Meeting Title: Urban Stems Data Sync Date: 2026-01-05 Meeting participants: Demilade Agboola, Emily Giant
WEBVTT
1 00:00:09.790 ⇒ 00:00:11.219 Emily Giant: Why, hello!
2 00:00:12.910 ⇒ 00:00:14.779 Demilade Agboola: Hello, Happy New Year.
3 00:00:15.110 ⇒ 00:00:16.849 Emily Giant: Happy New Year! Are you back home?
4 00:00:17.340 ⇒ 00:00:20.840 Demilade Agboola: Yes, yes, I am. I’ve been back since, like,
5 00:00:21.410 ⇒ 00:00:24.350 Demilade Agboola: late… well, mid to, like, late December.
6 00:00:24.910 ⇒ 00:00:25.680 Emily Giant: Okay.
7 00:00:25.680 ⇒ 00:00:28.719 Demilade Agboola: Yeah, hosted my sister and her family for Christmas, so…
8 00:00:28.970 ⇒ 00:00:29.710 Emily Giant: Aww.
9 00:00:30.150 ⇒ 00:00:31.779 Demilade Agboola: That was quite… that was pretty cool.
10 00:00:32.140 ⇒ 00:00:36.799 Emily Giant: Yeah. So you survived the Minnesota winter?
11 00:00:37.380 ⇒ 00:00:45.049 Demilade Agboola: Well, I mean, it wasn’t… it gets worse in January, so I survived the first part of it, but, it was pretty cool, it was pretty nice.
12 00:00:45.320 ⇒ 00:00:48.790 Emily Giant: Yeah, it’s really pretty. It’s just so cold there.
13 00:00:49.590 ⇒ 00:00:55.330 Demilade Agboola: Yes. It was, like, the ZD was, like, negative 4 before I left.
14 00:00:55.450 ⇒ 00:00:56.340 Demilade Agboola: Yeah.
15 00:00:56.490 ⇒ 00:00:58.810 Demilade Agboola: Yeah, that’s pretty bad. And that’s not the worst I’ve seen.
16 00:00:59.090 ⇒ 00:01:05.060 Emily Giant: Oh, no. Like, people would die trying to get into their cars there. They would just, like, freeze with their key.
17 00:01:05.190 ⇒ 00:01:23.440 Emily Giant: Yeah, so it gets bad, but I had friends visit from Arizona and South Carolina for my birthday, and they were just so cold the whole time, and it was not that bad. It was, like, 32 Fahrenheit. And I was like, guys, you can do this. Like, it’s not that cold. Get a cup of coffee. You’re okay.
18 00:01:24.560 ⇒ 00:01:26.979 Demilade Agboola: Yeah, 32 isn’t the worst, it’s definitely not.
19 00:01:26.980 ⇒ 00:01:27.430 Emily Giant: Meh.
20 00:01:27.430 ⇒ 00:01:30.849 Demilade Agboola: Worst I’ve seen, yeah. But how’s your birthday, though?
21 00:01:31.040 ⇒ 00:01:54.720 Emily Giant: it was awesome. It’s still, like, I’ve been so sick the last, like, month that I… I had, like, a karaoke party, and my brother’s band that, like, disbanded in 2010, they surprised me and, like, came back and played, and they were popular in the South, they were great, but it was, like, B20 again. But, like, I couldn’t speak to anyone the whole time, because I had no voice, so I was just like.
22 00:01:56.360 ⇒ 00:02:10.779 Emily Giant: it was sick, but it was really, really fun. I just needed time off so badly, like, we all did, that, like, the whole engagement with y’all was, like, so intense that all I wanted to do was sleep as soon as my vacation hit, so…
23 00:02:10.780 ⇒ 00:02:16.760 Emily Giant: It was good. It was necessary. But yeah, were you able to keep up tennis while you were doing all this traveling and stuff?
24 00:02:17.390 ⇒ 00:02:24.919 Demilade Agboola: No, so I was off tennis for, like, 4 weeks when I was traveling, but since I’ve been back, I’ve been at it,
25 00:02:25.220 ⇒ 00:02:27.749 Demilade Agboola: I have a tournament that starts this week, so…
26 00:02:28.020 ⇒ 00:02:29.880 Emily Giant: Oh, a tournament!
27 00:02:29.880 ⇒ 00:02:30.510 Demilade Agboola: Yeah.
28 00:02:30.820 ⇒ 00:02:33.629 Emily Giant: What? You’re now competing?
29 00:02:34.950 ⇒ 00:02:39.440 Demilade Agboola: I mean, maybe not successfully, but…
30 00:02:39.820 ⇒ 00:02:49.359 Emily Giant: It’s still so awesome, though. Like, you don’t need to be, like, the master of something to just, like, get your foot in the door, because that’s how you get good, but that is so awesome that you’re doing that.
31 00:02:49.850 ⇒ 00:02:54.360 Demilade Agboola: Yeah, yeah, so I have… my first game is on Thursday, so that should be fun.
32 00:02:54.800 ⇒ 00:03:00.289 Demilade Agboola: But yeah, I’m… Looking forward to it. I…
33 00:03:00.700 ⇒ 00:03:04.429 Demilade Agboola: I got this app, it’s called Swing Vision. It helps you get, like.
34 00:03:04.790 ⇒ 00:03:09.380 Demilade Agboola: Like, analytics on your game, so you can start, like, if you put it high enough.
35 00:03:09.520 ⇒ 00:03:16.059 Demilade Agboola: We can start to see where your shots land, where you hit the ball from, how fast you hit the ball.
36 00:03:16.340 ⇒ 00:03:17.030 Emily Giant: Whoa!
37 00:03:17.030 ⇒ 00:03:22.679 Demilade Agboola: It takes how many of the first serves go in, how many of the second serves go in, like, percentage-wise.
38 00:03:22.860 ⇒ 00:03:25.880 Demilade Agboola: So I’ve been using it for…
39 00:03:26.200 ⇒ 00:03:28.120 Demilade Agboola: Like, a week or a week and a half now.
40 00:03:28.350 ⇒ 00:03:36.990 Demilade Agboola: It’s been fun. I’ve done a couple self-practices with it. My percentages have been going up, so I’m beginning to figure some parts of it out.
41 00:03:37.120 ⇒ 00:03:38.969 Demilade Agboola: But it’s fun, it’s nice, I think.
42 00:03:38.970 ⇒ 00:03:45.080 Emily Giant: Pretty cool. Is it on… this is maybe a stupid question, is it on your watch, or your phone, or like, where is the app?
43 00:03:45.270 ⇒ 00:03:49.639 Demilade Agboola: So it’s a bit of both. So it’s on your phone, but for…
44 00:03:50.150 ⇒ 00:03:58.649 Demilade Agboola: control, you should… so I got a… that’s another story. At, like, 11.30, I got… I went out to, like, 11.30 PM, I went to get a…
45 00:03:58.880 ⇒ 00:04:03.570 Demilade Agboola: the watch, because I don’t have an app watch. I have a… the one I use every day is a Pixel watch.
46 00:04:03.730 ⇒ 00:04:04.350 Emily Giant: Yeah.
47 00:04:04.490 ⇒ 00:04:09.849 Demilade Agboola: Yeah, so I got the Apple Watch specifically because of that, because the app is iOS only.
48 00:04:10.460 ⇒ 00:04:12.340 Emily Giant: Oh, man. Well…
49 00:04:12.510 ⇒ 00:04:14.130 Demilade Agboola: I got it, it’s pretty cool.
50 00:04:14.300 ⇒ 00:04:33.059 Demilade Agboola: allows me to track stuff, lets me easily, just see what I’m doing. Like, it’ll tell you things like, oh, on your forehand, 70% of your cross-cut shots went in, but when you went down the line, it was only 40% that went in. So it allows you to kind of know
51 00:04:33.260 ⇒ 00:04:50.040 Demilade Agboola: what you need to work on. It lets you know the speed you’re using to go, like, cross-court versus down the line. On your backhand, too, it also lets you know that kind of thing. It lets you know the winners, it lets you know, like, how many shots were bad or good shots. Like, you just… you kind of get context of what’s going on.
52 00:04:50.040 ⇒ 00:04:50.690 Emily Giant: Yeah.
53 00:04:51.520 ⇒ 00:04:52.720 Demilade Agboola: And that was…
54 00:04:52.720 ⇒ 00:04:53.330 Emily Giant: white.
55 00:04:53.330 ⇒ 00:04:57.820 Demilade Agboola: with your coach and be like, yo, I think we need to work on this, this sort of shots, and it’s pretty cool.
56 00:04:57.820 ⇒ 00:05:02.810 Emily Giant: That is wild. Like, so do you set your phone up so it can, like, see the court?
57 00:05:02.810 ⇒ 00:05:03.520 Demilade Agboola: Yeah, yeah.
58 00:05:03.520 ⇒ 00:05:04.040 Emily Giant: Okay.
59 00:05:04.130 ⇒ 00:05:11.809 Demilade Agboola: So you can see the court. And then, usually, depending on, like, it’s getting pretty good at identifying me, but it would ask you.
60 00:05:12.090 ⇒ 00:05:16.529 Demilade Agboola: To tap the… where you are in the, like, the shots in the…
61 00:05:17.010 ⇒ 00:05:31.950 Demilade Agboola: on each point, and just be like, okay, so this was during each point, all right, gotcha. So he kind of knows, also, you hit the forehand cross-court, you hit the backhand here, you… it was flat, it was top spin, did you slice the ball, things like that, like, it would start putting that together.
62 00:05:32.140 ⇒ 00:05:37.220 Demilade Agboola: And you can kind of see, you know, which of yourselves went in, where you placed them.
63 00:05:37.720 ⇒ 00:05:40.309 Demilade Agboola: Like, you can actually… let me… let me see if I can…
64 00:05:40.740 ⇒ 00:05:57.500 Emily Giant: That is so crazy to me that, like, I mean, I… I can conceptualize of it because of, like, it looking at the court, but it being able to tell, like, your personal movements… I mean, I don’t know why. I’m impressed, because I have a machine write code for me half the day, so…
65 00:05:57.650 ⇒ 00:06:00.630 Emily Giant: Yeah, but it’s the world we live in. Crazy.
66 00:06:00.960 ⇒ 00:06:07.229 Demilade Agboola: That’s pretty cool. Like, I mean, obviously, it’s not 100, like, it’s not 100% accurate, so this is showing…
67 00:06:08.710 ⇒ 00:06:12.339 Demilade Agboola: Can I get it? No, this is… so this is me.
68 00:06:12.340 ⇒ 00:06:17.820 Emily Giant: I tried to zoom in on you, and it literally, like, hid the screen. Okay, so show me again. Sorry.
69 00:06:17.820 ⇒ 00:06:20.340 Demilade Agboola: It’s my blood background thingy that.
70 00:06:20.340 ⇒ 00:06:20.970 Emily Giant: Oh.
71 00:06:20.970 ⇒ 00:06:21.590 Demilade Agboola: Union.
72 00:06:21.910 ⇒ 00:06:24.380 Emily Giant: Hold it, like, back a little bit.
73 00:06:24.610 ⇒ 00:06:25.669 Demilade Agboola: So, can you see.
74 00:06:25.670 ⇒ 00:06:27.450 Emily Giant: Oh my god, yeah.
75 00:06:27.710 ⇒ 00:06:33.580 Demilade Agboola: So it shows where my shots were hit. So this is shot placement, where I placed the shots.
76 00:06:33.770 ⇒ 00:06:34.760 Emily Giant: Volley…
77 00:06:35.260 ⇒ 00:06:36.510 Demilade Agboola: Hit the shots from.
78 00:06:37.300 ⇒ 00:06:39.259 Emily Giant: Oh my gosh, that’s really cool.
79 00:06:39.260 ⇒ 00:06:45.029 Demilade Agboola: So you can kind of see some of the orange is like my backhand. So you can kind of tell I like my forehand quite a bit.
80 00:06:45.220 ⇒ 00:06:46.280 Emily Giant: Yeah.
81 00:06:46.280 ⇒ 00:06:47.619 Demilade Agboola: Yeah. Alright, Linda.
82 00:06:47.620 ⇒ 00:06:50.980 Emily Giant: I play, I always, like, I… it thinks…
83 00:06:51.250 ⇒ 00:07:02.140 Emily Giant: my brain thinks I’m left-handed. Every time I hit it, I’ll throw it to my left hand. We talked about this once. I would love to see, like, how often I actually hit with my right hand when I play tennis. That is so cool. What’s it called?
84 00:07:02.480 ⇒ 00:07:04.900 Demilade Agboola: It’s called Swing Vision. It’s pretty cool.
85 00:07:05.620 ⇒ 00:07:10.370 Emily Giant: I have an Apple Watch, but I haven’t updated it in, like, years, so…
86 00:07:10.530 ⇒ 00:07:13.169 Emily Giant: Swing Vision. That is so cool.
87 00:07:13.750 ⇒ 00:07:22.950 Demilade Agboola: Yeah, so there’s just a bunch of stuff, so that’s allowing me to, like, get better and figure out what… what’s going on. It shows you how many shots hit the net.
88 00:07:23.160 ⇒ 00:07:26.829 Emily Giant: Yeah. Like, it just shows you all that stuff, so you can kind of get context of…
89 00:07:26.830 ⇒ 00:07:34.480 Demilade Agboola: oh, I need to really work on my backhand. So, like, in the last rally, so this… the rally I played on, I believe it was Sunday or Saturday.
90 00:07:34.940 ⇒ 00:07:40.130 Demilade Agboola: It just was, like, your… only 44% of your backhands went in.
91 00:07:40.350 ⇒ 00:07:47.870 Demilade Agboola: 41% of them went down the line, so my back hands were weak on that day, but 77% of my forehands, cross-court, went in.
92 00:07:48.280 ⇒ 00:07:48.760 Emily Giant: Nice.
93 00:07:48.760 ⇒ 00:07:51.110 Demilade Agboola: then down the line, we’re in, so…
94 00:07:51.800 ⇒ 00:07:57.020 Emily Giant: it’s like, okay, I definitely need to look at, like, my backhand and just walk on that, get a couple shots going.
95 00:07:57.020 ⇒ 00:08:01.060 Demilade Agboola: get better rhythm with that, you know, things like that. It’s helpful, it’s really helpful.
96 00:08:01.590 ⇒ 00:08:18.399 Emily Giant: It’s always fun, I mean, I know that tennis is already a game, but it’s fun to gamify practice, because practice can get just, like, so frustrating when you feel like things aren’t changing. I think I’m just gonna, like, throw my racket, like, his face? John McEnroe, just, like.
97 00:08:18.400 ⇒ 00:08:24.990 Demilade Agboola: Rockets are too expensive to do that, and I don’t have sponsors, so I’m… I’m quite…
98 00:08:25.670 ⇒ 00:08:27.410 Emily Giant: In control of my temper.
99 00:08:27.720 ⇒ 00:08:29.280 Demilade Agboola: Yeah.
100 00:08:29.280 ⇒ 00:08:36.930 Emily Giant: I like ping pong for that reason. You can just split those bad boys in half, get your rage out, it’s great.
101 00:08:37.270 ⇒ 00:08:47.350 Emily Giant: I really need to get a ping pong table for the barn, and then I’ll work my way back up to tennis. But I’ve been so sick, like, I haven’t been able to run or exercise or anything for, like, a month.
102 00:08:47.490 ⇒ 00:08:48.440 Emily Giant: And…
103 00:08:48.550 ⇒ 00:09:00.859 Emily Giant: I just… you know when you get to that point where you’re like, I am starting from zero again. Like, I got into a good spot, and now it’s gonna be, like, back to zero with all of this, so I’m just dreading it, but it…
104 00:09:01.080 ⇒ 00:09:06.549 Emily Giant: It’s gotta happen. I don’t want to die in early death, so I guess I have to keep exercising.
105 00:09:07.190 ⇒ 00:09:08.989 Demilade Agboola: We don’t… we definitely don’t want that for you, so…
106 00:09:09.460 ⇒ 00:09:19.769 Emily Giant: Gotta keep my eye on the price, which is just being alive. So how did it go when, when I was out? I know that there were some… I saw a few, like.
107 00:09:20.370 ⇒ 00:09:26.800 Emily Giant: PRs go through, and a couple of things I was like, yeah. But just, like, general.
108 00:09:27.500 ⇒ 00:09:36.600 Demilade Agboola: I think January was mostly quiet, but there was a period where, like, certain dashboards weren’t loading. I think it appeared to be the Looker, like, LookML commits.
109 00:09:36.830 ⇒ 00:09:39.809 Emily Giant: Because it was one of those things where.
110 00:09:39.810 ⇒ 00:09:46.090 Demilade Agboola: Like, once we… because there weren’t any… Okay, so…
111 00:09:46.210 ⇒ 00:09:52.589 Demilade Agboola: Felipe hadn’t… Felipe said he last… he saw them work was on the 15th, I believe, of December.
112 00:09:52.680 ⇒ 00:09:53.790 Emily Giant: Someday.
113 00:09:53.790 ⇒ 00:09:59.020 Demilade Agboola: when he came back, like I said, I think he went on a holiday or something, and when he came back, they weren’t working.
114 00:09:59.340 ⇒ 00:10:02.579 Demilade Agboola: So, obviously, it was something that had changed within that period.
115 00:10:03.560 ⇒ 00:10:06.060 Demilade Agboola: There are not really any PRs to that effect.
116 00:10:08.820 ⇒ 00:10:27.690 Emily Giant: I noticed the same thing. And even today when I was back, I wanted to take a look at it with you. It seems to have resolved after I ran a couple commands, but I don’t understand why it worked. So, I would love to run it by you, but my mode and redshift were so effing slow that I was like.
117 00:10:27.880 ⇒ 00:10:30.239 Emily Giant: Something is terribly wrong here.
118 00:10:30.370 ⇒ 00:10:43.520 Emily Giant: And so I ran a command that Cursor told me to run, which was, like, stopping any other jobs that may be running in the background, from my instance, and that seems to have sped it up, but still, it was, like, eerily slow.
119 00:10:44.200 ⇒ 00:10:48.660 Demilade Agboola: Okay, but did that resolve the, dashboard issues?
120 00:10:48.920 ⇒ 00:10:51.260 Demilade Agboola: I don’t think so, they’re still really slow.
121 00:10:53.030 ⇒ 00:10:53.830 Demilade Agboola: Gotcha.
122 00:10:57.410 ⇒ 00:11:05.550 Emily Giant: I even thought, like, did we get downgraded in Redshift? Like, do we just not have as much, like, processing power? Because…
123 00:11:05.620 ⇒ 00:11:10.879 Emily Giant: nothing really changed. I looked at, like, the commits, but only in dbt.
124 00:11:10.950 ⇒ 00:11:30.920 Emily Giant: looked at the commits between, like, December 22nd and now to see if anything, like, jumped out at me. And, like, there are a couple things that may be affecting it, but I wouldn’t think that they would affect Looker. And Looker, like, should be running much, much faster with the new data tables, and it’s just…
125 00:11:31.510 ⇒ 00:11:33.430 Emily Giant: Not, for some reason.
126 00:11:35.270 ⇒ 00:11:39.340 Emily Giant: And one of the things was, like, so I split out
127 00:11:39.520 ⇒ 00:11:49.149 Emily Giant: the order level data from this, like, line item level data, which… that’s how we had set it up. And, in order to pull in, like, product information.
128 00:11:49.280 ⇒ 00:11:58.599 Emily Giant: two orders, the join… let me actually share a screen, this might… I did reverse it, but I’m sure that this still isn’t the best way to have done it.
129 00:11:59.010 ⇒ 00:11:59.680 Demilade Agboola: Oh, okay.
130 00:12:03.630 ⇒ 00:12:06.350 Emily Giant: So… That’s dbt.
131 00:12:07.550 ⇒ 00:12:08.530 Emily Giant: Fuller.
132 00:12:09.560 ⇒ 00:12:23.289 Emily Giant: So, in the LookML, one of the joins, had an OR in it, and I think it was, like, killing the runtime. Looker is just not good at OR conditions at all.
133 00:12:23.570 ⇒ 00:12:24.430 Demilade Agboola: Yeah.
134 00:12:24.860 ⇒ 00:12:27.650 Emily Giant: So, if I go to New Model Structure…
135 00:12:30.840 ⇒ 00:12:35.210 Emily Giant: Orders… Oh, no, I need to actually go to the model.
136 00:12:40.270 ⇒ 00:12:46.860 Emily Giant: Okay, those are all ands. It was… in the product… this.
137 00:12:48.300 ⇒ 00:13:05.339 Emily Giant: So, subscriptions still don’t look like they’re working, and it runs, like, really slowly. This one, the DIM Products Union All, also used to have a… an OR instead of a coalesce. Ever since fixing that, it runs a lot faster, but the
138 00:13:05.920 ⇒ 00:13:07.190 Emily Giant: this…
139 00:13:08.010 ⇒ 00:13:21.700 Emily Giant: subscriptions just, like, are bonking for some reason in the line item sales and order level data. I did have to reach out to Polytomic to have them build a new table, because without letting me know, they…
140 00:13:21.860 ⇒ 00:13:31.590 Emily Giant: launched a new table for gift subscriptions, so, like, all the work we had done to amortize the revenue, it wasn’t working, and I couldn’t figure out why. And it was because it was like, oh.
141 00:13:32.100 ⇒ 00:13:40.539 Emily Giant: Sorry, there’s a whole other table for gift subscriptions, so that was one of the issues, which I know how to solve that, but,
142 00:13:41.040 ⇒ 00:13:43.730 Emily Giant: Yeah, take a look at this, I can send it to you, too.
143 00:13:43.970 ⇒ 00:13:49.190 Demilade Agboola: But when you say it’s not working, is it, like, the join isn’t, like, it’s not properly joining, or…
144 00:13:49.190 ⇒ 00:13:54.540 Emily Giant: Yeah, it’s not properly, like, pulling orders that are related to subscriptions.
145 00:13:54.930 ⇒ 00:13:56.580 Demilade Agboola: Oh, gotcha, gotcha.
146 00:13:57.930 ⇒ 00:14:00.789 Emily Giant: It’s missing a bunch of them, or…
147 00:14:00.790 ⇒ 00:14:07.019 Demilade Agboola: And is that coming from… is that coming from the raw table, or is that post, transformation?
148 00:14:07.510 ⇒ 00:14:09.700 Emily Giant: That should be coming from the raw table.
149 00:14:10.460 ⇒ 00:14:20.309 Emily Giant: So, subscription’s order line ID is not null. I think it’s this order line ID, and it all kind of, like, boils back to…
150 00:14:20.530 ⇒ 00:14:30.889 Emily Giant: the problem where if an order isn’t marked as delivered in Shopify, it doesn’t generate certain IDs that should tie it to other IDs.
151 00:14:31.160 ⇒ 00:14:39.029 Emily Giant: And that is just not something that we can do anything about, but I don’t know if that’s the case every time. So that’s why I backed it up with this, like.
152 00:14:39.600 ⇒ 00:14:41.690 Emily Giant: suborder ID situation.
153 00:14:42.880 ⇒ 00:14:48.629 Emily Giant: But it’s… It’s like it’s too many conditions for something that should be really reliable.
154 00:14:50.590 ⇒ 00:14:56.410 Demilade Agboola: Yeah, I’m not a big fan of the ore going on there.
155 00:14:58.810 ⇒ 00:15:02.510 Demilade Agboola: So, if we use the suborder ID,
156 00:15:02.750 ⇒ 00:15:07.619 Demilade Agboola: Instead, does that… is that more reliable? Do we get more joins, or…
157 00:15:07.850 ⇒ 00:15:09.200 Demilade Agboola: Is it about the same thing.
158 00:15:10.480 ⇒ 00:15:13.159 Emily Giant: Actually, not sure.
159 00:15:14.530 ⇒ 00:15:20.890 Demilade Agboola: Because I’m thinking, like, if we have one that’s just kind of reliable, let’s use that.
160 00:15:22.120 ⇒ 00:15:33.320 Emily Giant: It’s the whole product-level thing of, like, if they want to see the products that were sent within the subscriptions… well, you know what, before I start making suppositions, I can just run… like, this is…
161 00:15:33.660 ⇒ 00:15:36.049 Emily Giant: One of the dashboards that just never…
162 00:15:36.810 ⇒ 00:15:42.419 Emily Giant: Runs in time and, like, bonks out, but… Let me do new…
163 00:15:44.390 ⇒ 00:15:53.730 Emily Giant: order-level sales data. So if I was just pulling subscriptions from… let’s say is subscription.
164 00:15:58.970 ⇒ 00:16:00.020 Emily Giant: Yes?
165 00:16:01.460 ⇒ 00:16:04.410 Emily Giant: And let’s do a date.
166 00:16:11.160 ⇒ 00:16:13.139 Emily Giant: Let’s do accrued.
167 00:16:17.000 ⇒ 00:16:19.230 Emily Giant: And subscription B.
168 00:16:36.060 ⇒ 00:16:37.210 Emily Giant: No results.
169 00:16:38.570 ⇒ 00:16:41.440 Emily Giant: Yeah, it’s… Curious.
170 00:16:45.590 ⇒ 00:16:51.499 Emily Giant: So if I run… In mode, for the underlying table.
171 00:17:07.640 ⇒ 00:17:09.469 Emily Giant: Fingers are getting ahead of me here.
172 00:17:27.089 ⇒ 00:17:30.730 Emily Giant: Don’t… we’re… Billing date, maybe, or is…
173 00:17:31.640 ⇒ 00:17:33.379 Emily Giant: Let’s see if there’s a created…
174 00:17:38.600 ⇒ 00:17:40.730 Emily Giant: Whatever, billing date.
175 00:17:41.270 ⇒ 00:17:45.980 Emily Giant: It’s greater than or equal to… Current date…
176 00:17:48.110 ⇒ 00:17:51.160 Demilade Agboola: It’s a typo, so the analytics has double A.
177 00:17:52.740 ⇒ 00:17:53.700 Emily Giant: What did I do?
178 00:17:54.190 ⇒ 00:17:55.920 Demilade Agboola: But analytics has doubled here.
179 00:17:56.170 ⇒ 00:17:57.199 Emily Giant: Oh, yeah.
180 00:18:29.360 ⇒ 00:18:32.670 Demilade Agboola: And it’s the… Fox subscriptions up to date.
181 00:18:34.900 ⇒ 00:18:39.409 Emily Giant: Doesn’t look like it… But it runs every 45 minutes.
182 00:18:49.430 ⇒ 00:18:52.350 Emily Giant: That is… not promising.
183 00:18:55.930 ⇒ 00:18:59.149 Emily Giant: Let’s try subscription start date instead of billing date.
184 00:19:01.710 ⇒ 00:19:04.180 Demilade Agboola: That looks really weird, that’s almost a year ago.
185 00:19:04.570 ⇒ 00:19:05.250 Emily Giant: Yeah.
186 00:19:06.060 ⇒ 00:19:08.660 Emily Giant: So that doesn’t even make a bit of sense.
187 00:19:24.350 ⇒ 00:19:27.339 Emily Giant: Yeah, that’s really, really strange.
188 00:19:27.530 ⇒ 00:19:28.390 Demilade Agboola: No.
189 00:19:42.550 ⇒ 00:19:44.320 Demilade Agboola: Can you go to Catalog?
190 00:19:47.020 ⇒ 00:19:48.089 Emily Giant: Over here?
191 00:19:48.660 ⇒ 00:19:51.259 Demilade Agboola: Yeah, so you can still put in the, like.
192 00:19:52.660 ⇒ 00:19:57.559 Demilade Agboola: And search for fax subscriptions on to see, like, how often it’s run.
193 00:20:01.070 ⇒ 00:20:03.490 Demilade Agboola: Okay, so there is a warning sign.
194 00:20:03.720 ⇒ 00:20:06.360 Demilade Agboola: Can you hover over the orange thing?
195 00:20:06.600 ⇒ 00:20:08.160 Demilade Agboola: It has steel options, so…
196 00:20:08.160 ⇒ 00:20:08.860 Emily Giant: be…
197 00:20:09.140 ⇒ 00:20:16.429 Demilade Agboola: Okay… Does it allow you to hover the still upstream sources, or…
198 00:20:18.280 ⇒ 00:20:20.070 Emily Giant: No. No, but this…
199 00:20:20.350 ⇒ 00:20:21.630 Demilade Agboola: Alright, let’s…
200 00:20:21.630 ⇒ 00:20:27.960 Emily Giant: upscale upstream sources in that half of that model is legacy, so it just doesn’t run.
201 00:20:28.330 ⇒ 00:20:29.080 Demilade Agboola: Fair enough.
202 00:20:33.230 ⇒ 00:20:34.800 Emily Giant: Here, let me go back to…
203 00:20:38.430 ⇒ 00:20:39.699 Demilade Agboola: Let me try and see.
204 00:20:50.700 ⇒ 00:20:55.639 Emily Giant: So… Intuptions Loop with Floral SKU.
205 00:20:56.950 ⇒ 00:21:00.560 Emily Giant: Let me see if that… I’ll open it in another window so we can go back and forth.
206 00:21:02.490 ⇒ 00:21:07.930 Emily Giant: Because this one should not have stale upstream sources, because it’s not connected to any of the legacy.
207 00:21:37.800 ⇒ 00:21:39.090 Emily Giant: Okay.
208 00:21:44.680 ⇒ 00:21:46.379 Emily Giant: Does it tell you how stale?
209 00:21:50.290 ⇒ 00:21:54.820 Emily Giant: This has stale upstream sources? Oh, it does not have freshness check configured.
210 00:21:58.450 ⇒ 00:22:01.549 Emily Giant: It has been refreshed in the past 30 days, okay?
211 00:22:04.260 ⇒ 00:22:06.070 Demilade Agboola: After subscriptions…
212 00:22:20.660 ⇒ 00:22:22.490 Demilade Agboola: So they’re building that…
213 00:22:28.790 ⇒ 00:22:31.560 Emily Giant: So the raw model has stale upstream sources?
214 00:22:35.570 ⇒ 00:22:39.380 Demilade Agboola: I’m actually even trying to figure out, like, Where…
215 00:22:49.500 ⇒ 00:22:55.090 Emily Giant: Let me see in… Polytomic.
216 00:23:22.570 ⇒ 00:23:23.270 Emily Giant: What?
217 00:23:24.020 ⇒ 00:23:25.710 Emily Giant: Has this always looked like this?
218 00:23:26.770 ⇒ 00:23:27.360 Demilade Agboola: Whoa.
219 00:23:29.130 ⇒ 00:23:30.240 Emily Giant: Polyatomic.
220 00:23:31.030 ⇒ 00:23:32.260 Emily Giant: connections.
221 00:23:32.260 ⇒ 00:23:33.930 Demilade Agboola: Two to bulk, yeah, connections.
222 00:23:34.050 ⇒ 00:23:36.070 Demilade Agboola: Oh, bulk sinks, actually.
223 00:23:36.610 ⇒ 00:23:39.380 Demilade Agboola: So bulk syncs is where you see what’s going on.
224 00:23:39.380 ⇒ 00:23:40.620 Emily Giant: There’s… okay.
225 00:23:41.010 ⇒ 00:23:43.240 Demilade Agboola: Yeah, so loop to redshift Sync, yeah.
226 00:23:51.130 ⇒ 00:23:53.460 Emily Giant: Where do I see, like, the columns?
227 00:23:58.610 ⇒ 00:23:59.430 Emily Giant: Here.
228 00:24:07.270 ⇒ 00:24:11.689 Emily Giant: There used to be one where it would show, like, how many… Columns it updated.
229 00:24:17.210 ⇒ 00:24:18.349 Demilade Agboola: How many columns?
230 00:24:19.420 ⇒ 00:24:20.880 Emily Giant: Yeah, here, total records.
231 00:24:22.330 ⇒ 00:24:23.139 Demilade Agboola: Oh, yeah, yeah.
232 00:24:31.720 ⇒ 00:24:32.440 Emily Giant: Hello?
233 00:24:35.600 ⇒ 00:24:37.380 Emily Giant: 2 million versus…
234 00:24:42.160 ⇒ 00:24:43.740 Emily Giant: Does that feel weird to you?
235 00:24:45.510 ⇒ 00:24:50.120 Emily Giant: Unless that was the new table that they… Customer details.
236 00:24:50.960 ⇒ 00:24:53.320 Demilade Agboola: Hmm, cause there’s a start time on…
237 00:24:54.210 ⇒ 00:24:57.930 Demilade Agboola: So, customer details had a start time in December.
238 00:24:58.280 ⇒ 00:25:01.479 Demilade Agboola: He finished until… the 2nd of January.
239 00:25:02.670 ⇒ 00:25:03.630 Emily Giant: What!
240 00:25:03.810 ⇒ 00:25:06.060 Demilade Agboola: And he had 2 million tables.
241 00:25:09.340 ⇒ 00:25:12.280 Demilade Agboola: That’s… That’s quite something, alright.
242 00:25:12.280 ⇒ 00:25:13.590 Emily Giant: Yeah.
243 00:25:13.590 ⇒ 00:25:17.640 Demilade Agboola: Was it… was it an initial sync, or… I’m guessing it’s initial syn.
244 00:25:17.640 ⇒ 00:25:22.780 Emily Giant: It must have been, because they did work over the holiday when I asked them for that new table.
245 00:25:22.780 ⇒ 00:25:23.310 Demilade Agboola: Yeah.
246 00:25:23.310 ⇒ 00:25:24.710 Emily Giant: That must have been what it was.
247 00:25:27.190 ⇒ 00:25:29.550 Emily Giant: But is it… this is total records.
248 00:25:30.560 ⇒ 00:25:31.160 Demilade Agboola: Yeah.
249 00:25:32.350 ⇒ 00:25:35.559 Emily Giant: At the beginning of time, not, like, total new records.
250 00:25:38.000 ⇒ 00:25:44.089 Demilade Agboola: I mean, if you ran for 121 hours, I believe it did sync the 2 million records.
251 00:25:47.020 ⇒ 00:25:50.530 Emily Giant: That makes sense to me more than these other ones that, like, don’t change.
252 00:25:54.840 ⇒ 00:25:57.109 Demilade Agboola: Can you click on, like, the term…
253 00:25:58.200 ⇒ 00:26:01.339 Demilade Agboola: There’s a 1 million one below or something.
254 00:26:02.640 ⇒ 00:26:04.010 Emily Giant: Here’s another 2 mil.
255 00:26:04.010 ⇒ 00:26:07.180 Demilade Agboola: Yeah, I’m guessing… can you click on that as well? Oh, yeah, right, this.
256 00:26:07.550 ⇒ 00:26:10.509 Demilade Agboola: Alright, so it’s the customer details, so it does appear that…
257 00:26:12.330 ⇒ 00:26:20.390 Demilade Agboola: once a while, you get… can you ask, or can you look into it? I’m curious as to… this.
258 00:26:21.960 ⇒ 00:26:23.050 Demilade Agboola: Wine.
259 00:26:23.270 ⇒ 00:26:26.159 Demilade Agboola: What’s in that table, and why does it seem to take so long?
260 00:26:46.790 ⇒ 00:26:49.690 Emily Giant: Not much.
261 00:26:51.530 ⇒ 00:26:57.040 Emily Giant: It’s just like… A customer-level look at their subscription.
262 00:27:03.150 ⇒ 00:27:04.690 Emily Giant: I don’t need to use those tables.
263 00:27:05.680 ⇒ 00:27:09.100 Demilade Agboola: So it’s like an active council, so it’s like a…
264 00:27:09.950 ⇒ 00:27:12.490 Demilade Agboola: But that’s weird. If it is…
265 00:27:13.510 ⇒ 00:27:19.320 Demilade Agboola: An aggregate of the customer details, like number of cancel subscriptions, it shouldn’t be that heavy.
266 00:27:20.150 ⇒ 00:27:23.080 Emily Giant: Yeah, it… that’s a lot. It’s very weird.
267 00:27:23.340 ⇒ 00:27:24.030 Demilade Agboola: Hmm.
268 00:27:31.700 ⇒ 00:27:36.990 Emily Giant: It’s like it’s running all of the… Historical subscribers as well.
269 00:27:37.560 ⇒ 00:27:41.540 Emily Giant: Which I’m guessing is what’s happening. Like, they ingested it or something.
270 00:27:43.470 ⇒ 00:27:51.569 Emily Giant: earlier today, and like, when Felipe was saying things were running really slowly, this table would have taken 10 minutes to run, so it is running fast.
271 00:27:52.490 ⇒ 00:27:54.760 Emily Giant: Very interesting.
272 00:27:57.080 ⇒ 00:27:58.790 Emily Giant: Like, this is nothing, though.
273 00:28:03.550 ⇒ 00:28:06.609 Emily Giant: Like, this person doesn’t even have any subscriptions.
274 00:28:10.630 ⇒ 00:28:13.720 Demilade Agboola: So it’s just basically every single customer…
275 00:28:14.770 ⇒ 00:28:17.020 Emily Giant: That has a Shopify ID, I think.
276 00:28:17.270 ⇒ 00:28:18.000 Demilade Agboola: Hmm.
277 00:28:18.990 ⇒ 00:28:20.610 Demilade Agboola: And it’s a raw table.
278 00:28:21.850 ⇒ 00:28:23.160 Emily Giant: Yeah. Oh, no.
279 00:28:23.370 ⇒ 00:28:25.359 Demilade Agboola: Like, it’s gotten married from…
280 00:28:25.360 ⇒ 00:28:25.790 Emily Giant: Not the right.
281 00:28:25.790 ⇒ 00:28:26.800 Demilade Agboola: from Luke.
282 00:28:28.250 ⇒ 00:28:29.220 Emily Giant: It’s this.
283 00:28:34.980 ⇒ 00:28:37.590 Emily Giant: Or is it… Customers.
284 00:28:40.790 ⇒ 00:28:42.509 Emily Giant: Customers… customer details.
285 00:28:42.510 ⇒ 00:28:43.550 Demilade Agboola: And I thought, yeah.
286 00:29:00.000 ⇒ 00:29:01.070 Emily Giant: A…
287 00:29:08.100 ⇒ 00:29:09.799 Emily Giant: Essentially the same thing.
288 00:29:13.570 ⇒ 00:29:16.260 Demilade Agboola: Yeah, it’s gone, it’s gone with the same thing. I think…
289 00:29:16.630 ⇒ 00:29:23.789 Demilade Agboola: So that’s just the raw data that comes from Loop. They basically are trying to sum up everything about every customer.
290 00:29:23.970 ⇒ 00:29:25.369 Demilade Agboola: They seem to have.
291 00:29:26.360 ⇒ 00:29:27.080 Emily Giant: Yep.
292 00:29:28.080 ⇒ 00:29:32.910 Demilade Agboola: I don’t know if you necessarily need it. Can you do a count all? Like, how many rows there are?
293 00:29:44.260 ⇒ 00:29:48.130 Emily Giant: That is… 2,647…
294 00:29:49.030 ⇒ 00:29:49.990 Demilade Agboola: Don’t think through it.
295 00:29:50.140 ⇒ 00:29:50.760 Emily Giant: I’m…
296 00:29:50.760 ⇒ 00:29:57.559 Demilade Agboola: Can we talk… okay. It might be helpful to talk to the polyatomic team to make this an incremental push.
297 00:29:57.810 ⇒ 00:29:58.720 Emily Giant: Yeah.
298 00:29:58.720 ⇒ 00:30:05.550 Demilade Agboola: Because you don’t necessarily need… To update every single thing, every single time, like…
299 00:30:05.550 ⇒ 00:30:06.230 Emily Giant: Yeah.
300 00:30:06.230 ⇒ 00:30:10.840 Demilade Agboola: You don’t… You’re only gonna get more rows as time goes on.
301 00:30:10.940 ⇒ 00:30:13.139 Demilade Agboola: And you don’t want another 4D push.
302 00:30:15.220 ⇒ 00:30:16.480 Emily Giant: I’ll talk to them.
303 00:30:16.490 ⇒ 00:30:17.220 Demilade Agboola: Okay.
304 00:30:17.370 ⇒ 00:30:25.990 Emily Giant: That is… not quite right. Now, if I… to count everything from… loop orders.
305 00:30:27.420 ⇒ 00:30:29.600 Emily Giant: Or even, yeah, orders.
306 00:30:29.890 ⇒ 00:30:34.650 Emily Giant: Because that should be every subscription order, and it should be several thousand a week.
307 00:30:44.070 ⇒ 00:30:46.100 Emily Giant: That’s just not that many rows.
308 00:30:54.690 ⇒ 00:30:58.780 Demilade Agboola: just… 62… Thousand in total.
309 00:31:01.520 ⇒ 00:31:02.740 Demilade Agboola: That seems quite low.
310 00:31:03.050 ⇒ 00:31:03.670 Emily Giant: Yeah.
311 00:31:05.700 ⇒ 00:31:09.899 Demilade Agboola: So is this a polyatomic tin or a luke thing? This will be the next…
312 00:31:10.330 ⇒ 00:31:12.670 Emily Giant: Is a good question.
313 00:31:14.390 ⇒ 00:31:15.250 Emily Giant: Max.
314 00:31:17.240 ⇒ 00:31:18.860 Emily Giant: was created at.
315 00:31:28.150 ⇒ 00:31:30.910 Emily Giant: Okay, that’s today, so that’s good.
316 00:31:31.320 ⇒ 00:31:32.950 Demilade Agboola: Can you do the minimum, please?
317 00:31:40.010 ⇒ 00:31:41.250 Emily Giant: Makes perfect sense.
318 00:31:41.410 ⇒ 00:31:45.269 Emily Giant: That’s the day we started flipping over subscriptions to Loop.
319 00:31:47.750 ⇒ 00:31:51.759 Demilade Agboola: So in, like, roughly a year and two months-ish.
320 00:31:53.590 ⇒ 00:31:55.410 Demilade Agboola: There are only 62,000 orders.
321 00:31:56.040 ⇒ 00:31:57.560 Emily Giant: That just doesn’t seem right.
322 00:31:59.120 ⇒ 00:32:05.630 Emily Giant: So, if there’s… Like, roughly, say, at the lowest end, 500 a week.
323 00:32:07.470 ⇒ 00:32:08.180 Demilade Agboola: Yeah.
324 00:32:08.180 ⇒ 00:32:10.330 Emily Giant: That the math doesn’t math.
325 00:32:12.150 ⇒ 00:32:21.979 Emily Giant: So something’s… missing from… one of these platforms. It could be Loop. Loop is terrible. So…
326 00:32:29.470 ⇒ 00:32:31.889 Emily Giant: Let me check real quick if they have, like, a.
327 00:32:38.530 ⇒ 00:32:45.860 Demilade Agboola: Alright, so there’s a drop-off in… that… in rows from in subscriptions merge loop subs with others.
328 00:32:45.970 ⇒ 00:32:50.810 Demilade Agboola: So the downstream model, which is in subscriptions loop with floral SKU.
329 00:32:52.170 ⇒ 00:32:54.860 Demilade Agboola: And that’s what feeds fax subscriptions, so, like.
330 00:32:57.590 ⇒ 00:33:04.299 Demilade Agboola: The max billing date in the first one is the 31st of December last year.
331 00:33:05.340 ⇒ 00:33:11.490 Demilade Agboola: And then… What we’re looking at now, which is what we’re seeing, is the 12th…
332 00:33:11.740 ⇒ 00:33:18.800 Demilade Agboola: Of March last year, so… There seems to be a drop-off in that logic.
333 00:33:21.740 ⇒ 00:33:23.260 Demilade Agboola: So what’s going on there…
334 00:33:28.660 ⇒ 00:33:30.750 Emily Giant: So it’s the loop with Floral SKU.
335 00:33:33.950 ⇒ 00:33:36.140 Demilade Agboola: Yeah, so Luke Lafrovsky…
336 00:33:37.690 ⇒ 00:33:42.690 Demilade Agboola: Seems to be even then, I may want to go a little step further. Give me a second.
337 00:34:49.679 ⇒ 00:34:54.560 Emily Giant: I wonder if it’s in this, like… Exclusion list here.
338 00:34:57.600 ⇒ 00:35:03.799 Demilade Agboola: Alright, so let me show you my screen so I can show you how I sort of figured out stuff that’s going on.
339 00:35:27.060 ⇒ 00:35:28.439 Demilade Agboola: Can you see my screen?
340 00:35:28.900 ⇒ 00:35:29.420 Emily Giant: Yep.
341 00:35:30.250 ⇒ 00:35:36.109 Demilade Agboola: Alright, so this is the first… This is the raw…
342 00:35:36.800 ⇒ 00:35:41.429 Demilade Agboola: or, like, our staging copy of, like, the polyatonic loop orders.
343 00:35:42.260 ⇒ 00:35:47.909 Demilade Agboola: Yeah, so the max billing date is today, right, or yesterday, which makes sense, yeah.
344 00:35:49.160 ⇒ 00:35:53.920 Demilade Agboola: Then this is the down model immediately downstream of it.
345 00:35:54.210 ⇒ 00:36:00.549 Demilade Agboola: Which serves… And you can see that we’ve lost some rows, for some reason. Yep.
346 00:36:01.270 ⇒ 00:36:05.740 Demilade Agboola: So now we have it as late last year, like, the last day of last year.
347 00:36:05.920 ⇒ 00:36:11.829 Demilade Agboola: So we’ve lost orders for some reason that happened this year, so we’ll need to look into the joins going on here.
348 00:36:12.270 ⇒ 00:36:21.400 Demilade Agboola: But then even further is the next one, which is here, and that’s when we start to see March of last year, which is way in the past.
349 00:36:21.950 ⇒ 00:36:27.940 Demilade Agboola: And then that’s obviously propagated into… That’s obviously propagated into this.
350 00:36:28.450 ⇒ 00:36:38.050 Demilade Agboola: So, yep. Yeah, so we can see from here… This is the…
351 00:36:38.670 ⇒ 00:36:44.349 Demilade Agboola: Yeah, so you can see that this is the loop we thought, that uses, like, the staging…
352 00:36:45.000 ⇒ 00:36:46.229 Emily Giant: loop orders.
353 00:36:48.830 ⇒ 00:36:51.100 Demilade Agboola: And… let me see…
354 00:36:51.790 ⇒ 00:36:57.510 Demilade Agboola: Okay, we’re joining from looped subs to loop orders, which I guess is where we’re losing some…
355 00:36:57.850 ⇒ 00:37:03.539 Demilade Agboola: Are all loop subs, loops, loop orders? Like, are all loop orders, subscription orders?
356 00:37:04.200 ⇒ 00:37:05.020 Emily Giant: Yes.
357 00:37:05.440 ⇒ 00:37:13.900 Demilade Agboola: So… Why would there be some subs that don’t have… Orders.
358 00:37:16.180 ⇒ 00:37:18.289 Emily Giant: I doubt I do not know.
359 00:37:18.810 ⇒ 00:37:22.410 Emily Giant: Is it just… do I need to, like, flip the join around?
360 00:37:23.010 ⇒ 00:37:29.520 Demilade Agboola: Yeah, I think that will help. We won’t lose orders. Potentially, we might need to…
361 00:37:31.720 ⇒ 00:37:34.810 Demilade Agboola: Look and see if there are some…
362 00:37:38.630 ⇒ 00:37:40.140 Demilade Agboola: Okay…
363 00:37:43.210 ⇒ 00:37:46.140 Demilade Agboola: Let me just do some further digging in.
364 00:38:00.220 ⇒ 00:38:01.679 Demilade Agboola: Trying to see where…
365 00:38:13.780 ⇒ 00:38:16.469 Demilade Agboola: So, the subscription ID…
366 00:38:38.430 ⇒ 00:38:40.600 Demilade Agboola: Let’s first check and see if there are any null…
367 00:38:40.750 ⇒ 00:38:44.319 Demilade Agboola: subscription IDs, which would obviously affect the join.
368 00:38:46.480 ⇒ 00:38:50.070 Demilade Agboola: There are none, that’s good, that’s very helpful.
369 00:38:50.480 ⇒ 00:38:55.369 Demilade Agboola: Oh… So, therefore, the question is, how many…
370 00:38:55.610 ⇒ 00:39:00.219 Demilade Agboola: Subscription IDs are in here that are not in here.
371 00:39:02.790 ⇒ 00:39:04.629 Demilade Agboola: So, let’s see that.
372 00:39:49.360 ⇒ 00:39:52.220 Demilade Agboola: So there are a couple subscription IDs where…
373 00:39:53.610 ⇒ 00:39:56.419 Demilade Agboola: They’re here, they’re not in here.
374 00:39:57.240 ⇒ 00:40:01.180 Demilade Agboola: And that’s why we’re having a weird join. Let me see…
375 00:40:02.840 ⇒ 00:40:05.630 Demilade Agboola: Turn off from this, where this is not in there.
376 00:40:06.680 ⇒ 00:40:19.040 Demilade Agboola: Order by… What’s the dates… Orders, so we have the billing dates.
377 00:40:19.790 ⇒ 00:40:22.330 Demilade Agboola: Reviewing date, descending…
378 00:40:52.730 ⇒ 00:40:56.780 Demilade Agboola: Okay, so these are the orders, some created even today.
379 00:40:57.640 ⇒ 00:41:00.880 Demilade Agboola: That are… that seem to be in loop orders.
380 00:41:01.420 ⇒ 00:41:07.219 Demilade Agboola: But I’m not in… The subscriptions table.
381 00:41:08.970 ⇒ 00:41:09.610 Emily Giant: Hmm.
382 00:41:10.150 ⇒ 00:41:13.759 Demilade Agboola: So, there are 2,076 of them.
383 00:41:14.280 ⇒ 00:41:16.170 Demilade Agboola: Actually, no, 20,000 in some sense.
384 00:41:16.170 ⇒ 00:41:17.340 Emily Giant: Yeah.
385 00:41:18.500 ⇒ 00:41:23.709 Demilade Agboola: Not bad. Here they are.
386 00:41:24.370 ⇒ 00:41:26.090 Demilade Agboola: And you said, let me try…
387 00:41:26.210 ⇒ 00:41:32.249 Demilade Agboola: Looking at them in Shopify, and seeing… Seeing if that helps.
388 00:41:33.150 ⇒ 00:41:33.770 Emily Giant: Cheers.
389 00:41:47.900 ⇒ 00:41:51.039 Emily Giant: This tracks. They’ve had a lot of problems where
390 00:41:51.210 ⇒ 00:41:53.819 Emily Giant: Subscriptions are not exporting from Loop.
391 00:41:55.440 ⇒ 00:41:55.980 Demilade Agboola: Gotcha.
392 00:41:55.980 ⇒ 00:42:05.790 Emily Giant: So, that makes a lot of sense to me, that the one table would have the data and the other wouldn’t, based on the tech problems they’ve been having.
393 00:42:10.140 ⇒ 00:42:11.480 Demilade Agboola: Yes, subscriptions.
394 00:42:13.360 ⇒ 00:42:14.829 Emily Giant: Does it have a little…
395 00:42:15.860 ⇒ 00:42:20.880 Emily Giant: Does it have a link in that page that you were just on to, like, go to loop?
396 00:42:21.930 ⇒ 00:42:25.019 Emily Giant: Because if it doesn’t, that means it didn’t, like, yeah, it does.
397 00:42:25.820 ⇒ 00:42:26.810 Demilade Agboola: And we’ll…
398 00:42:32.540 ⇒ 00:42:34.750 Demilade Agboola: I’m not sure I have loop access.
399 00:42:37.520 ⇒ 00:42:40.249 Emily Giant: I think it’s just urban stems, but…
400 00:42:45.470 ⇒ 00:42:55.100 Emily Giant: If you send me that, I’ll look into that, though. There’s something up, for sure. Maybe it’s a gift subscription, and it’s that table that we never had before.
401 00:42:55.370 ⇒ 00:42:56.650 Demilade Agboola: Yeah, alright.
402 00:42:56.910 ⇒ 00:43:01.539 Demilade Agboola: So let me just… instead of giving you stuff, let me just send you this query, I’ll send it to your Slack.
403 00:43:03.920 ⇒ 00:43:10.190 Demilade Agboola: So at least you kind of have an idea of where things are falling off at the individual stages.
404 00:43:11.180 ⇒ 00:43:13.620 Demilade Agboola: on… So, yeah.
405 00:43:14.380 ⇒ 00:43:15.050 Emily Giant: Perfect.
406 00:43:16.150 ⇒ 00:43:20.669 Demilade Agboola: And this just also helps you… I’ll send this to you as well, so at least you have an idea of…
407 00:43:20.880 ⇒ 00:43:26.380 Demilade Agboola: As you’re troubleshooting, like, you can kind of see where you need to bridge off the gaps.
408 00:43:26.720 ⇒ 00:43:27.430 Emily Giant: Yeah.
409 00:43:27.730 ⇒ 00:43:29.870 Demilade Agboola: Super helpful.
410 00:43:29.870 ⇒ 00:43:30.400 Emily Giant: fault.
411 00:43:34.130 ⇒ 00:43:35.710 Emily Giant: I’ll see if, like…
412 00:43:37.490 ⇒ 00:43:48.459 Emily Giant: I was waiting for the new year to kind of, like, get our schedule going again. I don’t know what your schedule looks like, but if we could do this, like, once a week, with a model, and just, like, troubleshoot where I’m stuck.
413 00:43:50.060 ⇒ 00:43:52.140 Emily Giant: I feel like that would be really helpful.
414 00:43:53.540 ⇒ 00:43:55.700 Demilade Agboola: Okay, hmm…
415 00:43:56.790 ⇒ 00:44:04.190 Emily Giant: And I can… I’m sure Zach will say yes, but just have to run it by him since it’s a different contract, blah blah.
416 00:44:04.790 ⇒ 00:44:10.140 Demilade Agboola: Alright, sounds good. Just let me know, and I’ll be able to figure out how to make it work.
417 00:44:10.630 ⇒ 00:44:16.449 Emily Giant: Oh, you just saved me, like, 5 hours of my life trying to figure this out, so I feel like that’s money well spent.
418 00:44:17.340 ⇒ 00:44:28.620 Demilade Agboola: No, that’s all good, it’s all good. So, yeah, once you’re able to just figure out, like, why they’re missing, like, subscriptions, the subscriptions table, specifically, is missing certain, like, orders.
419 00:44:30.130 ⇒ 00:44:30.980 Demilade Agboola: Because…
420 00:44:31.310 ⇒ 00:44:43.949 Demilade Agboola: Oh, not necessarily they’re missing certain orders, where there’s certain sufficient IDs missing, that we cannot then tie the orders to. So that’s why we’re losing orders as we… when we make that join. Once we figure out that sufficiency, we’ll issue…
421 00:44:44.120 ⇒ 00:45:00.600 Demilade Agboola: whether it’s a loop thing, whether it’s a polyatomic thing, or, you know, if it’s a urban stems thing, either. We just kind of figure out, like, where the drop-off is coming from. That would allow us to be able to catch up with some of these fact subscription issues.
422 00:45:00.920 ⇒ 00:45:07.750 Emily Giant: I think a lot of it will be fixed with just, like, swapping that join, but still, there’s gonna be information missing from the subscription.
423 00:45:07.750 ⇒ 00:45:09.790 Demilade Agboola: Yeah, exactly.
424 00:45:10.510 ⇒ 00:45:13.530 Emily Giant: Always something, but,
425 00:45:14.030 ⇒ 00:45:30.180 Emily Giant: like, next week, I… I’m still getting a lot of failing tests on the historical revenue models. It’s not the new ones that are the issue. It’s, like, the old ones where, like, tax is negative. Like, certain things are just funky that I would love help just, like, sorting those out so that
426 00:45:30.290 ⇒ 00:45:30.830 Emily Giant: It’s…
427 00:45:30.830 ⇒ 00:45:31.510 Demilade Agboola: Look at this.
428 00:45:31.510 ⇒ 00:45:46.079 Emily Giant: reliable. But that’s what I’ll ask. And I told Zach that before break, I was like, I just need some backup on these revenue models that isn’t you guys, because you don’t know these models, only Demulade does, and myself, and it would take you…
429 00:45:46.210 ⇒ 00:45:53.099 Emily Giant: a month to, like, sort through and figure out what’s even going on in half of them, so… I think that that would be, like.
430 00:45:53.340 ⇒ 00:45:57.569 Emily Giant: Really helpful, just to, like, get it done.
431 00:45:58.380 ⇒ 00:45:59.440 Emily Giant: Fast.
432 00:45:59.900 ⇒ 00:46:10.409 Emily Giant: Alright, cool. Well, thanks for holding down the fort while I was gone. Is there a certain day that I can say is, like, kind of open for you in, like, a time frame, just so, like…
433 00:46:10.810 ⇒ 00:46:14.690 Emily Giant: Can… Make sure we don’t have any other stuff going on.
434 00:46:16.480 ⇒ 00:46:22.659 Demilade Agboola: I would say usually my more open days tend to come towards the end of the week, so, like, Thursdays-ish should be fine.
435 00:46:22.990 ⇒ 00:46:25.040 Emily Giant: Yeah, I’d say same for me.
436 00:46:25.230 ⇒ 00:46:27.870 Emily Giant: So… Stays.
437 00:46:28.780 ⇒ 00:46:34.339 Emily Giant: Okay, cool. Alright, well, it was lovely to see you! It’s been such a long time!
438 00:46:34.340 ⇒ 00:46:35.840 Demilade Agboola: Yes, he has, yes he has.
439 00:46:36.050 ⇒ 00:46:36.620 Emily Giant: That’s.
440 00:46:36.620 ⇒ 00:46:37.709 Demilade Agboola: I know you’re doing well.
441 00:46:37.870 ⇒ 00:46:42.509 Emily Giant: No, you, you too. I hope your coffee consumption has stayed high.
442 00:46:42.730 ⇒ 00:46:48.910 Demilade Agboola: Oh yeah, I got a coffee machine, and so now I just buy the beans, and I just grind in, just…
443 00:46:48.910 ⇒ 00:46:51.389 Emily Giant: Oh, you’re living a good life now.
444 00:46:53.390 ⇒ 00:47:00.789 Emily Giant: At me, like, because I always do, like, pour over, and, like, I do the slow coffee, but when people come over, they’re so annoyed, because it takes forever.
445 00:47:00.790 ⇒ 00:47:15.670 Emily Giant: But it feels better to me. But my parents got me a coffee machine for Christmas, too, because they were like, we don’t want to come over. Like, you take way too long to make the coffee. I’m like, alright, fine. So it’s on, like, a top shelf in my closet that I can bring down if they come over.
446 00:47:15.670 ⇒ 00:47:18.569 Emily Giant: and drink subpar coffee. It’s just not a good one.
447 00:47:18.570 ⇒ 00:47:20.679 Emily Giant: It’s not a nice one, but…
448 00:47:20.860 ⇒ 00:47:33.010 Emily Giant: Yeah. If I could send you coffee, I would, but, like, all the places I use do not ship overseas. So, next time you’re in the States, I’ll send it to your girlfriend’s house, and you guys can have… from the coffee place where I used to work.
449 00:47:33.240 ⇒ 00:47:34.530 Demilade Agboola: Okay, sounds good.
450 00:47:34.770 ⇒ 00:47:38.670 Emily Giant: Alright, well, I will hopefully see you next week, if not, like, the week after.
451 00:47:39.120 ⇒ 00:47:40.400 Demilade Agboola: Alright, sounds good.
452 00:47:40.400 ⇒ 00:47:41.699 Emily Giant: Oh, I’m looking out.
453 00:47:42.010 ⇒ 00:47:43.300 Demilade Agboola: Got it then. Bye.