Meeting Title: US x BF | Standup Date: 2025-05-30 Meeting participants: Caio Velasco, Amber Lin, Demilade Agboola, Emily Giant
WEBVTT
1 00:00:48.000 ⇒ 00:00:49.319 Amber Lin: Hi Kyle.
2 00:00:51.020 ⇒ 00:00:52.699 Caio Velasco: Hi! Amber! How are you?
3 00:00:52.730 ⇒ 00:00:57.800 Amber Lin: Hi, I’m good. I just got into New York City right now. Actually.
4 00:00:58.590 ⇒ 00:01:01.090 Caio Velasco: Oh, you were in Washington before right? DC.
5 00:01:01.090 ⇒ 00:01:06.400 Amber Lin: I know it was so fast. I was like probably a lot recently.
6 00:01:06.400 ⇒ 00:01:07.689 Caio Velasco: At the moment, or.
7 00:01:08.490 ⇒ 00:01:08.895 Amber Lin: Hmm.
8 00:01:09.300 ⇒ 00:01:12.900 Caio Velasco: Are you traveling like as a nomad, or like because of.
9 00:01:12.900 ⇒ 00:01:23.889 Amber Lin: I would love to do digital nomad. But right now I’m just traveling. Have you been doing like the digital nomad life lifestyle at all?
10 00:01:25.214 ⇒ 00:01:32.029 Caio Velasco: Not really. I always had the reason to like either move somewhere or yeah.
11 00:01:32.030 ⇒ 00:01:32.470 Amber Lin: Oh!
12 00:01:32.470 ⇒ 00:01:34.970 Caio Velasco: Yeah, so like, just normally, not really.
13 00:01:36.380 ⇒ 00:01:43.610 Amber Lin: Hmm! I see. I mean, honestly, if I got the chance to just move, I would rather just move to be very honest.
14 00:01:44.260 ⇒ 00:01:45.539 Caio Velasco: True, true, true.
15 00:01:46.030 ⇒ 00:01:48.339 Amber Lin: Yeah. Oh, hi.
16 00:01:49.320 ⇒ 00:01:50.130 Caio Velasco: Hello!
17 00:01:51.580 ⇒ 00:02:00.950 Amber Lin: Hi, I’m trying to. Good morning. I was just looking at the tickets, you added in linear. I’m trying to
18 00:02:01.661 ⇒ 00:02:07.699 Amber Lin: convert that chunk of text into tickets and would love to also get some.
19 00:02:08.060 ⇒ 00:02:10.130 Amber Lin: Yeah. Sorry for me. Hi, Emily.
20 00:02:10.139 ⇒ 00:02:15.229 Emily Giant: Hi! Sorry I’m late. I was writing to Zack. We have overlapping meetings and.
21 00:02:15.230 ⇒ 00:02:16.190 Amber Lin: Oh!
22 00:02:16.190 ⇒ 00:02:21.345 Emily Giant: Know that like I was coming here. Not there. But Hi!
23 00:02:21.980 ⇒ 00:02:22.570 Amber Lin: Hello!
24 00:02:22.570 ⇒ 00:02:27.000 Amber Lin: I don’t like. I don’t think you’re late. Usually like the 1st
25 00:02:27.330 ⇒ 00:02:32.249 Amber Lin: 10. Well, 1st 5 min, and just whatever everybody comes.
26 00:02:33.340 ⇒ 00:02:34.090 Amber Lin: Good time.
27 00:02:35.070 ⇒ 00:02:35.690 Amber Lin: Lips.
28 00:02:35.690 ⇒ 00:02:39.484 Caio Velasco: In Brazil the 1st 30 min. You’re okay. I’m just kidding.
29 00:02:41.440 ⇒ 00:02:43.849 Emily Giant: Well, we’re gonna get along with that because I’m just
30 00:02:45.110 ⇒ 00:02:48.779 Emily Giant: 2 min after the hour. Is my my special time.
31 00:02:49.270 ⇒ 00:02:49.930 Emily Giant: Really.
32 00:02:50.290 ⇒ 00:02:50.650 Amber Lin: Okay.
33 00:02:50.650 ⇒ 00:02:52.890 Emily Giant: Into every meeting. 2 min late.
34 00:02:58.350 ⇒ 00:02:59.190 Amber Lin: What.
35 00:02:59.660 ⇒ 00:03:01.270 Demilade Agboola: I said, that’s time for coffee.
36 00:03:01.800 ⇒ 00:03:02.349 Emily Giant: Oh, you!
37 00:03:02.350 ⇒ 00:03:02.700 Amber Lin: Hello!
38 00:03:02.700 ⇒ 00:03:07.913 Emily Giant: Running around with like a pour over in my hand like making it at the same time
39 00:03:09.010 ⇒ 00:03:10.519 Emily Giant: definitely coffee time.
40 00:03:11.250 ⇒ 00:03:17.799 Amber Lin: Yeah, today I got a mushroom coffee, but I don’t really know what how different it is.
41 00:03:18.364 ⇒ 00:03:24.570 Emily Giant: my partner drinks that like because he doesn’t want to have like too much caffeine all the time, and I’m like, Why.
42 00:03:25.678 ⇒ 00:03:28.730 Emily Giant: but he says it’s okay, like, like, not.
43 00:03:28.730 ⇒ 00:03:29.410 Amber Lin: Okay.
44 00:03:29.410 ⇒ 00:03:30.969 Emily Giant: Placement. But let me know what you think.
45 00:03:30.970 ⇒ 00:03:38.669 Amber Lin: It tastes. I think they have coffee in this mushroom coffee, so it still tastes like coffee, even though it’s mushroom coffee.
46 00:03:38.670 ⇒ 00:03:41.449 Emily Giant: Maybe that’s the trick, is, it can’t be just mushroom coffee.
47 00:03:42.441 ⇒ 00:03:45.417 Amber Lin: I believe so
48 00:03:46.560 ⇒ 00:04:11.809 Amber Lin: let me share my screen. I was just looking at the part that you and do shared. I was trying to convert them into tickets, and I just wanted to clarify a few things before we get started. So I noticed that we were also talking about say, subscription and sales is that just related to the inventory? More, or is that kind of the next step of the revenue part.
49 00:04:12.698 ⇒ 00:04:15.380 Emily Giant: It is related to the inventory mart. They are.
50 00:04:15.380 ⇒ 00:04:16.350 Amber Lin: Okay.
51 00:04:16.350 ⇒ 00:04:23.550 Emily Giant: Curious about like what is happening with the inventory especially concerning like, how many.
52 00:04:23.550 ⇒ 00:04:23.950 Amber Lin: That’s all.
53 00:04:23.950 ⇒ 00:04:26.669 Emily Giant: Non like go to like the August.
54 00:04:26.670 ⇒ 00:04:28.450 Emily Giant: If sales are occurring.
55 00:04:28.450 ⇒ 00:04:29.700 Amber Lin: Like, so.
56 00:04:29.700 ⇒ 00:04:33.899 Emily Giant: That team and the revenue and inventory teams are to your point like very intertwined.
57 00:04:34.480 ⇒ 00:04:42.080 Emily Giant: This is just for the movement of the inventory, and is not really like the top or bottom line in any way. It’s just the unit.
58 00:04:42.080 ⇒ 00:04:43.139 Amber Lin: I see, I see.
59 00:04:44.080 ⇒ 00:04:55.670 Amber Lin: Okay, let me. So looking at these, do you guys think this is something that we’re gonna do? This is, we’re not gonna finish this this cycle right?
60 00:04:55.920 ⇒ 00:04:56.610 Emily Giant: No.
61 00:04:57.060 ⇒ 00:04:57.510 Amber Lin: Yeah.
62 00:04:57.510 ⇒ 00:04:58.010 Emily Giant: Oh, you!
63 00:04:58.850 ⇒ 00:04:59.190 Emily Giant: There’s.
64 00:04:59.190 ⇒ 00:04:59.570 Amber Lin: No.
65 00:04:59.570 ⇒ 00:05:00.200 Emily Giant: Like.
66 00:05:00.200 ⇒ 00:05:00.680 Amber Lin: Yeah.
67 00:05:01.380 ⇒ 00:05:02.549 Emily Giant: Think when we pointed at.
68 00:05:02.550 ⇒ 00:05:03.440 Amber Lin: Know what it was, either.
69 00:05:03.440 ⇒ 00:05:08.100 Emily Giant: It was like 3 cycles, 2 and a half cycle.
70 00:05:08.430 ⇒ 00:05:08.800 Emily Giant: They do.
71 00:05:08.800 ⇒ 00:05:19.607 Amber Lin: Yeah, yeah, just for inventory. I agree. I remember. So where should we start first? st We can. Because right now, I think this ticket.
72 00:05:21.300 ⇒ 00:05:26.179 Amber Lin: I think this is done like we pretty much, very long messages.
73 00:05:26.180 ⇒ 00:05:26.820 Emily Giant: Yeah.
74 00:05:26.820 ⇒ 00:05:27.720 Amber Lin: So
75 00:05:27.840 ⇒ 00:05:42.129 Amber Lin: know what tickets to have. I think this is done, and we can look at the tickets that we just created. And then we can say, Okay, what do we wanna do? First, st because there’s quite a few things
76 00:05:43.365 ⇒ 00:05:44.700 Amber Lin: yeah, for sure.
77 00:05:45.247 ⇒ 00:05:49.722 Emily Giant: And definitely ones that we can like, divide and conquer.
78 00:05:51.310 ⇒ 00:05:54.450 Emily Giant: demo. Lottie, what are your thoughts on this? Because you, you know, I’m like a
79 00:05:55.220 ⇒ 00:05:57.570 Emily Giant: get in there and start ripping away at tasks. But
80 00:05:58.650 ⇒ 00:06:05.800 Emily Giant: As far as the most logical starting point I would I would stink.
81 00:06:07.170 ⇒ 00:06:08.380 Emily Giant: Let me see this ticket?
82 00:06:08.990 ⇒ 00:06:09.600 Emily Giant: Okay.
83 00:06:09.600 ⇒ 00:06:10.928 Demilade Agboola: I guess I’m like
84 00:06:12.700 ⇒ 00:06:16.649 Demilade Agboola: I think eventually, and the supply chain will come last, because we’ll need to finish.
85 00:06:16.650 ⇒ 00:06:17.110 Emily Giant: Love it.
86 00:06:17.110 ⇒ 00:06:18.400 Demilade Agboola: What we currently have.
87 00:06:18.600 ⇒ 00:06:24.390 Demilade Agboola: and then try to make a version of what we have for inventory and supply chain.
88 00:06:24.570 ⇒ 00:06:28.590 Demilade Agboola: which is kind of different from what we’re currently doing. So that will be the last.
89 00:06:29.778 ⇒ 00:06:32.640 Amber Lin: This is gonna be last. Let me move it to the bottom.
90 00:06:34.852 ⇒ 00:06:40.490 Demilade Agboola: In terms of so auditing the models. The patches will come first.st That’s kind of where.
91 00:06:41.140 ⇒ 00:06:42.410 Emily Giant: What I thought too, so.
92 00:06:42.410 ⇒ 00:06:43.010 Amber Lin: Awesome.
93 00:06:43.010 ⇒ 00:06:44.250 Emily Giant: Realigned there.
94 00:06:44.560 ⇒ 00:06:48.310 Demilade Agboola: So we would have that come first.st
95 00:06:48.950 ⇒ 00:06:52.310 Demilade Agboola: Then, once we have the audit done.
96 00:06:52.980 ⇒ 00:06:56.839 Demilade Agboola: we will need to start looking through the different tasks now.
97 00:06:57.520 ⇒ 00:07:05.980 Demilade Agboola: and I would suggest that we do or unless.
98 00:07:05.980 ⇒ 00:07:06.450 Amber Lin: Yeah.
99 00:07:06.990 ⇒ 00:07:08.610 Demilade Agboola: So borders first.st
100 00:07:09.870 ⇒ 00:07:11.749 Demilade Agboola: And we go to Ims for deliveries.
101 00:07:13.390 ⇒ 00:07:16.189 Demilade Agboola: So it’s kind of the same like logic, for like.
102 00:07:16.190 ⇒ 00:07:17.179 Emily Giant: Yeah, it is.
103 00:07:17.774 ⇒ 00:07:28.470 Demilade Agboola: Deliveries and then, maybe in parallel, we can have the lower tasks like the code base cleanup.
104 00:07:28.720 ⇒ 00:07:33.740 Amber Lin: So things like and calendar enhancements is kind of like later right.
105 00:07:34.472 ⇒ 00:07:37.810 Demilade Agboola: I mean to be fair. That’s like a like a small task that can be.
106 00:07:37.810 ⇒ 00:07:38.270 Amber Lin: Yeah.
107 00:07:38.270 ⇒ 00:07:38.990 Amber Lin: Okay. Okay.
108 00:07:39.240 ⇒ 00:07:40.780 Demilade Agboola: There’s something in there.
109 00:07:40.780 ⇒ 00:07:47.920 Amber Lin: Sounds good. I was looking at this part, so I assume these 2. Each of them are different tickets, right?
110 00:07:49.670 ⇒ 00:07:54.080 Demilade Agboola: They should be sub to Oms for deliveries and Oms supporters. My bad.
111 00:07:54.080 ⇒ 00:08:08.820 Amber Lin: Oh, okay, sounds good and so great. I was gonna ask, like, this is 2 weeks. We need to break it down. But I think we already have it broken down? Would you know how long each of these tickets would take, or, do we have to spend some time to break them down further.
112 00:08:09.150 ⇒ 00:08:09.820 Amber Lin: perfect.
113 00:08:10.510 ⇒ 00:08:15.990 Demilade Agboola: I mean, I think we will have some time to break them down further.
114 00:08:16.600 ⇒ 00:08:21.610 Demilade Agboola: because I think 2 weeks is a conservative estimate. It might go over. Because again.
115 00:08:22.470 ⇒ 00:08:25.920 Demilade Agboola: we’re literally tearing apart models that like what? 500 lines
116 00:08:27.310 ⇒ 00:08:42.249 Demilade Agboola: trying to break it apart and then redesign it. And like trunking is based on like this is old logic to the old. Logic lives here. New logic lives here, work and all that stuff. So it might take a bit longer, but that’s fine.
117 00:08:42.559 ⇒ 00:08:44.860 Demilade Agboola: We will start off with
118 00:08:46.120 ⇒ 00:08:47.299 Amber Lin: I think we should.
119 00:08:48.430 ⇒ 00:08:51.469 Demilade Agboola: Oh, no, actually, we’ll need to do.
120 00:08:53.080 ⇒ 00:09:00.329 Demilade Agboola: Sorry we’ll need to do. We’ll need. I think we’ll need to include the adjustment types first, st just to get an idea of what that looks like
121 00:09:00.550 ⇒ 00:09:01.080 Demilade Agboola: with it.
122 00:09:01.440 ⇒ 00:09:07.420 Demilade Agboola: And then we now start building the new model, so it would be one to
123 00:09:09.740 ⇒ 00:09:24.389 Demilade Agboola: 3 and 4 are kind of part of doing 2. I don’t know how to explain beyond that. But like, when you’re building the new models, we’re gonna have to be separating the legacy logic as well as well as partitioning the by time.
124 00:09:25.060 ⇒ 00:09:41.079 Amber Lin: Great. So it seems like we need to. Well, audit was existing, and then kind of spike on how we’re gonna do it. If we were if we don’t know yet how to do it. And then we’re gonna build the new models. And I guess this is this goes after this
125 00:09:41.520 ⇒ 00:09:46.620 Amber Lin: like include additional adjustments. After we build the new models or.
126 00:09:46.780 ⇒ 00:09:47.340 Demilade Agboola: Right.
127 00:09:47.460 ⇒ 00:09:48.949 Demilade Agboola: I mean it it.
128 00:09:49.250 ⇒ 00:09:52.899 Demilade Agboola: I think we could. It could come before after. I think we could just.
129 00:09:52.900 ⇒ 00:09:54.160 Amber Lin: Oh, okay.
130 00:09:54.570 ⇒ 00:09:58.360 Demilade Agboola: It depends on how like, how much lift that is
131 00:09:58.630 ⇒ 00:10:08.069 Demilade Agboola: potentially, it might be easy to just add it. If it’s a simple thing of like, oh, it’s a quick join to this table, and then we need to call it when it’s this that could be done.
132 00:10:08.070 ⇒ 00:10:08.490 Amber Lin: Or no.
133 00:10:08.680 ⇒ 00:10:12.390 Demilade Agboola: And then you will just be part of the new logic.
134 00:10:12.390 ⇒ 00:10:33.210 Amber Lin: Alright. Sounds good. Sounds good, I think we’ll just. We’ll just sorry I didn’t mean to spend so much time on this I will just look at it further in the grooming. I think I don’t yet understand what’s going on, but I think you have a pretty good idea. Emily has a pretty good idea, and then we’ll we’ll talk about it in the grooming, and then we’ll have more
135 00:10:33.450 ⇒ 00:10:35.119 Amber Lin: concrete of like
136 00:10:35.320 ⇒ 00:10:42.059 Amber Lin: a roadmap, and how long each one would take once we do the grooming. But I think, for even for
137 00:10:42.270 ⇒ 00:10:46.690 Amber Lin: now, like these are, this is something that we can do immediately. Right.
138 00:10:49.580 ⇒ 00:10:50.860 Demilade Agboola: Yes, definitely.
139 00:10:53.310 ⇒ 00:10:58.200 Amber Lin: so if this comes first, st let me just add add that in
140 00:11:08.890 ⇒ 00:11:19.960 Amber Lin: anyways that’s that’s really awesome, let me go to the current cycle, and then we can just look at these few things. So we got that ticket done.
141 00:11:20.180 ⇒ 00:11:24.440 Amber Lin: I think Utam gave an answer on this 1st one.
142 00:11:25.030 ⇒ 00:11:28.349 Emily Giant: Yeah. And I only reached out directly to polytonic. And they’re.
143 00:11:28.350 ⇒ 00:11:28.730 Amber Lin: Okay.
144 00:11:28.730 ⇒ 00:11:33.924 Emily Giant: Let me read it. It sounds like they were willing to work on it. They’re really awesome.
145 00:11:34.230 ⇒ 00:11:34.750 Amber Lin: Good.
146 00:11:36.310 ⇒ 00:11:46.239 Emily Giant: Okay, Utam did hop in here, too. 2 options. Here we can implement to the apply for multiple schedules. You can use external orchestrator like Utam says.
147 00:11:48.110 ⇒ 00:11:48.570 Amber Lin: Okay.
148 00:11:48.570 ⇒ 00:11:50.770 Emily Giant: Okay. You told me to ask how urgent it is.
149 00:11:50.770 ⇒ 00:11:51.589 Amber Lin: There’s a little.
150 00:11:51.590 ⇒ 00:11:52.490 Emily Giant: And
151 00:11:52.610 ⇒ 00:12:01.600 Emily Giant: it it is relatively urgent. That was just this morning that he asked. I would say. This needs to be done like as soon as possible, so.
152 00:12:01.600 ⇒ 00:12:02.200 Amber Lin: Okay. Okay.
153 00:12:02.200 ⇒ 00:12:05.550 Emily Giant: I’ll I’ll answer you, Tom, but this ticket is definitely in progress. I’m like.
154 00:12:05.970 ⇒ 00:12:06.390 Amber Lin: Work.
155 00:12:06.390 ⇒ 00:12:07.540 Emily Giant: Towards resolution.
156 00:12:07.750 ⇒ 00:12:09.509 Demilade Agboola: That’s a great question. Wait.
157 00:12:09.510 ⇒ 00:12:22.439 Demilade Agboola: You want it as soon as possible. The solution would probably be to take a new tool like dark star airflow, because the it’s ready built, and it that’s literally what it’s meant for to orchestrate flows
158 00:12:23.253 ⇒ 00:12:25.939 Demilade Agboola: to create like all these custom flows.
159 00:12:26.400 ⇒ 00:12:30.790 Demilade Agboola: but like with polytomic, even if they’re gonna work on it, they said, it will take a couple of weeks.
160 00:12:32.170 ⇒ 00:12:33.200 Emily Giant: Oh, okay.
161 00:12:33.480 ⇒ 00:12:38.530 Demilade Agboola: Yeah, so it will. We’ll we’ll we’re preparing a doc for that, like internally, we’re talking about it.
162 00:12:39.180 ⇒ 00:12:44.879 Demilade Agboola: So we’ll come up with a like pro pro con of like either approach. And you you guys can make a decision.
163 00:12:44.880 ⇒ 00:12:45.700 Emily Giant: Okay.
164 00:12:47.550 ⇒ 00:13:05.560 Amber Lin: Yeah. And Emily, when we is the same as said, When would you need this pot? I know as soon as possible, but is a week, a good estimate where it’s like next Tuesday, something something that will be acceptable? Or is this something that can go on for like 2 more weeks.
165 00:13:06.100 ⇒ 00:13:12.135 Emily Giant: I wouldn’t say that the teams would be thrilled about it going on for 2 more weeks, so a week would be.
166 00:13:12.410 ⇒ 00:13:16.870 Amber Lin: Okay, a week is the latest deadline. Okay? Sounds good.
167 00:13:18.971 ⇒ 00:13:23.489 Amber Lin: Yeah. Cause even migrating to the doctor. And airflow will also take some time. But.
168 00:13:23.490 ⇒ 00:13:24.100 Emily Giant: Yeah.
169 00:13:24.474 ⇒ 00:13:32.335 Amber Lin: Sounds good. Let’s talk about it internally. I probably need to ask you, Tom, and then we’ll give you guys a
170 00:13:38.498 ⇒ 00:13:48.520 Amber Lin: okay, sounds good and then updates on this
171 00:13:50.140 ⇒ 00:13:53.050 Amber Lin: that I used to have every day. Really, yeah.
172 00:13:54.318 ⇒ 00:13:58.109 Demilade Agboola: Cause like it seems.
173 00:13:58.110 ⇒ 00:13:59.600 Amber Lin: But now it’s like nauseating.
174 00:13:59.600 ⇒ 00:14:01.679 Demilade Agboola: Everything in Gpt seems fine.
175 00:14:02.630 ⇒ 00:14:08.650 Demilade Agboola: and I’m not sure if the problem still exist. But I haven’t necessarily seen, like the the point.
176 00:14:08.650 ⇒ 00:14:09.910 Amber Lin: I don’t have time point of failure.
177 00:14:09.910 ⇒ 00:14:10.630 Amber Lin: Also, just add that.
178 00:14:10.630 ⇒ 00:14:14.009 Demilade Agboola: I will just like Pop on with me today. But
179 00:14:14.170 ⇒ 00:14:18.220 Demilade Agboola: 15 min, 20 min, and just kind of see how that’s coming along.
180 00:14:18.220 ⇒ 00:14:19.747 Amber Lin: Hi, how are you?
181 00:14:21.081 ⇒ 00:14:25.360 Amber Lin: So is the current state that it?
182 00:14:25.570 ⇒ 00:14:30.880 Amber Lin: I guess one is this report updated right now? Have you checked.
183 00:14:33.560 ⇒ 00:14:36.320 Demilade Agboola: Yeah, it looks. I’ve looked at the
184 00:14:36.540 ⇒ 00:14:40.190 Demilade Agboola: yeah, it is. So that’s why I’m like, what’s like. It seems fine.
185 00:14:40.190 ⇒ 00:14:41.010 Amber Lin: Interesting.
186 00:14:41.200 ⇒ 00:14:43.200 Amber Lin: Yeah, that’s why I say, report
187 00:14:44.030 ⇒ 00:14:50.590 Amber Lin: report is updated. So we can. I guess that now this is more of a investigate. If this is a 1 time issue.
188 00:14:50.590 ⇒ 00:14:55.670 Emily Giant: Yeah, I think it was like a weekend. We have a different schedule for the weekend, and something.
189 00:14:55.670 ⇒ 00:14:56.410 Amber Lin: Oh!
190 00:14:56.580 ⇒ 00:14:58.480 Emily Giant: Incorrect with the weekends.
191 00:15:00.610 ⇒ 00:15:05.039 Demilade Agboola: Yeah, so that’s kind of like things just look fine. So I was a bit like, Oh.
192 00:15:05.560 ⇒ 00:15:09.304 Emily Giant: Same here, I was like, Okay.
193 00:15:10.230 ⇒ 00:15:18.739 Amber Lin: Okay, I’m gonna I move the priority. And I say, we move the deadline. We should should probably still investigate if it
194 00:15:19.030 ⇒ 00:15:30.410 Amber Lin: still happens, cause. This does create some for you guys to look at the reports it it is an issue if it stops again. But I mean, if it’s updated. Now, I
195 00:15:30.510 ⇒ 00:15:34.400 Amber Lin: I think we can take some more time on this.
196 00:15:35.700 ⇒ 00:15:36.460 Amber Lin: It’s better.
197 00:15:37.104 ⇒ 00:15:44.850 Amber Lin: How long would it take? Would you say to investigate? If it’s a 1 time issue.
198 00:15:47.748 ⇒ 00:15:51.630 Demilade Agboola: I mean, we have an idea of when the issues happen. So it’ll probably just be a.
199 00:15:52.380 ⇒ 00:15:56.379 Demilade Agboola: Going back Dvt jobs and seeing like what coincides with
200 00:15:56.780 ⇒ 00:15:59.830 Demilade Agboola: like, what failures coincide with the lack of fresh.
201 00:16:00.880 ⇒ 00:16:09.890 Amber Lin: Okay, okay, would you be able to look at it? Say, this week or next.
202 00:16:10.250 ⇒ 00:16:14.980 Amber Lin: maybe Monday or Tuesday, to see if this issue comes up again over the weekend.
203 00:16:16.033 ⇒ 00:16:23.150 Demilade Agboola: I think, potentially next week this kind of a lot going on on my plate. So I will see if I could.
204 00:16:23.150 ⇒ 00:16:29.599 Amber Lin: Yeah, yeah. Sounds good cause if we check next week as well, we can see what’s if it happened again.
205 00:16:30.060 ⇒ 00:16:31.080 Amber Lin: I don’t know.
206 00:16:32.380 ⇒ 00:16:33.240 Demilade Agboola: Agreed.
207 00:16:35.630 ⇒ 00:16:40.080 Amber Lin: Sounds good. I’m gonna put the deadline on next
208 00:16:41.782 ⇒ 00:16:55.039 Amber Lin: check on this one. So how’s progress on this?
209 00:16:58.320 ⇒ 00:16:59.610 Amber Lin: I literally.
210 00:17:00.220 ⇒ 00:17:05.530 Demilade Agboola: So I reach out to polytomic directly, and it appears that we could actually.
211 00:17:05.530 ⇒ 00:17:05.920 Amber Lin: Thank you.
212 00:17:05.920 ⇒ 00:17:07.670 Demilade Agboola: Just do it from their end.
213 00:17:08.240 ⇒ 00:17:11.329 Demilade Agboola: and that will probably be the easiest thing. So I oh.
214 00:17:11.339 ⇒ 00:17:13.799 Demilade Agboola: think with them, and just see what happens.
215 00:17:19.380 ⇒ 00:17:25.679 Amber Lin: Do they say when they would be able to figure it out by? Because I don’t want us to get stuck?
216 00:17:26.349 ⇒ 00:17:29.259 Amber Lin: I mean, we’re testing respond to us.
217 00:17:29.260 ⇒ 00:17:37.309 Demilade Agboola: I want to be fair. They’re like very responsive under like
218 00:17:38.640 ⇒ 00:17:41.930 Demilade Agboola: Where the exact point of view comes in from.
219 00:17:45.503 ⇒ 00:17:47.110 Amber Lin: Okay, awesome.
220 00:17:48.000 ⇒ 00:17:51.739 Amber Lin: And you think it’s it’s okay to let them handle that.
221 00:17:52.380 ⇒ 00:17:53.290 Amber Lin: I just.
222 00:17:54.371 ⇒ 00:17:55.980 Demilade Agboola: I think it’s fine. It’s.
223 00:17:55.980 ⇒ 00:17:56.380 Amber Lin: So.
224 00:17:56.380 ⇒ 00:18:00.270 Demilade Agboola: If they’re if they’re gonna but it’s when they create the table that you know.
225 00:18:00.472 ⇒ 00:18:01.079 Amber Lin: Let’s do that.
226 00:18:01.080 ⇒ 00:18:02.220 Demilade Agboola: Access issues happen.
227 00:18:03.390 ⇒ 00:18:07.190 Demilade Agboola: We learn like, if they can handle it like, if we learn what they’re doing.
228 00:18:07.665 ⇒ 00:18:12.084 Demilade Agboola: it would. We could just see how it works, but like either ways, it’s fine to be honest.
229 00:18:12.330 ⇒ 00:18:12.900 Amber Lin: Okay.
230 00:18:13.768 ⇒ 00:18:21.090 Amber Lin: Okay. Sounds good any change in the deadline or anything. So this should be.
231 00:18:21.540 ⇒ 00:18:24.660 Amber Lin: I’m putting it in escalation.
232 00:18:25.130 ⇒ 00:18:27.759 Amber Lin: I would say we should expect it by
233 00:18:29.040 ⇒ 00:18:33.010 Amber Lin: like end of the cycle, maybe, or next one.
234 00:18:33.950 ⇒ 00:18:34.490 Demilade Agboola: Dude. This is.
235 00:18:37.700 ⇒ 00:18:38.030 Demilade Agboola: yeah.
236 00:18:38.030 ⇒ 00:18:39.399 Amber Lin: Oh, this is wrong!
237 00:18:39.770 ⇒ 00:18:41.310 Demilade Agboola: Yeah, that’s that’s correct.
238 00:18:41.920 ⇒ 00:18:42.580 Amber Lin: Oops.
239 00:18:43.179 ⇒ 00:18:44.499 Amber Lin: Okay, here we go.
240 00:18:46.060 ⇒ 00:18:47.270 Amber Lin: Okay.
241 00:18:52.400 ⇒ 00:18:58.519 Amber Lin: alright. Kyle, any updates on your end? I know you also sent something in this.
242 00:18:59.554 ⇒ 00:18:59.799 Caio Velasco: Yeah.
243 00:18:59.800 ⇒ 00:19:00.180 Amber Lin: Thank you.
244 00:19:00.511 ⇒ 00:19:04.160 Caio Velasco: So for the dashboard, I think, I talked to Emily
245 00:19:04.606 ⇒ 00:19:08.899 Caio Velasco: via slack, and we are gonna have a meeting on Monday, and I think.
246 00:19:08.900 ⇒ 00:19:09.390 Amber Lin: Hmm.
247 00:19:10.340 ⇒ 00:19:14.472 Caio Velasco: Also, I don’t know, Emily. Would you share something with me as well? Right like?
248 00:19:15.100 ⇒ 00:19:22.079 Caio Velasco: Well, whatever you can share until then it’s fine. Otherwise we can also start on Monday. That would be okay as well. It’s up to you.
249 00:19:22.430 ⇒ 00:19:30.360 Emily Giant: That’s what I’m working on today. I I’m just finishing up my looker user audit. And then I’m gonna move on to like filling you in where I can, and then
250 00:19:31.139 ⇒ 00:19:35.290 Emily Giant: that way, we can have a leg up on Monday when we meet.
251 00:19:36.400 ⇒ 00:19:36.930 Emily Giant: Yeah.
252 00:19:36.930 ⇒ 00:19:39.259 Caio Velasco: Perfect. So for that one. I think we are good.
253 00:19:39.260 ⇒ 00:19:40.619 Amber Lin: Want that. Go and talk.
254 00:19:41.220 ⇒ 00:19:43.020 Caio Velasco: For the next one.
255 00:19:45.700 ⇒ 00:19:46.270 Caio Velasco: It audit.
256 00:19:46.270 ⇒ 00:19:46.910 Amber Lin: Events.
257 00:19:47.140 ⇒ 00:19:49.799 Caio Velasco: It’s just like.
258 00:19:52.415 ⇒ 00:19:56.839 Amber Lin: So for this.
259 00:19:56.840 ⇒ 00:19:57.405 Caio Velasco: One
260 00:19:57.970 ⇒ 00:19:59.260 Amber Lin: So.
261 00:19:59.260 ⇒ 00:20:04.429 Caio Velasco: So what I did so far is I’m putting everything in the in that spreadsheet.
262 00:20:04.430 ⇒ 00:20:05.040 Amber Lin: But sometimes.
263 00:20:05.378 ⇒ 00:20:13.509 Caio Velasco: I created some new tabs. They’re called ingestion tabs. One. It’s by source. The other one is by table level, also by source.
264 00:20:14.630 ⇒ 00:20:20.260 Caio Velasco: And what I’m doing is with Hevo. I was able to use their Api to
265 00:20:21.050 ⇒ 00:20:26.729 Caio Velasco: just pull data that I wanted was just easier to do that, even though it took me some time to build the script
266 00:20:27.851 ⇒ 00:20:32.630 Caio Velasco: for polytonic it was quite easy, because there was just one there, so that was easy.
267 00:20:32.630 ⇒ 00:20:33.489 Amber Lin: It’s recyclable.
268 00:20:33.490 ⇒ 00:20:44.989 Caio Velasco: And for stitch. I couldn’t see an Api key, maybe because of the plan you guys have. But I was also able to kind of scrape.
269 00:20:45.370 ⇒ 00:20:47.089 Caio Velasco: Use a script to script the
270 00:20:47.420 ⇒ 00:20:49.560 Caio Velasco: the website. So I’m working on it now, and.
271 00:20:49.560 ⇒ 00:20:50.270 Amber Lin: We’ll turn on.
272 00:20:50.270 ⇒ 00:20:57.340 Caio Velasco: I already have the sources which is good, but then, now I have to see if I can go and get the tables by source, which is a bit more.
273 00:20:58.090 ⇒ 00:21:01.219 Caio Velasco: But then, at least we wouldn’t need to ask them to, you know.
274 00:21:01.220 ⇒ 00:21:01.620 Amber Lin: It’s there.
275 00:21:01.620 ⇒ 00:21:05.000 Caio Velasco: Would be on a new plan or something. I think this, yes, sure. Yeah.
276 00:21:06.300 ⇒ 00:21:08.790 Amber Lin: Okay. Awesome.
277 00:21:10.660 ⇒ 00:21:27.388 Amber Lin: I guess, for both of these might. I want to ask if you think both of them are on track to finish, because right now, I know. So for this one, we have all the tables. What’s gonna happen after we get all the tables? And how far is it from
278 00:21:27.830 ⇒ 00:21:29.579 Amber Lin: being finished? Right?
279 00:21:30.060 ⇒ 00:21:30.790 Amber Lin: Oh, okay.
280 00:21:30.790 ⇒ 00:21:36.079 Caio Velasco: So I think. Well, 1st thing would have to be to have all the tables right. I think this is almost done.
281 00:21:36.300 ⇒ 00:21:36.520 Amber Lin: Yeah.
282 00:21:36.828 ⇒ 00:21:47.299 Caio Velasco: After that, then we have to like, understand? I’ll have to go through them and also understand what each of them means. And and also that’s why I have other 2 columns over there.
283 00:21:47.300 ⇒ 00:21:47.640 Amber Lin: Okay.
284 00:21:47.640 ⇒ 00:21:55.530 Caio Velasco: Find the business unit, another one with a short description, just so that we get familiar with with the table. Maybe Emily can help us also.
285 00:21:56.090 ⇒ 00:21:56.420 Caio Velasco: So.
286 00:21:56.420 ⇒ 00:21:58.840 Amber Lin: Columns just to see if it’s making sense.
287 00:21:58.840 ⇒ 00:22:00.150 Amber Lin: Do you remember yesterday.
288 00:22:00.608 ⇒ 00:22:03.359 Caio Velasco: And then we would have to.
289 00:22:03.360 ⇒ 00:22:03.780 Amber Lin: Oh!
290 00:22:03.780 ⇒ 00:22:06.430 Caio Velasco: Do like an acceptance criteria, or something that we.
291 00:22:06.930 ⇒ 00:22:12.909 Caio Velasco: You know. Go one by one, and see. How would I define if they are duplicate or not? I think this would.
292 00:22:13.620 ⇒ 00:22:17.790 Caio Velasco: It would take some work, because it is indeed a lot of tables, for sure.
293 00:22:17.790 ⇒ 00:22:19.269 Amber Lin: I think so far.
294 00:22:19.270 ⇒ 00:22:26.280 Caio Velasco: We have, like 440 tables. So far, so.
295 00:22:26.770 ⇒ 00:22:28.089 Amber Lin: Gosh, okay.
296 00:22:28.882 ⇒ 00:22:39.200 Amber Lin: is there any? I think you’ll you’ll figure it out like maybe AI can help make faster. But like you’re really good at it. I don’t have to tell you anything.
297 00:22:39.390 ⇒ 00:22:47.469 Amber Lin: And then, yeah, I guess we tag it. And then we annotate. If we keep room consolidate essentially.
298 00:22:47.650 ⇒ 00:23:00.929 Caio Velasco: Yeah, yeah, let’s see that. For now we did some data engineering work to get things we need. And now we would have to do more like, let’s say, business work to really understand, like the sources, the tables, and how we would. Would that find a redundancy or
299 00:23:01.120 ⇒ 00:23:01.710 Caio Velasco: something.
300 00:23:02.143 ⇒ 00:23:17.330 Amber Lin: So it sounds like the 1st step. You’re pretty much done. Just a little bit stuck on stage, and the second step you probably would help from Emily, and then the 3rd step is
301 00:23:17.330 ⇒ 00:23:18.470 Amber Lin: perfect. Do you see?
302 00:23:18.470 ⇒ 00:23:22.350 Amber Lin: Think that this is a good deadline?
303 00:23:22.690 ⇒ 00:23:23.030 Caio Velasco: We.
304 00:23:23.030 ⇒ 00:23:28.469 Amber Lin: Finish in another week. Or do you think like the 1st 2 steps can be finished?
305 00:23:28.820 ⇒ 00:23:31.840 Caio Velasco: Yeah. 1, st 2. Steps. Yes, yes, yes.
306 00:23:31.840 ⇒ 00:23:32.400 Amber Lin: Okay.
307 00:23:34.069 ⇒ 00:23:52.049 Amber Lin: that’s good. So probably would need to extend that I’m gonna confirm how that shifts our cycles. But this is really good to know.
308 00:23:52.900 ⇒ 00:24:02.980 Caio Velasco: But this is good work, because it’s starting. It’s starting with the sources it’s super good to like. If you organize that, then everything falling from there should be easier, I hope.
309 00:24:03.220 ⇒ 00:24:12.369 Amber Lin: Totally. Totally. I think this work is just really, really important. And I’ll help other areas as well. Similar, I guess, similar. For this
310 00:24:12.790 ⇒ 00:24:15.260 Amber Lin: we’ve, I think.
311 00:24:17.285 ⇒ 00:24:26.030 Amber Lin: Emily, and I guess. What are the steps similarly toward this? So we looked at all the dashboards.
312 00:24:27.060 ⇒ 00:24:32.239 Amber Lin: Right? We list all dashboards. We
313 00:24:39.310 ⇒ 00:24:40.750 Amber Lin: What do you guys think?
314 00:24:42.531 ⇒ 00:24:52.950 Amber Lin: And also like, what would? What would you be aiming for in your meeting to do.
315 00:24:52.950 ⇒ 00:24:53.380 Caio Velasco: Yeah. Go ahead.
316 00:24:53.380 ⇒ 00:24:53.790 Amber Lin: Yes.
317 00:24:55.485 ⇒ 00:24:56.630 Amber Lin: Okay.
318 00:24:56.630 ⇒ 00:24:58.320 Emily Giant: Yeah, you you go ahead. I’m.
319 00:24:58.320 ⇒ 00:25:04.480 Caio Velasco: So, yeah, so I think, yeah, well, listing for sure. Then categorizing, I think that’s what we would
320 00:25:05.210 ⇒ 00:25:13.549 Caio Velasco: doing the 1st meeting just to like, get acquainted with the dashboard and everything that’s happening, the business function, the most used one like Zach mentioned
321 00:25:14.180 ⇒ 00:25:17.779 Caio Velasco: meeting. You have, like the the comment up there.
322 00:25:17.780 ⇒ 00:25:18.280 Amber Lin: How.
323 00:25:18.280 ⇒ 00:25:23.820 Caio Velasco: And yeah, I think that would be the 1st step to to understand, like what can be fit.
324 00:25:23.920 ⇒ 00:25:31.090 Caio Velasco: how to subset the dashboard. I think this would be the 1st step for sure. That’s probably what Emily will try to do with me.
325 00:25:31.090 ⇒ 00:25:32.581 Emily Giant: That that makes sense to me.
326 00:25:34.030 ⇒ 00:25:40.420 Amber Lin: Sounds good. And after that we kind of lag.
327 00:25:41.480 ⇒ 00:25:54.170 Amber Lin: Okay? So it seems like, that’s the 1st step. Do you say that this like what is a good? Is this still a good deadline for.
328 00:25:54.170 ⇒ 00:25:57.809 Caio Velasco: Yeah, yeah. I think both can be done. Yes.
329 00:25:57.810 ⇒ 00:26:08.159 Amber Lin: Okay, okay, sounds good. So by next Tuesday we will have categorized and flag them. And then next step, we’ll be able to present it to the team.
330 00:26:09.990 ⇒ 00:26:20.489 Caio Velasco: Yes, yes, for the last one. Let me sorry. It was Tuesday or Friday, just to, because I saw 6, 3, and I usually use like different days, a month.
331 00:26:20.490 ⇒ 00:26:29.420 Amber Lin: I know I know the Us. One is very confusing. This is 6, 3 would be Tuesday, but.
332 00:26:29.930 ⇒ 00:26:35.760 Caio Velasco: Yeah. Well, I think for those 2, for each different 10.
333 00:26:35.760 ⇒ 00:26:36.125 Amber Lin: Hmm!
334 00:26:36.490 ⇒ 00:26:41.170 Caio Velasco: I think we can get an outcome from the meeting, and then we see. But I think it’s a good step. Yeah.
335 00:26:41.170 ⇒ 00:26:46.269 Amber Lin: Okay, okay, okay, sounds good. And then we’ll we’ll see what next we need to do.
336 00:26:46.450 ⇒ 00:26:47.060 Caio Velasco: Yep.
337 00:26:49.790 ⇒ 00:26:52.435 Amber Lin: Yeah, okay, sounds good.
338 00:26:53.370 ⇒ 00:27:02.509 Amber Lin: you guys are meeting next Monday, like, today, we’re looking at this one. I believe.
339 00:27:02.510 ⇒ 00:27:03.370 Emily Giant: Yes.
340 00:27:03.370 ⇒ 00:27:09.950 Amber Lin: Okay, so this is for who’s taking us this on? Sorry we shouldn’t be doing this in.
341 00:27:11.882 ⇒ 00:27:19.340 Amber Lin: Okay, I don’t know if Demo Lade has.
342 00:27:19.340 ⇒ 00:27:22.554 Demilade Agboola: I mean, yeah, this is yeah.
343 00:27:27.290 ⇒ 00:27:30.909 Demilade Agboola: So yes, I will talk to audit it. So I can start work on Monday, though.
344 00:27:31.628 ⇒ 00:27:36.650 Amber Lin: Okay, so this is for who’s the next Monday?
345 00:27:37.980 ⇒ 00:27:39.920 Demilade Agboola: When I begin, yeah.
346 00:27:39.920 ⇒ 00:27:47.510 Amber Lin: Oh, never mind. Okay. Sounds good. We have another grooming next Monday. We could just talk about it then
347 00:27:47.900 ⇒ 00:27:48.880 Amber Lin: hot water.
348 00:27:49.940 ⇒ 00:27:50.650 Demilade Agboola: Sounds good.
349 00:27:50.650 ⇒ 00:27:55.080 Amber Lin: Is there anything I know, Kyle, still working on these 2?
350 00:27:56.151 ⇒ 00:28:02.920 Amber Lin: Any other things in progress? Because these who are not not on our plate anymore.
351 00:28:03.420 ⇒ 00:28:09.220 Amber Lin: I just don’t know what we’re looking at today. I know Emily’s giving the Async updates to file.
352 00:28:11.150 ⇒ 00:28:12.350 Amber Lin: And then.
353 00:28:16.640 ⇒ 00:28:21.459 Amber Lin: is there anything you’re working on today? Or is this? Is it.
354 00:28:22.952 ⇒ 00:28:25.219 Demilade Agboola: Nothing from my end. I think we’re pretty good.
355 00:28:25.930 ⇒ 00:28:34.090 Amber Lin: Okay. Sounds good. Alright. Thank you all for the meeting. It went a little bit over because we have some new tasks that we had to add today.
356 00:28:35.390 ⇒ 00:28:36.290 Emily Giant: Alright. Thank you.
357 00:28:36.620 ⇒ 00:28:39.620 Amber Lin: Yeah, thank you so much. Alright, bye, bye.
358 00:28:39.620 ⇒ 00:28:40.270 Caio Velasco: Alright, bye-bye.
359 00:28:40.980 ⇒ 00:28:45.289 Amber Lin: And that is just I don’t feel like water damage up there.
360 00:28:52.260 ⇒ 00:28:55.290 Amber Lin: Oh, devil! Are you still in this meeting?