Meeting Title: US x BF | Standup Date: 2025-06-04 Meeting participants: Demilade Agboola, Emily Giant, Amber Lin, Caio Velasco
WEBVTT
1 00:00:17.380 ⇒ 00:00:19.404 Emily Giant: Good morning or afternoon.
2 00:00:23.550 ⇒ 00:00:25.050 Demilade Agboola: Hi, Emily, how are you?
3 00:00:25.690 ⇒ 00:00:28.940 Emily Giant: I’m okay. I just ran in the door from the Vet.
4 00:00:29.494 ⇒ 00:00:34.970 Emily Giant: We took one of the barn cats because we have like a cat colony that lives outside, and
5 00:00:35.190 ⇒ 00:00:51.779 Emily Giant: 2 of them were sick, and my partner dropped one of them when we were going to the car. So I just spent the last hour chasing a cat that isn’t even my cat outside of the veterinary clinic. So I’m a little frazzled. But but I’m okay.
6 00:00:52.360 ⇒ 00:00:54.369 Demilade Agboola: It sounds like it’s been quite the morning.
7 00:00:54.690 ⇒ 00:00:57.719 Emily Giant: It has been quite the morning. It’s the like
8 00:00:58.630 ⇒ 00:01:04.450 Emily Giant: the fact that it is not actually our animal that like
9 00:01:04.910 ⇒ 00:01:22.060 Emily Giant: why, we have invested this much feeling in these like feral cats, they’re neighborhood cats. I don’t know if you have programs like this like where y’all live, but you can like have them clipped and chipped and neutered, and then, as long as you like agree to feed them they’ll do it for cheap.
10 00:01:22.080 ⇒ 00:01:40.490 Emily Giant: and we live in the country, so the word is out. All of the cats come to our house like this appeared on the porch, and she’s precious. But then she was bleeding this morning in places she shouldn’t be bleeding, so we took her in, and then, when we were going to the car, another one of the cats was like limping towards us, and we were like Oh.
11 00:01:40.490 ⇒ 00:01:41.150 Amber Lin: You.
12 00:01:41.420 ⇒ 00:01:47.969 Emily Giant: Get in the car you’re coming into so $200 later for cats that are not ours.
13 00:01:48.390 ⇒ 00:01:55.970 Emily Giant: One of them was, he scratched my partner in the parking lot, and anyone would have dropped the cat like he like went for him.
14 00:01:56.050 ⇒ 00:02:20.380 Emily Giant: And so we were running around the veterinary clinic, trying to catch him for like 30, 40 min, and he was just in like a thicket. It was so stupid. All of it was ridiculous. I was like, how like we have. This is our fault, like we created this, but I almost didn’t make it to this meeting. I missed all of my morning meetings. Anyway, I have
15 00:02:20.520 ⇒ 00:02:22.960 Emily Giant: created a cat colony, and.
16 00:02:22.960 ⇒ 00:02:23.710 Amber Lin: Oh, wow!
17 00:02:23.710 ⇒ 00:02:25.709 Emily Giant: Not on purpose.
18 00:02:27.060 ⇒ 00:02:29.959 Amber Lin: I’m jealous and glad I’m not there.
19 00:02:29.960 ⇒ 00:02:30.450 Emily Giant: Yeah.
20 00:02:30.450 ⇒ 00:02:30.770 Amber Lin: Okay.
21 00:02:30.770 ⇒ 00:02:32.560 Emily Giant: Yeah, they’re cute when they’re cute.
22 00:02:32.560 ⇒ 00:02:33.869 Demilade Agboola: On the bright side
23 00:02:34.150 ⇒ 00:02:38.319 Demilade Agboola: on the bright side. When the cats take over the planets, they would you would have good representation.
24 00:02:38.320 ⇒ 00:02:45.009 Emily Giant: Yes, exactly. I have good cat, Karma, that’s what. Matt. Way back he looked at the cats and was like, we’re building good cat, Karma.
25 00:02:46.845 ⇒ 00:03:02.810 Emily Giant: I have a friend who’s a comedian, and one of her. She only only tells stories that are like real and one of the stories she tells is that her dad, who is like kind of nuts. He started feeding the raccoons in his neighborhood, dog food with chocolate chips in it to the point.
26 00:03:02.810 ⇒ 00:03:03.810 Amber Lin: Wow!
27 00:03:04.042 ⇒ 00:03:06.830 Emily Giant: There wound up being like a hundred 20 raccoons that we
28 00:03:08.190 ⇒ 00:03:26.219 Emily Giant: door and like run their hands down it when he wouldn’t feed them, and it wound up being like $400 a month to feed these raccoons. It was like an infestation, but I feel like that’s the direction I’m heading with these cats like they’re going to be at our window like like running their claws down it.
29 00:03:26.780 ⇒ 00:03:33.299 Emily Giant: anyway. I I think that our team should be here, but we don’t have to wait on them.
30 00:03:34.020 ⇒ 00:03:34.820 Amber Lin: Okay.
31 00:03:34.820 ⇒ 00:03:35.290 Emily Giant: Started.
32 00:03:35.290 ⇒ 00:03:42.890 Amber Lin: There’s a few things we wanted to ask Alex about, especially with Red Shift, I believe, but other than that we should be good to start
33 00:03:45.399 ⇒ 00:03:50.069 Amber Lin: as a can. Everyone just spend
34 00:03:50.498 ⇒ 00:03:59.149 Amber Lin: a minute or 2 to update our tickets. So any statuses that we need to update. I’ll just we just take a minute or 2 to do that.
35 00:03:59.470 ⇒ 00:04:00.100 Emily Giant: Okay.
36 00:04:13.470 ⇒ 00:04:19.580 Emily Giant: sorry I’m writing, Alex. I’m sorry. What was that we’re doing? Updates.
37 00:04:19.779 ⇒ 00:04:23.119 Amber Lin: Yeah, just update any ticket statuses that we need to do.
38 00:04:23.289 ⇒ 00:04:28.559 Amber Lin: I think most most of it is updated. But just wanna make sure.
39 00:04:29.410 ⇒ 00:04:35.710 Emily Giant: Were you able to take a look at that automation? I tagged you in? I wanna make sure it’s not too annoying to.
40 00:04:35.710 ⇒ 00:04:40.870 Amber Lin: I saw it. I think it’s great. That it can just be automated and put in there.
41 00:04:41.060 ⇒ 00:04:42.360 Emily Giant: I am like.
42 00:04:42.820 ⇒ 00:04:46.870 Emily Giant: if there’s something I’m doing twice. I’m like, I can’t do it like this. It has to only be.
43 00:04:47.235 ⇒ 00:04:47.600 Amber Lin: Hmm.
44 00:04:47.600 ⇒ 00:04:49.070 Emily Giant: So, yeah.
45 00:04:49.070 ⇒ 00:04:49.720 Amber Lin: Right.
46 00:04:49.720 ⇒ 00:04:55.760 Emily Giant: Okay. Is there a certain thing? I added the word new to the title when it comes over. But is there.
47 00:04:56.160 ⇒ 00:05:00.209 Emily Giant: Anything else you want added to the ticket when it’s created.
48 00:05:00.821 ⇒ 00:05:04.490 Amber Lin: Let me go find that ticket.
49 00:05:04.490 ⇒ 00:05:06.360 Emily Giant: And there was a real one, too.
50 00:05:06.500 ⇒ 00:05:07.890 Amber Lin: That it’ll bear with me.
51 00:05:07.890 ⇒ 00:05:08.530 Emily Giant: You.
52 00:05:10.305 ⇒ 00:05:11.160 Amber Lin: Hmm!
53 00:05:11.890 ⇒ 00:05:15.650 Amber Lin: Trying to find the ticket now is a little bit buried in all the stuff.
54 00:05:15.990 ⇒ 00:05:20.040 Amber Lin: Would you mind tagging me in it, or just sharing your screen and looking at it.
55 00:05:20.040 ⇒ 00:05:22.229 Emily Giant: Yeah, yeah, let me.
56 00:05:22.610 ⇒ 00:05:23.870 Amber Lin: Share my screen.
57 00:05:23.870 ⇒ 00:05:27.950 Amber Lin: Oh, I saw the new update receiving.
58 00:05:30.460 ⇒ 00:05:32.450 Emily Giant: How to share. Got it.
59 00:05:32.560 ⇒ 00:05:33.220 Amber Lin: Okay.
60 00:05:33.480 ⇒ 00:05:34.630 Emily Giant: Share.
61 00:05:35.110 ⇒ 00:05:37.190 Emily Giant: Want to do my little screen.
62 00:05:39.280 ⇒ 00:05:43.650 Emily Giant: Sorry I’m so not used to zoom.
63 00:05:43.650 ⇒ 00:05:44.436 Amber Lin: So, yeah.
64 00:05:44.830 ⇒ 00:05:51.190 Emily Giant: I like it. I like it better. I just don’t know how to use it. Okay, so if I go to linear.
65 00:05:59.250 ⇒ 00:06:02.299 Emily Giant: it doesn’t. It doesn’t want to do it. Okay, come on.
66 00:06:02.300 ⇒ 00:06:05.300 Amber Lin: It’s it’s okay. I I think I found it. Let me share my screen.
67 00:06:05.300 ⇒ 00:06:05.789 Emily Giant: Okay.
68 00:06:06.540 ⇒ 00:06:07.395 Amber Lin: Yeah.
69 00:06:09.650 ⇒ 00:06:11.400 Amber Lin: Here is the issue.
70 00:06:13.160 ⇒ 00:06:17.340 Emily Giant: Yes, that’s the 1. 0, photos didn’t come through. Okay.
71 00:06:20.628 ⇒ 00:06:23.179 Amber Lin: I think that’s the only one I see.
72 00:06:23.180 ⇒ 00:06:27.099 Emily Giant: Only I erased the other one, but this one should.
73 00:06:27.830 ⇒ 00:06:29.879 Amber Lin: Be the same like.
74 00:06:30.310 ⇒ 00:06:32.760 Emily Giant: Automated conditions that come through.
75 00:06:33.570 ⇒ 00:06:40.029 Emily Giant: I just wanna know, like goes into the correct spot like, do you want it to go directly into backlog? Or should it go somewhere else.
76 00:06:40.871 ⇒ 00:06:50.040 Amber Lin: Yeah, I can go in the backlog, and we can groom it right because we’re doing a grooming session later. And we can look at all these tickets and move them to the respective places.
77 00:06:50.040 ⇒ 00:06:51.020 Emily Giant: Okay. Great.
78 00:06:51.020 ⇒ 00:06:54.590 Amber Lin: Okay, yeah, I’m going back to
79 00:06:54.890 ⇒ 00:06:58.310 Amber Lin: the current cycle. I assume everything is updated.
80 00:06:59.346 ⇒ 00:07:04.110 Amber Lin: Okay, let’s look at this one, I hope, is Alex here yet?
81 00:07:05.128 ⇒ 00:07:06.800 Emily Giant: I just pinged him.
82 00:07:07.010 ⇒ 00:07:07.850 Amber Lin: Hmm.
83 00:07:07.850 ⇒ 00:07:09.690 Emily Giant: And I haven’t heard back from him, but.
84 00:07:09.690 ⇒ 00:07:10.320 Amber Lin: Okay.
85 00:07:10.680 ⇒ 00:07:14.070 Emily Giant: Yeah, I can always take the questions to him as well.
86 00:07:14.070 ⇒ 00:07:15.877 Amber Lin: Okay. Sounds good.
87 00:07:17.090 ⇒ 00:07:18.420 Emily Giant: I believe.
88 00:07:19.030 ⇒ 00:07:20.143 Amber Lin: Oh, Kyle,
89 00:07:22.100 ⇒ 00:07:26.180 Amber Lin: Can you talk about what’s blocked here? I know there was something going on.
90 00:07:28.013 ⇒ 00:07:32.030 Caio Velasco: Yes. So I have access to aws.
91 00:07:32.535 ⇒ 00:07:52.049 Caio Velasco: And, for example, when I went to the red seat part and and I, I didn’t see basically anything there. So then I went to the I am Iam part, which is where the user roles and everything should be, and I don’t have any, or I don’t have a role or a user or something around those lines.
92 00:07:52.637 ⇒ 00:08:00.380 Caio Velasco: And also in redshift. I don’t see any cluster. So there’s some, probably something related to permissions or something, so
93 00:08:00.560 ⇒ 00:08:05.040 Caio Velasco: so I can’t access, and I think.
94 00:08:06.363 ⇒ 00:08:11.509 Amber Lin: So what you, what you need is access to redship. Is that is that correct?
95 00:08:12.810 ⇒ 00:08:19.040 Caio Velasco: Yes, if there’s it’s in the Channel, I can try to find the link, and you can post if it’s easier.
96 00:08:19.980 ⇒ 00:08:30.889 Caio Velasco: But it’s basically well, I don’t know what I need. Actually, I think it’s related to Iam credentials, permissions. So that I can see things in redshift.
97 00:08:31.507 ⇒ 00:08:37.429 Amber Lin: I see, do you know what you need from Alex, or is this sort of a meet something, you need to figure out in a meeting.
98 00:08:39.240 ⇒ 00:08:58.629 Caio Velasco: I think theoretically, should be in his end. If he is the one who provides those permissions. It’s just like I’m a new user. I have to be able to see redshift so I don’t know a meeting. If it’s needed or not, then I don’t know, because I didn’t have experience with this before.
99 00:08:59.320 ⇒ 00:09:17.669 Emily Giant: I think that Alex and Kyle need to partner on this as soon as possible, because when demalade joined the team it took him like a week to get the correct access. So at least, we have the experience of like having to get him through those permissions. But, Demo Lotte, do you remember that like how difficult it was.
100 00:09:17.670 ⇒ 00:09:26.279 Demilade Agboola: Yeah, it’s just there was just a lot of like weird stuff happening. But like, eventually I got in and I was able to, you know, be useful to the
101 00:09:26.650 ⇒ 00:09:35.490 Demilade Agboola: but yeah, I I do know it can be frustrating. But I definitely know Alex is the person to meet. I also looked through the like.
102 00:09:37.670 ⇒ 00:09:43.009 Demilade Agboola: Grants like the user users. We access the database. And Kyle’s name isn’t there? So.
103 00:09:43.200 ⇒ 00:09:43.860 Amber Lin: Hmm!
104 00:09:45.098 ⇒ 00:09:55.600 Amber Lin: Is it possible? Emily, to for you to look at Alex Calendar and suggest a few times like, can we book it from here, or do we have to talk to him? Okay.
105 00:09:56.620 ⇒ 00:10:04.415 Emily Giant: Let me pull up his calendar. You can move ahead. I’ll pull up his calendar and then put something on like today, because.
106 00:10:04.740 ⇒ 00:10:10.390 Amber Lin: Awesome. Yeah, yeah. And this is blocking. The other queries.
107 00:10:10.530 ⇒ 00:10:11.560 Emily Giant: Yeah.
108 00:10:11.885 ⇒ 00:10:20.360 Amber Lin: Question, since double edit, I think you do have access to this. Would you be able to do the query, or does this have to be.
109 00:10:20.600 ⇒ 00:10:25.649 Amber Lin: Kyle. I’m just thinking about ways to get around this until Kyle gets unblocked.
110 00:10:28.640 ⇒ 00:10:31.509 Demilade Agboola: Yeah, I could do this. I could use this query if.
111 00:10:32.640 ⇒ 00:10:41.969 Amber Lin: Okay, I mean, even, maybe the I don’t know. Maybe Kyle logs into your account or something.
112 00:10:43.030 ⇒ 00:10:45.060 Amber Lin: Here.
113 00:10:45.340 ⇒ 00:10:58.380 Amber Lin: yeah. Cause we want this to be done, so we can send. Send the list to Emily, and I don’t think the query will take too long, because all we need is the table name. And just these 3,
114 00:10:59.340 ⇒ 00:11:01.150 Amber Lin: these 3 things? Right?
115 00:11:04.060 ⇒ 00:11:08.279 Amber Lin: So maybe I, how long do you think this
116 00:11:09.670 ⇒ 00:11:12.389 Amber Lin: is this? Gonna take more than half a day.
117 00:11:14.380 ⇒ 00:11:21.609 Demilade Agboola: But once the query is ready, I’m sure it is like the the hard part will be figuring out a query, but like it shouldn’t be.
118 00:11:21.930 ⇒ 00:11:25.289 Amber Lin: I see Kyle, did you already have a query figured out.
119 00:11:26.670 ⇒ 00:11:32.010 Caio Velasco: No, no, not yet, because it’s something that I have to try. And error normally so.
120 00:11:32.610 ⇒ 00:11:35.139 Amber Lin: Oh, I see, I see.
121 00:11:40.360 ⇒ 00:11:45.620 Amber Lin: Let’s see, I think this is pretty important. Let’s say that.
122 00:11:45.900 ⇒ 00:11:48.649 Amber Lin: Let’s say dem Laude will have to take this.
123 00:11:48.910 ⇒ 00:11:52.159 Amber Lin: then I know something else. We have to get pushed back.
124 00:11:55.060 ⇒ 00:11:58.419 Demilade Agboola: Yes, if if I take it I could do it today.
125 00:12:00.495 ⇒ 00:12:01.330 Demilade Agboola: Yeah.
126 00:12:04.870 ⇒ 00:12:06.300 Amber Lin: Yeah, sounds good.
127 00:12:09.300 ⇒ 00:12:12.280 Amber Lin: Oh, awesome.
128 00:12:13.380 ⇒ 00:12:20.370 Amber Lin: So I would say, this is improv to do, I guess
129 00:12:20.610 ⇒ 00:12:26.659 Amber Lin: to do. And then I’m gonna create another ticket to, I hope.
130 00:12:28.210 ⇒ 00:12:34.480 Amber Lin: Name, spell your name wrong, please access to shit.
131 00:12:42.460 ⇒ 00:12:44.849 Amber Lin: I’ll give that to Alex.
132 00:12:48.380 ⇒ 00:12:49.730 Emily Giant: Excuse me, goodness.
133 00:12:49.730 ⇒ 00:12:50.590 Amber Lin: Bless you!
134 00:12:51.575 ⇒ 00:12:54.669 Amber Lin: Right, this is something to do.
135 00:12:58.340 ⇒ 00:13:04.429 Amber Lin: and then all right, great, so unblock that.
136 00:13:04.740 ⇒ 00:13:06.850 Amber Lin: And let’s look at.
137 00:13:09.970 ⇒ 00:13:12.350 Amber Lin: Oh, dear! Is it the cat hair.
138 00:13:13.020 ⇒ 00:13:13.579 Emily Giant: Yup!
139 00:13:14.140 ⇒ 00:13:17.770 Amber Lin: Oh, my! I want to have a cat, but I’m I’m allergic.
140 00:13:17.950 ⇒ 00:13:21.840 Emily Giant: Me, too, only to the barn one, though not to my actual cat.
141 00:13:22.704 ⇒ 00:13:24.400 Amber Lin: That’s so funny.
142 00:13:24.400 ⇒ 00:13:25.690 Emily Giant: Oh, my! Gosh!
143 00:13:27.350 ⇒ 00:13:32.319 Amber Lin: Demo, is this is this one updated? Is is still in progress.
144 00:13:32.320 ⇒ 00:13:49.109 Demilade Agboola: Oh, no, like, I just added the bottom. Yeah. So it’s done. The major issue was just like one particular model that wasn’t necessarily built correctly. But I see the other things are like adding tests and stuff, but that will come in as we’re trying to like. Build out.
145 00:13:49.110 ⇒ 00:13:52.610 Amber Lin: So the model. Okay? So I can call this, I can call this one done.
146 00:13:52.880 ⇒ 00:13:56.670 Demilade Agboola: Yeah, but I would. I would add a document to that effect. But.
147 00:13:57.105 ⇒ 00:13:57.540 Amber Lin: Great.
148 00:13:57.570 ⇒ 00:13:58.500 Demilade Agboola: But yeah.
149 00:13:59.620 ⇒ 00:14:00.620 Amber Lin: Sounds good.
150 00:14:01.897 ⇒ 00:14:05.379 Amber Lin: I know that one’s still going on.
151 00:14:07.424 ⇒ 00:14:12.410 Demilade Agboola: Actually, I I have done something on 54 oh.
152 00:14:12.410 ⇒ 00:14:12.850 Amber Lin: Right.
153 00:14:12.850 ⇒ 00:14:18.510 Demilade Agboola: So I I have run the script. That’s that, documented the script.
154 00:14:19.317 ⇒ 00:14:26.540 Demilade Agboola: So the script has allowed. At least I tested it, for one thing, and it allowed us to be able to test like the staging models.
155 00:14:27.350 ⇒ 00:14:29.909 Demilade Agboola: When we’re trying before we end this product.
156 00:14:30.400 ⇒ 00:14:47.009 Demilade Agboola: So it’s just something to keep an eye out on just like if it works all day if there any issues and things like that, but just like for the general team. So Emily and Kyle and Otam, just if you ever push a Pr. Just like it should work. Now, if he doesn’t, please let me know.
157 00:14:48.160 ⇒ 00:14:50.056 Amber Lin: Okay, so is this,
158 00:14:51.200 ⇒ 00:14:57.350 Amber Lin: is this done? Is this, is there something else we need to do for, say, automatic brands.
159 00:14:57.819 ⇒ 00:15:04.559 Demilade Agboola: So right now it’s just the waiting game, just to see if it breaks and just see if anything goes wrong. But like, set up. Yeah.
160 00:15:05.653 ⇒ 00:15:09.109 Amber Lin: To see what breaks
161 00:15:10.525 ⇒ 00:15:15.499 Amber Lin: over here, we said we wanna I don’t know if this still applies. But
162 00:15:15.700 ⇒ 00:15:18.800 Amber Lin: should we check this with Emily and Alex.
163 00:15:19.992 ⇒ 00:15:23.590 Demilade Agboola: Yeah, that’s kind of why, I just, you know, just mention the team. Now, yeah. So
164 00:15:23.730 ⇒ 00:15:29.349 Demilade Agboola: like, they’re they have a heads up that like things should work, and if things aren’t working, they should let me know.
165 00:15:30.080 ⇒ 00:15:31.479 Amber Lin: Sounds good, so I should.
166 00:15:31.480 ⇒ 00:15:35.310 Emily Giant: Prs that I need to have reviewed and push
167 00:15:35.610 ⇒ 00:15:39.790 Emily Giant: through this flow, so I’ll be able to test it.
168 00:15:39.790 ⇒ 00:15:43.049 Amber Lin: Hmm! So should I put, I should put this in testing right.
169 00:15:43.050 ⇒ 00:15:43.730 Emily Giant: I don’t know.
170 00:15:43.730 ⇒ 00:15:52.760 Amber Lin: I’ll put this in testing great and progress progress owns.
171 00:15:53.060 ⇒ 00:15:56.929 Amber Lin: Were we able? Let me see if a voice responded.
172 00:15:58.562 ⇒ 00:16:02.987 Demilade Agboola: So I wish has responded to me. He’s mentioned. We have
173 00:16:04.480 ⇒ 00:16:05.270 Amber Lin: 1, 6.
174 00:16:05.270 ⇒ 00:16:06.549 Demilade Agboola: Give me one second.
175 00:16:06.670 ⇒ 00:16:10.760 Demilade Agboola: Basically, he mentioned that we do have a template. But it’s
176 00:16:10.900 ⇒ 00:16:17.839 Demilade Agboola: it was with polytomi ingested data. But it’s still shopify. So it’s just basically going to be trying to fit
177 00:16:18.698 ⇒ 00:16:28.499 Demilade Agboola: the shopify modeling onto this for order lines, order, product and customer and transaction. So I will look at that, and I will send that over to Emily.
178 00:16:29.180 ⇒ 00:16:29.870 Demilade Agboola: Today.
179 00:16:29.870 ⇒ 00:16:30.454 Amber Lin: What’s
180 00:16:32.816 ⇒ 00:16:39.889 Amber Lin: Sounds like from a wish that needs more fitting for
181 00:16:40.700 ⇒ 00:16:43.619 Amber Lin: 3, 4, 7, 8, 2, and 3.
182 00:16:44.540 ⇒ 00:16:46.230 Amber Lin: Okay, great.
183 00:16:47.592 ⇒ 00:16:51.160 Amber Lin: So say, that’s okay.
184 00:16:53.480 ⇒ 00:16:56.210 Amber Lin: Great. This is in progress.
185 00:16:58.262 ⇒ 00:17:05.440 Amber Lin: Let’s go through the this. So I think we need Alex for that.
186 00:17:09.940 ⇒ 00:17:14.060 Amber Lin: We have a few days for that one alright
187 00:17:14.420 ⇒ 00:17:17.670 Amber Lin: here. Oh, never mind, that was the same thing that we looked at.
188 00:17:18.680 ⇒ 00:17:19.589 Amber Lin: Let me
189 00:17:23.940 ⇒ 00:17:29.440 Amber Lin: hmm right message.
190 00:17:31.170 ⇒ 00:17:35.660 Amber Lin: We have usage stats, we have accuracy.
191 00:17:36.370 ⇒ 00:17:43.390 Amber Lin: Oh, think that’s everything in this cycle?
192 00:17:44.710 ⇒ 00:17:54.000 Amber Lin: Let me go. Look at this. Did we miss anything very nice? Table usage?
193 00:17:55.290 ⇒ 00:17:56.680 Amber Lin: Apply for decision.
194 00:17:58.750 ⇒ 00:18:03.660 Amber Lin: Okay, that’s good looking at Looker.
195 00:18:08.400 ⇒ 00:18:13.620 Amber Lin: Oh, my bad should have this should have been in cycle.
196 00:18:14.750 ⇒ 00:18:15.640 Amber Lin: Oh.
197 00:18:23.940 ⇒ 00:18:27.350 Amber Lin: oh, I don’t wanna assign this now, because
198 00:18:27.580 ⇒ 00:18:31.479 Amber Lin: we already had the sprint started. But this is something that we need to do.
199 00:18:32.230 ⇒ 00:18:33.000 Amber Lin: Oops.
200 00:18:34.680 ⇒ 00:18:40.466 Demilade Agboola: Yeah, that’s fine. I think I’ll just. I just need to hop on a call with Emily for that. To be honest.
201 00:18:41.480 ⇒ 00:18:48.300 Amber Lin: And I think we could just kind of slog through. Maybe not every single thing, because there are a lot of like just get general direction.
202 00:18:49.120 ⇒ 00:18:51.793 Demilade Agboola: Then use that direction to be able to like
203 00:18:52.470 ⇒ 00:18:55.119 Demilade Agboola: filter things better, until, like the more.
204 00:18:55.120 ⇒ 00:18:55.630 Amber Lin: Thanks.
205 00:18:55.630 ⇒ 00:18:58.579 Demilade Agboola: The reliable models and explores basically.
206 00:19:01.460 ⇒ 00:19:04.887 Amber Lin: Sounds good. I I know we have. You have the
207 00:19:06.859 ⇒ 00:19:14.830 Amber Lin: the red ship usage now. So I would say that probably we do this later, this week or earlier next week.
208 00:19:15.090 ⇒ 00:19:23.930 Amber Lin: Let me see if this cycle is ending. Oh, never mind. So we could maybe do this later this week for initial, for, like an initial call.
209 00:19:24.340 ⇒ 00:19:30.210 Amber Lin: and then we can start stuff for next cycle. As well.
210 00:19:31.090 ⇒ 00:19:34.380 Amber Lin: Think this is should be pretty good for.
211 00:19:36.378 ⇒ 00:19:44.389 Emily Giant: Demo Lade, as a step to that. If we want to like, break this into any kind of tasks, I can add a tab to the
212 00:19:44.480 ⇒ 00:20:08.500 Emily Giant: spreadsheet that we’ve been using, and like for the top models, do you? Would it be helpful if I just wrote down like the known issues there, there is not a single model that’s reliable. So I but I am very smiling. But I’m very briefed on like which ones we use, and what’s wrong with them. So maybe it would be easier if I started. And then you and I could like.
213 00:20:08.780 ⇒ 00:20:13.980 Emily Giant: go through the problems and look at them in real time. I’m just trying to think of the most efficient way to do this.
214 00:20:14.500 ⇒ 00:20:23.319 Demilade Agboola: Yeah, I was when the reason why I laughed was, I was going to say, Kyle, welcome to the to, I think.
215 00:20:25.460 ⇒ 00:20:36.230 Demilade Agboola: But yeah, I know that a lot of models have issues. I think what we should try and do in terms of reliabilities, maybe try and create like a scale, right? So maybe
216 00:20:36.900 ⇒ 00:20:43.939 Demilade Agboola: to 10. So let’s just say 10 is the most unreliable to like, one is like the. It’s
217 00:20:44.360 ⇒ 00:20:59.600 Demilade Agboola: yeah. One is like the lowest level of reliability. So like we can say, there’s no one, or like highest level of reliability. So there are no one models. Maybe there are 2 or 3. And then so like, once we know that anything about 5, we can just go like, yeah, we need to redo these like conference.
218 00:20:59.600 ⇒ 00:21:00.260 Amber Lin: Hmm.
219 00:21:00.526 ⇒ 00:21:04.519 Demilade Agboola: But anything between like one to 5, we can say, Okay, how do we like.
220 00:21:04.930 ⇒ 00:21:05.610 Amber Lin: Improve them.
221 00:21:05.610 ⇒ 00:21:07.480 Demilade Agboola: Improve them, or build around them.
222 00:21:08.490 ⇒ 00:21:10.129 Demilade Agboola: But I think that might just be faster.
223 00:21:10.260 ⇒ 00:21:10.910 Amber Lin: Yeah.
224 00:21:11.850 ⇒ 00:21:12.779 Emily Giant: I like that.
225 00:21:13.010 ⇒ 00:21:16.000 Amber Lin: Do we have a spreadsheet of all the DVD models we have
226 00:21:17.440 ⇒ 00:21:20.879 Amber Lin: cause. I I think it makes sense to go off a list and go mark them.
227 00:21:20.880 ⇒ 00:21:21.700 Emily Giant: One, by one.
228 00:21:21.910 ⇒ 00:21:22.810 Amber Lin: Okay.
229 00:21:23.360 ⇒ 00:21:23.880 Amber Lin: Okay.
230 00:21:24.180 ⇒ 00:21:29.640 Amber Lin: Great spreadsheet. I don’t know if we happen yet at all.
231 00:21:29.920 ⇒ 00:21:32.000 Amber Lin: Dbt, models.
232 00:21:35.160 ⇒ 00:21:45.350 Emily Giant: I can also like make a confluence article, too, or some something that’s like more permanent. If you don’t want me to junk up that working spreadsheet that’s being used.
233 00:21:47.146 ⇒ 00:21:51.949 Amber Lin: Depends. On which spreadsheet are we talking about? Do we already have a spreadsheet that
234 00:21:52.080 ⇒ 00:21:54.250 Amber Lin: does that? Does this?
235 00:21:55.241 ⇒ 00:22:00.538 Amber Lin: Yes, there’s utam has one that he shared. Let me share it in the chat.
236 00:22:11.750 ⇒ 00:22:14.479 Emily Giant: He initially had used it like during scoping.
237 00:22:14.710 ⇒ 00:22:21.289 Emily Giant: But kind of we continue to use it to just like add
238 00:22:21.490 ⇒ 00:22:23.119 Emily Giant: more information as we got it.
239 00:22:23.120 ⇒ 00:22:24.699 Amber Lin: Oh, I see.
240 00:22:28.280 ⇒ 00:22:32.940 Amber Lin: Kyle, we’re using a new data platform documentation. Or is this one that.
241 00:22:32.940 ⇒ 00:22:34.720 Caio Velasco: Yeah, that’s the one that’s.
242 00:22:34.720 ⇒ 00:22:35.300 Amber Lin: Oh, okay.
243 00:22:35.300 ⇒ 00:22:36.350 Caio Velasco: Created.
244 00:22:36.350 ⇒ 00:22:45.589 Amber Lin: Okay, dB, so it would be in Dbt data sources. Data source. Right? Okay?
245 00:22:45.950 ⇒ 00:22:46.910 Amber Lin: I mean.
246 00:22:47.240 ⇒ 00:23:03.999 Amber Lin: if we’re just, we can, essentially, what we need is one row for rating like rate from one to 10 and one row for just comments of what’s not working. I think it won’t clutter up things too much, and it helps. It keeps everything in the right place.
247 00:23:05.200 ⇒ 00:23:08.779 Emily Giant: Okay, I added. Columnia is now rating, and then.
248 00:23:11.090 ⇒ 00:23:18.240 Amber Lin: And then comments right comments that you probably have received from all the stakeholders, and saying, This is not working.
249 00:23:19.220 ⇒ 00:23:19.940 Emily Giant: Yep.
250 00:23:25.780 ⇒ 00:23:28.930 Demilade Agboola: Yeah, pretty sure we could like, I could just update the list.
251 00:23:29.980 ⇒ 00:23:31.350 Demilade Agboola: All models.
252 00:23:33.810 ⇒ 00:23:36.289 Demilade Agboola: I could probably do that today as well.
253 00:23:37.120 ⇒ 00:23:40.029 Demilade Agboola: But there are a lot of models. So I’m not sure
254 00:23:41.070 ⇒ 00:23:44.249 Demilade Agboola: how we, how you want to necessarily prioritize that.
255 00:23:45.450 ⇒ 00:23:49.819 Emily Giant: I can also, like someone. So many of them are legacy.
256 00:23:50.296 ⇒ 00:24:00.040 Emily Giant: have documentation of that, too, that, like I can for comments, I can add legacy to those. I don’t think it will actually take all that long, considering how many of them are inactive.
257 00:24:00.880 ⇒ 00:24:08.139 Amber Lin: Yeah, I think the demo that you bring up good point. We could put this to the next cycle. I just put it in. But maybe we have enough with
258 00:24:09.920 ⇒ 00:24:14.880 Amber Lin: wait this this cycle ends Jill.
259 00:24:15.720 ⇒ 00:24:18.869 Amber Lin: A lot of sense. Oh, we have a week.
260 00:24:19.120 ⇒ 00:24:21.569 Amber Lin: We have a week left, so.
261 00:24:21.940 ⇒ 00:24:28.450 Demilade Agboola: I I mean to be fair, Emily, that’s doing the really hard part. I would just need to create the the spreadsheet and just kind of append it to.
262 00:24:29.620 ⇒ 00:24:30.060 Amber Lin: Okay.
263 00:24:30.430 ⇒ 00:24:31.200 Demilade Agboola: Oh!
264 00:24:32.380 ⇒ 00:24:47.329 Caio Velasco: Just one thing that tab the data sources one. There are things there, but you can erase everything. Because that was just a 1st attempt. I had to go over the repo. But that tab, specifically, you can. You can use it. You can erase the data over there. Those, those
265 00:24:47.430 ⇒ 00:24:50.759 Caio Velasco: those Dbt. Models that are listed so feel free.
266 00:24:52.640 ⇒ 00:24:53.739 Demilade Agboola: Gotcha will do.
267 00:24:53.740 ⇒ 00:24:54.330 Amber Lin: Okay?
268 00:24:58.250 ⇒ 00:24:59.530 Amber Lin: So
269 00:25:01.410 ⇒ 00:25:08.119 Amber Lin: yeah, I’m looking at our what we have on our plate right now. So we have a week left and
270 00:25:08.480 ⇒ 00:25:14.260 Amber Lin: has to do the query, the retro tables.
271 00:25:14.440 ⇒ 00:25:18.540 Amber Lin: and then look at the auditing, the inventory
272 00:25:19.230 ⇒ 00:25:32.940 Amber Lin: stuff. I think that’s the main 2 that will take some time, and maybe we can squeeze in and verify accuracy. Maybe I should assign this. I think we can assign this to Emily, but I don’t know, Emily, how much do you have on your plate?
273 00:25:33.200 ⇒ 00:25:34.870 Emily Giant: If we can do this this week.
274 00:25:34.870 ⇒ 00:25:45.400 Emily Giant: it’s 1 of those where, like it might be a ton. It might not be. But I do have like issues that were submitted in the last couple of days, having to do with like
275 00:25:45.890 ⇒ 00:25:47.920 Emily Giant: the inventory and accuracy ticket, though.
276 00:25:47.920 ⇒ 00:25:48.239 Amber Lin: I said.
277 00:25:48.240 ⇒ 00:25:55.480 Emily Giant: Which I was like. Maybe if I can work with them on that, since it’s under the validating, the image.
278 00:25:55.480 ⇒ 00:25:56.420 Amber Lin: True.
279 00:25:56.761 ⇒ 00:26:02.570 Emily Giant: That’s something we could partner on. Because I do think that’s a smaller tweak. I’m having
280 00:26:03.000 ⇒ 00:26:07.370 Emily Giant: a lot of issues with our component data which I have for
281 00:26:07.580 ⇒ 00:26:12.180 Emily Giant: 6 months. But it’s like hit a wall this week. Is
282 00:26:12.400 ⇒ 00:26:14.569 Emily Giant: there any way that I could like
283 00:26:15.180 ⇒ 00:26:21.479 Emily Giant: expedite the sharing of the shopify work that your partner, I think his name was like a a wash.
284 00:26:22.650 ⇒ 00:26:23.130 Amber Lin: Esh.
285 00:26:23.130 ⇒ 00:26:23.450 Emily Giant: Any.
286 00:26:23.450 ⇒ 00:26:23.860 Amber Lin: Yeah.
287 00:26:23.860 ⇒ 00:26:26.460 Emily Giant: Patient on that, so that I can.
288 00:26:27.210 ⇒ 00:26:31.380 Emily Giant: I’m having trouble understanding what is a normal
289 00:26:32.063 ⇒ 00:26:37.619 Emily Giant: what is normalized data for a certain feed versus what was inaccurately
290 00:26:39.510 ⇒ 00:26:45.180 Emily Giant: coded in so like our soligo flows. That’s causing
291 00:26:46.300 ⇒ 00:26:50.620 Emily Giant: what I would call unexpected results, like.
292 00:26:50.620 ⇒ 00:26:51.650 Amber Lin: I see, I see.
293 00:26:51.650 ⇒ 00:27:12.972 Emily Giant: Yeah, like, our component data is supposed to be like just the component skew so there’s only one. And our other data is like the parent skew. And there’s not a table anymore that, like correctly pairs, the parent skew with the component skew. And I do not want to hard code it because that’s not sustainable.
294 00:27:13.840 ⇒ 00:27:19.529 Emily Giant: I yeah, I need to figure out a way to or to find a table that is.
295 00:27:19.530 ⇒ 00:27:20.000 Amber Lin: Oh!
296 00:27:20.000 ⇒ 00:27:20.880 Emily Giant: Have
297 00:27:21.060 ⇒ 00:27:31.769 Emily Giant: a component level skew in shopify. I found one that kind of does. But like I’m just seeing so much strange behavior in the shopify Hevo flows that.
298 00:27:32.400 ⇒ 00:27:37.510 Emily Giant: I’m trying to find any information I can on what is expected.
299 00:27:38.990 ⇒ 00:27:43.066 Amber Lin: Do you think our table can be shared? I’ll leave it to you.
300 00:27:44.055 ⇒ 00:27:48.779 Demilade Agboola: Top urban stems priority for me today.
301 00:27:49.560 ⇒ 00:27:53.639 Demilade Agboola: So I’ll do that, and then I’ll move on to the query.
302 00:27:53.880 ⇒ 00:27:56.749 Demilade Agboola: and then also the tables for
303 00:27:56.910 ⇒ 00:27:58.559 Demilade Agboola: for you to be able to rate them.
304 00:28:00.860 ⇒ 00:28:01.450 Emily Giant: Okay.
305 00:28:01.600 ⇒ 00:28:05.469 Amber Lin: Okay, sounds good.
306 00:28:06.340 ⇒ 00:28:11.899 Amber Lin: Okay, that sounds good. Let me edit this
307 00:28:17.510 ⇒ 00:28:19.000 Amber Lin: verify.
308 00:28:23.240 ⇒ 00:28:25.009 Amber Lin: Okay? Sounds good.
309 00:28:27.380 ⇒ 00:28:31.169 Amber Lin: Kyle, what’s what are you working on right now? Today?
310 00:28:31.990 ⇒ 00:28:36.669 Caio Velasco: So I worked on the dashboard list
311 00:28:37.456 ⇒ 00:28:45.190 Caio Velasco: from Looker. So I was able to pull that. And do I created because I never use it, looker before honestly. So.
312 00:28:45.190 ⇒ 00:28:45.520 Amber Lin: And.
313 00:28:45.865 ⇒ 00:29:01.780 Caio Velasco: But I was able to pull all dashboards and also a few user usage statistics. And from from what I it’s already in that in that spreadsheet as well. In the dashboard there is a dashboard tab which is just unique names for dashboards.
314 00:29:02.280 ⇒ 00:29:05.919 Caio Velasco: And then there’s another one dashboard audit, which is more.
315 00:29:05.920 ⇒ 00:29:06.339 Amber Lin: Let me know!
316 00:29:06.340 ⇒ 00:29:08.210 Caio Velasco: Whole user statistics.
317 00:29:08.688 ⇒ 00:29:12.410 Caio Velasco: So just for a unique dashboard, there are almost 800.
318 00:29:12.540 ⇒ 00:29:14.260 Caio Velasco: So a lot.
319 00:29:15.158 ⇒ 00:29:21.979 Caio Velasco: But yeah, but at least we have some user statistics and things that help can help us decide.
320 00:29:22.450 ⇒ 00:29:23.250 Amber Lin: Along the way
321 00:29:23.440 ⇒ 00:29:30.919 Amber Lin: I see one last thing, Emily, were you able to find Alex Calendar? I want to book that for Kyle, so he gets access.
322 00:29:34.030 ⇒ 00:29:38.830 Caio Velasco: I think Alex just replied to Tom in the in the Channel.
323 00:29:39.500 ⇒ 00:29:43.730 Caio Velasco: And maybe he understood what was happening. I’m not sure
324 00:29:48.210 ⇒ 00:29:49.239 Caio Velasco: is what
325 00:30:03.060 ⇒ 00:30:04.560 Caio Velasco: I mean. I think you’re on mute.
326 00:30:08.000 ⇒ 00:30:08.660 Emily Giant: Yeah.
327 00:30:09.227 ⇒ 00:30:26.229 Emily Giant: so I can try running the query that Utam used to give access Prior. And if that doesn’t work, then I’ll try to nail Alex down. He’s typing right now, though I’m hoping we can just like resolve this, Async. It was just
328 00:30:26.550 ⇒ 00:30:30.129 Emily Giant: last time that, like I wanted to make sure to nip it in the bud.
329 00:30:30.400 ⇒ 00:30:31.110 Amber Lin: Okay.
330 00:30:32.580 ⇒ 00:30:35.860 Emily Giant: And then is he coming to the grooming session? Is he on that invite.
331 00:30:36.100 ⇒ 00:30:38.208 Amber Lin: I hope so. I hope so.
332 00:30:41.831 ⇒ 00:30:47.610 Amber Lin: Oh, no, let me, Alex add Alex oops!
333 00:30:49.530 ⇒ 00:30:51.950 Emily Giant: Looks like he DM’d you some credentials, Kyle?
334 00:30:52.950 ⇒ 00:30:55.920 Caio Velasco: Yes, I just got them. Let me let me see.
335 00:30:59.010 ⇒ 00:31:02.050 Amber Lin: Oh, so great! I think that should solve the issue.
336 00:31:02.220 ⇒ 00:31:05.819 Amber Lin: Should Alex be in the grooming. I don’t think so.
337 00:31:06.340 ⇒ 00:31:07.840 Emily Giant: We may not need to be.
338 00:31:08.060 ⇒ 00:31:11.530 Amber Lin: Yeah, he’s probably really really busy.
339 00:31:12.170 ⇒ 00:31:15.469 Amber Lin: Oh, Adam is optional, but he doesn’t need to be there.
340 00:31:18.518 ⇒ 00:31:20.539 Amber Lin: So I have to hop now. Yeah.
341 00:31:20.540 ⇒ 00:31:31.310 Amber Lin: me, too. Me, too. But hopefully that gets resolved. Keep us, Kyle. Keep us noted, and we’ll see you guys in the grooming.
342 00:31:31.310 ⇒ 00:31:31.980 Demilade Agboola: Brian.