04.600 Uttam Kumaran: This is also like
464 00:41:05.140 ⇒ 00:41:10.849 Uttam Kumaran: I would leave this in backlog, but it’s like a low priority. We’re not having any like.
465 00:41:10.970 ⇒ 00:41:14.490 Uttam Kumaran: We’re not seeing any blocks or anything anymore. Right? Emily. So.
466 00:41:14.710 ⇒ 00:41:15.390 Emily Giant: No.
467 00:41:15.390 ⇒ 00:41:19.079 Uttam Kumaran: Okay? So yeah, I don’t, unless unless Alex is like does
468 00:41:19.690 ⇒ 00:41:22.309 Uttam Kumaran: where the bill is too high. I don’t think this matters, for now.
469 00:41:31.850 ⇒ 00:41:40.909 Amber Lin: Okay, so that observability set of slack email alerts for job failures
470 00:41:41.060 ⇒ 00:41:44.310 Amber Lin: do we already have? That? Is that what Meta plan helps us do.
471 00:41:46.694 ⇒ 00:41:51.160 Uttam Kumaran: Yes, oh, yes, it
472 00:41:51.160 ⇒ 00:41:55.959 Uttam Kumaran: it does, but we haven’t sort of confirmed it yet, like confirmed that we’re gonna be using it. So.
473 00:41:55.960 ⇒ 00:41:56.730 Amber Lin: Okay.
474 00:41:57.439 ⇒ 00:42:02.719 Uttam Kumaran: Is there the question here like, Are we gonna keep doing this, or is we do this cycle? Or what’s the question here.
475 00:42:02.720 ⇒ 00:42:04.600 Amber Lin: Oh, no, we’re group just grooming the backlog.
476 00:42:04.600 ⇒ 00:42:08.410 Amber Lin: Oh, yeah, we still need this. Tickets. Are the priorities correct?
477 00:42:08.890 ⇒ 00:42:12.310 Uttam Kumaran: Yeah, I would. I would leave that one. Yeah.
478 00:42:15.430 ⇒ 00:42:19.639 Uttam Kumaran: I think the way the way it’s gonna get implemented will decide. Probably tomorrow.
479 00:42:30.140 ⇒ 00:42:31.070 Amber Lin: alright.
480 00:42:32.150 ⇒ 00:42:38.170 Amber Lin: So next one as source freshness checks.
481 00:42:38.720 ⇒ 00:42:39.570 Uttam Kumaran: Same thing.
482 00:42:40.920 ⇒ 00:42:41.450 Amber Lin: Okay.
483 00:42:42.150 ⇒ 00:42:44.180 Amber Lin: Park related.
484 00:42:45.150 ⇒ 00:42:47.430 Amber Lin: Better play.
485 00:42:54.280 ⇒ 00:43:02.679 Amber Lin: Okay, alright. Theft. Broad scheme. Is that also the same thing.
486 00:43:06.230 ⇒ 00:43:10.889 Uttam Kumaran: This is not the same thing, but low priority.
487 00:43:11.420 ⇒ 00:43:12.270 Amber Lin: Okay?
488 00:43:18.830 ⇒ 00:43:22.680 Amber Lin: And the last one, okay.
489 00:43:22.680 ⇒ 00:43:23.540 Uttam Kumaran: Yeah, low.
490 00:43:23.990 ⇒ 00:43:25.750 Amber Lin: And move to backlog.
491 00:43:25.870 ⇒ 00:43:26.930 Amber Lin: Are we ever.
492 00:43:26.930 ⇒ 00:43:33.239 Uttam Kumaran: It’s like the last thing we should. Yeah. But like 59, we, it’s like the last thing we’ll ever do.
493 00:43:33.430 ⇒ 00:43:36.479 Uttam Kumaran: Same with 61. Probably they’re like a very low priority.
494 00:43:39.430 ⇒ 00:43:43.630 Uttam Kumaran: The yeah. I think it’s the way this is is fine.
495 00:43:43.630 ⇒ 00:43:51.490 Amber Lin: Okay, sounds good. So I won’t think about like while we’re still through inventory for everything. I’m not probably not gonna add these to cycle.
496 00:43:51.490 ⇒ 00:43:52.450 Uttam Kumaran: Yeah.
497 00:43:54.160 ⇒ 00:43:58.369 Amber Lin: Okay, Polytomers, let us stay there. I don’t know if we’re deciding on that.
498 00:44:00.230 ⇒ 00:44:01.710 Amber Lin: Turn off.
499 00:44:02.390 ⇒ 00:44:04.750 Amber Lin: Okay, we did drop them.
500 00:44:05.910 ⇒ 00:44:07.820 Amber Lin: Think this is part of
501 00:44:08.340 ⇒ 00:44:14.750 Amber Lin: this is part of that that’s duplicate. Oh, right, this is a duplicate, because we already this is essentially layer. 2.
502 00:44:17.493 ⇒ 00:44:18.940 Caio Velasco: The explore.
503 00:44:19.240 ⇒ 00:44:19.980 Amber Lin: Yes.
504 00:44:23.010 ⇒ 00:44:33.080 Caio Velasco: Not necessarily because we might have already deleted them, but not necessarily. But those are very small in terms, like percentage points. In fact, we
505 00:44:33.390 ⇒ 00:44:38.759 Caio Velasco: already deprecated at 2 almost 2,000 tables, and those might lead us to like 5 tables.
506 00:44:39.170 ⇒ 00:44:42.969 Caio Velasco: So not very important, but still something that we could do. Yeah.
507 00:44:43.730 ⇒ 00:44:48.470 Amber Lin: Okay, but that’s different from your.
508 00:44:49.340 ⇒ 00:44:49.730 Caio Velasco: That’s.
509 00:44:49.840 ⇒ 00:44:51.560 Amber Lin: There, too, is for views.
510 00:44:51.560 ⇒ 00:44:58.370 Caio Velasco: Yeah, there’s a different views. Yeah, yeah. Sorry. The one is like, looker views, which is something very different than I could theoretically not very different.
511 00:44:58.370 ⇒ 00:45:05.501 Amber Lin: Oh, okay, I see. So this
512 00:45:06.260 ⇒ 00:45:08.999 Amber Lin: are we gonna do next cycle?
513 00:45:09.730 ⇒ 00:45:17.690 Caio Velasco: Well, I think if we continue the the those layers, I would be deprecating views and redshift then.
514 00:45:18.210 ⇒ 00:45:22.219 Caio Velasco: we could go back to this one and see if there is anything missing. For example.
515 00:45:22.220 ⇒ 00:45:24.900 Uttam Kumaran: Are you? Are you just moving these to?
516 00:45:25.480 ⇒ 00:45:28.190 Uttam Kumaran: So we’re in redshift, or are you? Are you dropping them.
517 00:45:28.700 ⇒ 00:45:34.680 Caio Velasco: So what I did was I moved to an archive schema and.
518 00:45:35.560 ⇒ 00:45:36.090 Amber Lin: Drop, the.
519 00:45:36.090 ⇒ 00:45:41.130 Caio Velasco: Then I dropped. Yeah, then I compared it. If everything, all the counts were the same between original.
520 00:45:41.130 ⇒ 00:45:41.530 Caio Velasco: Okay?
521 00:45:41.530 ⇒ 00:45:45.210 Caio Velasco: And then, yeah. Then I dropped in the origin, the origin.
522 00:45:45.210 ⇒ 00:45:48.539 Uttam Kumaran: So is this next one, like dropping the archives.
523 00:45:50.120 ⇒ 00:45:50.689 Caio Velasco: No.
524 00:45:50.690 ⇒ 00:45:52.399 Amber Lin: That’s what we assume.
525 00:45:53.060 ⇒ 00:45:56.140 Uttam Kumaran: Well like I would leave the I would just leave the archives.
526 00:45:56.500 ⇒ 00:45:59.479 Amber Lin: Okay. So we should not do this.
527 00:46:00.070 ⇒ 00:46:04.649 Uttam Kumaran: I guess, like Kai, I just to confirm that is this. This is related to
528 00:46:05.420 ⇒ 00:46:09.720 Uttam Kumaran: this is dropping the non archived ones. So we should keep this. Probably.
529 00:46:09.720 ⇒ 00:46:11.627 Caio Velasco: Yeah, I think it’s because we were
530 00:46:12.090 ⇒ 00:46:24.469 Caio Velasco: discussing them when we were doing them. So I think this one would be the app. The next step of the other one but the other one before we just copy. Right. So now we we already copied and dropped the original destination.
531 00:46:24.620 ⇒ 00:46:29.769 Caio Velasco: and kept the the ones in archive. So I think the the other one is duplicated kind of
532 00:46:30.330 ⇒ 00:46:32.500 Caio Velasco: so this one looks like it’s.
533 00:46:32.500 ⇒ 00:46:33.150 Uttam Kumaran: This one’s done.
534 00:46:33.150 ⇒ 00:46:34.060 Amber Lin: Yeah, cause, we’re doing.
535 00:46:34.060 ⇒ 00:46:34.510 Uttam Kumaran: Have.
536 00:46:35.363 ⇒ 00:46:35.790 Amber Lin: Sorry.
537 00:46:35.790 ⇒ 00:46:39.390 Uttam Kumaran: Sorry. Do you have the the commands you ran by chance.
538 00:46:40.080 ⇒ 00:46:43.050 Caio Velasco: Yeah, I I have a repo for everything I do.
539 00:46:43.310 ⇒ 00:46:43.650 Uttam Kumaran: Okay.
540 00:46:43.650 ⇒ 00:46:44.330 Amber Lin: Okay, cool. If you can.
541 00:46:44.330 ⇒ 00:46:45.770 Uttam Kumaran: Can you? Can you link?
542 00:46:47.780 ⇒ 00:46:52.570 Uttam Kumaran: The commands here just so like in case we need to come back to it.
543 00:46:53.010 ⇒ 00:46:54.819 Caio Velasco: Yeah, yeah, definitely, definitely.
544 00:46:55.000 ⇒ 00:46:59.189 Uttam Kumaran: Because I’m just. This is, I just get really nervous. So.
545 00:46:59.190 ⇒ 00:47:09.759 Caio Velasco: No, no, I have. Every time I find, like a finisher script. I also do ask to do like a Markdown thought process of everything so that anyone could do it.
546 00:47:11.760 ⇒ 00:47:14.750 Uttam Kumaran: Just in case if someone comes back to this and is like, What do we run again?
547 00:47:15.230 ⇒ 00:47:15.650 Caio Velasco: Yeah.
548 00:47:15.650 ⇒ 00:47:17.380 Amber Lin: Perfect. Okay?
549 00:47:18.670 ⇒ 00:47:21.780 Amber Lin: Probably I need to make another ticket for
550 00:47:22.210 ⇒ 00:47:25.240 Amber Lin: this. Drop them when they’re not accurate.
551 00:47:27.660 ⇒ 00:47:33.979 Caio Velasco: Yeah, yeah, this is something that we we need to go back and and rethink about the accuracy things. Yeah.
552 00:47:33.980 ⇒ 00:47:38.540 Amber Lin: Hmm, okay. And I assume that one would be low priority.
553 00:47:43.630 ⇒ 00:47:58.579 Caio Velasco: Well, it depends how we are, how sure we are about the accuracy. Let’s say map or measures we had, and then how would them be linked to those tables in terms of define if they have to be dropped or not?
554 00:47:58.720 ⇒ 00:48:03.279 Caio Velasco: This is something we need to discuss again. Maybe after we did all the ones above.
555 00:48:04.390 ⇒ 00:48:08.470 Amber Lin: Okay, okay, so that would be a conversation that comes up.
556 00:48:08.810 ⇒ 00:48:13.370 Amber Lin: Say, put it there. Remove that out of cycle.
557 00:48:13.570 ⇒ 00:48:14.580 Amber Lin: Oh.
558 00:48:19.390 ⇒ 00:48:25.129 Caio Velasco: And today I was working on the on the layer, 2 for fusion. Well, he gave me a bit of a headache.
559 00:48:25.360 ⇒ 00:48:31.409 Caio Velasco: but I know kind of getting the point. Now, yeah, this is difficult. Everything has to be done, and I can start procedures, and
560 00:48:31.940 ⇒ 00:48:38.759 Caio Velasco: sometimes they have. Redshift has limitations as well. So we have to adapt to those things. And yeah, so I’ll get it.
561 00:48:39.020 ⇒ 00:48:39.760 Amber Lin: Okay.
562 00:48:39.920 ⇒ 00:48:43.000 Amber Lin: Lastly, let’s look at Looker.
563 00:48:43.700 ⇒ 00:48:52.639 Amber Lin: So it’s same things of just cleaning. I think there’s same cleaning up afterwards. Let’s see.
564 00:48:53.880 ⇒ 00:48:59.949 Amber Lin: Okay, this is a I think, Emily, this is a new one. Do we still need this.
565 00:49:01.010 ⇒ 00:49:06.217 Emily Giant: Not in any upcoming cycle, but that will definitely be needed down the line.
566 00:49:07.520 ⇒ 00:49:09.109 Amber Lin: How many points would that be.
567 00:49:10.180 ⇒ 00:49:15.547 Emily Giant: A lot. That’s going to be like nested under the finance mart.
568 00:49:15.960 ⇒ 00:49:16.700 Amber Lin: Oh.
569 00:49:24.810 ⇒ 00:49:29.379 Amber Lin: so this is like a when we tackle the finance mart. We’re going to tackle this type of thing.
570 00:49:30.420 ⇒ 00:49:35.929 Amber Lin: Okay, I’m gonna give it many points. We’ll figure that out.
571 00:49:39.110 ⇒ 00:49:40.770 Amber Lin: Medium, low.
572 00:49:41.340 ⇒ 00:49:42.190 Emily Giant: Low.
573 00:49:42.190 ⇒ 00:49:42.830 Amber Lin: Okay?
574 00:49:47.556 ⇒ 00:49:55.620 Amber Lin: That one consolidate options for product type and looker to avoid confusion.
575 00:49:55.800 ⇒ 00:50:00.890 Emily Giant: We don’t need that one anymore. That’s a byproduct of what we’re doing elsewhere.
576 00:50:01.050 ⇒ 00:50:03.510 Amber Lin: Okay, great.
577 00:50:03.750 ⇒ 00:50:09.520 Amber Lin: So data. Governance document for a looker.
578 00:50:13.160 ⇒ 00:50:15.830 Uttam Kumaran: Yeah, I guess I don’t know what this is related to.
579 00:50:17.790 ⇒ 00:50:20.409 Emily Giant: About the changes, but it’s low priority.
580 00:50:20.410 ⇒ 00:50:26.620 Amber Lin: Okay, this will be unless we have a documentation ticket
581 00:50:27.100 ⇒ 00:50:31.000 Amber Lin: which we don’t. So this is documentation
582 00:50:40.610 ⇒ 00:50:43.469 Amber Lin: estimate. How long will that take?
583 00:50:43.600 ⇒ 00:50:45.469 Amber Lin: 2 points? 1 point.
584 00:50:45.890 ⇒ 00:50:48.710 Uttam Kumaran: I mean I don’t. I just don’t know what like goes into this.
585 00:50:49.290 ⇒ 00:50:53.340 Amber Lin: I don’t know, either. But do we have any documentation for looker
586 00:50:54.470 ⇒ 00:50:58.349 Amber Lin: for urban studies? Do we need a documentation for looker?
587 00:50:59.070 ⇒ 00:51:00.279 Amber Lin: I’m not sure.
588 00:51:00.280 ⇒ 00:51:02.119 Emily Giant: Y’all need to do that like.
589 00:51:02.630 ⇒ 00:51:11.229 Amber Lin: Do people, I guess, do people know where to look for data may maybe this is more of a in like a marked definition, mark, the march
590 00:51:11.400 ⇒ 00:51:12.590 Amber Lin: documentation thing.
591 00:51:13.100 ⇒ 00:51:15.259 Amber Lin: Maybe we don’t need it here. You’re right.
592 00:51:15.670 ⇒ 00:51:22.040 Amber Lin: Okay, castled migrate.
593 00:51:22.180 ⇒ 00:51:25.650 Amber Lin: Look, Ml. Logic to Dbt. Marts.
594 00:51:27.750 ⇒ 00:51:29.340 Emily Giant: So that’s also like
595 00:51:29.530 ⇒ 00:51:33.820 Emily Giant: a byproduct of everything else that we’re doing. So it doesn’t need to be a separate ticket.
596 00:51:34.710 ⇒ 00:51:35.470 Amber Lin: Oh!
597 00:51:35.720 ⇒ 00:51:44.660 Emily Giant: Well, maybe it does, you know. Maybe it does, just to make sure that, like the derived tables in looker that are still being used, are all migrated.
598 00:51:46.030 ⇒ 00:51:50.300 Emily Giant: But it’s too general of a ticket to be all that useful.
599 00:51:50.890 ⇒ 00:51:53.920 Uttam Kumaran: Yeah. So this probably has to get broken down further. So I would
600 00:51:55.170 ⇒ 00:52:00.230 Uttam Kumaran: put that in the title or, yeah, in this.
601 00:52:00.400 ⇒ 00:52:01.190 Uttam Kumaran: Yeah.
602 00:52:04.690 ⇒ 00:52:06.470 Amber Lin: Okay. 5, points.
603 00:52:08.557 ⇒ 00:52:13.540 Uttam Kumaran: I this. I don’t think we have the criteria to do this, so I don’t can’t point this at all.
604 00:52:13.540 ⇒ 00:52:16.760 Amber Lin: I’m gonna make it bigger, and then we’ll divide it.
605 00:52:16.930 ⇒ 00:52:19.729 Amber Lin: And then priority of this.
606 00:52:21.580 ⇒ 00:52:21.910 Emily Giant: Hello!
607 00:52:21.910 ⇒ 00:52:23.960 Uttam Kumaran: 5 meet. Yeah. Medium or low.
608 00:52:24.550 ⇒ 00:52:25.190 Amber Lin: Okay.
609 00:52:28.810 ⇒ 00:52:34.140 Amber Lin: What about folder structure and lock prod.
610 00:52:35.130 ⇒ 00:52:37.149 Amber Lin: This is at the end of everything.
611 00:52:37.560 ⇒ 00:52:38.190 Emily Giant: Yeah.
612 00:52:41.240 ⇒ 00:52:46.029 Amber Lin: 5, 3, 1, 3 points, priorities.
613 00:52:46.030 ⇒ 00:52:48.670 Emily Giant: Sure 3 is fine, low priority.
614 00:52:52.110 ⇒ 00:52:54.460 Amber Lin: Fresh natals to dashboards.
615 00:52:57.850 ⇒ 00:52:59.510 Emily Giant: Low priority.
616 00:53:00.730 ⇒ 00:53:03.180 Uttam Kumaran: I mean, this is something the analyst team can probably do.
617 00:53:03.180 ⇒ 00:53:03.870 Emily Giant: Yeah.
618 00:53:10.730 ⇒ 00:53:12.840 Amber Lin: okay, right?
619 00:53:13.000 ⇒ 00:53:19.130 Amber Lin: And then migrate high cost. Pdt, certificate
620 00:53:28.310 ⇒ 00:53:35.990 Amber Lin: is this is this the same thing as, my like is this a part of the ticket for that.
621 00:53:40.820 ⇒ 00:53:47.850 Uttam Kumaran: They’re they’re related. Yeah, I think I think the Pdts are something that we should definitely take on.
622 00:53:50.390 ⇒ 00:53:53.890 Uttam Kumaran: This is a little bit of a higher like. I would put a medium priority.
623 00:53:54.380 ⇒ 00:53:54.870 Amber Lin: Okay.
624 00:53:54.870 ⇒ 00:53:57.650 Uttam Kumaran: And it should happen sometime in the next month or so.
625 00:53:58.060 ⇒ 00:54:04.119 Amber Lin: Okay, then we need to probably define it a little bit clearer.
626 00:54:05.340 ⇒ 00:54:08.433 Uttam Kumaran: Yeah, I mean you. You’ll have to go in and look and find all the
627 00:54:08.800 ⇒ 00:54:12.899 Uttam Kumaran: the Pdts, and then create a plan to migrate them.
628 00:54:12.900 ⇒ 00:54:13.510 Amber Lin: Yeah.
629 00:54:20.770 ⇒ 00:54:22.880 Amber Lin: all right, 5 points.
630 00:54:23.000 ⇒ 00:54:23.990 Amber Lin: It’s good.
631 00:54:24.690 ⇒ 00:54:30.510 Amber Lin: And lastly.
632 00:54:35.190 ⇒ 00:54:40.590 Amber Lin: refracture sales, data inventory data explores.
633 00:54:40.950 ⇒ 00:54:44.069 Amber Lin: That’s something we talked about recently.
634 00:54:57.850 ⇒ 00:54:58.790 Emily Giant: With every.
635 00:55:00.150 ⇒ 00:55:02.259 Emily Giant: Thing that we’re doing right like.
636 00:55:03.410 ⇒ 00:55:07.680 Emily Giant: I’m not sure about this as like a separate ticket.
637 00:55:08.210 ⇒ 00:55:14.280 Uttam Kumaran: Yeah, I don’t think this necessarily needs to be a separate ticket so long as the marts are getting used there. Then, yeah, it’s.
638 00:55:14.870 ⇒ 00:55:15.380 Amber Lin: Awesome.
639 00:55:16.140 ⇒ 00:55:17.600 Amber Lin: So duplicate.
640 00:55:18.380 ⇒ 00:55:23.029 Amber Lin: Alright, let’s cleaned up. So, looker, we’re mostly done. We’re just
641 00:55:23.970 ⇒ 00:55:32.739 Amber Lin: like talking to the stakeholders, making sure that they on boarded and then some point in time next month we’ll do this one.
642 00:55:34.540 ⇒ 00:55:39.300 Amber Lin: Okay, anything in ad hoc
643 00:55:43.400 ⇒ 00:55:45.900 Amber Lin: that one. Do we still need that, Emily.
644 00:55:47.710 ⇒ 00:55:49.980 Emily Giant: No, I mean
645 00:55:52.670 ⇒ 00:56:00.599 Emily Giant: not in this board necessarily, but I definitely still have outstanding items to do with this. But it doesn’t need to be a volunteer board.
646 00:56:00.600 ⇒ 00:56:03.080 Amber Lin: Okay, sounds good.
647 00:56:04.120 ⇒ 00:56:07.460 Amber Lin: So I think that’s
648 00:56:07.960 ⇒ 00:56:16.969 Amber Lin: all of it. I think this was very productive grooming session. Thank you all for joining once. We. I think the most important thing now is to work on
649 00:56:17.390 ⇒ 00:56:26.310 Amber Lin: revenue, making sure we have that technical design document, and then after that, we’ll be able to groom revenue because we’ll know what we need to do.
650 00:56:29.000 ⇒ 00:56:29.800 Emily Giant: Agreed.
651 00:56:30.480 ⇒ 00:56:31.250 Amber Lin: Okay.
652 00:56:32.160 ⇒ 00:56:33.020 Emily Giant: Alright, cool.
653 00:56:33.420 ⇒ 00:56:34.500 Amber Lin: Thanks, everybody.
654 00:56:35.040 ⇒ 00:56:35.560 Uttam Kumaran: Okay?
655 00:56:36.640 ⇒ 00:56:37.160 Uttam Kumaran: Bye.
656 00:56:37.160 ⇒ 00:56:38.250 Caio Velasco: Bye.