Meeting Title: Sync on shopify data Date: 2026-01-27 Meeting participants: Casie Aviles, Pranav Narahari, Bobby Palmieri, Samuel Roberts
WEBVTT
1 00:00:09.470 ⇒ 00:00:10.510 Pranav Narahari: Hey, Casey.
2 00:00:11.530 ⇒ 00:00:12.200 Casie Aviles: Thank you for that.
3 00:00:14.420 ⇒ 00:00:15.660 Pranav Narahari: We’ve been alright.
4 00:00:16.250 ⇒ 00:00:24.900 Casie Aviles: Yeah, thank you. I’m just… I just wanted to sync on what I left off with the… with Lilo.
5 00:00:26.200 ⇒ 00:00:26.820 Pranav Narahari: Yeah.
6 00:00:27.250 ⇒ 00:00:28.989 Casie Aviles: Let me quickly share.
7 00:00:31.550 ⇒ 00:00:39.000 Casie Aviles: So… Yeah, right now, what I saw was that we were… Let me check again.
8 00:00:39.750 ⇒ 00:00:42.409 Casie Aviles: So there were syncs that were having errors.
9 00:00:44.100 ⇒ 00:00:46.499 Casie Aviles: So it would end up being incomplete.
10 00:00:49.310 ⇒ 00:00:53.920 Casie Aviles: But I believe we were able to get at least the customers and some of the other
11 00:00:54.640 ⇒ 00:00:56.190 Casie Aviles: Objects here, so…
12 00:00:56.550 ⇒ 00:01:02.639 Casie Aviles: Like, and another thing with… with AirByte is it looks like it doesn’t get, like, all of the…
13 00:01:04.690 ⇒ 00:01:08.170 Casie Aviles: Objects here, or streams, so it calls it streams.
14 00:01:09.650 ⇒ 00:01:15.340 Casie Aviles: Some of them will be… so, for example, like, I selected all 36, but…
15 00:01:15.660 ⇒ 00:01:16.160 Pranav Narahari: Yep.
16 00:01:16.160 ⇒ 00:01:17.070 Casie Aviles: they won’t…
17 00:01:17.840 ⇒ 00:01:26.940 Casie Aviles: This is a new sync, so the past sync, the one that we did yesterday, was… I selected all 36, and…
18 00:01:27.240 ⇒ 00:01:29.459 Casie Aviles: They… not all of them were there.
19 00:01:29.580 ⇒ 00:01:33.280 Casie Aviles: hmm.
20 00:01:33.280 ⇒ 00:01:36.970 Pranav Narahari: So you’re saying, like, even after you selected it, it still didn’t pull in that data?
21 00:01:38.110 ⇒ 00:01:44.550 Casie Aviles: Yeah, that’s… that’s, what’s happening, and I think that’s because it’s trying to avoid, like.
22 00:01:44.730 ⇒ 00:01:50.489 Casie Aviles: It’s either it’s the rate-limiting thing, or like… So we avoid, like, timeouts.
23 00:01:50.750 ⇒ 00:01:52.720 Casie Aviles: Or it’s because of the error.
24 00:01:56.010 ⇒ 00:01:57.719 Pranav Narahari: I see. So…
25 00:01:57.830 ⇒ 00:02:04.039 Pranav Narahari: For, like, future syncs, like, for, like, the 24-hour syncs, will they be able to bring in
26 00:02:04.330 ⇒ 00:02:08.410 Pranav Narahari: the data, or… Or no.
27 00:02:09.799 ⇒ 00:02:12.359 Casie Aviles: That’s… that’s what’s unclear about
28 00:02:12.529 ⇒ 00:02:22.259 Casie Aviles: air byte, because I selected everything, but it only did, like, 8 streams. Not sure if you can see that here. It says 8 streams synced.
29 00:02:23.029 ⇒ 00:02:25.369 Casie Aviles: And then one stream incomplete.
30 00:02:26.239 ⇒ 00:02:29.929 Casie Aviles: But the others would have this status here, queued for next thing.
31 00:02:30.590 ⇒ 00:02:31.100 Pranav Narahari: Oh, okay.
32 00:02:31.100 ⇒ 00:02:31.760 Casie Aviles: Oh…
33 00:02:32.530 ⇒ 00:02:41.250 Pranav Narahari: It also might just be… yeah, it could be a rate-limiting thing, where they’re just syncing a few fields at a time, and then after these sync, the next ones will sync.
34 00:02:41.860 ⇒ 00:02:45.029 Casie Aviles: Yeah, that’s… that’s… What might be happening.
35 00:02:47.410 ⇒ 00:02:53.640 Casie Aviles: Okay, so what, what else? So we do have, like, data already on… Mother dove, at least, so…
36 00:02:53.960 ⇒ 00:02:54.630 Pranav Narahari: Okay.
37 00:02:55.100 ⇒ 00:02:57.310 Casie Aviles: This is for customers.
38 00:02:58.710 ⇒ 00:03:02.929 Casie Aviles: And it looks like we have 348K rows for that.
39 00:03:03.470 ⇒ 00:03:13.149 Casie Aviles: And, yeah, you can… if you’re able to, like, go into Mother Doc, you can also inspect it here. You should be able to get in using just
40 00:03:13.930 ⇒ 00:03:15.820 Casie Aviles: the Stitch, login.
41 00:03:16.100 ⇒ 00:03:21.320 Casie Aviles: I haven’t really… Dug too much into each…
42 00:03:21.870 ⇒ 00:03:25.460 Casie Aviles: Table here, because there’s a lot of tables, and some of them
43 00:03:25.900 ⇒ 00:03:32.659 Casie Aviles: Or… don’t have any rows, because, you know, that’s the same thing where it wasn’t synced, like, all of them.
44 00:03:32.780 ⇒ 00:03:38.440 Casie Aviles: Which is why I’m asked… I guess it would be great if we could also know which
45 00:03:38.700 ⇒ 00:03:44.000 Casie Aviles: tables we want to prioritize, maybe AirByte might… be having…
46 00:03:46.090 ⇒ 00:03:50.700 Casie Aviles: Might have trouble getting everything, so maybe we don’t need all of the…
47 00:03:51.540 ⇒ 00:04:02.030 Pranav Narahari: Yeah, I think what we should start off with is just pulling in the data for forecasting, and then pulling in the data for the Slack reports, and then…
48 00:04:02.520 ⇒ 00:04:10.150 Pranav Narahari: And then also, I think Bobby just mentioned, too, like, we just kind of need the data that is being used for, like, the Orca…
49 00:04:10.390 ⇒ 00:04:12.029 Pranav Narahari: like…
50 00:04:12.660 ⇒ 00:04:13.470 Casie Aviles: Reports.
51 00:04:13.690 ⇒ 00:04:16.179 Pranav Narahari: So if we just match that, we should be good.
52 00:04:16.329 ⇒ 00:04:21.509 Pranav Narahari: Yeah, so he says we should mimic the orca structure as much as possible, they have all the data we need in using Airbyte.
53 00:04:21.640 ⇒ 00:04:31.570 Pranav Narahari: So… Yeah, we can… So, when he’s referring to Orca…
54 00:04:32.060 ⇒ 00:04:40.609 Pranav Narahari: I believe, like, I’m just looking up Orca, like, in our Slack channel. There is this, Newton Golf database Google Sheet.
55 00:04:40.760 ⇒ 00:04:43.020 Pranav Narahari: That he says that he pulls straight from…
56 00:04:44.580 ⇒ 00:04:47.420 Pranav Narahari: That basically comes from Orca, and…
57 00:04:47.570 ⇒ 00:04:50.009 Pranav Narahari: Let’s see what that brings in.
58 00:04:50.160 ⇒ 00:04:54.389 Pranav Narahari: It brings in a bunch of data, let me pull in the Shopify one specifically.
59 00:04:55.760 ⇒ 00:04:59.389 Pranav Narahari: There’s quite a few stuff from Shopify, actually.
60 00:05:01.550 ⇒ 00:05:11.009 Pranav Narahari: Yeah, I’m just gonna send this to you, too, but we can look at it right now. Actually, I, yeah, I don’t know how long you want it to be on, I wasn’t sure if you just wanted to do a quick sync, or…
61 00:05:11.440 ⇒ 00:05:16.229 Pranav Narahari: Yeah, if you need a hop, feel free, because I know you’re… you’re not feeling well.
62 00:05:16.590 ⇒ 00:05:19.489 Casie Aviles: Yeah, yeah, all good, I mean, I can, I can…
63 00:05:20.220 ⇒ 00:05:22.229 Casie Aviles: I’ll at least finish this video.
64 00:05:22.790 ⇒ 00:05:23.250 Pranav Narahari: Okay.
65 00:05:23.250 ⇒ 00:05:25.640 Casie Aviles: And then I’ll hop once we’re all good now.
66 00:05:27.050 ⇒ 00:05:27.690 Pranav Narahari: Perfect.
67 00:05:29.850 ⇒ 00:05:32.960 Casie Aviles: Okay… It’s this spreadsheet.
68 00:05:34.840 ⇒ 00:05:39.749 Pranav Narahari: Yeah, so at the bottom, there’s a few different… Pages. Yeah.
69 00:05:39.750 ⇒ 00:05:44.300 Casie Aviles: Before I drop. Okay… Let me see…
70 00:05:44.860 ⇒ 00:05:48.680 Pranav Narahari: So it says orders, revenue… .
71 00:05:48.680 ⇒ 00:05:50.609 Casie Aviles: reduce revenue discounts.
72 00:05:50.730 ⇒ 00:05:52.070 Casie Aviles: These ones. Yes.
73 00:05:52.070 ⇒ 00:05:59.869 Pranav Narahari: There’s also that customer type, which is huge. New versus return customer. That’s one thing I want to see, because I wasn’t able to…
74 00:06:00.020 ⇒ 00:06:02.400 Pranav Narahari: Pull in that info from…
75 00:06:02.940 ⇒ 00:06:09.820 Pranav Narahari: the MCP. For whatever reason, it’s been, like, such a pain for me to, like, figure out how to do that. I was gonna…
76 00:06:09.930 ⇒ 00:06:16.440 Pranav Narahari: sync up with Sam to see if, like, he wanted to take a stab at it. But I think maybe it just makes sense…
77 00:06:16.540 ⇒ 00:06:19.970 Pranav Narahari: To just hold off on it until we have this,
78 00:06:20.640 ⇒ 00:06:28.280 Pranav Narahari: this data warehouse set up? So yeah, let’s try to find that field first. Let’s, like, make sure that we’re pulling that in, Mother Duck.
79 00:06:28.850 ⇒ 00:06:31.660 Casie Aviles: Okay, yeah, customer type, right?
80 00:06:32.030 ⇒ 00:06:33.009 Pranav Narahari: customer type, yep.
81 00:06:35.280 ⇒ 00:06:35.980 Casie Aviles: Hmm.
82 00:06:36.220 ⇒ 00:06:38.270 Casie Aviles: Not sure if we have that here.
83 00:06:50.950 ⇒ 00:06:54.160 Pranav Narahari: Is that what it’s called on the datasheet, customer type?
84 00:06:58.930 ⇒ 00:07:02.820 Casie Aviles: It’s not… I can’t… how do I see the entire thing?
85 00:07:03.050 ⇒ 00:07:06.370 Pranav Narahari: Oh, is it not… it’s not allowing you to expand it?
86 00:07:06.370 ⇒ 00:07:07.230 Casie Aviles: Yeah…
87 00:07:07.550 ⇒ 00:07:09.080 Pranav Narahari: Oh, weird. Okay.
88 00:07:09.420 ⇒ 00:07:11.550 Pranav Narahari: Let me just… let me see if I can figure it out.
89 00:07:19.740 ⇒ 00:07:20.929 Casie Aviles: Okay, then don’t…
90 00:07:41.590 ⇒ 00:07:44.059 Pranav Narahari: I just made a copy of it, we’ll see if that helps.
91 00:07:47.760 ⇒ 00:07:48.540 Pranav Narahari: Yep.
92 00:07:49.040 ⇒ 00:07:51.770 Pranav Narahari: It’s called Customer Placeholder.
93 00:07:52.070 ⇒ 00:07:54.609 Pranav Narahari: Or customer underscore type. Yeah.
94 00:07:55.050 ⇒ 00:07:58.069 Pranav Narahari: There’s… because there’s two fields that are showing the same data.
95 00:07:59.120 ⇒ 00:08:00.240 Casie Aviles: Oh, okay.
96 00:08:00.950 ⇒ 00:08:01.910 Casie Aviles: Jesus now.
97 00:08:05.520 ⇒ 00:08:11.200 Casie Aviles: Let’s see… Customer… yeah, customer… no.
98 00:08:14.590 ⇒ 00:08:15.490 Casie Aviles: Hmm.
99 00:08:16.470 ⇒ 00:08:19.419 Pranav Narahari: Okay, so we’re not pulling in the… interesting.
100 00:08:21.800 ⇒ 00:08:26.499 Casie Aviles: We do have Customer Journey. It might just be named differently, though.
101 00:08:26.500 ⇒ 00:08:27.240 Pranav Narahari: Yeah.
102 00:08:27.940 ⇒ 00:08:28.910 Pranav Narahari: That’s true.
103 00:08:31.010 ⇒ 00:08:36.290 Pranav Narahari: Do you want to just… can you look up data, too? Like, can you look up the word, like, return?
104 00:08:39.419 ⇒ 00:08:41.169 Casie Aviles: Let me… let me see…
105 00:08:45.449 ⇒ 00:08:47.859 Casie Aviles: Oh, it’s for order refunds.
106 00:08:48.680 ⇒ 00:08:50.790 Pranav Narahari: Order refunds, okay.
107 00:08:51.420 ⇒ 00:08:56.589 Pranav Narahari: That’s not exactly what we need. Yes, we probably can’t look up… the exact data.
108 00:08:57.010 ⇒ 00:09:00.190 Casie Aviles: Yeah, I think… We’ll have to pull…
109 00:09:00.790 ⇒ 00:09:05.149 Casie Aviles: What we can, and then maybe we can inspect here, or…
110 00:09:05.660 ⇒ 00:09:06.010 Pranav Narahari: Yeah.
111 00:09:06.010 ⇒ 00:09:07.650 Casie Aviles: Not sure if they even do, like.
112 00:09:08.030 ⇒ 00:09:14.100 Casie Aviles: If they’re doing any transformations, once they’ve gotten the data from AirByte.
113 00:09:15.370 ⇒ 00:09:16.360 Pranav Narahari: Yeah.
114 00:09:16.360 ⇒ 00:09:18.019 Casie Aviles: They might be doing, like, a…
115 00:09:18.170 ⇒ 00:09:24.280 Casie Aviles: an additional step where they process the data from Airbyte. That’s why it looks like this, because
116 00:09:25.410 ⇒ 00:09:30.650 Casie Aviles: The customer’s table doesn’t… Look exactly like this.
117 00:09:33.110 ⇒ 00:09:37.880 Pranav Narahari: Right, yeah. Should I pull in Bobby into this call, or should I just have a separate conversation with him?
118 00:09:40.870 ⇒ 00:09:47.079 Casie Aviles: I mean, it’s fine, so we can confirm if he’s on the… yeah, if he’s on.
119 00:09:47.450 ⇒ 00:09:48.950 Pranav Narahari: If he’s online, I can pull him in.
120 00:09:49.540 ⇒ 00:09:50.150 Casie Aviles: Yeah.
121 00:09:50.500 ⇒ 00:09:51.180 Pranav Narahari: Okay.
122 00:10:55.380 ⇒ 00:10:57.280 Pranav Narahari: So maybe what we need to do is, like.
123 00:10:58.190 ⇒ 00:11:05.110 Pranav Narahari: just to see if we pulled in that data, come up with a SQL query that… Pulls in, like.
124 00:11:05.430 ⇒ 00:11:12.629 Pranav Narahari: return as, like, does, like, a find all on, like, the term return.
125 00:11:12.630 ⇒ 00:11:13.370 Casie Aviles: Who knows?
126 00:11:13.370 ⇒ 00:11:21.370 Pranav Narahari: But, like, yeah, we don’t know if there’s any data transformation happening. Like, what if it’s just a Boolean, and then, you know, they just map that to…
127 00:11:21.490 ⇒ 00:11:24.819 Pranav Narahari: Return or new, like, after the fact.
128 00:11:25.290 ⇒ 00:11:26.459 Casie Aviles: Yeah, that’s true.
129 00:11:27.450 ⇒ 00:11:29.979 Pranav Narahari: But, I guess it’s worth a shot, we can still try it.
130 00:11:37.020 ⇒ 00:11:44.440 Casie Aviles: Because they’re not the same… In terms of, like, the columns, the structure is different.
131 00:11:44.860 ⇒ 00:11:49.890 Casie Aviles: I think it’s possible that they’re… Joining it with…
132 00:11:50.000 ⇒ 00:11:53.129 Casie Aviles: A different table, like Customer Journey.
133 00:11:54.260 ⇒ 00:11:55.350 Pranav Narahari: Right, right.
134 00:11:59.170 ⇒ 00:12:03.300 Casie Aviles: Yeah, looks like we also have… Customer journey here.
135 00:12:03.490 ⇒ 00:12:06.690 Casie Aviles: Or wait… let’s see if we can join… No.
136 00:12:27.610 ⇒ 00:12:28.590 Bobby Palmieri: What’s up, folks?
137 00:12:30.140 ⇒ 00:12:31.040 Pranav Narahari: Hey, Bobby.
138 00:12:31.630 ⇒ 00:12:33.140 Bobby Palmieri: How are ya?
139 00:12:33.140 ⇒ 00:12:49.660 Pranav Narahari: Pretty good, pretty good. Yeah, so we’re just looking into that Orca notebook that you sent us a while back, and yeah, we select that there’s that customer type field there, as well as, like, customer,
140 00:12:50.100 ⇒ 00:13:01.120 Pranav Narahari: what is it called? Customer… I think it was just called customer placeholder. So, we’re assuming that these fields are just, like, renamed after the fact, probably via, like, some SQL query.
141 00:13:01.230 ⇒ 00:13:02.349 Pranav Narahari: Okay.
142 00:13:03.410 ⇒ 00:13:05.860 Pranav Narahari: Yeah, can you give us a little bit of, like…
143 00:13:06.230 ⇒ 00:13:14.050 Pranav Narahari: insight about, like, how these tables were created? Like, was it from, like, some SQL query that you have access to? Or…
144 00:13:14.410 ⇒ 00:13:15.640 Pranav Narahari: From elsewhere.
145 00:13:16.190 ⇒ 00:13:19.290 Bobby Palmieri: It’s not.
146 00:13:21.190 ⇒ 00:13:24.489 Pranav Narahari: It’s just pulled directly from, like, on the data.
147 00:13:24.490 ⇒ 00:13:30.260 Bobby Palmieri: So I just have access to that, because they, like, linked us to, like, the backend spreadsheet.
148 00:13:30.830 ⇒ 00:13:34.309 Bobby Palmieri: Transparently, I’m not sure we’re supposed to have access to this.
149 00:13:34.310 ⇒ 00:13:34.690 Pranav Narahari: God.
150 00:13:35.320 ⇒ 00:13:37.460 Bobby Palmieri: You know, in that regard.
151 00:13:38.210 ⇒ 00:13:43.480 Pranav Narahari: Gotcha. Okay, so, yeah, so then basically… Yeah, Casey and I…
152 00:13:43.770 ⇒ 00:13:56.739 Pranav Narahari: we’re pretty sure that this is being generated from some SQL query, and that’s why we’re not able to see the customer underscore type field, like, being pulled straight from Shopify.
153 00:13:56.740 ⇒ 00:13:57.100 Bobby Palmieri: Could you…
154 00:13:57.460 ⇒ 00:13:58.650 Pranav Narahari: Most likely.
155 00:14:00.880 ⇒ 00:14:05.140 Bobby Palmieri: Well, do you have orders?
156 00:14:06.660 ⇒ 00:14:08.640 Pranav Narahari: Yeah, so there’s an order stable.
157 00:14:08.920 ⇒ 00:14:15.060 Casie Aviles: Yeah, there’s an orders table, but… It’s not yet… See if it’s populated.
158 00:14:15.890 ⇒ 00:14:19.920 Casie Aviles: Oh, there are no rows right now, so we’re going to do a sync for that.
159 00:14:21.290 ⇒ 00:14:24.950 Casie Aviles: But yes, we do have, an orders table.
160 00:14:26.060 ⇒ 00:14:29.080 Pranav Narahari: Yeah, so let’s just look at the fields here, though.
161 00:14:29.880 ⇒ 00:14:35.329 Bobby Palmieri: Is there a new or returning? Because that’s what I plug in. I think it should be, like, new or returning.
162 00:14:38.770 ⇒ 00:14:39.880 Bobby Palmieri: Type in new.
163 00:14:48.270 ⇒ 00:14:51.770 Pranav Narahari: Can you also take a look at the smart collections, Casey?
164 00:14:54.060 ⇒ 00:14:57.720 Bobby Palmieri: Collections is probably going to be product-related, if I had to guess.
165 00:14:57.720 ⇒ 00:14:59.339 Pranav Narahari: Gotcha.
166 00:15:00.660 ⇒ 00:15:03.849 Pranav Narahari: I thought it could be, like, a custom, like, filter.
167 00:15:11.510 ⇒ 00:15:13.450 Casie Aviles: Yeah, I don’t think he has.
168 00:15:16.890 ⇒ 00:15:17.610 Bobby Palmieri: Let’s see…
169 00:15:31.170 ⇒ 00:15:33.749 Bobby Palmieri: Alright, and… taking a look.
170 00:15:44.080 ⇒ 00:15:46.100 Bobby Palmieri: Where are we right now? This is AirByte.
171 00:15:46.660 ⇒ 00:15:55.640 Casie Aviles: Yeah, we’re… so what we did here is we’re trying to get, like, All the streams first, but…
172 00:15:56.950 ⇒ 00:16:05.579 Casie Aviles: I think for, like, to prevent timeouts and rate limiting, it’s not syncing, like, everything, so we also wanted to check if
173 00:16:06.900 ⇒ 00:16:13.320 Casie Aviles: We know, like, if there’s any streams or objects here that we want to prioritize,
174 00:16:17.910 ⇒ 00:16:26.599 Casie Aviles: And yeah, also, like, how we’re able to construct this, this table from the Newton Golf database, since it doesn’t look like…
175 00:16:27.760 ⇒ 00:16:36.269 Casie Aviles: From the raw export, like, we have, like, we have the same Colon, since… This is the customer’s…
176 00:16:36.390 ⇒ 00:16:39.599 Casie Aviles: This is the data that we pulled for our customers, and…
177 00:16:40.280 ⇒ 00:16:46.869 Casie Aviles: It’s… it doesn’t exactly match this one, so that’s why we thought there might be some transformation happening.
178 00:16:47.360 ⇒ 00:16:51.250 Casie Aviles: Before it gets to this… state.
179 00:16:56.860 ⇒ 00:17:03.340 Bobby Palmieri: I’m gonna send you… This, just from chat.
180 00:17:09.440 ⇒ 00:17:14.750 Bobby Palmieri: It looks like you may just need to do, like, a second, like…
181 00:17:14.970 ⇒ 00:17:18.819 Bobby Palmieri: Order stream and customer stream, and then, like, an incremental sync.
182 00:17:30.700 ⇒ 00:17:32.969 Bobby Palmieri: Maybe that’s… I just sent you a link on…
183 00:17:33.720 ⇒ 00:17:35.519 Bobby Palmieri: Maybe the best way to do it.
184 00:17:37.850 ⇒ 00:17:42.059 Pranav Narahari: Gotcha, so yeah, maybe we need to check that customers table, too, to see if we’re…
185 00:17:42.230 ⇒ 00:17:46.319 Pranav Narahari: If there’s a field that shows whether a customer is new or returning.
186 00:17:46.780 ⇒ 00:17:50.979 Pranav Narahari: I know that customer data is actually already populated, so we could…
187 00:17:51.430 ⇒ 00:17:53.329 Pranav Narahari: Take a look there as well.
188 00:17:59.270 ⇒ 00:17:59.970 Casie Aviles: Okay.
189 00:18:00.780 ⇒ 00:18:02.510 Casie Aviles: Also, yeah.
190 00:18:03.180 ⇒ 00:18:05.909 Casie Aviles: I think we’ll just have to dive, dive into the…
191 00:18:07.010 ⇒ 00:18:12.140 Casie Aviles: the data DORA expert that we have, and okay.
192 00:18:15.320 ⇒ 00:18:17.780 Pranav Narahari: It’d be nice if Shopify allowed us to just, like.
193 00:18:17.960 ⇒ 00:18:21.849 Pranav Narahari: Pull in the data programmatically from custom reports that we built.
194 00:18:22.000 ⇒ 00:18:24.050 Bobby Palmieri: I don’t know why…
195 00:18:27.640 ⇒ 00:18:29.619 Bobby Palmieri: Because Watergraph does that.
196 00:18:31.280 ⇒ 00:18:33.290 Pranav Narahari: Autographed from a report.
197 00:18:36.720 ⇒ 00:18:37.850 Bobby Palmieri: Yeah.
198 00:18:47.070 ⇒ 00:18:48.300 Bobby Palmieri: I’m missing…
199 00:18:49.240 ⇒ 00:18:58.599 Pranav Narahari: Casey, do you know what all of these fields, actually, sorry, all these tables for fulfillments refer to? Like, there’s a bunch here that have, like.
200 00:19:00.260 ⇒ 00:19:03.060 Pranav Narahari: Extra, like, characters at the end.
201 00:19:03.480 ⇒ 00:19:11.280 Pranav Narahari: Better, more or less, probably just, like, like, random. To just keep them unique.
202 00:19:12.980 ⇒ 00:19:13.920 Casie Aviles: No.
203 00:19:14.810 ⇒ 00:19:20.190 Pranav Narahari: Okay. Yeah, I can look into that. That could have something to do with the custom reports that we build.
204 00:19:22.380 ⇒ 00:19:28.160 Bobby Palmieri: Let me know… Yeah, I’m not sure.
205 00:19:28.160 ⇒ 00:19:28.740 Pranav Narahari: Yeah.
206 00:19:30.030 ⇒ 00:19:41.120 Pranav Narahari: Basically, yeah, sorting all this data is going to be, like, a huge chunk of just, like, the forecasting. But the good thing, it’s, like, a one-time thing. It’s just, like, okay, we get just, like, this huge…
207 00:19:41.220 ⇒ 00:19:44.610 Pranav Narahari: like, sum of data, like, Casey, how many, like, records were we pulling in?
208 00:19:45.200 ⇒ 00:19:47.540 Bobby Palmieri: Because it was saying, like, on air bite, right?
209 00:19:48.780 ⇒ 00:19:51.550 Casie Aviles: Yeah, I think we were getting, like, 200.
210 00:19:53.050 ⇒ 00:19:54.490 Casie Aviles: 100,000.
211 00:19:55.410 ⇒ 00:19:59.920 Pranav Narahari: Yeah, that was, like, early yesterday, though, so I wonder if it’s even more now.
212 00:20:00.270 ⇒ 00:20:02.450 Pranav Narahari: Yeah, 125…
213 00:20:03.130 ⇒ 00:20:06.069 Bobby Palmieri: Yeah, I wonder, like, do we maybe want to…
214 00:20:06.550 ⇒ 00:20:13.830 Bobby Palmieri: I don’t know what the cost structure is of… Airbite, and, like… Mother Duck, but…
215 00:20:14.570 ⇒ 00:20:17.019 Bobby Palmieri: Like, there’s probably some stuff that we don’t need.
216 00:20:17.790 ⇒ 00:20:31.170 Pranav Narahari: Yeah, totally. That’s what Casey and I were talking about. We’re gonna basically replicate what you have in that Orca Google Sheet, and then even if that seems like too much, like, we’ll check in with you, because…
217 00:20:31.330 ⇒ 00:20:39.289 Pranav Narahari: what we see the data, from our perspective, that we need is, like, the Slack reports, as well as the forecasting tool.
218 00:20:41.060 ⇒ 00:20:42.639 Bobby Palmieri: Early on everything.
219 00:20:42.640 ⇒ 00:20:43.719 Pranav Narahari: Early on, yeah.
220 00:20:44.070 ⇒ 00:20:48.250 Bobby Palmieri: Yeah, my thought would be, like, just mimic what we have in Orca.
221 00:20:48.570 ⇒ 00:20:58.369 Bobby Palmieri: Okay. As close as possible, because I do think that, like, the future state will include all of this. My guess is that they have this for a reason. Right.
222 00:20:59.100 ⇒ 00:21:00.919 Bobby Palmieri: So yeah, that would be my thought there.
223 00:21:01.380 ⇒ 00:21:10.630 Pranav Narahari: Okay, so just basically, specifically for Shopify, look at these, like, 4… I think there’s 4 tables, or 4 sheets at the bottom, just, like, make sure we’re capturing all that data.
224 00:21:11.110 ⇒ 00:21:11.680 Bobby Palmieri: Yup.
225 00:21:11.880 ⇒ 00:21:15.790 Bobby Palmieri: My guess would be that,
226 00:21:18.240 ⇒ 00:21:21.240 Bobby Palmieri: And then my guess would be…
227 00:21:23.130 ⇒ 00:21:28.600 Bobby Palmieri: that Meta, Google, etc. is probably, you know, the same.
228 00:21:29.730 ⇒ 00:21:35.209 Bobby Palmieri: Like, all of the day… like, all of the things that they display in their app are what we’re gonna wanna display in Stitch.
229 00:21:35.780 ⇒ 00:21:36.220 Pranav Narahari: Yep.
230 00:21:36.500 ⇒ 00:21:41.579 Bobby Palmieri: Like, if you look at Meta, you look at Google, like, I would just mimic that across pretty much everything.
231 00:21:42.550 ⇒ 00:21:52.000 Pranav Narahari: Yeah, yeah, that makes sense. And I think we’re already in, like, a good place for Meta specifically, like, where… how to pull in that data, since we’re pulling it via the API, like…
232 00:21:52.180 ⇒ 00:21:52.570 Bobby Palmieri: Yup.
233 00:21:52.570 ⇒ 00:21:58.499 Pranav Narahari: Pretty easily, like, I’m not concerned about that at all. And then… Okay.
234 00:21:59.030 ⇒ 00:22:03.069 Pranav Narahari: Yeah, Shopify’s just, for whatever reason, being a little bit tricky, but…
235 00:22:03.320 ⇒ 00:22:08.680 Pranav Narahari: Good thing is, it’s just, like, we had to figure it out just, like, one time, and…
236 00:22:08.800 ⇒ 00:22:22.090 Pranav Narahari: Yeah, since we’re in the process of the data warehouse, too, like, just enabling the… the Shopify Slack reports to just pull in straight from the data warehouse is, like, that’ll be a good first test for us to make sure, you know, things are working, so…
237 00:22:22.240 ⇒ 00:22:24.669 Pranav Narahari: Yeah, we’ll just, we’ll just go about things that way.
238 00:22:25.820 ⇒ 00:22:32.079 Bobby Palmieri: I just sent, how Claude had, like, a different way of doing it.
239 00:22:33.160 ⇒ 00:22:35.840 Bobby Palmieri: I think you’re gonna have to do it by, like…
240 00:22:36.890 ⇒ 00:22:39.830 Bobby Palmieri: Order, like, first order created app.
241 00:22:41.240 ⇒ 00:22:47.719 Bobby Palmieri: I think that’s the way to do it. Each of these customer profiles probably have… A number of orders.
242 00:22:48.470 ⇒ 00:22:49.030 Pranav Narahari: Yep.
243 00:22:52.840 ⇒ 00:22:56.260 Bobby Palmieri: Alright, let me know if you guys need another set of eyes on anything.
244 00:22:56.950 ⇒ 00:22:59.100 Pranav Narahari: Totally. Thanks, Bobby, thanks for hopping in.
245 00:23:00.000 ⇒ 00:23:00.730 Casie Aviles: Thank you.
246 00:23:05.380 ⇒ 00:23:06.400 Casie Aviles: Okay.
247 00:23:10.460 ⇒ 00:23:16.399 Casie Aviles: Right, so it sounds like we’re still going to have to do, like, another step, right, in between.
248 00:23:16.680 ⇒ 00:23:18.340 Casie Aviles: After the export.
249 00:23:20.200 ⇒ 00:23:27.750 Pranav Narahari: Yeah, there may also be, like, some custom stuff we can do in Airbyte itself.
250 00:23:27.880 ⇒ 00:23:34.880 Pranav Narahari: So then save it into a table, within, within Mother Duck.
251 00:23:37.670 ⇒ 00:23:48.399 Pranav Narahari: That may be overcomplicating it, maybe we do it after the fact, maybe we just save the data as is. Because, honestly, saving the data as is may not be an issue, because…
252 00:23:49.750 ⇒ 00:23:52.599 Pranav Narahari: Like, doing a query on that data to then…
253 00:23:52.880 ⇒ 00:23:56.680 Pranav Narahari: like, format a table in real time, I think maybe…
254 00:23:58.270 ⇒ 00:24:02.009 Pranav Narahari: less difficult, and also these Slack reports aren’t…
255 00:24:02.350 ⇒ 00:24:08.840 Pranav Narahari: like, they’re being sent on, like, a daily cadence. In the forecasting tool, too, like…
256 00:24:12.290 ⇒ 00:24:15.099 Pranav Narahari: I mean, the forecasting tool is looking at different data, and it’s…
257 00:24:15.550 ⇒ 00:24:18.039 Casie Aviles: So I’m not as concerned about that, so…
258 00:24:20.370 ⇒ 00:24:21.350 Pranav Narahari: Yeah.
259 00:24:22.630 ⇒ 00:24:33.459 Pranav Narahari: So, when you used AirByte in the past, have you… you’re just kind of pulling in the data directly, right? Not doing any manipulations on it to create separate tables within whatever database you’re storing it in?
260 00:24:34.380 ⇒ 00:24:45.149 Casie Aviles: No, so it, ideally, we just get everything, and then once it’s loaded here in the warehouse, we can then do, like, any transformations that we want to do.
261 00:24:45.950 ⇒ 00:24:48.300 Pranav Narahari: Okay, yeah, I like that idea.
262 00:24:50.050 ⇒ 00:24:56.349 Casie Aviles: Yeah, I… just that way we won’t miss any data, and then we have everything we need.
263 00:24:57.030 ⇒ 00:24:57.690 Pranav Narahari: Yep.
264 00:24:57.690 ⇒ 00:25:01.939 Casie Aviles: the only thing I’m worried about here is, like.
265 00:25:02.840 ⇒ 00:25:09.979 Casie Aviles: it’s… it’s… it’s not… when I… when I run a sync, it’s not getting everything, right, because some of them are…
266 00:25:10.670 ⇒ 00:25:14.249 Casie Aviles: like I said, are queued for next sync, so…
267 00:25:16.180 ⇒ 00:25:20.980 Casie Aviles: I’m not sure what the best way is to handle that with AirByte.
268 00:25:21.880 ⇒ 00:25:29.019 Casie Aviles: I think that’s… that’s my only thing there.
269 00:25:30.110 ⇒ 00:25:33.530 Casie Aviles: But if the sync is good, then we can get
270 00:25:34.490 ⇒ 00:25:38.439 Casie Aviles: If it runs successfully, then we can get, you know, the stuff that we need.
271 00:25:39.080 ⇒ 00:25:41.070 Casie Aviles: Which I… yeah.
272 00:25:41.780 ⇒ 00:25:42.120 Casie Aviles: The.
273 00:25:46.830 ⇒ 00:25:47.490 Pranav Narahari: Yeah.
274 00:25:48.160 ⇒ 00:25:50.920 Pranav Narahari: I think, Sam’s gonna hop in, too.
275 00:25:51.310 ⇒ 00:25:55.139 Pranav Narahari: We’ll see, if he has, like, some input on this.
276 00:26:02.060 ⇒ 00:26:05.410 Pranav Narahari: Which, yeah, hey, Sam.
277 00:26:05.800 ⇒ 00:26:07.550 Samuel Roberts: No, I don’t hear anything anymore, right?
278 00:26:09.690 ⇒ 00:26:14.740 Pranav Narahari: Casey, what is the… In Mother Duck, which…
279 00:26:15.770 ⇒ 00:26:19.799 Pranav Narahari: which data… like, what is it called? I see that there’s, like…
280 00:26:20.030 ⇒ 00:26:21.950 Pranav Narahari: Thanks. Yeah, we can hear you, Sam.
281 00:26:22.220 ⇒ 00:26:24.100 Samuel Roberts: Okay, cool, sorry, I couldn’t hear anything for a second.
282 00:26:24.590 ⇒ 00:26:25.559 Samuel Roberts: What’d I miss?
283 00:26:27.280 ⇒ 00:26:33.229 Pranav Narahari: Yeah, we’re just looking, like, for the right data, specifically for Slack reports.
284 00:26:33.440 ⇒ 00:26:37.029 Pranav Narahari: So yeah, is the data going into AirByte Test?
285 00:26:37.800 ⇒ 00:26:39.439 Casie Aviles: Yes, okay.
286 00:26:39.550 ⇒ 00:26:51.579 Casie Aviles: I did two tests. The first one was with my personal account, which is here in MyDB, but I created a new one, so it’s much, you know, easier to find. It’s the airbag test.
287 00:26:51.940 ⇒ 00:26:55.350 Casie Aviles: And should be under this main schema, so…
288 00:26:55.760 ⇒ 00:26:59.860 Pranav Narahari: Gotcha. Yeah, Sam, I’ll fill you in real quick.
289 00:26:59.860 ⇒ 00:27:00.500 Samuel Roberts: Sure.
290 00:27:00.740 ⇒ 00:27:05.240 Pranav Narahari: So… for Shopify, that’s obviously been, like,
291 00:27:05.700 ⇒ 00:27:13.150 Pranav Narahari: Kind of just, like, an issue for us for the past, like, week or so, just pulling in the right data.
292 00:27:13.420 ⇒ 00:27:16.580 Pranav Narahari: The data they’re looking for specifically is…
293 00:27:16.870 ⇒ 00:27:22.709 Pranav Narahari: New customer data, and new as in, are they a new customer to the brand?
294 00:27:23.240 ⇒ 00:27:24.150 Pranav Narahari: So… Right.
295 00:27:24.820 ⇒ 00:27:26.080 Pranav Narahari: They…
296 00:27:26.230 ⇒ 00:27:35.589 Pranav Narahari: Orca is able to pull in that data, and Bobby has a little bit of behind-the-scenes look to know that, okay, their stack is using Airbyte,
297 00:27:35.850 ⇒ 00:27:44.960 Pranav Narahari: And they’re able to pull in this data somehow, right? So this field right here that Casey’s highlighting, customer type, they’re able to differentiate between new and return.
298 00:27:46.980 ⇒ 00:28:03.530 Pranav Narahari: we aren’t able to find this specific field, customer underscore type, within the data warehouse. Likely, they’re, you know, they’re running some SQL query to create a separate table at which then they are loading into Google Sheets.
299 00:28:04.450 ⇒ 00:28:05.210 Pranav Narahari: So…
300 00:28:05.400 ⇒ 00:28:07.410 Samuel Roberts: Basically, we’re trying to figure out…
301 00:28:07.660 ⇒ 00:28:17.310 Pranav Narahari: what that SQL query is. Bobby was like, yeah, they shared me this, like, notebook, likely they were not supposed to share it with him, like, this isn’t something that they were.
302 00:28:17.310 ⇒ 00:28:17.690 Samuel Roberts: does, you know.
303 00:28:18.010 ⇒ 00:28:23.359 Pranav Narahari: transparent about. It’s, I mean, that makes sense. It’s probably, like, core to their business. Yeah.
304 00:28:23.460 ⇒ 00:28:28.850 Pranav Narahari: So, we basically need to reconstruct whatever that SQL query was.
305 00:28:29.070 ⇒ 00:28:30.050 Pranav Narahari: And…
306 00:28:31.250 ⇒ 00:28:37.190 Pranav Narahari: we also need to make sure we’re pulling in the source data as well, so there’s also… that’s another component to this, like.
307 00:28:37.720 ⇒ 00:28:45.310 Pranav Narahari: Not all the fields are syncing at the same time. Some of them are saying they’re queued for sync, specifically, like, the customer data.
308 00:28:45.710 ⇒ 00:28:46.800 Pranav Narahari: Hmm.
309 00:28:47.000 ⇒ 00:28:50.710 Pranav Narahari: Probably not the customer, the… I think it was the order data, right, Casey?
310 00:28:50.990 ⇒ 00:28:52.300 Casie Aviles: Yeah, organs.
311 00:28:52.300 ⇒ 00:28:54.060 Pranav Narahari: Yeah, order still hasn’t synced.
312 00:28:54.180 ⇒ 00:28:55.710 Pranav Narahari: So…
313 00:28:56.130 ⇒ 00:29:05.099 Pranav Narahari: Yeah, I’m… what I’m leaning towards right now is that we should start from even bare bones… like, even more bare bones, which is…
314 00:29:05.290 ⇒ 00:29:12.110 Pranav Narahari: let’s not pull in all of this data, like, abandon checkouts, collections, fulfillments, like…
315 00:29:12.260 ⇒ 00:29:24.819 Pranav Narahari: let’s just focus maybe on the data specifically for Slack reports right now. Let’s solve that problem, and then we can, like, click on a few more fields that maybe bring us to the point where we’re pulling in all the data that
316 00:29:24.920 ⇒ 00:29:30.600 Pranav Narahari: is, that we can see within that, ORCA notebook.
317 00:29:31.990 ⇒ 00:29:32.730 Pranav Narahari: So…
318 00:29:32.730 ⇒ 00:29:37.300 Samuel Roberts: Okay, so we probably need to sync the orders and the customers?
319 00:29:38.090 ⇒ 00:29:42.049 Pranav Narahari: Yeah, orders and customers is the… The main two.
320 00:29:42.050 ⇒ 00:29:42.710 Samuel Roberts: Okay.
321 00:29:43.010 ⇒ 00:29:43.650 Pranav Narahari: Yeah.
322 00:29:45.710 ⇒ 00:29:47.749 Pranav Narahari: Might be the only two, honestly.
323 00:29:48.420 ⇒ 00:29:50.409 Samuel Roberts: For now, I think that would probably be…
324 00:29:51.010 ⇒ 00:29:51.840 Pranav Narahari: Yeah.
325 00:29:55.070 ⇒ 00:29:58.129 Casie Aviles: Yeah, because I think it’s… it might be…
326 00:29:58.700 ⇒ 00:30:02.739 Casie Aviles: struggling to get, like, all of them. There are currently… there are 33.
327 00:30:03.370 ⇒ 00:30:06.380 Casie Aviles: Objects or streams here that we have.
328 00:30:07.390 ⇒ 00:30:15.480 Pranav Narahari: Yeah. And I think also in the settings, we can just set it to, like, just give us the data from the last, you know, 7 days.
329 00:30:16.480 ⇒ 00:30:18.850 Pranav Narahari: Instead of, like, the last 2 years.
330 00:30:20.250 ⇒ 00:30:21.690 Casie Aviles: I said the desk.
331 00:30:21.900 ⇒ 00:30:25.690 Casie Aviles: Yeah, for last year, but I think we can go even. You can do it.
332 00:30:25.690 ⇒ 00:30:27.809 Pranav Narahari: Yeah, we can go even, like, just for, like.
333 00:30:27.810 ⇒ 00:30:28.330 Samuel Roberts: Okay.
334 00:30:28.610 ⇒ 00:30:34.020 Pranav Narahari: Make sure we’re getting the data. Since we’re not testing forecasting right now, we’re just testing Slack reports.
335 00:30:34.480 ⇒ 00:30:37.330 Pranav Narahari: Yeah.
336 00:30:39.190 ⇒ 00:30:43.630 Samuel Roberts: Alright, so then once we have the… Orders and the customers…
337 00:30:43.630 ⇒ 00:30:44.140 Casie Aviles: talk.
338 00:30:44.530 ⇒ 00:30:48.199 Samuel Roberts: what I’m seeing here, just on Google’s AI overview, is just…
339 00:30:48.510 ⇒ 00:30:52.309 Samuel Roberts: They define it as a new order has… a new customer has one order.
340 00:30:52.800 ⇒ 00:31:00.600 Samuel Roberts: Returning as more than one. So if we can just do a lookup, On that customer’s total orders.
341 00:31:01.250 ⇒ 00:31:04.469 Samuel Roberts: And… and that should give us new and returning.
342 00:31:04.980 ⇒ 00:31:05.360 Pranav Narahari: Yup.
343 00:31:05.360 ⇒ 00:31:06.690 Samuel Roberts: Right. Okay.
344 00:31:06.690 ⇒ 00:31:07.320 Pranav Narahari: Right.
345 00:31:07.920 ⇒ 00:31:10.910 Casie Aviles: And I haven’t… Dive…
346 00:31:11.360 ⇒ 00:31:19.990 Casie Aviles: into, like, you know, I haven’t done, like, an exploration on each table. I literally just got… tried to get everything
347 00:31:20.480 ⇒ 00:31:22.659 Casie Aviles: Yeah. Probably worth looking into.
348 00:31:23.320 ⇒ 00:31:24.540 Casie Aviles: What weekends.
349 00:31:24.540 ⇒ 00:31:26.350 Samuel Roberts: Open up customers for a second.
350 00:31:26.610 ⇒ 00:31:27.909 Samuel Roberts: Is there anything in there?
351 00:31:29.250 ⇒ 00:31:30.040 Casie Aviles: Yes.
352 00:31:30.580 ⇒ 00:31:31.319 Casie Aviles: There should…
353 00:31:31.320 ⇒ 00:31:33.039 Samuel Roberts: There’s a… yeah.
354 00:31:33.900 ⇒ 00:31:35.220 Casie Aviles: Let me refresh.
355 00:31:35.660 ⇒ 00:31:37.200 Casie Aviles: Running my query.
356 00:31:56.280 ⇒ 00:31:58.040 Samuel Roberts: That’s a big query too, isn’t it, though?
357 00:32:01.380 ⇒ 00:32:06.390 Samuel Roberts: So, I think, should there be something here that has orders?
358 00:32:08.500 ⇒ 00:32:16.699 Samuel Roberts: On this customer… Last order name… order count, there it is. Orders count.
359 00:32:18.000 ⇒ 00:32:22.610 Casie Aviles: And I also have last order ID, although I’m not sure if that’s what we need.
360 00:32:23.450 ⇒ 00:32:26.739 Samuel Roberts: Okay, yeah, so I think the strategy here will have to be…
361 00:32:27.950 ⇒ 00:32:31.660 Samuel Roberts: Like, for the orders in a given time period.
362 00:32:32.090 ⇒ 00:32:36.050 Samuel Roberts: Query against the customer order count.
363 00:32:36.360 ⇒ 00:32:37.130 Samuel Roberts: Right.
364 00:32:37.790 ⇒ 00:32:45.669 Pranav Narahari: Yeah. Okay. And maybe even last order ID, like, if it’s null, we can just, assume that that’s a new customer.
365 00:32:48.880 ⇒ 00:32:52.950 Samuel Roberts: I mean, a new customer will only have one order, and it’ll be whatever order you looked up from the…
366 00:32:54.520 ⇒ 00:32:55.049 Pranav Narahari: Oh, yeah.
367 00:32:55.050 ⇒ 00:32:55.630 Samuel Roberts: You know, like.
368 00:32:55.630 ⇒ 00:32:57.799 Pranav Narahari: It’ll never be null, I guess, yeah.
369 00:32:58.360 ⇒ 00:33:03.659 Samuel Roberts: Yeah, if you’re going from orders to customers… Then it should be…
370 00:33:06.900 ⇒ 00:33:07.950 Samuel Roberts: Yeah.
371 00:33:11.840 ⇒ 00:33:12.370 Samuel Roberts: Okay.
372 00:33:12.370 ⇒ 00:33:17.660 Pranav Narahari: Yes, I think you’re right, yeah, so we’ll just go off of order count being 1.
373 00:33:18.490 ⇒ 00:33:21.289 Samuel Roberts: Yeah, one or more than one, should be the…
374 00:33:21.560 ⇒ 00:33:22.990 Casie Aviles: Oh, okay, yeah.
375 00:33:25.010 ⇒ 00:33:26.289 Casie Aviles: Yeah, that makes sense.
376 00:33:27.060 ⇒ 00:33:28.369 Samuel Roberts: Yeah, so if, if…
377 00:33:30.560 ⇒ 00:33:35.140 Samuel Roberts: Yeah, so, like, I don’t know exactly how the reports work, but if it’s, like, the last 7 days, right?
378 00:33:36.770 ⇒ 00:33:40.349 Samuel Roberts: Then, for every order, Or we’d have to join…
379 00:33:40.700 ⇒ 00:33:43.309 Samuel Roberts: Something, which we could probably do a pretty quick…
380 00:33:44.470 ⇒ 00:33:49.789 Samuel Roberts: SQL call, that’ll just add, like, is new customer to that order, if we can…
381 00:33:50.700 ⇒ 00:33:52.710 Samuel Roberts: SQL blew our way to that.
382 00:33:53.670 ⇒ 00:33:54.310 Pranav Narahari: Yep.
383 00:34:02.570 ⇒ 00:34:03.250 Samuel Roberts: Alright.
384 00:34:03.470 ⇒ 00:34:10.209 Samuel Roberts: So what’s the plan from here, then? We need to re-sync just customers and orders, is that the idea? Or…
385 00:34:10.699 ⇒ 00:34:18.989 Pranav Narahari: I think so, unless, so Casey just updated the replication start date. I wonder if they’re gonna say they’re completed now.
386 00:34:19.499 ⇒ 00:34:26.879 Pranav Narahari: Yeah, if you go into the data now, Casey, like, is it still saying, like, Sinking, or…
387 00:34:27.749 ⇒ 00:34:29.939 Pranav Narahari: Because I assume it would have gone to this date.
388 00:34:33.500 ⇒ 00:34:37.429 Casie Aviles: I think it’s still the… 2025 date.
389 00:34:38.010 ⇒ 00:34:39.060 Pranav Narahari: So, we probably…
390 00:34:39.060 ⇒ 00:34:41.860 Casie Aviles: We need to do a new sync with a new date.
391 00:34:42.620 ⇒ 00:34:43.310 Samuel Roberts: Okay.
392 00:34:43.650 ⇒ 00:34:44.570 Pranav Narahari: That makes sense.
393 00:34:48.120 ⇒ 00:34:55.460 Casie Aviles: Yeah, this takes around… I think the last one took 3 hours or something, so… I don’t want to, like, wait for… for the…
394 00:34:55.469 ⇒ 00:34:59.229 Samuel Roberts: Can we just think, like, the last month or so, just for testing purposes?
395 00:34:59.860 ⇒ 00:35:03.780 Casie Aviles: Okay, so I guess I’ll go and cancel this now, since…
396 00:35:03.780 ⇒ 00:35:08.300 Samuel Roberts: Yeah, just for now, let’s just get some data in to figure out the shape and everything.
397 00:35:08.740 ⇒ 00:35:09.430 Pranav Narahari: Yeah.
398 00:35:10.930 ⇒ 00:35:11.560 Samuel Roberts: Okay.
399 00:35:16.060 ⇒ 00:35:18.229 Samuel Roberts: Something went wrong with the connector, what was that?
400 00:35:18.900 ⇒ 00:35:21.460 Casie Aviles: Yeah, that’s also another… oh no.
401 00:35:25.840 ⇒ 00:35:29.429 Casie Aviles: That’s one of the errors that I found in the logs.
402 00:35:30.200 ⇒ 00:35:32.569 Samuel Roberts: Fulfillment’s incomplete, is that what we’re talking about here?
403 00:35:35.580 ⇒ 00:35:37.289 Samuel Roberts: That one, too, orders, okay.
404 00:35:38.500 ⇒ 00:35:41.500 Samuel Roberts: Can you click the, little arrow on that warning?
405 00:35:42.480 ⇒ 00:35:43.390 Samuel Roberts: On the right.
406 00:35:58.140 ⇒ 00:36:01.199 Samuel Roberts: Yeah, just dumped that in and see what it… oh, is this what it was? Okay.
407 00:36:02.760 ⇒ 00:36:06.670 Casie Aviles: Everybody’s trying to look up, I feel, that came through as an empty object with zero.
408 00:36:07.070 ⇒ 00:36:09.739 Samuel Roberts: You can’t ingest an empty struct, ugh, okay.
409 00:36:12.800 ⇒ 00:36:15.999 Pranav Narahari: This looks like it has to do with fulfillment, though, or is it also orders?
410 00:36:16.820 ⇒ 00:36:18.329 Samuel Roberts: It was also orders down there.
411 00:36:18.840 ⇒ 00:36:23.100 Pranav Narahari: Okay, in the… That message that I only read fulfillments.
412 00:36:23.280 ⇒ 00:36:26.349 Samuel Roberts: Yeah, I think it was just that one that was the report up there, but…
413 00:36:27.480 ⇒ 00:36:31.130 Casie Aviles: It did load some… orders.
414 00:36:31.360 ⇒ 00:36:34.150 Casie Aviles: For fulfillment, it’s zero also.
415 00:36:35.090 ⇒ 00:36:35.800 Pranav Narahari: Yeah.
416 00:36:37.390 ⇒ 00:36:42.069 Pranav Narahari: Yeah, let’s… let’s just try with just orders and customers.
417 00:36:42.420 ⇒ 00:36:45.560 Samuel Roberts: Yeah. We’ll see what happens, and if not, we’ll get a specific.
418 00:36:45.760 ⇒ 00:36:50.300 Pranav Narahari: error for… Four orders, and then we can try to debug that.
419 00:36:53.400 ⇒ 00:36:54.260 Samuel Roberts: Sounds good.
420 00:36:58.830 ⇒ 00:37:00.180 Pranav Narahari: Do we update settings, too?
421 00:37:04.890 ⇒ 00:37:07.599 Pranav Narahari: Sorry, maybe not settings, just for,
422 00:37:07.720 ⇒ 00:37:09.359 Pranav Narahari: Like, when do we want to sync it?
423 00:37:09.830 ⇒ 00:37:10.790 Pranav Narahari: Until…
424 00:37:12.530 ⇒ 00:37:13.650 Samuel Roberts: River 24.
425 00:37:14.450 ⇒ 00:37:17.630 Pranav Narahari: So I’m not saying it’s properly. I meant, like,
426 00:37:17.830 ⇒ 00:37:19.920 Pranav Narahari: Like, the start date for the data?
427 00:37:19.920 ⇒ 00:37:21.439 Samuel Roberts: Oh, oh, how far back, yeah.
428 00:37:21.440 ⇒ 00:37:22.880 Pranav Narahari: How far back, sorry, yeah.
429 00:37:26.440 ⇒ 00:37:28.809 Casie Aviles: Yeah, it should be for the last week.
430 00:37:29.020 ⇒ 00:37:30.520 Pranav Narahari: Honestly, that’s perfect, because those…
431 00:37:30.520 ⇒ 00:37:31.690 Samuel Roberts: That’s what I’m pronoun.
432 00:37:31.900 ⇒ 00:37:37.820 Pranav Narahari: The Slack report is only for 7 days, so… like, it only pulls in data from the last 7 days, so… this is perfect.
433 00:37:37.820 ⇒ 00:37:38.490 Samuel Roberts: Thank you.
434 00:37:40.100 ⇒ 00:37:43.510 Casie Aviles: Okay, so it’s going… starting now, so… just need…
435 00:37:43.510 ⇒ 00:37:43.960 Pranav Narahari: Okay.
436 00:37:43.960 ⇒ 00:37:45.230 Casie Aviles: Monitor this.
437 00:37:45.970 ⇒ 00:37:46.860 Samuel Roberts: Okay, yeah.
438 00:37:47.190 ⇒ 00:37:52.020 Samuel Roberts: Keep an eye on that. Let us know if it hits another error or pinches.
439 00:37:52.680 ⇒ 00:37:57.260 Pranav Narahari: Yeah, and Casey, I can take this over, too. I know you’re still recovering, so.
440 00:37:57.260 ⇒ 00:37:58.139 Samuel Roberts: Oh, yeah.
441 00:37:59.040 ⇒ 00:38:06.480 Pranav Narahari: Yeah, if we want to hop off now, and then I can take over, and then, yeah, Case, if we need anything, I can just, like, Slack you. Okay. Up to you.
442 00:38:06.480 ⇒ 00:38:07.010 Casie Aviles: Yes.
443 00:38:08.180 ⇒ 00:38:11.600 Casie Aviles: Sure. You just log in using Stitch.
444 00:38:11.820 ⇒ 00:38:13.250 Casie Aviles: the… yeah.
445 00:38:13.380 ⇒ 00:38:14.390 Casie Aviles: This one.
446 00:38:14.390 ⇒ 00:38:14.950 Samuel Roberts: Yep.
447 00:38:15.160 ⇒ 00:38:18.590 Casie Aviles: For both, for both mother duck and Airbite. Okay.
448 00:38:18.590 ⇒ 00:38:24.200 Samuel Roberts: Okay, yeah, I added the AirByte login there, so it should prompt you on Google, or on 1Password for the Google, so…
449 00:38:24.950 ⇒ 00:38:25.870 Pranav Narahari: Yep, yep.
450 00:38:26.120 ⇒ 00:38:39.149 Pranav Narahari: Yeah, Sam, so I’m gonna probably focus more so on this, as well as just getting, like, the… I’m gonna also have the UI out today for the forecasting.
451 00:38:39.720 ⇒ 00:38:40.750 Pranav Narahari: So…
452 00:38:41.230 ⇒ 00:38:48.230 Pranav Narahari: the… what Bobby and I kind of decided on in, like, the thread this morning, as well as just, like, in this call, is like, okay.
453 00:38:48.630 ⇒ 00:39:01.580 Pranav Narahari: Shopify reports are a little bit more complex, let’s just get it set up so, like, once data warehousing is, like, set, we have the data there, then Slack… the Shopify Slack report can be, like, the first one that we, like.
454 00:39:01.620 ⇒ 00:39:09.340 Pranav Narahari: Test with, like, the end-to-end flow of, like, air bite, mother, mother duck, and then,
455 00:39:09.450 ⇒ 00:39:10.790 Pranav Narahari: you know, the Slack report.
456 00:39:11.810 ⇒ 00:39:12.330 Pranav Narahari: Within.
457 00:39:12.330 ⇒ 00:39:13.820 Samuel Roberts: Okay, I think that makes sense.
458 00:39:13.820 ⇒ 00:39:14.280 Pranav Narahari: Yeah.
459 00:39:14.280 ⇒ 00:39:15.450 Samuel Roberts: Yeah, I think, I think.
460 00:39:15.450 ⇒ 00:39:18.890 Pranav Narahari: When the data, like, Like, in real time, and doing calculations.
461 00:39:18.890 ⇒ 00:39:22.550 Samuel Roberts: Right, that was just kind of a stopgap until we had this anyway, so I think that’s fine.
462 00:39:22.750 ⇒ 00:39:26.730 Pranav Narahari: Yeah, we shouldn’t spend too much extra time on that. I think I already spent too much time on it.
463 00:39:27.160 ⇒ 00:39:29.450 Samuel Roberts: Yeah, okay. Sounds like a good plan, then.
464 00:39:30.830 ⇒ 00:39:40.750 Samuel Roberts: And then… What else are we… So I’m just pulling up… Vinny real quick.
465 00:39:41.810 ⇒ 00:39:47.970 Samuel Roberts: So that’s moving… That’s moving. Sorry, I’m just looking here. Okay, a warehouse, spike on air bite.
466 00:39:48.440 ⇒ 00:39:49.200 Samuel Roberts: Yeah.
467 00:39:49.200 ⇒ 00:39:55.059 Pranav Narahari: Also, did you give an update on, the… Claude Skills.
468 00:39:55.400 ⇒ 00:39:58.430 Samuel Roberts: Yeah, I slacked them in the,
469 00:39:58.600 ⇒ 00:40:03.550 Samuel Roberts: Well, before I got off last night, I let them know in that thread that it was good, maybe I should have tagged them.
470 00:40:03.550 ⇒ 00:40:06.870 Pranav Narahari: Oh, live in staging, yeah, yeah, okay, I missed that. Totally. Okay, cool.
471 00:40:08.500 ⇒ 00:40:12.929 Pranav Narahari: Oh, okay, okay, yeah. In my message, I sent that, like, you might send it later, but you had already sent it to us.
472 00:40:12.930 ⇒ 00:40:16.609 Samuel Roberts: Yeah, yeah, I’d already… yeah, that’s the problem with Slack, it doesn’t always show you, like, what’s going on in the threads.
473 00:40:16.970 ⇒ 00:40:18.370 Samuel Roberts: Yeah. Once you’re tagged there.
474 00:40:19.230 ⇒ 00:40:21.149 Pranav Narahari: Yup. Okay, cool.
475 00:40:24.370 ⇒ 00:40:25.000 Samuel Roberts: Anything else?
476 00:40:25.000 ⇒ 00:40:25.639 Casie Aviles: Thank you.
477 00:40:26.610 ⇒ 00:40:27.130 Pranav Narahari: Oh, really?
478 00:40:27.130 ⇒ 00:40:28.109 Samuel Roberts: I feel better, Casey.
479 00:40:28.470 ⇒ 00:40:31.770 Pranav Narahari: Yeah, feel better, Casey. Yep.
480 00:40:31.890 ⇒ 00:40:34.240 Pranav Narahari: Yeah, actually, Sam, one other thing is…
481 00:40:34.910 ⇒ 00:40:38.739 Pranav Narahari: I’ve noticed, like, that they haven’t added anybody into production.
482 00:40:41.440 ⇒ 00:40:43.409 Pranav Narahari: Like, they haven’t really been using production.
483 00:40:44.130 ⇒ 00:40:52.940 Samuel Roberts: That’s fine. I think they have a whole, like, they want to roll it out a certain way, and I’m not sure if that’s being held up by this. I didn’t think that was, because, like, they were using staging, I thought.
484 00:40:53.790 ⇒ 00:40:54.470 Pranav Narahari: Yeah.
485 00:40:55.010 ⇒ 00:40:56.670 Samuel Roberts: Did he say anything about that just now?
486 00:40:56.670 ⇒ 00:40:59.210 Pranav Narahari: No, they just haven’t responded to that.
487 00:40:59.210 ⇒ 00:41:02.619 Samuel Roberts: Okay, let’s not stress about that yet, but, yeah.
488 00:41:03.580 ⇒ 00:41:04.889 Samuel Roberts: We’ll keep an eye on it.
489 00:41:05.460 ⇒ 00:41:13.189 Pranav Narahari: Yeah. Yeah, I think this is a little bit of, like, what I’m learning is, like, we need to… I’m trying to figure out, like, what’s important to them, which is what’s not important to them.
490 00:41:13.990 ⇒ 00:41:14.650 Samuel Roberts: Yeah.
491 00:41:14.650 ⇒ 00:41:28.869 Pranav Narahari: Seems like, yeah, the staging, not super… I mean, sorry, the production environment, having people on board is not the most pressing thing right now. It’s like, honestly, like, this forecasting stuff is the most pressing.
492 00:41:28.980 ⇒ 00:41:32.120 Pranav Narahari: So…
493 00:41:32.790 ⇒ 00:41:44.899 Pranav Narahari: Yeah. I guess that’s just, like, some things I’m learning along the way about, like, what they want to be updated on, like, consistently and constantly, versus, like, some stuff that, like, is not even as relevant to them, so…
494 00:41:45.610 ⇒ 00:41:46.020 Samuel Roberts: Right.
495 00:41:46.020 ⇒ 00:41:46.800 Pranav Narahari: Pulling that up.
496 00:41:47.440 ⇒ 00:41:49.099 Samuel Roberts: Okay, yeah, no, that’s a good call-out.
497 00:41:49.970 ⇒ 00:41:50.970 Samuel Roberts: I think…
498 00:41:52.590 ⇒ 00:42:04.430 Samuel Roberts: I mean, we can ask them more specifically, like, you know, when do they plan on onboarding people? We’re still waiting on that CSV to upload stuff, so, like, I don’t know if that’s part of it, too. They just don’t want to get people in there until they’re done with that, and they got other stuff going on, so I don’t know.
499 00:42:04.880 ⇒ 00:42:05.890 Samuel Roberts: I think…
500 00:42:05.960 ⇒ 00:42:17.389 Pranav Narahari: part… partly for them is, like, they want the tool to be, like, ready to use, because, like, they can’t control what everybody, like, at their agency is, like, using as a tool, and so…
501 00:42:17.390 ⇒ 00:42:17.840 Samuel Roberts: Right.
502 00:42:17.840 ⇒ 00:42:18.970 Pranav Narahari: Kind of trying to sell…
503 00:42:23.310 ⇒ 00:42:24.180 Samuel Roberts: Yeah.
504 00:42:26.760 ⇒ 00:42:30.909 Samuel Roberts: Okay, well, I mean, I think the only thing to do is just get this forecasting stuff as pushed as we can.
505 00:42:32.680 ⇒ 00:42:33.740 Samuel Roberts: At this point.
506 00:42:37.280 ⇒ 00:42:38.790 Samuel Roberts: Okay,
507 00:42:42.340 ⇒ 00:42:44.049 Samuel Roberts: Yeah, okay, are you still there?
508 00:42:45.350 ⇒ 00:42:46.250 Samuel Roberts: -Oh.
509 00:42:47.870 ⇒ 00:42:50.049 Samuel Roberts: You can hear me, I can’t hear anything you’re saying.
510 00:43:04.340 ⇒ 00:43:05.040 Samuel Roberts: Okay.
511 00:43:05.160 ⇒ 00:43:10.509 Samuel Roberts: I’m gonna hop off if you can hear me, I’ll message you on Slack if there’s some kind of issue with Zoom right now.