Meeting Title: Pranav <> Casie: Lilo EP Handoff Date: 2026-01-29 Meeting participants: Casie Aviles, Pranav Narahari
WEBVTT
1 00:00:14.350 ⇒ 00:00:15.340 Casie Aviles: Apron enough.
2 00:00:16.610 ⇒ 00:00:17.500 Pranav Narahari: Hey, Casey.
3 00:00:21.510 ⇒ 00:00:23.500 Casie Aviles: Yeah, yeah, how are you?
4 00:00:24.220 ⇒ 00:00:26.060 Pranav Narahari: Good, good. How are you? How are you feeling now?
5 00:00:27.030 ⇒ 00:00:30.709 Casie Aviles: Yeah, thanks, feeling better now, thankfully.
6 00:00:31.150 ⇒ 00:00:32.550 Pranav Narahari: Nice. That’s good to hear.
7 00:00:34.580 ⇒ 00:00:39.279 Casie Aviles: Alright, okay, so I think for the handover, I would just,
8 00:00:40.050 ⇒ 00:00:44.620 Casie Aviles: Would love some… would love to know, like, what are the stuff that you want to…
9 00:00:44.920 ⇒ 00:00:46.619 Pranav Narahari: Hand over to me, like.
10 00:00:46.890 ⇒ 00:00:48.949 Casie Aviles: Which ones do should I start?
11 00:00:49.060 ⇒ 00:00:54.619 Casie Aviles: Like, which, like, for example, the GATT chart, and if there’s any other
12 00:00:54.950 ⇒ 00:00:57.899 Casie Aviles: Artifacts that I should be taking care of now.
13 00:00:58.650 ⇒ 00:01:02.050 Pranav Narahari: Yeah, I would say for Lilo, it’s pretty simple,
14 00:01:02.150 ⇒ 00:01:11.739 Pranav Narahari: I think Notion is kind of, like, the heaviest lift, usually, which we are starting based on a new client. We haven’t wrote a ton of documentation.
15 00:01:12.460 ⇒ 00:01:12.840 Casie Aviles: I see.
16 00:01:12.840 ⇒ 00:01:22.929 Pranav Narahari: And we haven’t wrote a ton in, Notion, or we haven’t written anything in Notion yet, I think. Maybe Sam has, but nothing EP-related.
17 00:01:23.750 ⇒ 00:01:24.889 Casie Aviles: I see. Okay.
18 00:01:24.890 ⇒ 00:01:28.889 Pranav Narahari: Yeah, so the main stuff is linear.
19 00:01:29.130 ⇒ 00:01:35.279 Pranav Narahari: And… the Gantt chart, and also the GitHub repo. So…
20 00:01:35.780 ⇒ 00:01:42.990 Pranav Narahari: Oh, okay. The GitHub repo actually has a lot of information pertaining to PRDs, estimates.
21 00:01:43.000 ⇒ 00:01:44.320 Casie Aviles: in timeline.
22 00:01:44.380 ⇒ 00:01:54.199 Pranav Narahari: So, yeah, let me show you each one of these one by one. So, linear and Gantt charts easy, so let me send… let me just send those over to you first.
23 00:02:22.290 ⇒ 00:02:30.329 Pranav Narahari: I think you need to be added to the Lilo Social Instagant if you’re not already, so… I don’t have permissions to do that, but Rico can do that for you.
24 00:02:30.760 ⇒ 00:02:33.710 Casie Aviles: Sure, sure, I think I’m there. Yeah, I can see it.
25 00:02:34.090 ⇒ 00:02:36.949 Pranav Narahari: Oh, you can already see it? Okay, perfect. And then,
26 00:02:37.090 ⇒ 00:02:44.380 Pranav Narahari: Let me send you a direct link to… the Lilo board as well.
27 00:02:46.080 ⇒ 00:02:47.450 Pranav Narahari: Okay, there we go.
28 00:02:49.650 ⇒ 00:02:50.450 Casie Aviles: Okay.
29 00:02:56.430 ⇒ 00:02:58.040 Casie Aviles: Alright, yep, I see it.
30 00:02:58.670 ⇒ 00:03:06.490 Pranav Narahari: Okay, cool. Yeah, any, like, specific questions about those? I think those are more or less straightforward.
31 00:03:09.430 ⇒ 00:03:11.040 Casie Aviles: Or, actually…
32 00:03:11.140 ⇒ 00:03:22.219 Pranav Narahari: if you wanna… maybe share your screen real quick for the Instagant, I can walk through a few different things on there, since we scoped out not just this project, but also future projects.
33 00:03:23.480 ⇒ 00:03:25.429 Casie Aviles: Okay, so this sustains the gun.
34 00:03:26.760 ⇒ 00:03:32.840 Pranav Narahari: Yeah. So yeah, right now we’re in the forecasting phase.
35 00:03:33.100 ⇒ 00:03:38.210 Pranav Narahari: So… I think you’re pretty familiar about what is needed for that.
36 00:03:38.590 ⇒ 00:03:44.520 Pranav Narahari: If not, you know, we can talk more. And there’s also a lot of docs, too, that maybe you can read first, and then…
37 00:03:44.790 ⇒ 00:03:48.079 Pranav Narahari: you can ask more questions after.
38 00:03:48.790 ⇒ 00:03:50.010 Pranav Narahari: So…
39 00:03:50.740 ⇒ 00:03:59.010 Pranav Narahari: Yeah, after… and then, so there’s also that ad hoc phase at the bottom, that’s where we added the Nano Banana, as well as these 3 other…
40 00:03:59.240 ⇒ 00:04:08.920 Pranav Narahari: milestones that, so actually that one, Claude Skills, you can just even mark that as completed, because Sam completed that.
41 00:04:10.540 ⇒ 00:04:16.089 Pranav Narahari: But… Yeah, so that’s kind of, like, where I throw in ad hoc stuff.
42 00:04:19.620 ⇒ 00:04:20.010 Pranav Narahari: And then…
43 00:04:20.019 ⇒ 00:04:20.389 Casie Aviles: if you scroll.
44 00:04:20.390 ⇒ 00:04:21.860 Pranav Narahari: Drill down even further.
45 00:04:22.060 ⇒ 00:04:22.780 Casie Aviles: Yeah, okay.
46 00:04:22.780 ⇒ 00:04:25.849 Pranav Narahari: And then to the right, there’s, like, Phase 3.
47 00:04:26.020 ⇒ 00:04:30.069 Casie Aviles: So, yeah, you’ll be reading docs about this as well. We’ve already…
48 00:04:30.620 ⇒ 00:04:33.619 Pranav Narahari: Yeah, we’ve already kind of, scoped it out.
49 00:04:33.860 ⇒ 00:04:36.430 Casie Aviles: And that should be in the GitHub brief already.
50 00:04:36.430 ⇒ 00:04:40.930 Pranav Narahari: Yep, it’s all in the GitHub repo, so yeah, that’s gonna be what I’m gonna show you next. So…
51 00:04:41.150 ⇒ 00:04:43.519 Pranav Narahari: Yeah, let me send you a link, and then we can…
52 00:04:44.950 ⇒ 00:04:49.530 Pranav Narahari: You can just, like, keep sharing your screen, and then I can show you where everything is.
53 00:04:50.520 ⇒ 00:04:53.459 Pranav Narahari: Yeah, so… actually, yeah, if you just go to the main…
54 00:04:53.580 ⇒ 00:04:55.909 Pranav Narahari: Branch, you’ll be able to see it.
55 00:04:56.120 ⇒ 00:04:58.719 Pranav Narahari: So, yeah, if you go into PRDs.
56 00:05:01.700 ⇒ 00:05:10.790 Pranav Narahari: And then, yeah, you’ll see these 6 docs. So, there are… there’s the PRDs, and then right after it is a work estimate. The work estimates are what I…
57 00:05:11.030 ⇒ 00:05:12.110 Pranav Narahari: Road out.
58 00:05:13.390 ⇒ 00:05:24.730 Pranav Narahari: Based off of the PRDs, and each phase has these three sections, like POC, V1, and then… or, sorry, POC, MVP, and then V1.
59 00:05:25.700 ⇒ 00:05:26.380 Casie Aviles: Okay.
60 00:05:26.690 ⇒ 00:05:27.300 Pranav Narahari: Yeah.
61 00:05:28.030 ⇒ 00:05:31.419 Pranav Narahari: So… that’s kinda how things are organized.
62 00:05:32.480 ⇒ 00:05:39.490 Pranav Narahari: There’s a… a sense… There’s a little bit there about, the hours estimates for each thing.
63 00:05:39.860 ⇒ 00:05:40.810 Casie Aviles: Mmm, okay.
64 00:05:40.810 ⇒ 00:05:41.360 Pranav Narahari: Yeah.
65 00:05:42.000 ⇒ 00:05:44.210 Pranav Narahari: The thing is, is, like, this…
66 00:05:44.660 ⇒ 00:05:49.940 Pranav Narahari: Was what we initially scoped, and then we increased the…
67 00:05:50.370 ⇒ 00:05:57.169 Pranav Narahari: Or we decreased, the timeline for things, like, we… we kind of, squished it, so…
68 00:05:57.880 ⇒ 00:06:03.169 Pranav Narahari: All of these things may not still be in the Gantt, like all the deliverables.
69 00:06:04.240 ⇒ 00:06:09.069 Pranav Narahari: However, I think the hours ranges are still accurate. I don’t think those have been decreased at all.
70 00:06:10.420 ⇒ 00:06:18.570 Pranav Narahari: So, everything still should be, like, up-to-date, and there should be, like, no data discrepancies between this and Instagant.
71 00:06:19.730 ⇒ 00:06:27.090 Casie Aviles: Okay, great. I’m… I’ll just dive into this some more, and I’ll, if I have any questions, yeah, I’ll let you guys know.
72 00:06:27.750 ⇒ 00:06:28.520 Pranav Narahari: Perfect.
73 00:06:29.020 ⇒ 00:06:32.110 Pranav Narahari: But yeah, these are the 3 main things.
74 00:06:33.460 ⇒ 00:06:36.240 Casie Aviles: Okay, we also have SOWs here.
75 00:06:39.410 ⇒ 00:06:40.270 Pranav Narahari: Yeah.
76 00:06:42.430 ⇒ 00:06:48.600 Pranav Narahari: I don’t even think I… I think I read those SOWs, like, early on. I don’t really know how…
77 00:06:48.800 ⇒ 00:06:50.540 Pranav Narahari: Relevant they still are.
78 00:06:51.770 ⇒ 00:06:52.640 Casie Aviles: I see.
79 00:06:53.320 ⇒ 00:06:53.990 Pranav Narahari: Yeah.
80 00:06:54.570 ⇒ 00:06:57.230 Pranav Narahari: But, you know, I think the more you read, the better.
81 00:06:57.740 ⇒ 00:07:04.959 Pranav Narahari: And any questions you have, like, in the next, like, week or so, we’ll… we’ll definitely clear it up, you know?
82 00:07:05.290 ⇒ 00:07:06.909 Casie Aviles: Sure. Yeah, feels good.
83 00:07:06.980 ⇒ 00:07:10.169 Pranav Narahari: Any questions if you’re confused about scope or whatever.
84 00:07:10.170 ⇒ 00:07:22.689 Casie Aviles: Yeah, have you guys established, like, a cadence with, or, like, meeting with Sam, or… and… so it’s going to be us three now, right? Since, you’ll be CSO, and then Sam will be…
85 00:07:23.080 ⇒ 00:07:24.619 Casie Aviles: The service lead, right?
86 00:07:25.020 ⇒ 00:07:25.620 Pranav Narahari: Yep.
87 00:07:26.290 ⇒ 00:07:28.909 Casie Aviles: Okay, and, okay.
88 00:07:29.680 ⇒ 00:07:35.279 Casie Aviles: I was just asking, like, if you guys already have, like, a set schedule or cadence for that, or…
89 00:07:35.870 ⇒ 00:07:37.539 Casie Aviles: I can propose a new one.
90 00:07:37.620 ⇒ 00:07:43.930 Pranav Narahari: Yeah, so I did actually have a repeating meeting set up with Sam, but we ended up not really needing them.
91 00:07:44.390 ⇒ 00:07:45.380 Casie Aviles: Oh, okay.
92 00:07:45.380 ⇒ 00:07:55.660 Pranav Narahari: I had it, like, set up, like, once a week, just to, like, align on, like, SL and, EP stuff. I love those type of meetings, like, we can also…
93 00:07:58.040 ⇒ 00:08:03.839 Pranav Narahari: We could also do the same, on, like, our end?
94 00:08:03.870 ⇒ 00:08:07.229 Casie Aviles: Like, me and you can also have, like, you know, weekly or…
95 00:08:07.230 ⇒ 00:08:13.769 Pranav Narahari: multiple times per week, like, syncs on stuff, if there’s… especially in the beginning, maybe you have more questions.
96 00:08:14.780 ⇒ 00:08:19.759 Pranav Narahari: But yeah, feel free to throw whatever you want on the calendar. We don’t have anything that I need to transfer over to you.
97 00:08:20.200 ⇒ 00:08:21.550 Casie Aviles: Okay, sure.
98 00:08:21.700 ⇒ 00:08:22.530 Casie Aviles: Alrighty.
99 00:08:22.840 ⇒ 00:08:25.220 Casie Aviles: Yeah, I’ll look for the best time, then.
100 00:08:26.970 ⇒ 00:08:28.130 Casie Aviles: Okay.
101 00:08:28.280 ⇒ 00:08:32.259 Casie Aviles: So I think, yeah, that’s… that should be pretty clear.
102 00:08:32.929 ⇒ 00:08:37.809 Casie Aviles: I think, some things that I can work on is to just polish this a bit.
103 00:08:38.490 ⇒ 00:08:40.059 Casie Aviles: the Notion one.
104 00:08:41.809 ⇒ 00:08:47.050 Casie Aviles: This, this should be the… this is it, right? The Notion page that we have for Lilo.
105 00:08:48.660 ⇒ 00:08:53.049 Pranav Narahari: Maybe, yeah, I haven’t really dove into this. I may be,
106 00:08:53.880 ⇒ 00:08:56.680 Pranav Narahari: I’m not really sure who made this.
107 00:08:57.190 ⇒ 00:08:58.920 Casie Aviles: Oh… Yeah.
108 00:09:01.560 ⇒ 00:09:03.770 Casie Aviles: Yeah, maybe someone from ops would…
109 00:09:03.770 ⇒ 00:09:06.150 Pranav Narahari: Yeah, I think the ops team has been working on this.
110 00:09:06.690 ⇒ 00:09:08.750 Casie Aviles: Okay, yeah, I can just,
111 00:09:09.780 ⇒ 00:09:17.929 Casie Aviles: keep this updated, then, and keep it aligned, but it looks like the mean… Sort, stuff to keep.
112 00:09:18.220 ⇒ 00:09:22.320 Casie Aviles: Or to maintain would be the Gantt chart, linear, and then this one. Okay.
113 00:09:22.810 ⇒ 00:09:23.420 Pranav Narahari: Yup.
114 00:09:23.840 ⇒ 00:09:28.340 Casie Aviles: Alright, I think that’s… that’s all I had for… that’s all… yeah.
115 00:09:29.090 ⇒ 00:09:32.499 Casie Aviles: If I ever just have any questions, then I’ll let you guys know, but…
116 00:09:33.420 ⇒ 00:09:39.229 Casie Aviles: I’ll probably just, go through cursor and, you know, Ask some questions.
117 00:09:39.400 ⇒ 00:09:42.909 Casie Aviles: Based on this, so that’s… that should be helpful.
118 00:09:43.510 ⇒ 00:09:44.680 Pranav Narahari: Cool, yeah.
119 00:09:47.790 ⇒ 00:09:54.529 Pranav Narahari: Yeah, sounds good to me. I was also wondering, do you have a few more minutes we can maybe talk through some of the Shopify data stuff?
120 00:09:54.850 ⇒ 00:10:02.679 Casie Aviles: Yeah, actually, I think that we should talk a bit about that, where did they laugh off?
121 00:10:05.270 ⇒ 00:10:12.779 Pranav Narahari: I believe the latest was you were able to get Polytomic to work and actually bring in the new customer field as well?
122 00:10:12.930 ⇒ 00:10:15.269 Pranav Narahari: And so I kind of want to just,
123 00:10:16.180 ⇒ 00:10:19.140 Pranav Narahari: Cross-check with the dashboard right now.
124 00:10:19.670 ⇒ 00:10:24.159 Pranav Narahari: And see if, like, all these values look right. Which, that would be a huge win.
125 00:10:26.360 ⇒ 00:10:33.390 Casie Aviles: Sure. I think… Like, there were some… there were some failures still.
126 00:10:33.680 ⇒ 00:10:36.130 Casie Aviles: with the sink, but I don’t think it was as…
127 00:10:37.480 ⇒ 00:10:44.520 Casie Aviles: Yeah, some of the objects here did not… fully… sync… well, let’s see…
128 00:10:44.940 ⇒ 00:10:49.609 Casie Aviles: Yeah, that would be order refunds and transactions, but the other ones are completed.
129 00:10:51.030 ⇒ 00:10:51.780 Pranav Narahari: Okay.
130 00:10:52.540 ⇒ 00:10:59.550 Pranav Narahari: You, you showed, like, a table in that, in the chat, too, that sh… that said something about, like…
131 00:11:00.290 ⇒ 00:11:02.170 Pranav Narahari: Let me see…
132 00:11:02.570 ⇒ 00:11:04.209 Casie Aviles: Oh, yeah, for the, for the…
133 00:11:04.210 ⇒ 00:11:06.799 Pranav Narahari: Account, and total spend, and then…
134 00:11:06.800 ⇒ 00:11:07.140 Casie Aviles: customer.
135 00:11:07.140 ⇒ 00:11:07.770 Pranav Narahari: type.
136 00:11:08.580 ⇒ 00:11:13.020 Pranav Narahari: Yeah, can I just, like… I’m guessing you, like, create a SQL query to pull in that info?
137 00:11:14.070 ⇒ 00:11:16.179 Casie Aviles: Yeah. That should be on my third doc.
138 00:11:16.300 ⇒ 00:11:17.310 Casie Aviles: Okay.
139 00:11:17.310 ⇒ 00:11:17.940 Pranav Narahari: Perfect.
140 00:11:18.300 ⇒ 00:11:19.790 Pranav Narahari: Yeah, let’s.
141 00:11:19.790 ⇒ 00:11:22.710 Casie Aviles: Oh, this is the Roma account. Let me log out.
142 00:11:30.280 ⇒ 00:11:33.660 Pranav Narahari: Let’s see what we can do for maybe,
143 00:11:34.300 ⇒ 00:11:44.539 Pranav Narahari: updating that query to, like, squish together basically all the customers per day. So we just want to see, like, the per day order count for customer type being new.
144 00:11:44.970 ⇒ 00:11:49.050 Pranav Narahari: And then the total spent also summed.
145 00:11:50.740 ⇒ 00:11:51.410 Casie Aviles: Okay.
146 00:11:51.910 ⇒ 00:11:55.819 Pranav Narahari: Yeah, that way I can compare it to the values in Shopify.
147 00:11:56.390 ⇒ 00:12:12.590 Pranav Narahari: And if they look close, because there’s gonna be some data discrepancy because of time zone issues and some other, like, issues that Bobby told me about. So if they’re pretty close, though, especially, like, on the, like, the basis of, like, week to week.
148 00:12:12.760 ⇒ 00:12:17.100 Pranav Narahari: Yeah. Then I feel pretty confident about the… that we’re doing it the right way.
149 00:12:18.290 ⇒ 00:12:19.970 Casie Aviles: Okay, yeah.
150 00:12:21.130 ⇒ 00:12:26.529 Casie Aviles: So this was… I think this is the query that I used. Okay.
151 00:12:27.750 ⇒ 00:12:28.760 Pranav Narahari: Okay, cool.
152 00:12:29.180 ⇒ 00:12:33.350 Casie Aviles: So, this one should show, like, new and returning…
153 00:12:34.240 ⇒ 00:12:40.540 Casie Aviles: customers. But I also was just playing around, and I wanted to, like, generate
154 00:12:41.180 ⇒ 00:12:49.400 Casie Aviles: Or, like, mimic, you know, the one that we… have on… the Newton… database.
155 00:12:50.010 ⇒ 00:12:57.559 Casie Aviles: So I think… This is… I haven’t really shared this yet, but this is what… I got yesterday.
156 00:13:01.470 ⇒ 00:13:02.570 Pranav Narahari: Oh, let me see that.
157 00:13:04.070 ⇒ 00:13:05.339 Casie Aviles: Yeah, this one.
158 00:13:07.770 ⇒ 00:13:08.990 Casie Aviles: So I tried to learn…
159 00:13:08.990 ⇒ 00:13:09.750 Pranav Narahari: Yeah, just…
160 00:13:10.510 ⇒ 00:13:22.299 Pranav Narahari: This data is interesting, especially since, like, you’re getting the new and returning field, which I wasn’t seeing before, so… yeah, it looks like Polytomic is definitely giving us more data.
161 00:13:22.480 ⇒ 00:13:24.560 Casie Aviles: I’ll do it.
162 00:13:24.910 ⇒ 00:13:28.210 Pranav Narahari: Okay, so you synced it into Google, that’s good.
163 00:13:29.020 ⇒ 00:13:37.600 Casie Aviles: Yeah, I tried to basically copy how they did it here, so that involved, like, a lot of aggregation and joining. I’m not too sure exactly what
164 00:13:38.300 ⇒ 00:13:41.669 Casie Aviles: You know, how all of this is working, but…
165 00:13:41.950 ⇒ 00:13:48.879 Casie Aviles: I just got it from ChatGPT, but I gave it all the schemas, and the columns, and…
166 00:13:49.290 ⇒ 00:13:52.659 Casie Aviles: Was able to generate this, where we have, you know.
167 00:13:53.340 ⇒ 00:13:54.870 Pranav Narahari: Gotcha. Demand.
168 00:13:55.260 ⇒ 00:13:55.760 Casie Aviles: So far.
169 00:13:55.760 ⇒ 00:13:59.090 Pranav Narahari: If I go into Mother Duck right now, I should be able to see this too, right?
170 00:13:59.330 ⇒ 00:14:01.580 Casie Aviles: Yes, just run the query.
171 00:14:01.950 ⇒ 00:14:04.010 Casie Aviles: This one, the topmost one.
172 00:14:05.000 ⇒ 00:14:05.600 Pranav Narahari: Yep.
173 00:14:06.200 ⇒ 00:14:10.270 Pranav Narahari: Okay, yeah, let me play around with this for a little bit, and .
174 00:14:10.380 ⇒ 00:14:12.060 Casie Aviles: Okay. See if the data…
175 00:14:12.440 ⇒ 00:14:18.860 Pranav Narahari: a li- like… Is in sync with what… or the data is actually pulling what we need.
176 00:14:18.900 ⇒ 00:14:20.140 Casie Aviles: Yes.
177 00:14:20.140 ⇒ 00:14:27.470 Pranav Narahari: But yeah, okay, this looks… this looks promising, though. One other option that we saw was using Shopify QL.
178 00:14:27.620 ⇒ 00:14:32.640 Pranav Narahari: And if we use Shopify QL, we can literally pull the…
179 00:14:33.140 ⇒ 00:14:42.659 Pranav Narahari: the query from the Shopify report. So, maybe… let me just share my screen real quick and show you what Shopify dashboard looks like.
180 00:14:49.810 ⇒ 00:14:50.960 Pranav Narahari: One second.
181 00:14:57.930 ⇒ 00:15:03.380 Pranav Narahari: Yeah, so… Bobby created this dashboard for me to, like, cross-check the numbers.
182 00:15:03.590 ⇒ 00:15:05.110 Pranav Narahari: So… Hmm.
183 00:15:05.880 ⇒ 00:15:10.430 Pranav Narahari: Here… there’s a… a query that…
184 00:15:10.780 ⇒ 00:15:13.080 Pranav Narahari: Theoretically, we should just be able to paste this.
185 00:15:13.210 ⇒ 00:15:19.779 Pranav Narahari: into a… the Shopify QL API, and we should be able to get all of.
186 00:15:19.780 ⇒ 00:15:20.899 Casie Aviles: the values.
187 00:15:21.530 ⇒ 00:15:22.220 Pranav Narahari: tier.
188 00:15:23.780 ⇒ 00:15:29.370 Casie Aviles: I see. So we won’t even need to… we won’t even need an ELT tool then.
189 00:15:29.720 ⇒ 00:15:30.240 Casie Aviles: And God.
190 00:15:30.240 ⇒ 00:15:35.849 Pranav Narahari: Well, I think what we would like to do is still have an ELT tool in, like, a data warehouse.
191 00:15:35.850 ⇒ 00:15:36.710 Casie Aviles: Hmm.
192 00:15:38.960 ⇒ 00:15:43.299 Pranav Narahari: But… If we’re not able to pull the data, then we’ll have to do it this way.
193 00:15:43.480 ⇒ 00:15:47.530 Pranav Narahari: Because the ELT tool makes it way simpler, right? We’re not writing any code.
194 00:15:47.530 ⇒ 00:15:48.329 Casie Aviles: I’m treating it.
195 00:15:48.330 ⇒ 00:15:50.070 Pranav Narahari: Custom, like, requests.
196 00:15:50.530 ⇒ 00:15:51.870 Casie Aviles: Yeah, that’s true.
197 00:15:52.650 ⇒ 00:16:01.279 Pranav Narahari: So… Like, option, like, option 1 would be to try to, like.
198 00:16:01.800 ⇒ 00:16:12.970 Pranav Narahari: pull the data from what you, got from… through Polytonic into Mother Duck, but if that fails, then we’ll have to probably go, yeah, the Shopify QL route.
199 00:16:14.480 ⇒ 00:16:15.170 Casie Aviles: Okay, cool.
200 00:16:15.170 ⇒ 00:16:22.399 Pranav Narahari: Which I think is fine. One other thing that Sam and I were talking about was, is a data warehouse a little bit overkill for this project?
201 00:16:22.610 ⇒ 00:16:35.969 Pranav Narahari: We are leaning a little bit towards that. We thought maybe we can just make a data warehouse, because, you know, it’s been a topic of conversation, and then maybe in the future it actually does make sense, but…
202 00:16:35.970 ⇒ 00:16:36.760 Casie Aviles: Yeah.
203 00:16:36.910 ⇒ 00:16:43.629 Pranav Narahari: We’re also thinking, like, Postgres DBs are, like, you know, they’ll still be pretty performant if we’re using, like.
204 00:16:43.790 ⇒ 00:16:50.390 Pranav Narahari: GraphQL, like, we’re querying the exact data we need, so, like, it’s gonna be performant as well.
205 00:16:52.420 ⇒ 00:16:54.670 Pranav Narahari: Yeah, it could… I don’t…
206 00:16:54.900 ⇒ 00:16:59.959 Pranav Narahari: We’re not trying to, like, create a bunch of different dashboards and a bunch of visualizations, so…
207 00:17:00.300 ⇒ 00:17:03.060 Pranav Narahari: You know, there’s not gonna be a spring varying happening, so…
208 00:17:04.030 ⇒ 00:17:16.239 Pranav Narahari: We’ll see. We’ll have to obviously make a decision very soon. Probably, like, by Monday or Tuesday next week, we should have figured out exactly, like, what’s going on with Shopify, and…
209 00:17:17.490 ⇒ 00:17:18.200 Casie Aviles: Okay.
210 00:17:18.450 ⇒ 00:17:21.619 Pranav Narahari: Yeah, make a decision there. But… yeah.
211 00:17:21.750 ⇒ 00:17:27.579 Pranav Narahari: I’m… these are just kind of the… what we were… Sam and I were talking about yesterday that, you know, we just,
212 00:17:27.760 ⇒ 00:17:32.820 Pranav Narahari: haven’t looped you into yet, so I just wanted to make sure you were… Okay, yeah.
213 00:17:33.340 ⇒ 00:17:35.100 Casie Aviles: Yeah, thanks, yep.
214 00:17:35.500 ⇒ 00:17:41.910 Casie Aviles: So, this is… this is just going to be for the POC, right? What word?
215 00:17:45.980 ⇒ 00:17:49.790 Casie Aviles: Sorry, what we’re, this, this… What we’re seeing here is…
216 00:17:49.940 ⇒ 00:17:52.870 Casie Aviles: We’re just pulling in Shopify for the POC.
217 00:17:55.440 ⇒ 00:18:04.249 Pranav Narahari: we’re pulling it in for the POC, for sure, like, so by next week, we need to present the POC. However, like, we…
218 00:18:04.810 ⇒ 00:18:09.410 Pranav Narahari: Don’t want to… like, this process should still work for production as well.
219 00:18:10.010 ⇒ 00:18:11.070 Casie Aviles: Yeah, okay.
220 00:18:11.270 ⇒ 00:18:13.999 Pranav Narahari: And so, yeah, we have to think about it as…
221 00:18:14.430 ⇒ 00:18:18.100 Pranav Narahari: It shouldn’t just be for one brand, it needs to work across brands.
222 00:18:18.410 ⇒ 00:18:19.680 Pranav Narahari: I see.
223 00:18:19.790 ⇒ 00:18:20.660 Pranav Narahari: Yeah.
224 00:18:20.770 ⇒ 00:18:29.100 Pranav Narahari: which I don’t think is an issue, like, the way that we would do it, I can’t see it not being, scalable across all brands.
225 00:18:31.880 ⇒ 00:18:40.100 Pranav Narahari: I think you have some experience, I’m guessing, with, like, ABC or maybe a different client, like, using a data warehouse. Is that right?
226 00:18:40.930 ⇒ 00:18:48.340 Casie Aviles: Yeah, I have a bit of experience, but it’s not my specialty, but yeah, I’ve used a little bit of…
227 00:18:48.340 ⇒ 00:18:53.160 Pranav Narahari: I guess based on, like, that client, and comparing it to this client, Are there…
228 00:18:53.550 ⇒ 00:19:09.129 Pranav Narahari: was the data warehouse, like, the right… the right decision for that client, and also, did they have the same needs as Lilo? Or are they, like, completely different? Like, I’m just wondering, what is your, like, opinion on whether we go the data warehouse route versus just a regular, like, Postgres table?
229 00:19:10.660 ⇒ 00:19:21.559 Casie Aviles: Yeah, so for ABC, we are storing it in a… we are storing, like, the conversation logs in a data warehouse, right? Like, the same one that we… that I sent you, the export.
230 00:19:21.930 ⇒ 00:19:23.010 Pranav Narahari: Gotcha.
231 00:19:23.370 ⇒ 00:19:28.490 Casie Aviles: But, yeah, we’re using that… To create, like, a dashboard.
232 00:19:28.940 ⇒ 00:19:36.769 Casie Aviles: So… I think it… it’s… It’s fine that it’s in a data warehouse.
233 00:19:37.530 ⇒ 00:19:40.620 Casie Aviles: However, like…
234 00:19:41.540 ⇒ 00:19:49.510 Casie Aviles: for another client that I worked on, which was also concerned with getting, like, marketing data and all of that.
235 00:19:50.370 ⇒ 00:19:57.860 Casie Aviles: We were doing it in such that it was just, you know, automations.
236 00:19:58.620 ⇒ 00:20:01.229 Casie Aviles: We were just getting automations from…
237 00:20:02.060 ⇒ 00:20:08.269 Casie Aviles: like, web scraping automations and APIs, you know, custom scripts, and putting it inside, like.
238 00:20:08.540 ⇒ 00:20:11.380 Casie Aviles: A spreadsheet, but that wasn’t scalable.
239 00:20:11.670 ⇒ 00:20:17.530 Casie Aviles: So… Eventually, we needed to use, like, a data warehouse.
240 00:20:17.630 ⇒ 00:20:22.090 Casie Aviles: And I think that’s… that should… that makes much more sense there.
241 00:20:22.230 ⇒ 00:20:26.249 Casie Aviles: But if you’re… if you guys think that it’s not, like…
242 00:20:27.110 ⇒ 00:20:33.850 Casie Aviles: Like, we can… we can just do with a Postgres table, and I think that should be fine, you know? Or… yeah.
243 00:20:35.100 ⇒ 00:20:41.039 Casie Aviles: Like, a data warehouse would be great if we’re going to do, like, some dashboarding, analytics.
244 00:20:41.550 ⇒ 00:20:44.329 Casie Aviles: And then if we, like, need to do a lot of…
245 00:20:44.650 ⇒ 00:20:50.640 Casie Aviles: you know, querying, I think that that would be great, but if we’re not gonna do a lot of querying.
246 00:20:51.290 ⇒ 00:20:57.170 Casie Aviles: then I think… We can’t… we… it’s fine if we don’t do a data warehouse.
247 00:20:59.120 ⇒ 00:21:01.110 Pranav Narahari: Gotcha. Okay, that’s good to know.
248 00:21:01.340 ⇒ 00:21:02.610 Pranav Narahari: We’ll,
249 00:21:03.850 ⇒ 00:21:16.939 Pranav Narahari: we’ll basically just, like, see what happens with, Polyatomic. Right now, like, it was pretty simple to set up with, like, that integration with Polyatomic and Mother Duck, right? We just had to make sure, like, all the data is there.
250 00:21:17.810 ⇒ 00:21:18.589 Pranav Narahari: But, yeah.
251 00:21:18.590 ⇒ 00:21:19.220 Casie Aviles: Hmm.
252 00:21:19.540 ⇒ 00:21:20.460 Casie Aviles: Could we…
253 00:21:20.500 ⇒ 00:21:28.199 Pranav Narahari: So, if we wanted to create, like, a custom ETL into Mother Duck using Shopify QL, that is an option, right?
254 00:21:29.950 ⇒ 00:21:38.430 Casie Aviles: I think so. I haven’t really gotten… I haven’t really looked at the… the API for Shopify, or…
255 00:21:38.580 ⇒ 00:21:43.559 Casie Aviles: Shopify QL, but… It should be doable, right? I mean, that’s… that’s…
256 00:21:44.110 ⇒ 00:21:46.569 Casie Aviles: We could do that, definitely, but it’s…
257 00:21:46.980 ⇒ 00:21:52.240 Casie Aviles: like, one thing to keep in mind, I guess, is that we’re gonna have to maintain that.
258 00:21:52.610 ⇒ 00:21:53.260 Pranav Narahari: Right.
259 00:21:53.960 ⇒ 00:21:55.000 Casie Aviles: So…
260 00:21:55.790 ⇒ 00:22:02.320 Casie Aviles: That would be on us to, like, make sure that the data is fresh, or that it’s syncing correctly, you know, so…
261 00:22:02.820 ⇒ 00:22:05.119 Pranav Narahari: Yep, yep, definitely more maintenance there.
262 00:22:06.980 ⇒ 00:22:09.609 Casie Aviles: Okay. I guess that would be the trade-off there.
263 00:22:10.270 ⇒ 00:22:17.060 Pranav Narahari: Yeah, but it might be the only option, I guess, right? Like… In either case, If,
264 00:22:17.560 ⇒ 00:22:23.900 Pranav Narahari: we go Postgres or Motherdeck, and the data, like, the custom integration.
265 00:22:24.250 ⇒ 00:22:31.770 Pranav Narahari: or, sorry, built-in integration that, Polytomic has with Shopify, if that’s not working.
266 00:22:32.280 ⇒ 00:22:38.750 Pranav Narahari: Then, our only option is to manage a custom… ETL…
267 00:22:39.290 ⇒ 00:22:40.070 Casie Aviles: Yeah.
268 00:22:40.070 ⇒ 00:22:41.100 Pranav Narahari: Right, yeah.
269 00:22:44.000 ⇒ 00:22:46.250 Pranav Narahari: Okay, sounds good. So…
270 00:22:48.090 ⇒ 00:22:56.890 Pranav Narahari: Yeah, I mean, for POC purposes, like, setting up that ETL for next week, we could definitely do. But, you know, we’ll just have to…
271 00:22:57.710 ⇒ 00:23:01.539 Pranav Narahari: I think it still is, like, we have the allotted time in the…
272 00:23:01.920 ⇒ 00:23:15.019 Pranav Narahari: in the, like, in our Gantt, essentially, like, in the budget for the project, to maintain that. Is it the best solution? Maybe not, if they start needing more Shopify data.
273 00:23:15.240 ⇒ 00:23:16.090 Pranav Narahari: You know…
274 00:23:16.650 ⇒ 00:23:23.310 Pranav Narahari: In the future, we just have to know that, okay, that’s going to increase the resourcing needed to implement.
275 00:23:23.930 ⇒ 00:23:24.660 Casie Aviles: Yeah.
276 00:23:25.120 ⇒ 00:23:25.850 Pranav Narahari: Yeah.
277 00:23:25.850 ⇒ 00:23:38.040 Casie Aviles: If we’re gonna need, like, more sources, then I think Allatomic might be… or, like, an ETL tool would be good, like, if we’re not just gonna get from Shopify, right? If we’re…
278 00:23:38.390 ⇒ 00:23:42.930 Casie Aviles: we need to get from other sources as well, then an ETL tool would make sense.
279 00:23:43.940 ⇒ 00:23:45.320 Pranav Narahari: Yeah, so well…
280 00:23:46.340 ⇒ 00:24:01.610 Pranav Narahari: Yeah, so I guess there is a… there’s a scenario where, you know, there’s, the Facebook ads platform, we had no issues with that with, Polytomic. Sorry, not with Polytomic, with, AirByte. We may still just be able to use Polytomic.
281 00:24:01.710 ⇒ 00:24:09.810 Pranav Narahari: for that, I have no issues, but let’s say with Shopify, that might be the only custom… Like…
282 00:24:11.310 ⇒ 00:24:14.919 Pranav Narahari: Data thing that we have to manage.
283 00:24:15.380 ⇒ 00:24:21.490 Pranav Narahari: So, we can still, like… with any future… let’s say…
284 00:24:21.640 ⇒ 00:24:36.360 Pranav Narahari: Yeah, so what might actually end up happening is we may still have, like, the Shopify integration with Polytomic, with all of the data, but for the custom pieces of data that we’re not able to pull from that, we may need to manage, like, a separate
285 00:24:36.920 ⇒ 00:24:42.649 Pranav Narahari: like… data sync using the Shopify QL API.
286 00:24:43.160 ⇒ 00:24:43.900 Casie Aviles: Wow.
287 00:24:45.600 ⇒ 00:24:47.629 Casie Aviles: Yeah, I think so, but…
288 00:24:47.630 ⇒ 00:24:58.060 Pranav Narahari: We just need to check with the polyatomic, like, connection that we have right now that you made. Are we getting the data? If we are, then okay, all of this conversation is just, like.
289 00:24:58.170 ⇒ 00:24:59.810 Pranav Narahari: It doesn’t matter, like, we’re.
290 00:24:59.810 ⇒ 00:25:00.580 Casie Aviles: Good.
291 00:25:00.580 ⇒ 00:25:04.569 Pranav Narahari: If not, that’s when we have to start looking into the alternatives.
292 00:25:05.440 ⇒ 00:25:06.910 Casie Aviles: Okay. Yeah. Yeah.
293 00:25:06.910 ⇒ 00:25:12.540 Pranav Narahari: So yeah, in the meantime, I have, like, 15 minutes before the next call with, Bobby. I will…
294 00:25:13.430 ⇒ 00:25:18.979 Pranav Narahari: I will kind of work on that SQL command and, let you know.
295 00:25:19.260 ⇒ 00:25:26.210 Pranav Narahari: what happens there. I’ll probably spend some time later today as well working on it too, so…
296 00:25:26.650 ⇒ 00:25:28.000 Pranav Narahari: I’ll keep you updated.
297 00:25:28.490 ⇒ 00:25:31.129 Casie Aviles: Okay, cool. Thank you, thank you very much.
298 00:25:31.220 ⇒ 00:25:32.829 Pranav Narahari: Yeah, no problem. Take Casey.
299 00:25:33.200 ⇒ 00:25:33.760 Casie Aviles: Bye-bye.