Meeting Title: Working session Date: 2025-06-18 Meeting participants: Demilade Agboola, Emily Giant
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1 00:00:49.810 ⇒ 00:00:51.050 Emily Giant: Morning.
2 00:00:54.000 ⇒ 00:00:55.929 Demilade Agboola: Hi! Good afternoon!
3 00:00:56.870 ⇒ 00:00:59.990 Emily Giant: And how are you doing.
4 00:01:00.833 ⇒ 00:01:05.190 Demilade Agboola: Pretty good bit tired, but pretty fine, otherwise how are you.
5 00:01:06.400 ⇒ 00:01:09.120 Emily Giant: Fine, alive, tired.
6 00:01:09.120 ⇒ 00:01:09.520 Demilade Agboola: Perfect.
7 00:01:09.970 ⇒ 00:01:20.570 Emily Giant: I’ve been having fist fights with for the last 48 h. So I’m like, but yeah, I’m hoping that
8 00:01:21.020 ⇒ 00:01:23.939 Emily Giant: I can give like clear enough
9 00:01:24.900 ⇒ 00:01:40.419 Emily Giant: if information about what’s going on so that we can like fix it together, because I’m just like getting blocked from doing any of the work for, like the bigger infrastructure project until this is fixed. And it’s just a giant
10 00:01:41.010 ⇒ 00:01:46.509 Emily Giant: waste of time, because I’m fixing models that we’re replacing in like a week. But
11 00:01:47.681 ⇒ 00:02:10.228 Emily Giant: but that’s okay we get. We get paid the same thing, no matter what it is that we’re doing. So that’s what I have to tell myself when I get to this point where I’m like, Oh, my God! Like, why don’t people want good data? They just want things like they’re working instead of instead of like actually work. But alright, I’ll share my screen.
12 00:02:10.900 ⇒ 00:02:16.520 Emily Giant: I added some stuff to the fellow notes, but I’m still not seeing my fellow notes pop up
13 00:02:17.050 ⇒ 00:02:18.963 Emily Giant: in whatever
14 00:02:21.910 ⇒ 00:02:26.480 Emily Giant: I know that there’s like a way to get this to pop up in.
15 00:02:29.820 ⇒ 00:02:36.179 Emily Giant: Let’s no in zoom, but it’s just not happening today, so
16 00:02:36.640 ⇒ 00:02:38.780 Emily Giant: I’ll share them some other way.
17 00:02:39.910 ⇒ 00:02:41.740 Emily Giant: Do do do
18 00:02:45.850 ⇒ 00:02:47.469 Emily Giant: so all right.
19 00:02:51.500 ⇒ 00:02:54.090 Emily Giant: This is the meeting.
20 00:02:55.360 ⇒ 00:02:59.750 Emily Giant: Just send all this to you in.
21 00:03:00.320 ⇒ 00:03:09.410 Emily Giant: I see like a notes thing in the zoom here.
22 00:03:09.930 ⇒ 00:03:10.665 Demilade Agboola: Yeah.
23 00:03:13.700 ⇒ 00:03:14.860 Emily Giant: Whatever.
24 00:03:14.860 ⇒ 00:03:15.659 Demilade Agboola: Things are working now.
25 00:03:16.345 ⇒ 00:03:20.159 Emily Giant: Sheet that will be good enough for today.
26 00:03:26.330 ⇒ 00:03:27.120 Emily Giant: Okay.
27 00:03:37.430 ⇒ 00:03:40.849 Emily Giant: how many other clients are you working with right now like a ton.
28 00:03:41.830 ⇒ 00:03:45.819 Demilade Agboola: Me. I just have one more to be honest. What? What? One active
29 00:03:47.399 ⇒ 00:03:51.349 Demilade Agboola: the other I have an eye cause like
30 00:03:51.900 ⇒ 00:04:03.229 Demilade Agboola: I’m 1 of the tech leads in Brainforge. So sometimes I just have an eye on certain things just in case anything goes wrong. But like, in terms of like active participation. It’s just urban stems on one more.
31 00:04:04.330 ⇒ 00:04:08.759 Emily Giant: I can’t imagine you could do more than 2 at a time. It’s so much work.
32 00:04:09.560 ⇒ 00:04:11.520 Emily Giant: anything like urban stems.
33 00:04:11.786 ⇒ 00:04:22.709 Demilade Agboola: I mean, yeah, at some point like your productivity does go down. If you’re not, if you’re on too many. So it’s kind of finding that sweet spot, and I also think, like different projects, require different things from you.
34 00:04:23.260 ⇒ 00:04:23.580 Emily Giant: Yeah.
35 00:04:23.830 ⇒ 00:04:38.459 Demilade Agboola: These projects. You know, I’m leading this project another project that might be supporting the project, like, you know, so different projects. It’s that balance of not leading too many projects, and not like being able to be useful in as many projects as possible.
36 00:04:38.660 ⇒ 00:04:39.570 Emily Giant: Yeah.
37 00:04:39.790 ⇒ 00:04:48.775 Emily Giant: Yeah. Well, it’s probably also like, more interesting to not just work with the same data all the time. Like, keep your brain happy.
38 00:04:49.480 ⇒ 00:05:07.270 Demilade Agboola: I mean, sometimes your brain can get too happy. But like, Oh, yeah, I I agree. I I think one of the cause I was in consulting before then I moved out of consulting. Then I’m back now. One of the things I did miss about consulting was like a regular job fell too slow.
39 00:05:07.800 ⇒ 00:05:08.380 Emily Giant: Yeah.
40 00:05:08.380 ⇒ 00:05:17.970 Demilade Agboola: It’s kind of felt like, Oh, you know, things need to happen so like slowly, and everything is like now. It was it was it was it was do my head in.
41 00:05:18.330 ⇒ 00:05:18.815 Emily Giant: Yeah.
42 00:05:20.060 ⇒ 00:05:20.860 Demilade Agboola: There’s Derek.
43 00:05:20.860 ⇒ 00:05:26.171 Emily Giant: So there are enough problems to keep you here for years. So.
44 00:05:26.580 ⇒ 00:05:27.910 Demilade Agboola: See that I can see that.
45 00:05:27.910 ⇒ 00:05:29.199 Emily Giant: Popping up all the time.
46 00:05:30.494 ⇒ 00:05:47.905 Emily Giant: So what’s happening now? And what’s been happening for like slowly over weeks? And we didn’t really know why is that in our looker reporting things are showing up with this. No shopify product profile instead of a product name. So
47 00:05:48.550 ⇒ 00:05:52.520 Emily Giant: people aren’t sure like what it is that
48 00:05:52.800 ⇒ 00:06:00.619 Emily Giant: the sales are against. Sorry. It just started storming. And I need to scream upstairs. So I’m gonna mute you for 2 seconds. I just need Matt to bring the cats inside.
49 00:06:00.620 ⇒ 00:06:01.730 Demilade Agboola: Okay. Sounds good.
50 00:06:19.440 ⇒ 00:06:24.900 Emily Giant: Okay, he’s getting the captain. So I know that
51 00:06:25.030 ⇒ 00:06:29.760 Emily Giant: this problem comes from a coalesce in Oms items xf.
52 00:06:36.260 ⇒ 00:06:48.999 Emily Giant: which is think it’s like one downstream from our product model, anyhow. Let’s do no.
53 00:06:49.000 ⇒ 00:06:57.097 Demilade Agboola: I was just just gonna say that I also think that, like going forward, the the products also needs, like its own review.
54 00:06:59.070 ⇒ 00:06:59.680 Emily Giant: What?
55 00:06:59.680 ⇒ 00:07:05.700 Demilade Agboola: Products like flow. It’s it’s on review, because it seems to be held together by thoughts and prayers.
56 00:07:05.930 ⇒ 00:07:09.750 Emily Giant: Yeah. Oh, I mean by like an atheist
57 00:07:10.540 ⇒ 00:07:36.020 Emily Giant: like last ditch effort to pray because they don’t know what else to do like. That’s what’s holding this together? It’s not good. Yeah, it’s on the watch list. So we’ve got all of these shopify product profile issues. In the reporting. Sometimes the skew comes through. Sometimes it doesn’t. It seems to be a combination of 2 different issues. One is the products Xf where the information like.
58 00:07:36.890 ⇒ 00:07:39.240 Emily Giant: Well, items not product skew.
59 00:07:39.850 ⇒ 00:07:51.939 Emily Giant: Anyhow, it’s legitimately deprecated. Shopify stopped supporting the table. There’s no updated skews. So I did in this branch, which is called products Xf model swap.
60 00:07:53.450 ⇒ 00:08:00.750 Emily Giant: I’m just slotting in this temporary model with the new shopify table.
61 00:08:01.582 ⇒ 00:08:07.550 Emily Giant: But I haven’t. I haven’t deployed it to production because I’m still seeing the issue.
62 00:08:07.780 ⇒ 00:08:12.589 Emily Giant: and it seems to be still happening in some cases.
63 00:08:12.900 ⇒ 00:08:23.759 Emily Giant: because shopify started generating these random skews like this, like weird string of
64 00:08:24.110 ⇒ 00:08:28.318 Emily Giant: whatever. Whenever people are purchasing a
65 00:08:29.440 ⇒ 00:08:55.100 Emily Giant: like custom bundle so like if somebody buys any hard good, any vase, chocolate, bar candle whatever, instead of having the individual skews shopify is generating this random string to associate with the order, and so there is nothing in the downstream models for it to join up to to say like these are the products that are in it, which is.
66 00:08:55.580 ⇒ 00:09:03.880 Demilade Agboola: But like, when you see random like, I do, you mean like like, it’s not found anywhere, even if you go to shopify itself like you can’t find it like the products that go.
67 00:09:04.510 ⇒ 00:09:06.600 Emily Giant: It does not exist. So like
68 00:09:07.225 ⇒ 00:09:09.700 Emily Giant: if I wanted to see
69 00:09:09.910 ⇒ 00:09:18.490 Emily Giant: everything in products or oms. So where? Where? I know that the the this isn’t happening.
70 00:09:18.730 ⇒ 00:09:22.980 Emily Giant: Product name is coming through is here. So if I pull everything from it
71 00:09:27.990 ⇒ 00:09:29.779 Emily Giant: true, we don’t need that.
72 00:09:31.220 ⇒ 00:09:40.460 Emily Giant: I can pull order numbers and then look it up in shopify to see like that.
73 00:09:40.630 ⇒ 00:09:49.400 Emily Giant: It’s actually like the dove and a glass vase. And I can pull that information from other models that are joined in here.
74 00:09:50.150 ⇒ 00:09:50.590 Demilade Agboola: Okay.
75 00:09:50.590 ⇒ 00:09:53.479 Emily Giant: It’s like figuring out the best way to do it. So
76 00:09:54.760 ⇒ 00:10:03.220 Emily Giant: this one, this is like a legitimate kitted skew, and I do not know why, something that is like
77 00:10:03.450 ⇒ 00:10:13.886 Emily Giant: a profiled product. Doesn’t have a skew, but I’m guessing these are from the deprecated model like these will actually be available.
78 00:10:14.730 ⇒ 00:10:21.539 Demilade Agboola: So what I’m what I’m saying is this, what I’m trying to understand is this, this skews that have no association to anything right now.
79 00:10:21.820 ⇒ 00:10:22.150 Emily Giant: Is it.
80 00:10:22.150 ⇒ 00:10:23.290 Demilade Agboola: Option of like
81 00:10:23.450 ⇒ 00:10:32.040 Demilade Agboola: in the previous system. They did? Or is this a function of like in any system? In every system? They’ve never had something that you could associate with that skew.
82 00:10:32.040 ⇒ 00:10:52.869 Emily Giant: Never have. They’re randomly generated. They. This is a new deployment that I didn’t know about. Where they started, randomly generating skews for any order with more than one bouquet product or more than just a floral product, because the fulfillment software was
83 00:10:54.069 ⇒ 00:10:55.609 Emily Giant: leaving out
84 00:10:56.230 ⇒ 00:11:13.969 Emily Giant: things that needed to go in an order because of how shopify was passing the payload back to our order fulfillment system. So this was like an askew, override type thing to bundle them together for fulfill for fulfillment purposes. However, this was like unbeknownst to me, so I didn’t
85 00:11:14.740 ⇒ 00:11:21.339 Emily Giant: do anything to the models to account for this happening.
86 00:11:21.500 ⇒ 00:11:24.650 Emily Giant: Let’s just pull this directly.
87 00:11:24.650 ⇒ 00:11:32.339 Demilade Agboola: But like the the. So these skills, you know, do we know the products that they they that were sold or associated with this skews.
88 00:11:32.550 ⇒ 00:11:35.579 Emily Giant: Yeah, we can. We can back our way into it.
89 00:11:35.800 ⇒ 00:11:45.340 Emily Giant: but no longer come through the way they used to, which would have been directly in. This
90 00:11:45.990 ⇒ 00:11:47.520 Emily Giant: item table.
91 00:11:48.450 ⇒ 00:11:53.770 Demilade Agboola: My my question with that is, if we know what the product is. Is there? Is there a way we can
92 00:11:54.020 ⇒ 00:11:56.640 Demilade Agboola: attribute the appropriate skew instead.
93 00:11:56.640 ⇒ 00:11:57.340 Emily Giant: Yes.
94 00:11:57.920 ⇒ 00:12:14.150 Emily Giant: yeah. So that’s what I need help doing is I know where all of the information is. I updated the upstream, the main upstream model that should feed this correctly. But now it’s just like plugging in the pieces and making sure that it actually
95 00:12:15.350 ⇒ 00:12:17.570 Emily Giant: works the way that I think that it will.
96 00:12:18.392 ⇒ 00:12:22.189 Emily Giant: Okay, what? The sorry. This is
97 00:12:22.550 ⇒ 00:12:29.550 Emily Giant: like a deleted line. Maybe this was not a good one to pull. So, for example, this is where
98 00:12:29.690 ⇒ 00:12:33.440 Emily Giant: it starts to not have a name. And then in the dag
99 00:12:34.740 ⇒ 00:12:38.729 Emily Giant: we know that it’s this order number. So what I can do is like.
100 00:12:39.330 ⇒ 00:12:45.219 Emily Giant: check, shopify, and go upstream to find where the actual product skews are coming through.
101 00:12:46.230 ⇒ 00:12:47.539 Demilade Agboola: Can we see that like.
102 00:12:50.190 ⇒ 00:12:54.640 Emily Giant: So I want to know what they are 1st to just have a control.
103 00:12:55.530 ⇒ 00:13:03.960 Emily Giant: Alright, so it’s the margo and the respace. So it should be this, these 2 skews, anyhow, in the dag
104 00:13:04.630 ⇒ 00:13:09.209 Emily Giant: it’s not coming through in this model. So if I go to
105 00:13:19.480 ⇒ 00:13:21.919 Emily Giant: items Xf, is it really sorry.
106 00:13:22.240 ⇒ 00:13:28.429 Emily Giant: So dim. Line item Union. This downstream dependency is where I would assume
107 00:13:28.530 ⇒ 00:13:35.082 Emily Giant: that the order information is coming through for these items. So this would be like the 1st place, I check
108 00:13:46.040 ⇒ 00:13:47.699 Emily Giant: Okay? So if I do.
109 00:14:15.630 ⇒ 00:14:18.569 Emily Giant: okay, so here it is. We’ve got
110 00:14:18.850 ⇒ 00:14:24.090 Emily Giant: the kitted skew, which is nonsense. And then the 2 actual products on the order.
111 00:14:27.910 ⇒ 00:14:29.970 Emily Giant: But when I go downstream
112 00:14:31.220 ⇒ 00:14:36.930 Emily Giant: to items Xf, only this stupid one is coming through and the other ones aren’t.
113 00:14:37.650 ⇒ 00:14:41.290 Emily Giant: I don’t know why. So
114 00:14:42.210 ⇒ 00:14:45.399 Emily Giant: if I open this model where that is coming from.
115 00:14:45.540 ⇒ 00:14:49.720 Emily Giant: and that’s what I’ve been seeing that like, okay, yes, we’re seeing what we need.
116 00:14:50.280 ⇒ 00:14:55.550 Emily Giant: But it is not coming through to
117 00:14:55.970 ⇒ 00:14:58.359 Emily Giant: the downstream model in a way that you can see the name
118 00:15:00.930 ⇒ 00:15:06.150 Emily Giant: So this goes to this, and somehow
119 00:15:06.740 ⇒ 00:15:08.970 Emily Giant: dim line out of Union right here.
120 00:15:10.260 ⇒ 00:15:13.059 Emily Giant: The the information is just getting erased.
121 00:15:25.380 ⇒ 00:15:32.600 Demilade Agboola: alright. So if my question is chemical, what’s the most upstream model that that would be available in?
122 00:15:32.840 ⇒ 00:15:38.080 Demilade Agboola: And does the app here there, like, yeah. Raw table, if possible.
123 00:15:38.550 ⇒ 00:15:45.350 Emily Giant: Yeah, so doom, it’s going to be that split line item
124 00:15:46.150 ⇒ 00:15:49.309 Emily Giant: and staging split line items and staging line items.
125 00:15:49.970 ⇒ 00:15:55.490 Emily Giant: So oms, line items, Oms split line items.
126 00:15:55.640 ⇒ 00:16:10.580 Demilade Agboola: Okay to see if they come through in the sources. If they come through in the sources. That means something’s going on in modeling. That’s not.
127 00:16:11.810 ⇒ 00:16:13.649 Demilade Agboola: Oh, we can. We know it’s upstream.
128 00:16:15.560 ⇒ 00:16:17.620 Emily Giant: These are all hevo tables.
129 00:16:18.270 ⇒ 00:16:21.219 Emily Giant: So I just need to figure out what the source name is. Hevo.
130 00:16:35.280 ⇒ 00:16:35.970 Emily Giant: Okay?
131 00:16:36.240 ⇒ 00:16:37.589 Emily Giant: In case.
132 00:16:48.000 ⇒ 00:16:51.759 Emily Giant: okay, I might have to just go to Hevo and look up what the table is called, because I can’t.
133 00:16:51.900 ⇒ 00:16:54.670 Emily Giant: Well, I guess I can compile it from staging.
134 00:16:54.930 ⇒ 00:17:00.579 Emily Giant: Oh, I am tired. My brain is not working.
135 00:17:03.850 ⇒ 00:17:05.649 Demilade Agboola: Coffee. Have you had your morning coffee?
136 00:17:06.230 ⇒ 00:17:08.869 Emily Giant: I have. It’s not enough. It’s not enough.
137 00:17:09.604 ⇒ 00:17:14.819 Emily Giant: I’m supposed, to have the next 2 days off which Alex was like. Yes, you can. As long as
138 00:17:14.960 ⇒ 00:17:19.229 Emily Giant: like the garbage is still on fire, and I’m like, well.
139 00:17:20.140 ⇒ 00:17:23.610 Emily Giant: I might as well cancel Christmas this year, because.
140 00:17:23.619 ⇒ 00:17:25.129 Demilade Agboola: Ever! Not on fire!
141 00:17:25.130 ⇒ 00:17:34.339 Emily Giant: Yeah, okay, I actually think that I’m closer to a fix than it appears. I just need help.
142 00:17:38.850 ⇒ 00:17:41.219 Emily Giant: so this is the source.
143 00:17:42.030 ⇒ 00:17:42.860 Emily Giant: Right.
144 00:17:46.980 ⇒ 00:17:48.400 Emily Giant: Hevo, pop!
145 00:17:49.040 ⇒ 00:17:55.039 Emily Giant: I cannot wait till we clean up redshift, because half of these are like, not even in use.
146 00:17:56.540 ⇒ 00:18:02.120 Emily Giant: Models or tables.
147 00:18:02.690 ⇒ 00:18:03.390 Demilade Agboola: Alright!
148 00:18:05.830 ⇒ 00:18:10.619 Emily Giant: Line items, line items. So it’s prod line items right here. Okay.
149 00:18:10.990 ⇒ 00:18:15.500 Emily Giant: this is the 1st one where it should appear, and.
150 00:18:21.440 ⇒ 00:18:26.640 Demilade Agboola: So it looks like it’s it looks like the weird numbers appear in here as well.
151 00:18:27.460 ⇒ 00:18:27.980 Demilade Agboola: Right.
152 00:18:27.980 ⇒ 00:18:33.349 Emily Giant: Okay is, yeah. I need to figure out like, how
153 00:18:33.690 ⇒ 00:18:36.170 Emily Giant: where the suborder Id lives in.
154 00:18:36.690 ⇒ 00:18:38.120 Demilade Agboola: The source.
155 00:18:42.210 ⇒ 00:18:44.409 Emily Giant: I think it’s the hevo ref id.
156 00:19:26.290 ⇒ 00:19:31.089 Emily Giant: Well, that’s running. I’ll set up the next. The other source where it’s supposed to come from.
157 00:19:39.070 ⇒ 00:19:40.400 Emily Giant: which is.
158 00:19:58.010 ⇒ 00:20:04.057 Emily Giant: I’m supposed to take my nephews to see the new Lilo and stitch movie tonight. And I feel like I’m just gonna be mean to him.
159 00:20:08.290 ⇒ 00:20:13.519 Emily Giant: Okay, so that’s still running.
160 00:20:25.770 ⇒ 00:20:28.240 Demilade Agboola: I want to quickly make a cup of coffee.
161 00:20:28.240 ⇒ 00:20:36.139 Emily Giant: Okay, go for it. I’m gonna go see if I have more in my kitchen as well while this. It’s still running. It’s a huge table. So it takes a minute.
162 00:20:36.800 ⇒ 00:20:38.790 Demilade Agboola: I mean literally just finished now, but.
163 00:20:43.750 ⇒ 00:20:45.421 Emily Giant: It’s waiting for you.
164 00:20:52.350 ⇒ 00:20:53.909 Emily Giant: Oh, my goodness.
165 00:21:07.500 ⇒ 00:21:09.340 Emily Giant: yeah, here.
166 00:21:14.300 ⇒ 00:21:16.789 Emily Giant: here, he’s a baby dog.
167 00:26:31.100 ⇒ 00:26:35.130 Emily Giant: my coffee is on order okay, so
168 00:26:51.470 ⇒ 00:26:52.220 Emily Giant: right.
169 00:26:56.820 ⇒ 00:26:58.840 Emily Giant: but this one is the
170 00:27:03.280 ⇒ 00:27:05.810 Emily Giant: keep. It all missed line items. And then
171 00:27:09.330 ⇒ 00:27:12.430 Emily Giant: these are the results from just line items.
172 00:27:15.070 ⇒ 00:27:22.599 Emily Giant: So staging obviously, is gonna bring in the most recent rows, because this is crazy.
173 00:27:23.110 ⇒ 00:27:30.810 Emily Giant: But all 3, all 3 skews are present here.
174 00:27:32.360 ⇒ 00:27:35.200 Emily Giant: and then in this one it’s just the crazy.
175 00:27:49.440 ⇒ 00:27:52.180 Demilade Agboola: I just I realized I was on mute. So that’s queue is waiting.
176 00:27:53.290 ⇒ 00:27:54.250 Emily Giant: No.
177 00:27:55.820 ⇒ 00:27:59.920 Demilade Agboola: From the from the Hevo table.
178 00:28:01.290 ⇒ 00:28:02.609 Emily Giant: What was the question? Sorry?
179 00:28:02.610 ⇒ 00:28:05.580 Demilade Agboola: So that table is weird directly from the Hevo table.
180 00:28:06.610 ⇒ 00:28:07.300 Emily Giant: Yeah.
181 00:28:08.120 ⇒ 00:28:14.060 Emily Giant: that’s like the source. These are the things that I’ve been like screaming about since the day you started.
182 00:28:14.200 ⇒ 00:28:20.870 Emily Giant: because they used to correctly separate kits from the individual items within a kit, and now they can mesh them all together.
183 00:28:21.600 ⇒ 00:28:25.160 Emily Giant: and that’s why nothing works so.
184 00:28:30.155 ⇒ 00:28:31.060 Demilade Agboola: So
185 00:28:33.310 ⇒ 00:28:40.500 Demilade Agboola: is this so? My question? So now I’m trying to think about it. Is this a hero problem, or is this a shopify problem?
186 00:28:41.520 ⇒ 00:28:42.520 Emily Giant: Hmm!
187 00:28:42.970 ⇒ 00:28:46.090 Emily Giant: I think it might be a Kibo problem.
188 00:28:48.990 ⇒ 00:28:51.099 Emily Giant: But here’s my issue.
189 00:28:51.460 ⇒ 00:29:00.389 Emily Giant: It’s saying the product name right here correctly, even though it’s this crazy ass Skew. It’s still saying the right product name. And so even
190 00:29:00.810 ⇒ 00:29:03.319 Emily Giant: with it being jacked up.
191 00:29:04.130 ⇒ 00:29:05.316 Emily Giant: Thank you.
192 00:29:06.110 ⇒ 00:29:08.740 Emily Giant: It still should carry through the product name.
193 00:29:10.990 ⇒ 00:29:18.570 Demilade Agboola: Yeah. But isn’t. Isn’t that it’s been? Isn’t your logic that if it doesn’t have something in joint zone that’s when you say no product profile, something
194 00:29:19.930 ⇒ 00:29:22.069 Demilade Agboola: like how we see the logic again.
195 00:29:22.410 ⇒ 00:29:24.269 Emily Giant: Let’s see. Okay, so
196 00:29:26.290 ⇒ 00:29:30.959 Emily Giant: it comes through correctly in this one. So we know it’s not broken there. It’s this.
197 00:29:31.800 ⇒ 00:29:38.289 Emily Giant: So if items is coming from dim light in union.
198 00:29:41.000 ⇒ 00:29:42.849 Demilade Agboola: So what is products? Xf.
199 00:29:44.240 ⇒ 00:29:47.029 Emily Giant: It’s just a table of shopify products.
200 00:29:47.170 ⇒ 00:29:50.420 Emily Giant: It’s not orders, only product skews.
201 00:29:50.590 ⇒ 00:29:53.479 Demilade Agboola: Alright. Can we go? Can we go back to Ms items? Xf.
202 00:29:57.720 ⇒ 00:30:01.980 Demilade Agboola: So we go no shopify product. If items, the skew like shipping.
203 00:30:02.650 ⇒ 00:30:06.469 Demilade Agboola: there should be revenue else products names. And this.
204 00:30:10.130 ⇒ 00:30:11.370 Demilade Agboola: okay.
205 00:30:19.790 ⇒ 00:30:22.840 Demilade Agboola: So the idea is the products name.
206 00:30:22.970 ⇒ 00:30:29.399 Demilade Agboola: It’s coming from products except not items, except though or dim line items.
207 00:30:31.210 ⇒ 00:30:32.250 Emily Giant: Correct.
208 00:30:32.820 ⇒ 00:30:39.460 Demilade Agboola: So, even if it comes through the orders which it is, if we don’t have it available in products except
209 00:30:39.930 ⇒ 00:30:44.210 Demilade Agboola: through shopify, it would not match, therefore no name will appear.
210 00:30:45.200 ⇒ 00:30:46.080 Emily Giant: Right.
211 00:30:47.034 ⇒ 00:30:52.150 Demilade Agboola: So here’s what we could do. We could.
212 00:30:54.260 ⇒ 00:31:05.960 Demilade Agboola: I think so. What we can do is flag just also looking. Shopify. Figure out why, that’s not coming through. If that if that’s coming products. So we do look through products except the source tables. If that comes through, if it doesn’t
213 00:31:06.450 ⇒ 00:31:09.530 Demilade Agboola: oh, we need to flag it, to shopify one.
214 00:31:09.670 ⇒ 00:31:23.629 Demilade Agboola: or if it’s from shopify, from evil. But then 2 is we also, then can create a feel safe of like, hey? If this normal thing doesn’t work, then we also I don’t know that case when use the products name from this.
215 00:31:24.320 ⇒ 00:31:26.039 Emily Giant: So I could do like a coalesce
216 00:31:26.430 ⇒ 00:31:32.169 Emily Giant: or something. Well, not even a coalesce like like you said a a case statement.
217 00:31:32.370 ⇒ 00:31:32.770 Demilade Agboola: Yes.
218 00:31:33.240 ⇒ 00:31:36.600 Emily Giant: Is that? Right? Okay, yeah. So
219 00:31:37.746 ⇒ 00:31:44.140 Emily Giant: it’s not going. So when product skew, like shipping, it’s not like that. The product skew is
220 00:31:44.540 ⇒ 00:31:47.680 Emily Giant: crazy. So none of those wild ones.
221 00:31:48.390 ⇒ 00:31:51.849 Emily Giant: I don’t want it to select the product skew.
222 00:31:52.240 ⇒ 00:31:56.050 Emily Giant: That is the one that it’s grabbing.
223 00:31:57.020 ⇒ 00:31:59.970 Emily Giant: But okay, here’s where I’m confused.
224 00:32:00.920 ⇒ 00:32:07.730 Emily Giant: If I go back to the table where it’s pulling the product skews, there are 3 lines. Why is it only grabbing one.
225 00:32:11.010 ⇒ 00:32:12.770 Demilade Agboola: Can you go back? Please.
226 00:32:13.261 ⇒ 00:32:16.698 Emily Giant: So items is dim. One item, union.
227 00:32:17.660 ⇒ 00:32:18.500 Demilade Agboola: Yeah.
228 00:32:18.500 ⇒ 00:32:20.290 Emily Giant: I run that for that order.
229 00:32:25.650 ⇒ 00:32:30.240 Emily Giant: My expectation is that I get all 3. Yep. So I get 3 product skews.
230 00:32:30.890 ⇒ 00:32:31.680 Demilade Agboola: Okay.
231 00:32:32.390 ⇒ 00:32:37.120 Emily Giant: But then when I go one downstream to this items, Xf.
232 00:32:40.800 ⇒ 00:32:42.869 Emily Giant: and maybe I’m wrong, maybe I just.
233 00:32:43.700 ⇒ 00:32:45.700 Demilade Agboola: I think maybe you ran the product skew.
234 00:32:46.220 ⇒ 00:32:47.110 Demilade Agboola: Hello.
235 00:33:00.180 ⇒ 00:33:02.310 Emily Giant: Okay, so it is pulling 3 lines.
236 00:33:02.580 ⇒ 00:33:11.319 Demilade Agboola: Yeah, can we go through the like? The part where we see the weirdness, the no products, the product where I don’t know what the the column name is right. Now.
237 00:33:12.180 ⇒ 00:33:23.809 Demilade Agboola: Alright, yeah. So at this point, what we need to do is we need to create a fail safe, so that even if it’s not, there’s no product name in products. But it’s coming through with the orders. You can just select
238 00:33:24.210 ⇒ 00:33:48.789 Demilade Agboola: the orders product name. But obviously, this is just again, we’re just putting Band-aid over stuff. We need to figure out why this is happening in the 1st place. So if he was not, if he was ingesting properly. Then why do we have products that coming through, shopify but do not exist in shopify products catalog. So we we have. If we can get list of that. We can fire that to shopify
239 00:33:48.910 ⇒ 00:33:55.159 Demilade Agboola: or like. Maybe the product. SIM, I don’t know exactly the process in which, like things get populated in shopify products.
240 00:33:55.280 ⇒ 00:34:00.119 Demilade Agboola: But we need to them and just be like, Yo, this, this is. This is what’s causing it.
241 00:34:00.972 ⇒ 00:34:12.220 Emily Giant: So I, okay, I’ll let Tasdiq know that this might, require some work on his end. But this is they’ll never have a product profile. These are like umbrellas for other
242 00:34:13.042 ⇒ 00:34:17.869 Emily Giant: anytime it has this like random string of numbered letters.
243 00:34:18.000 ⇒ 00:34:21.000 Emily Giant: What it’s indicating is that somebody bought 2 things.
244 00:34:21.449 ⇒ 00:34:24.099 Emily Giant: and I don’t even want this there
245 00:34:25.210 ⇒ 00:34:29.519 Emily Giant: like, it’s not a useful line to have in the data.
246 00:34:33.060 ⇒ 00:34:37.530 Emily Giant: Because it indicates nothing. These. This is the order.
247 00:34:37.960 ⇒ 00:34:47.710 Emily Giant: And then this is just garbage that’s coming through and obscuring the actual products.
248 00:35:10.853 ⇒ 00:35:11.820 Emily Giant: Excuse me.
249 00:35:13.190 ⇒ 00:35:16.279 Demilade Agboola: Alright. So everyone in this.
250 00:35:16.730 ⇒ 00:35:24.629 Demilade Agboola: So my, okay, can we take this like, we are looking string the ones where you said that should never have a product. Can we go look at what the product name is through the orders.
251 00:35:25.060 ⇒ 00:35:27.030 Emily Giant: Yes, so it is.
252 00:35:28.000 ⇒ 00:35:29.620 Emily Giant: It’s going to be different every time.
253 00:35:29.920 ⇒ 00:35:44.779 Emily Giant: But in this case, for this order it’s double. The margo and the respace like these are the products. And this is indicating, because of the k, the kit indicator in the middle that the customer bought a bouquet and an add on.
254 00:35:45.100 ⇒ 00:35:50.730 Emily Giant: and because, prior to this development, fix.
255 00:35:50.940 ⇒ 00:35:54.300 Emily Giant: the respace was getting left off of orders.
256 00:35:54.892 ⇒ 00:36:04.800 Emily Giant: So Tosd created this like shopify custom, bundle things to make sure that both items appeared on the pick ticket at the fulfillment centers.
257 00:36:05.290 ⇒ 00:36:06.170 Demilade Agboola: Okay.
258 00:36:06.550 ⇒ 00:36:11.650 Emily Giant: But but technically the name of it is
259 00:36:11.970 ⇒ 00:36:14.759 Emily Giant: double the Margo and the Reese face.
260 00:36:29.790 ⇒ 00:36:30.950 Emily Giant: You know it’s bad.
261 00:36:37.890 ⇒ 00:36:42.329 Demilade Agboola: So effectively. What what I’m thinking here is, if even if we get the
262 00:36:42.930 ⇒ 00:36:46.670 Demilade Agboola: even if we get the the appropriate name
263 00:36:47.270 ⇒ 00:36:51.079 Demilade Agboola: like for those ones we can’t get get. We can’t get an appropriate name effectively.
264 00:36:52.420 ⇒ 00:36:53.020 Emily Giant: Right.
265 00:36:54.090 ⇒ 00:37:02.999 Demilade Agboola: Alright. Is it? Is this something that you said is transient? So is there anything that’s caught in time somewhere? And the system somewhere? Or is it just like gone forever.
266 00:37:04.573 ⇒ 00:37:17.900 Emily Giant: This did not start happening until so I’m just time gating things from the point of migration, just to catch all of them because there are a couple that are lingering
267 00:37:18.810 ⇒ 00:37:26.260 Emily Giant: here. I can just actually pull the information instead of hypothesizing. Where product? Name?
268 00:38:08.840 ⇒ 00:38:11.730 Emily Giant: Okay? So these are very old, but
269 00:38:12.280 ⇒ 00:38:14.229 Emily Giant: I don’t care about those.
270 00:38:45.580 ⇒ 00:38:52.850 Emily Giant: okay, so these are still old orders. But we have, like thousands of
271 00:38:53.570 ⇒ 00:38:56.167 Emily Giant: orders, apparently, that have no profile.
272 00:38:57.480 ⇒ 00:39:02.409 Emily Giant: not gonna worry about those. Right now, I’m more interested in.
273 00:39:05.740 ⇒ 00:39:06.440 Emily Giant: Let me.
274 00:39:28.270 ⇒ 00:39:31.589 Emily Giant: So obviously, these are from like yesterday.
275 00:40:04.070 ⇒ 00:40:08.270 Emily Giant: So yeah, the vast majority are happening like recently.
276 00:40:08.790 ⇒ 00:40:13.000 Emily Giant: because I’m on page one of 49. And I’m just last month.
277 00:40:14.940 ⇒ 00:40:16.340 Demilade Agboola: Yeah. So it’s
278 00:40:16.610 ⇒ 00:40:22.579 Demilade Agboola: it’s a, it’s a new problem. I mean, there’s some old stuff, too, but like mostly new.
279 00:40:23.620 ⇒ 00:40:24.170 Emily Giant: Yep.
280 00:40:28.420 ⇒ 00:40:33.309 Demilade Agboola: Alright, can we try doing the fix and seeing how many that knocks out the way?
281 00:40:35.260 ⇒ 00:40:48.029 Demilade Agboola: So if we tried like order, so we can say, okay. So this was a problem that had 4 4,000 of account, but the exact zeros. But like the now, it’s down to this. But these ones are still gonna be hard to fix.
282 00:40:48.290 ⇒ 00:40:53.899 Demilade Agboola: because there are like skews that don’t necessarily have a product name associated with them.
283 00:40:57.710 ⇒ 00:41:04.550 Emily Giant: Okay, so items dot product skew as products queue sure
284 00:41:04.780 ⇒ 00:41:13.570 Emily Giant: case when items dot products to you like shipping, then shipping, else so the fix would be.
285 00:41:14.560 ⇒ 00:41:27.200 Emily Giant: Oh, that’s the thing it’s like, where do we want to eliminate those skews from coming in at all, since they’ll never join to anything, and they don’t actually indicate a product that we offer.
286 00:41:34.710 ⇒ 00:41:39.319 Demilade Agboola: I’m trying to think, because I mean, some of them do indicate products that you offer.
287 00:41:40.550 ⇒ 00:41:43.091 Emily Giant: Correct. It’s when they have
288 00:41:44.580 ⇒ 00:41:52.590 Emily Giant: digits and digits and like alphabet letters. That’s when they’re not a skew that we offer.
289 00:41:54.540 ⇒ 00:42:00.160 Emily Giant: So we could do some sort of like filter out
290 00:42:03.220 ⇒ 00:42:05.410 Demilade Agboola: Like that expression.
291 00:42:14.730 ⇒ 00:42:20.759 Demilade Agboola: so. But my! My question is what the product names in the orders associated with those queues, that that’s kind of what I want to know.
292 00:42:21.340 ⇒ 00:42:26.940 Demilade Agboola: Like, are those are those product names, something that we can use to try and map back training.
293 00:42:26.940 ⇒ 00:42:29.610 Emily Giant: I think so so like.
294 00:42:30.320 ⇒ 00:42:36.257 Emily Giant: Let me know if you think I’m going down a wrong path here. If I were to select all of these.
295 00:42:37.410 ⇒ 00:42:47.569 Emily Giant: All of these skews with the random generated tag and pulled the order numbers. My hypothesis would be that all of those orders
296 00:42:47.690 ⇒ 00:42:51.950 Emily Giant: also have the correct names of the items in that table.
297 00:42:52.850 ⇒ 00:42:56.582 Emily Giant: It’s just also pulling in.
298 00:42:58.630 ⇒ 00:43:04.760 Emily Giant: that additional placeholder skew that’s used for fulfillment that we do not want or need.
299 00:43:06.420 ⇒ 00:43:09.229 Emily Giant: So let me try this.
300 00:44:43.920 ⇒ 00:44:48.820 Emily Giant: So I’m like attempting to reverse engineer these order numbers.
301 00:44:49.990 ⇒ 00:44:51.649 Emily Giant: So now, if I
302 00:44:55.010 ⇒ 00:45:01.099 Emily Giant: do the same thing and then pull these orders. What I would expect to see is all of the
303 00:45:01.560 ⇒ 00:45:15.880 Emily Giant: like, the random skew and the 2 skews that should actually be pulled with each order, and that the dash k, 1 is extraneous, but that is only a hypothesis. So
304 00:45:16.630 ⇒ 00:45:20.010 Emily Giant: I need a macro, for how often I do this concatenate thing.
305 00:46:02.930 ⇒ 00:46:03.780 Emily Giant: Okay.
306 00:46:04.490 ⇒ 00:46:10.980 Emily Giant: so there should be 3 lines per order. If what I’m thinking is correct. And I’m already not seeing that.
307 00:46:14.080 ⇒ 00:46:18.420 Emily Giant: Okay, yeah, huh?
308 00:46:21.640 ⇒ 00:46:24.180 Emily Giant: Yeah. So that thing is happening where
309 00:46:25.470 ⇒ 00:46:28.870 Emily Giant: it’s not pulling in all of the lines for each order.
310 00:46:30.750 ⇒ 00:46:34.460 Emily Giant: Like, if I look and dash at this order.
311 00:46:34.850 ⇒ 00:46:35.520 Demilade Agboola: Yeah.
312 00:46:35.870 ⇒ 00:46:38.690 Emily Giant: It’s not gonna say nothing.
313 00:46:40.000 ⇒ 00:46:47.349 Emily Giant: Or that random skew. It’s going to say, products, yeah. Mother’s day roses, little words and glass face. There should be
314 00:46:47.800 ⇒ 00:46:48.920 Emily Giant: 4 lines.
315 00:46:51.610 ⇒ 00:46:52.840 Demilade Agboola: 4 lines or 3 lines.
316 00:46:53.530 ⇒ 00:46:56.039 Emily Giant: 4. Well, there should be 3.
317 00:46:56.170 ⇒ 00:47:06.480 Emily Giant: But because of this Bs, I’m expecting, I should say I’m expecting for so which one did I? Just if I pull that
318 00:47:08.400 ⇒ 00:47:10.140 Emily Giant: one upstream.
319 00:47:40.840 ⇒ 00:47:42.640 Emily Giant: WTF.
320 00:47:45.290 ⇒ 00:47:46.919 Emily Giant: This is not what I expected.
321 00:47:48.480 ⇒ 00:47:49.550 Demilade Agboola: Only 2.
322 00:47:50.520 ⇒ 00:47:51.120 Emily Giant: Yeah.
323 00:47:51.570 ⇒ 00:47:56.529 Demilade Agboola: Well is that it’s not the sub order. Id. Should it? Should it be the sub order id or the order id.
324 00:47:57.160 ⇒ 00:47:58.490 Demilade Agboola: or does it not matter.
325 00:47:58.810 ⇒ 00:47:59.979 Emily Giant: It shouldn’t matter.
326 00:48:00.750 ⇒ 00:48:01.990 Emily Giant: Not for this order.
327 00:48:06.410 ⇒ 00:48:08.869 Emily Giant: When did this happen? This is
328 00:48:14.600 ⇒ 00:48:15.350 Emily Giant: okay.
329 00:48:15.640 ⇒ 00:48:17.989 Emily Giant: Well, that’s a different result than I thought.
330 00:48:19.920 ⇒ 00:48:21.719 Emily Giant: So if I go back to our
331 00:48:21.890 ⇒ 00:48:25.740 Emily Giant: what would you do like? Try the hevo table again to see what’s happening here?
332 00:48:25.920 ⇒ 00:48:26.700 Demilade Agboola: Yeah.
333 00:48:46.040 ⇒ 00:48:46.750 Emily Giant: Oops.
334 00:49:04.800 ⇒ 00:49:10.860 Emily Giant: Okay, not not bad.
335 00:49:12.410 ⇒ 00:49:15.040 Emily Giant: Okay, at least we’re getting. I mean, this is not even
336 00:49:15.600 ⇒ 00:49:18.137 Emily Giant: the order. What am I looking at?
337 00:49:19.230 ⇒ 00:49:22.500 Emily Giant: Oh, okay. Sorry, Una memento.
338 00:49:23.710 ⇒ 00:49:27.901 Emily Giant: I was like, we don’t even sell the product that is on that line.
339 00:49:30.360 ⇒ 00:49:38.730 Emily Giant: okay. So this is usually what we see in this table, which is just the kitted skew.
340 00:49:39.160 ⇒ 00:49:46.643 Emily Giant: I mean, this is whack attack. That product skew doesn’t even have like the the flrl.
341 00:49:48.430 ⇒ 00:49:50.420 Emily Giant: and then the other hebo table.
342 00:49:52.310 ⇒ 00:49:55.289 Emily Giant: which I would think has like the itemized
343 00:49:55.530 ⇒ 00:50:00.379 Emily Giant: things within the order, not just the kitted line. Item.
344 00:50:32.090 ⇒ 00:50:34.460 Demilade Agboola: So it seems like we have bad data coming in.
345 00:50:34.860 ⇒ 00:50:39.189 Emily Giant: Yeah, yeah. And this has been since the migration. These are the tables that
346 00:50:40.580 ⇒ 00:50:46.580 Emily Giant: we need to entirely replace, because they just simply do not work as expected
347 00:50:46.840 ⇒ 00:50:51.819 Emily Giant: anymore, and nothing I have done in months has moved the needle.
348 00:50:55.250 ⇒ 00:51:04.170 Emily Giant: So these will. My guess is that they’ll be replaced with like shopify tables that don’t try to transform them to fit our oms.
349 00:51:05.300 ⇒ 00:51:11.119 Emily Giant: but that’s going to take forever, and everyone wants it to be fixed immediately. So
350 00:51:12.860 ⇒ 00:51:17.030 Emily Giant: I’m just trying to like duct tape it together for now.
351 00:51:20.080 ⇒ 00:51:24.770 Emily Giant: Okay, it says it succeeded. But I’m there. We go. Okay.
352 00:51:27.230 ⇒ 00:51:31.674 Emily Giant: So we’ve got a a lot of rows.
353 00:51:34.310 ⇒ 00:51:44.249 Emily Giant: and I see all of the correct items associated with the order in this table.
354 00:51:45.270 ⇒ 00:51:48.229 Emily Giant: So maybe it’s the staging model that’s hiding them.
355 00:51:50.610 ⇒ 00:51:56.169 Emily Giant: I don’t know and I also don’t see the crazy skew.
356 00:51:57.660 ⇒ 00:51:58.570 Demilade Agboola: -
357 00:52:02.340 ⇒ 00:52:05.169 Demilade Agboola: I think it’s possible the models may need to refresh.
358 00:52:05.650 ⇒ 00:52:08.300 Demilade Agboola: because I I believe they’re built incrementally right.
359 00:52:08.600 ⇒ 00:52:09.709 Emily Giant: Yes, they are.
360 00:52:09.710 ⇒ 00:52:10.750 Demilade Agboola: Yeah. So like.
361 00:52:10.910 ⇒ 00:52:24.020 Demilade Agboola: these things might need to be refreshed, especially if you’re not. If you’re joining on the like sub order id like incremental support. Id! It could just go like, Hey, this award id already exists. It’s not going to bother to update it.
362 00:52:24.690 ⇒ 00:52:25.770 Demilade Agboola: My wife.
363 00:52:26.300 ⇒ 00:52:30.640 Emily Giant: Yeah, alright, I can kick off a refresh, for sure.
364 00:52:30.720 ⇒ 00:52:37.160 Demilade Agboola: So can we see if like numbers change, I feel some will change. I don’t think we’ll fix every single thing.
365 00:52:38.538 ⇒ 00:52:43.559 Demilade Agboola: If those numbers change, especially numbers that are not associated with any other numbers.
366 00:52:43.750 ⇒ 00:52:48.599 Demilade Agboola: It means that, like by the time we add our second fix of being able to back up, using the
367 00:52:49.877 ⇒ 00:52:54.879 Demilade Agboola: order id product, name that should also, like the other product name that should also help as well.
368 00:52:55.190 ⇒ 00:53:00.089 Emily Giant: Yeah, that makes sense to me. Okay, so I would just go to like
369 00:53:00.290 ⇒ 00:53:02.790 Emily Giant: one of these ad hoc models and update it.
370 00:53:05.370 ⇒ 00:53:07.636 Emily Giant: So just this, just the
371 00:53:10.270 ⇒ 00:53:13.899 Emily Giant: staging models, or like, how would you?
372 00:53:15.170 ⇒ 00:53:17.880 Emily Giant: Should I do the entire downstream refresh?
373 00:53:18.080 ⇒ 00:53:21.440 Emily Giant: Or what? What should I target here.
374 00:53:26.990 ⇒ 00:53:28.229 Demilade Agboola: I think.
375 00:53:28.340 ⇒ 00:53:31.199 Demilade Agboola: Let’s see, what models do we?
376 00:53:32.710 ⇒ 00:53:38.759 Demilade Agboola: I think we can just basically look at the items. Xf, and just do everything downstream of items except.
377 00:53:39.050 ⇒ 00:53:40.380 Emily Giant: Okay, that makes sense.
378 00:53:40.380 ⇒ 00:53:42.539 Demilade Agboola: Or we can just try and look at items like stuff.
379 00:53:50.240 ⇒ 00:53:53.649 Emily Giant: A Dbt run models plus on this items except full refresh.
380 00:53:54.870 ⇒ 00:53:56.099 Emily Giant: Well, damn.
381 00:54:00.220 ⇒ 00:54:01.549 Emily Giant: it, does look small
382 00:54:13.450 ⇒ 00:54:14.679 Emily Giant: that is running.
383 00:54:15.790 ⇒ 00:54:22.530 Emily Giant: So I’m I’m gonna write this down so that I don’t get squirrely in my brain.
384 00:54:24.095 ⇒ 00:54:24.800 Emily Giant: Okay.
385 00:54:29.410 ⇒ 00:54:43.350 Emily Giant: recommended Patch. Would be use product name from staging line items.
386 00:54:44.140 ⇒ 00:54:53.930 Emily Giant: Slash Jim line item union instead of products. Xf.
387 00:54:57.580 ⇒ 00:55:06.910 Emily Giant: When no shopify profile is returned, am I understanding you? Right? This is what you’re proposing.
388 00:55:07.590 ⇒ 00:55:08.340 Demilade Agboola: Yeah.
389 00:55:09.810 ⇒ 00:55:11.820 Emily Giant: That makes a lot of sense to me.
390 00:55:13.860 ⇒ 00:55:14.610 Emily Giant: Check.
391 00:55:27.110 ⇒ 00:55:28.089 Emily Giant: And then
392 00:55:28.970 ⇒ 00:55:36.859 Emily Giant: One thing I wanted to ask in the last 5 min, and the hevo shopify.
393 00:55:37.080 ⇒ 00:55:42.989 Emily Giant: I’m going to replace that table one way or the other, because the old one’s never going to get updated again.
394 00:55:45.870 ⇒ 00:55:52.580 Emily Giant: But it looks like events keep not loading.
395 00:55:53.830 ⇒ 00:56:01.520 Emily Giant: And I I keep having to like, jump in and refresh this like daily to get things to update. And I can’t
396 00:56:02.060 ⇒ 00:56:07.750 Emily Giant: figure out like how to get this to like automatically fix itself.
397 00:56:08.070 ⇒ 00:56:15.735 Emily Giant: Id type. Varchar mapped incompatible like I’ve dropped this table and reloaded it and tried to fix the
398 00:56:17.950 ⇒ 00:56:20.750 Emily Giant: This type that it says is incompatible.
399 00:56:21.760 ⇒ 00:56:24.660 Emily Giant: And still every day I log in. And it’s this.
400 00:56:26.880 ⇒ 00:56:31.590 Demilade Agboola: Yeah, do you have access to like he was like, support team.
401 00:56:33.070 ⇒ 00:56:34.190 Emily Giant: Yeah, I do.
402 00:56:34.340 ⇒ 00:56:35.880 Emily Giant: Yeah, ultimately.
403 00:56:35.880 ⇒ 00:56:38.809 Demilade Agboola: If that’s 1 of the issues
404 00:56:39.170 ⇒ 00:56:41.253 Demilade Agboola: that pops up every day.
405 00:56:43.330 ⇒ 00:56:51.729 Demilade Agboola: yeah, like, I, I don’t think it’s something that’s the best way, like best productive, best way to spend time to help on it every day.
406 00:56:52.490 ⇒ 00:56:57.550 Emily Giant: Okay, yeah, I’ll I’ll pop into their support and ask if they can do something.
407 00:56:58.109 ⇒ 00:56:58.969 Emily Giant: All right.
408 00:57:00.260 ⇒ 00:57:10.220 Emily Giant: And then what I’m thinking for a future state is that we can use this like transformations to like from the source
409 00:57:11.610 ⇒ 00:57:13.120 Emily Giant: configure
410 00:57:13.340 ⇒ 00:57:20.966 Emily Giant: these fixes instead of having to do it just in the Dbt logic. But that’s like a last resort type thing.
411 00:57:21.740 ⇒ 00:57:29.259 Emily Giant: I’m gonna try this morning to just work in that logic and Oms items Xf to
412 00:57:29.910 ⇒ 00:57:32.159 Emily Giant: defer to the product name
413 00:57:32.300 ⇒ 00:57:37.960 Emily Giant: from the from the line items, tables, and see how many that fixes. And I’ll report back.
414 00:57:39.290 ⇒ 00:57:40.780 Emily Giant: Okay, start there.
415 00:57:40.780 ⇒ 00:57:48.880 Emily Giant: Yeah. Any amount of fix is good for morale. So
416 00:57:50.660 ⇒ 00:58:00.550 Emily Giant: and everyone else who works at urban stems. So all right cool. Do you have any other questions before?
417 00:58:00.790 ⇒ 00:58:05.020 Emily Giant: We do not see each other for 30 min, and then have another meeting.
418 00:58:06.060 ⇒ 00:58:12.020 Demilade Agboola: I do know I had some questions about some of the good
419 00:58:12.640 ⇒ 00:58:16.109 Demilade Agboola: did give me one second. I’m trying to open my drive.
420 00:58:31.970 ⇒ 00:58:35.240 Demilade Agboola: yeah, we’re thought running on time.
421 00:58:55.360 ⇒ 00:59:00.349 Demilade Agboola: All right. So I had a cop
422 00:59:03.630 ⇒ 00:59:09.330 Demilade Agboola: forest ones. Scary one second.
423 00:59:12.090 ⇒ 00:59:19.800 Demilade Agboola: Alright, yeah. So my one of my 1st questions was like the SS. 2 GB. 2 B to C customer group.
424 00:59:21.350 ⇒ 00:59:24.379 Demilade Agboola: What? What does what like? What does our customer group represent?
425 00:59:25.960 ⇒ 00:59:27.809 Emily Giant: The b, 2 BB, 2 c.
426 00:59:27.810 ⇒ 00:59:29.960 Demilade Agboola: Yeah, yeah, it’s not like a very.
427 00:59:31.900 ⇒ 00:59:35.710 Emily Giant: It’s just the logic of like. Is this a business to business
428 00:59:35.940 ⇒ 00:59:41.320 Emily Giant: sale versus business to customer, which we don’t have a ton of b 2 b sales. So
429 00:59:41.980 ⇒ 00:59:46.620 Emily Giant: I know that our we have like a couple business to business subscriptions.
430 00:59:47.120 ⇒ 00:59:51.140 Emily Giant: But I would have to ask Perry what she wants that model to be doing.
431 00:59:51.460 ⇒ 00:59:57.709 Demilade Agboola: Okay, I just know that, like, it comes in and just like, okay, so what’s going on there? It’s basically like a select all.
432 00:59:57.710 ⇒ 00:59:58.390 Emily Giant: Yeah.
433 00:59:58.570 ⇒ 01:00:06.170 Demilade Agboola: So what’s happening there? Also, I saw the there’s the when it comes to transactions. There is an Oms transactions, table.
434 01:00:07.150 ⇒ 01:00:07.790 Emily Giant: Yeah.
435 01:00:08.210 ⇒ 01:00:14.860 Demilade Agboola: So how is that different from like the one polytonic is interesting, like, what sort of like? What sort of transactions are those.
436 01:00:16.198 ⇒ 01:00:19.670 Emily Giant: The so those are financial transactions
437 01:00:23.330 ⇒ 01:00:30.139 Emily Giant: like dollar oms, transaction. There’s another one called Transactions Xf.
438 01:00:30.580 ⇒ 01:00:31.290 Demilade Agboola: Okay.
439 01:00:31.660 ⇒ 01:00:38.640 Emily Giant: The old like postgres table that we turned off. So it is the exact same thing.
440 01:00:39.050 ⇒ 01:00:39.690 Demilade Agboola: Oh!
441 01:00:39.690 ⇒ 01:00:44.149 Emily Giant: Working with the migration. So that’s why we did the whole polyatomic switch.
442 01:00:44.580 ⇒ 01:00:45.860 Demilade Agboola: Oh, gotcha!
443 01:00:45.860 ⇒ 01:00:50.330 Emily Giant: Yeah, so, totally, starting 11, 7, 24.
444 01:00:50.330 ⇒ 01:00:57.789 Demilade Agboola: Alright. So my question is like the Oms transactions. What are they like? What transactions do they represent? Like you? Said financial transactions. But of what though.
445 01:01:01.030 ⇒ 01:01:02.810 Emily Giant: Website, purchases.
446 01:01:04.020 ⇒ 01:01:07.740 Demilade Agboola: Okay, website, purchases, yeah.
447 01:01:07.740 ⇒ 01:01:11.640 Emily Giant: Totally replaced with a shopify table in the future.
448 01:01:11.880 ⇒ 01:01:18.520 Demilade Agboola: Okay? So I’m asking that because, like isn’t polyatomic, also tracking transactions that take place on flowers.
449 01:01:21.050 ⇒ 01:01:30.080 Emily Giant: Yes, yes, but it’s not polytomic. Doesn’t have the like purchase price discounting
450 01:01:30.190 ⇒ 01:01:34.390 Emily Giant: those type of things. It could, because technically like polytomic
451 01:01:34.820 ⇒ 01:01:40.007 Emily Giant: shopify and Netsuite are talking back and forth all the time, and
452 01:01:40.780 ⇒ 01:01:42.040 Emily Giant: That could
453 01:01:42.670 ⇒ 01:01:48.779 Emily Giant: easily be grabbed from that soligo flow. But I know that Alex doesn’t want to put any more stress on.
454 01:01:48.970 ⇒ 01:01:49.279 Demilade Agboola: Oh!
455 01:01:49.590 ⇒ 01:01:58.700 Emily Giant: Pulling data from Netsuite, because it’s already like crapping out when we have too much traffic. There’s like the concurrency is not great.
456 01:01:59.180 ⇒ 01:01:59.590 Demilade Agboola: Yeah.
457 01:01:59.980 ⇒ 01:02:04.315 Emily Giant: Shoot. I have to hop to my to another meeting. But
458 01:02:07.520 ⇒ 01:02:14.839 Emily Giant: I was. Gonna say, let’s do this after stand up. But then we have another meeting directly after standup. I think there might be 15 min, though, right
459 01:02:14.960 ⇒ 01:02:17.769 Emily Giant: between stand up and the grooming.
460 01:02:18.240 ⇒ 01:02:23.840 Demilade Agboola: Yeah, we’ll see. Like, I might have a lot of meetings today, so it might have to be like afternoon your time. But we’ll see.
461 01:02:24.180 ⇒ 01:02:25.852 Emily Giant: Okay? Well, well,
462 01:02:27.850 ⇒ 01:02:33.889 Emily Giant: we’ll do this at some point. Then, when we both don’t have meetings so like next year, next summer.
463 01:02:34.030 ⇒ 01:02:36.879 Emily Giant: cool. Alright. I’ll talk to you soon. I gotta go.
464 01:02:36.880 ⇒ 01:02:37.890 Demilade Agboola: Alright, bye.