Meeting Title: Magic Spoon SPINS sync Date: 2026-01-29 Meeting participants: Uttam Kumaran, Demilade Agboola, Ashwini Sharma
WEBVTT
1 00:00:37.540 ⇒ 00:00:38.390 Demilade Agboola: Hilton.
2 00:00:38.850 ⇒ 00:00:39.640 Uttam Kumaran: Hey, dude.
3 00:00:41.660 ⇒ 00:00:43.379 Demilade Agboola: Did you get any rest last night?
4 00:00:44.620 ⇒ 00:00:47.669 Uttam Kumaran: I did, a little bit.
5 00:00:49.650 ⇒ 00:00:50.589 Uttam Kumaran: How about you?
6 00:00:51.170 ⇒ 00:00:56.319 Demilade Agboola: I did, actually. I think I slept, like, 3AM or so, thereabout.
7 00:00:56.320 ⇒ 00:00:57.190 Uttam Kumaran: Okay.
8 00:01:04.430 ⇒ 00:01:09.550 Demilade Agboola: So on my end, I just need to… so today’s focus would just be the CSV, ones.
9 00:01:10.540 ⇒ 00:01:13.199 Demilade Agboola: Put that together. We’ll throw that into the model.
10 00:01:14.180 ⇒ 00:01:17.230 Demilade Agboola: And… boom, we should be done on this end.
11 00:01:17.570 ⇒ 00:01:21.189 Uttam Kumaran: Okay. So, we’ll be waiting for the spins data to integrate it.
12 00:01:22.780 ⇒ 00:01:23.970 Uttam Kumaran: Okay, cool.
13 00:01:25.580 ⇒ 00:01:29.479 Uttam Kumaran: Yeah, I wonder if you should just already start working on the spins data.
14 00:01:30.400 ⇒ 00:01:34.589 Ashwini Sharma: Yeah, I just wanted to finish the CTA also, so…
15 00:01:34.880 ⇒ 00:01:39.690 Ashwini Sharma: Okay, I’ll create… it won’t take much time to create that report.
16 00:01:40.010 ⇒ 00:01:45.829 Uttam Kumaran: Yeah, no, no, Ashwini, I keep telling… dude, I keep telling you, please, this Magic Spoon stuff is number one.
17 00:01:46.320 ⇒ 00:01:47.730 Ashwini Sharma: Okay, yeah.
18 00:01:47.730 ⇒ 00:01:51.020 Uttam Kumaran: I don’t know how… I don’t know how much I can tell you, like.
19 00:01:51.640 ⇒ 00:01:55.520 Uttam Kumaran: I don’t know how I can express that any differently, apart from they’re gonna fire us.
20 00:01:55.710 ⇒ 00:01:56.390 Uttam Kumaran: And…
21 00:01:56.390 ⇒ 00:01:57.710 Ashwini Sharma: Yeah, yeah.
22 00:01:57.710 ⇒ 00:02:01.060 Uttam Kumaran: Like, it’s gonna… it’s gonna basically be on you. So…
23 00:02:01.060 ⇒ 00:02:01.500 Ashwini Sharma: Alright.
24 00:02:01.500 ⇒ 00:02:08.940 Uttam Kumaran: I really… I don’t know how… I don’t… the way that the pipeline has been written, I can’t help there.
25 00:02:09.050 ⇒ 00:02:16.639 Uttam Kumaran: But, like, if you’re not able to do it, then I’m gonna have to just ask Awash, and we’re gonna just have to pair and transfer it over, because…
26 00:02:17.570 ⇒ 00:02:18.540 Uttam Kumaran: I don’t know how…
27 00:02:18.540 ⇒ 00:02:24.759 Ashwini Sharma: So I can make myself more clear. Yeah, I’ll give that sheet in another 15-20 minutes.
28 00:02:25.590 ⇒ 00:02:26.200 Uttam Kumaran: Okay.
29 00:02:28.250 ⇒ 00:02:31.630 Uttam Kumaran: Yeah, like, we have to close this out this week. Like.
30 00:02:31.630 ⇒ 00:02:34.669 Ashwini Sharma: But again, yeah, there is no data to compare it with.
31 00:02:34.670 ⇒ 00:02:35.490 Uttam Kumaran: I know, I know.
32 00:02:35.820 ⇒ 00:02:40.309 Uttam Kumaran: I know, we talked about this, but, like, what is the objective, right? We call this morning.
33 00:02:40.530 ⇒ 00:02:42.979 Uttam Kumaran: We’re gonna produce the aggregate views, right?
34 00:02:43.580 ⇒ 00:02:46.660 Uttam Kumaran: That’s not done yet, like, let’s finish that, please.
35 00:02:46.660 ⇒ 00:02:47.450 Ashwini Sharma: Okay, okay.
36 00:02:47.450 ⇒ 00:02:51.199 Uttam Kumaran: Or if you can’t finish it, you can give me the data and I can finish it. But, like.
37 00:02:51.420 ⇒ 00:02:54.720 Uttam Kumaran: We’re gonna get fired from this client if we don’t finish it today.
38 00:02:55.180 ⇒ 00:02:58.540 Ashwini Sharma: Yeah, 20 minutes, give me 20 minutes, I’ll just present it here.
39 00:02:58.540 ⇒ 00:03:05.990 Uttam Kumaran: I don’t wanna… I don’t mean to be very direct, I’m, like, not often like this, but it’s, like, it’s very important that we just try to do this right now.
40 00:03:07.530 ⇒ 00:03:10.189 Ashwini Sharma: Got it, yeah. I’ll just send it out here.
41 00:03:10.350 ⇒ 00:03:15.910 Uttam Kumaran: Okay. And then, Demi, I think, like, we will have, like, we have a raw export of the spins
42 00:03:16.460 ⇒ 00:03:20.339 Uttam Kumaran: data, so… Up to you if you want to start building on it.
43 00:03:23.030 ⇒ 00:03:29.040 Demilade Agboola: Is the raw export the only way it comes out as? Like, because the raw export doesn’t really seem to have, like.
44 00:03:29.770 ⇒ 00:03:33.610 Demilade Agboola: exact, weak landmarks.
45 00:03:34.850 ⇒ 00:03:39.019 Demilade Agboola: If you get what I mean. Like, how do I know what weeks,
46 00:03:39.580 ⇒ 00:03:44.410 Demilade Agboola: each theme belongs to. All I can see is, like, 4 weeks, 12 weeks, 24 weeks, and 52 weeks.
47 00:03:44.410 ⇒ 00:03:47.790 Uttam Kumaran: Yeah, Shwini, do we get, like, the actual week as, like, a date time?
48 00:03:49.490 ⇒ 00:03:57.759 Ashwini Sharma: Yeah, we do, right? Raw data has everything, like, whatever you want to create, you can create it out of that, except for those averages, TDP.
49 00:03:57.760 ⇒ 00:04:03.599 Uttam Kumaran: the week, like… The week itself, like, 52 weeks, 24 weeks, like.
50 00:04:03.950 ⇒ 00:04:05.689 Ashwini Sharma: Yeah, yeah. And the raw data…
51 00:04:05.780 ⇒ 00:04:07.479 Uttam Kumaran: Do we have, like, a date time?
52 00:04:08.120 ⇒ 00:04:20.619 Ashwini Sharma: We have an end date, right? So, for example, if you are talking about 4 weeks from end date, which is December 28th, right, you can always go back and then see what was the data like.
53 00:04:21.459 ⇒ 00:04:23.329 Uttam Kumaran: Can we just get one week every week?
54 00:04:24.140 ⇒ 00:04:27.320 Demilade Agboola: Yeah, that’s the question. Like, can we have, like, the granularity.
55 00:04:27.320 ⇒ 00:04:28.050 Ashwini Sharma: Yeah.
56 00:04:28.340 ⇒ 00:04:30.660 Demilade Agboola: That is what we have done, right?
57 00:04:31.070 ⇒ 00:04:38.730 Ashwini Sharma: That is what is there in raw data. So basically, if you look at raw data, you’ll see that, you know, there is a record for, let’s say, a week.
58 00:04:38.920 ⇒ 00:04:40.689 Ashwini Sharma: December 28th, right?
59 00:04:41.030 ⇒ 00:04:44.500 Ashwini Sharma: And then there is a… there are other records for it.
60 00:04:44.500 ⇒ 00:04:48.779 Demilade Agboola: Can’t… Can you send the raw data, please? Is there… because I… in the data.
61 00:04:48.780 ⇒ 00:04:49.690 Ashwini Sharma: It’s a dead end…
62 00:04:49.690 ⇒ 00:04:50.490 Demilade Agboola: Like that.
63 00:04:50.870 ⇒ 00:04:56.459 Ashwini Sharma: It’s there in redshift table, right? If you query it, you’ll get it. I can send you the table name.
64 00:04:57.090 ⇒ 00:05:02.180 Demilade Agboola: Alright, please do, so I can, like, look at that on this call, and we can maybe…
65 00:05:02.840 ⇒ 00:05:05.040 Demilade Agboola: Try to see that we’re on the same page.
66 00:05:05.780 ⇒ 00:05:08.490 Ashwini Sharma: Yeah, let me show you that.
67 00:05:09.350 ⇒ 00:05:12.529 Ashwini Sharma: So it’s there in the sheet also, right?
68 00:05:24.390 ⇒ 00:05:30.549 Demilade Agboola: Spins underscore commercial dot… Is the marketing inside one week?
69 00:05:31.670 ⇒ 00:05:35.900 Ashwini Sharma: So… So, yeah, yeah, this is the table name.
70 00:05:51.610 ⇒ 00:05:57.979 Uttam Kumaran: Okay, I’m gonna drop to the AI, AI team stand-up, but yeah, I guess, Demi, maybe you take a look, and then…
71 00:05:58.550 ⇒ 00:06:05.469 Uttam Kumaran: Yeah, ideally you guys can align on… on the format of the model so that you can also start on that. They’ll be happy to hear that.
72 00:06:06.450 ⇒ 00:06:07.210 Demilade Agboola: Okay, alright.
73 00:06:07.210 ⇒ 00:06:08.690 Uttam Kumaran: Okay, okay, thank you.
74 00:06:23.750 ⇒ 00:06:25.329 Ashwini Sharma: Oh, what it runs…
75 00:06:47.360 ⇒ 00:06:55.719 Ashwini Sharma: Just querying from one table is taking such a long time, right? So, you see this? You have a bunch of things over here. Geography, number of weeks, end date.
76 00:06:55.860 ⇒ 00:07:02.789 Ashwini Sharma: Product universe, right, reporting level, subcategory, and then you also have end date.
77 00:07:05.120 ⇒ 00:07:05.720 Demilade Agboola: Okay.
78 00:07:06.580 ⇒ 00:07:09.240 Ashwini Sharma: Hold on a second, I’ll just show you so that you…
79 00:07:09.930 ⇒ 00:07:13.530 Ashwini Sharma: time period or something is there. Let me just see…
80 00:07:13.890 ⇒ 00:07:16.050 Ashwini Sharma: End date, right? End date is there.
81 00:07:16.320 ⇒ 00:07:21.600 Ashwini Sharma: So maybe if I, if I just query something like this, maybe it’ll be easier for you to visualize it.
82 00:07:22.850 ⇒ 00:07:26.370 Demilade Agboola: So the end dates are… give me one second…
83 00:07:28.530 ⇒ 00:07:29.530 Demilade Agboola: Let me look at the calendar.
84 00:07:29.710 ⇒ 00:07:35.360 Ashwini Sharma: This one, right? So… Insulin.
85 00:07:36.540 ⇒ 00:07:37.310 Ashwini Sharma: Si.
86 00:07:37.680 ⇒ 00:07:39.040 Ashwini Sharma: Right over here, right?
87 00:07:40.070 ⇒ 00:07:40.849 Demilade Agboola: Can you see this?
88 00:07:40.850 ⇒ 00:07:41.290 Ashwini Sharma: End date.
89 00:07:42.080 ⇒ 00:07:58.330 Ashwini Sharma: So, end date is, see, for this record, the end date is like this, right? But then, for any other record, it could be something else. So, if I do an order by end date, right, you’ll see everything starts from, this one, 2025, 12, 28.
90 00:08:09.600 ⇒ 00:08:13.430 Ashwini Sharma: Oh, I should have given… descending. Sorry.
91 00:08:23.450 ⇒ 00:08:24.170 Ashwini Sharma: Yeah.
92 00:08:24.760 ⇒ 00:08:37.869 Ashwini Sharma: So let’s see… okay, I need to re-execute it again. But you see, right, it goes up to 2025.105, and that’s 52 weeks, minus of, like, Beta 25, 12, 28.
93 00:08:39.179 ⇒ 00:08:41.009 Demilade Agboola: Yes, but I want to see what the…
94 00:08:44.310 ⇒ 00:08:44.960 Ashwini Sharma: Yeah.
95 00:08:45.270 ⇒ 00:08:47.120 Ashwini Sharma: You can see this over here, right?
96 00:08:47.320 ⇒ 00:08:48.580 Demilade Agboola: Alright, so let me try something.
97 00:08:49.370 ⇒ 00:08:50.690 Demilade Agboola: Give me one second.
98 00:08:51.210 ⇒ 00:08:51.760 Ashwini Sharma: Yeah.
99 00:09:03.230 ⇒ 00:09:06.380 Demilade Agboola: Sorry, I’m trying to write the query. Okay, give me one second.
100 00:09:15.220 ⇒ 00:09:19.199 Demilade Agboola: Wait… Oh, okay. It’s end.
101 00:09:19.670 ⇒ 00:09:20.420 Demilade Agboola: Dudes.
102 00:09:24.590 ⇒ 00:09:26.270 Demilade Agboola: Sorry…
103 00:09:33.780 ⇒ 00:09:35.919 Demilade Agboola: Quick question. Is there…
104 00:09:37.700 ⇒ 00:09:40.739 Demilade Agboola: I might do something wrong, sorry, let me share my screen, so you can see what it…
105 00:09:47.560 ⇒ 00:09:51.069 Demilade Agboola: I can’t seem to see… Let’s say there’s not end it.
106 00:09:52.560 ⇒ 00:09:57.400 Ashwini Sharma: No, yeah, you have to enclose it in, double quotes, otherwise it won’t return that.
107 00:09:58.440 ⇒ 00:09:59.080 Demilade Agboola: Okay.
108 00:09:59.890 ⇒ 00:10:03.229 Ashwini Sharma: If there is an uppercase in the column name, you need to do that.
109 00:10:05.620 ⇒ 00:10:06.300 Demilade Agboola: Gotcha.
110 00:10:15.230 ⇒ 00:10:16.290 Demilade Agboola: Okay…
111 00:10:17.470 ⇒ 00:10:19.580 Ashwini Sharma: You put a distinct, and then just do an order.
112 00:10:19.580 ⇒ 00:10:20.250 Demilade Agboola: Yeah, yeah, yeah.
113 00:10:20.250 ⇒ 00:10:20.820 Ashwini Sharma: Nice.
114 00:10:21.790 ⇒ 00:10:24.789 Demilade Agboola: I put it this thing before, I forgot, I don’t put it this time.
115 00:10:30.890 ⇒ 00:10:36.249 Demilade Agboola: I want to be sure that they’re all going by, like, a weekly… okay, yes, they’re all doing a weekly jump.
116 00:10:37.380 ⇒ 00:10:39.830 Demilade Agboola: Okay.
117 00:10:40.080 ⇒ 00:10:47.860 Demilade Agboola: So, 28th… 12 is… a Sunday, so it’s basically every Sunday that ends as a week.
118 00:10:48.380 ⇒ 00:10:49.150 Demilade Agboola: Here.
119 00:10:55.720 ⇒ 00:10:57.969 Demilade Agboola: It’s basically every Sunday, lazy week. Okay.
120 00:10:58.140 ⇒ 00:10:58.950 Demilade Agboola: Alright.
121 00:10:59.490 ⇒ 00:11:00.890 Demilade Agboola: That… that’s fine.
122 00:11:04.620 ⇒ 00:11:06.549 Demilade Agboola: I’m trying to see…
123 00:11:08.250 ⇒ 00:11:17.139 Demilade Agboola: Alright, so I’ll start looking at the springs data and seeing how they want to aggregated. Can’t promise that I would have everything done, but I will start looking at that.
124 00:11:18.640 ⇒ 00:11:25.880 Demilade Agboola: Also, please, can you just, like, as best as possible, like, if we can start to, like, wrap up some of these, like, applications, it would be…
125 00:11:25.880 ⇒ 00:11:26.880 Ashwini Sharma: Yeah, yeah.
126 00:11:26.880 ⇒ 00:11:27.240 Demilade Agboola: tick.
127 00:11:27.240 ⇒ 00:11:29.710 Ashwini Sharma: I’ll take a little pressure off. Continue on that.
128 00:11:29.710 ⇒ 00:11:30.005 Demilade Agboola: So…
129 00:11:30.670 ⇒ 00:11:35.960 Ashwini Sharma: Yeah, I can tell Utam is feeling a lot of pressure, so… Yeah, yeah, I know. Okay.
130 00:11:36.380 ⇒ 00:11:39.690 Ashwini Sharma: I’ll just drop off this call and then start working on that.
131 00:11:40.200 ⇒ 00:11:41.319 Demilade Agboola: Alright, sounds good then.
132 00:11:42.930 ⇒ 00:11:43.770 Demilade Agboola: Take care.
133 00:11:44.250 ⇒ 00:11:45.159 Ashwini Sharma: Okay, alright.