Meeting Title: Magic Spoon Data Extraction Sync Date: 2026-01-28 Meeting participants: Uttam Kumaran, Ashwini Sharma
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1 00:00:18.080 ⇒ 00:00:18.750 Ashwini Sharma: Bump.
2 00:00:23.460 ⇒ 00:00:24.100 Uttam Kumaran: A.
3 00:00:24.680 ⇒ 00:00:25.790 Ashwini Sharma: Perfect.
4 00:00:34.120 ⇒ 00:00:39.490 Ashwini Sharma: Okay, so I was a little bit investigating, some more stuff over here.
5 00:00:41.640 ⇒ 00:00:43.410 Ashwini Sharma: Give me, give me a second.
6 00:00:43.610 ⇒ 00:00:44.250 Uttam Kumaran: No problem.
7 00:01:10.960 ⇒ 00:01:17.089 Ashwini Sharma: So the missing things, right? When I executed for a single line item, it appeared in the file.
8 00:01:17.540 ⇒ 00:01:22.849 Ashwini Sharma: But we’re at… You know, try to get data added in bulk, it didn’t, so…
9 00:01:23.040 ⇒ 00:01:28.099 Ashwini Sharma: Some more investigation would be required to figure out why that… That is happening.
10 00:01:28.510 ⇒ 00:01:29.170 Uttam Kumaran: Okay.
11 00:01:30.480 ⇒ 00:01:36.029 Ashwini Sharma: And regarding that, I’ve got the definitions of what it means.
12 00:01:37.280 ⇒ 00:01:40.459 Ashwini Sharma: I just want to, you know, apply this thing to…
13 00:01:41.360 ⇒ 00:01:46.269 Ashwini Sharma: What is this? 1, 2, 3, 4, 1, 2, 3, 4… Alright, category.
14 00:01:48.850 ⇒ 00:01:50.060 Ashwini Sharma: I’ll go to this one.
15 00:01:50.630 ⇒ 00:01:51.420 Ashwini Sharma: Alright.
16 00:01:52.990 ⇒ 00:02:01.820 Ashwini Sharma: This is… 4 weeks, 4 weeks… 4 weeks as a platform.
17 00:02:02.110 ⇒ 00:02:09.530 Ashwini Sharma: DDP… ACV, ACV is 60… ETV60.
18 00:02:10.830 ⇒ 00:02:11.590 Ashwini Sharma: Alright.
19 00:02:13.040 ⇒ 00:02:16.850 Ashwini Sharma: And average. What is the average of this one?
20 00:02:19.600 ⇒ 00:02:22.869 Ashwini Sharma: Okay, not exactly, but we are pretty close.
21 00:02:25.460 ⇒ 00:02:26.210 Uttam Kumaran: Okay.
22 00:02:27.590 ⇒ 00:02:34.240 Ashwini Sharma: The average, what I’m seeing over here, right, average is coming as 59.75, Right?
23 00:02:34.390 ⇒ 00:02:38.140 Ashwini Sharma: And in this case, average it reports as 60, right?
24 00:02:38.240 ⇒ 00:02:43.620 Ashwini Sharma: if we are going to look for a larger time frame, like, for example, 12 weeks, right? For 12 weeks.
25 00:02:43.980 ⇒ 00:02:48.520 Ashwini Sharma: ECV is reported as 59 over here from platform.
26 00:02:49.290 ⇒ 00:02:55.379 Ashwini Sharma: And if I go to 12, it’s 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12.
27 00:02:58.190 ⇒ 00:03:01.099 Ashwini Sharma: And it’s coming as 59.13.
28 00:03:05.440 ⇒ 00:03:10.290 Ashwini Sharma: But hold on a second. Average… average cannot be averaged out, right?
29 00:03:20.490 ⇒ 00:03:22.800 Ashwini Sharma: Yeah, this won’t work. This won’t work.
30 00:03:24.350 ⇒ 00:03:29.659 Ashwini Sharma: this is already an average value, right? Average… average ACV is an average value, it won’t…
31 00:03:30.370 ⇒ 00:03:31.110 Uttam Kumaran: Yeah.
32 00:03:31.110 ⇒ 00:03:44.109 Ashwini Sharma: This is a brand or item’s average distribution in a market, like a retail or channel, over a period of time. This number will represent a person presence in the market between 0% and 100%.
33 00:03:44.890 ⇒ 00:03:45.530 Uttam Kumaran: Okay.
34 00:03:46.480 ⇒ 00:03:48.910 Ashwini Sharma: Okay, what we can do over here is maybe…
35 00:03:48.910 ⇒ 00:03:52.109 Uttam Kumaran: But it’s just a… it’s just a number, right, for the time frame.
36 00:03:52.860 ⇒ 00:03:56.109 Ashwini Sharma: It’s a… it’s a number… it’s, it’s sort of… yeah.
37 00:03:56.110 ⇒ 00:03:56.880 Uttam Kumaran: It’s the one number.
38 00:03:57.780 ⇒ 00:03:58.920 Ashwini Sharma: Yes.
39 00:03:58.920 ⇒ 00:04:00.620 Uttam Kumaran: So if you go back to the sheet…
40 00:04:00.750 ⇒ 00:04:02.159 Ashwini Sharma: It’s a derived number.
41 00:04:03.250 ⇒ 00:04:07.700 Ashwini Sharma: Dude, it’s, it’s, so…
42 00:04:07.700 ⇒ 00:04:10.539 Uttam Kumaran: Did we get… we didn’t get the ACV at all, right?
43 00:04:11.210 ⇒ 00:04:15.459 Ashwini Sharma: No, we have the… we have the ACV values. For this record, we don’t have.
44 00:04:15.780 ⇒ 00:04:24.409 Ashwini Sharma: Right? For this record, we don’t have, but I have extracted this information separately in this sheet, right? So I have the ACV value for
45 00:04:24.560 ⇒ 00:04:25.439 Ashwini Sharma: What exactly does that mean?
46 00:04:25.440 ⇒ 00:04:31.009 Uttam Kumaran: This ACV is a flat number for this… for this time period.
47 00:04:31.330 ⇒ 00:04:32.730 Ashwini Sharma: For this week, yes.
48 00:04:32.730 ⇒ 00:04:35.550 Uttam Kumaran: Yeah, so there’s no, like, there’s no…
49 00:04:36.620 ⇒ 00:04:43.100 Uttam Kumaran: Yeah, like, I wouldn’t calculate… like, you can calculate an average on top of that, but, like, we’re not gonna be able to derive that, you know?
50 00:04:43.100 ⇒ 00:04:44.339 Ashwini Sharma: No, we won’t.
51 00:04:44.340 ⇒ 00:04:44.660 Uttam Kumaran: Okay.
52 00:04:44.660 ⇒ 00:04:45.760 Ashwini Sharma: Yeah.
53 00:04:46.030 ⇒ 00:04:50.310 Ashwini Sharma: I can make a suggestion on what we can do for this thing, because
54 00:04:50.420 ⇒ 00:04:56.810 Ashwini Sharma: Like, what we did was we got the record for every week, right? And then we are trying to calculate
55 00:04:56.960 ⇒ 00:04:59.770 Ashwini Sharma: Other stuff based on that.
56 00:04:59.930 ⇒ 00:05:02.189 Ashwini Sharma: But clearly, getting it for every week…
57 00:05:02.800 ⇒ 00:05:06.070 Ashwini Sharma: Not going to work, especially for things like this.
58 00:05:06.540 ⇒ 00:05:11.390 Ashwini Sharma: So what I can suggest is, like, we get it for Every week, and…
59 00:05:12.160 ⇒ 00:05:15.569 Ashwini Sharma: It’s corresponding, like, higher time frames.
60 00:05:19.820 ⇒ 00:05:21.050 Uttam Kumaran: Was that clear?
61 00:05:24.200 ⇒ 00:05:25.080 Uttam Kumaran: Okay.
62 00:05:25.710 ⇒ 00:05:27.019 Uttam Kumaran: But I guess, like…
63 00:05:28.060 ⇒ 00:05:33.879 Uttam Kumaran: I guess I just want to back up, like, I need… I want to ship something now, like, I’m running out of time.
64 00:05:34.240 ⇒ 00:05:34.570 Ashwini Sharma: Okay.
65 00:05:34.570 ⇒ 00:05:40.570 Uttam Kumaran: I need to get something out. So, are we good on definitions, and, like, where are we on overall status?
66 00:05:40.980 ⇒ 00:05:45.729 Ashwini Sharma: Yeah, so I’ve added I’ve added for all the records.
67 00:05:45.940 ⇒ 00:05:46.580 Uttam Kumaran: Okay.
68 00:05:46.800 ⇒ 00:05:50.729 Ashwini Sharma: Right, these things, let me remove the filter so that
69 00:05:56.570 ⇒ 00:05:57.500 Ashwini Sharma: Alright.
70 00:05:58.480 ⇒ 00:06:01.790 Ashwini Sharma: So, filter, remote, and you add this filter, right?
71 00:06:01.960 ⇒ 00:06:06.680 Ashwini Sharma: Okay, yeah, this, this… I think this might be ready to ship out. I’ve added
72 00:06:06.990 ⇒ 00:06:10.219 Ashwini Sharma: What is the discrepancy reason? Why the numbers differ?
73 00:06:10.390 ⇒ 00:06:14.490 Ashwini Sharma: And what needs to be done?
74 00:06:15.240 ⇒ 00:06:17.560 Ashwini Sharma: So that the numbers will not differ, right?
75 00:06:18.070 ⇒ 00:06:23.880 Ashwini Sharma: And there are 3 different categories of those differences, right? One is because
76 00:06:24.290 ⇒ 00:06:27.660 Ashwini Sharma: The timing difference between Spins API and Platform API?
77 00:06:28.030 ⇒ 00:06:32.190 Ashwini Sharma: the other one is the issue with the filters.
78 00:06:33.130 ⇒ 00:06:33.620 Ashwini Sharma: Because we.
79 00:06:33.620 ⇒ 00:06:42.810 Uttam Kumaran: So, like, maybe I can… maybe I can share, and I can walk through, and you can just narrate, so then I can… because I’ll be… I’ll try to sort of get it over to them.
80 00:06:43.060 ⇒ 00:06:44.260 Ashwini Sharma: Okay. So…
81 00:06:45.170 ⇒ 00:06:48.759 Uttam Kumaran: Here, I’m just sharing my screen.
82 00:06:49.830 ⇒ 00:06:57.370 Uttam Kumaran: So, okay, we have relatively new data from SPIN’s time difference, so when the data is subtracted from the platform.
83 00:06:57.630 ⇒ 00:07:04.040 Uttam Kumaran: Versus when data was… Okay, and then what is the formula?
84 00:07:07.380 ⇒ 00:07:09.340 Ashwini Sharma: Yeah, it just.
85 00:07:09.340 ⇒ 00:07:09.770 Uttam Kumaran: But if you don’.
86 00:07:09.770 ⇒ 00:07:11.470 Ashwini Sharma: If this is null, right, yeah.
87 00:07:11.470 ⇒ 00:07:12.360 Uttam Kumaran: Okay, okay.
88 00:07:13.570 ⇒ 00:07:14.520 Uttam Kumaran: Okay.
89 00:07:15.950 ⇒ 00:07:24.020 Uttam Kumaran: Great, so that’s all those. This could be because of the difference in timing of platform, the difference could be… should be within the thresholds. So what does this mean?
90 00:07:24.970 ⇒ 00:07:29.830 Ashwini Sharma: The difference is minimal, right? Like, 1 cent, or 10 cents, right? Yeah.
91 00:07:30.040 ⇒ 00:07:30.630 Uttam Kumaran: Okay.
92 00:07:30.850 ⇒ 00:07:32.429 Uttam Kumaran: And so these are the…
93 00:07:34.400 ⇒ 00:07:38.559 Ashwini Sharma: Yeah, if you make a change in the text over here, it will reflect in that.
94 00:07:38.720 ⇒ 00:07:43.310 Uttam Kumaran: Can I add, can I add, like, a header on this? Will it break?
95 00:07:43.310 ⇒ 00:07:45.460 Ashwini Sharma: It might break.
96 00:07:47.030 ⇒ 00:07:50.020 Ashwini Sharma: I don’t know. Oh, it didn’t, it didn’t. Yeah, yeah, yeah, go ahead.
97 00:07:50.240 ⇒ 00:07:50.870 Uttam Kumaran: Okay.
98 00:07:53.310 ⇒ 00:08:03.669 Uttam Kumaran: So… Instagram, let’s C, X… donation… Earlier information…
99 00:08:09.850 ⇒ 00:08:10.640 Uttam Kumaran: Okay.
100 00:08:15.770 ⇒ 00:08:21.290 Uttam Kumaran: filtering issue with Spins API by querying data, and it returns a corrupted file. So tell me about that one.
101 00:08:22.540 ⇒ 00:08:36.759 Ashwini Sharma: Sorry, which one? Filtering one, yeah. So, when we issued the filter to receive large amounts of data, that particular records was not included in the export, but when I’m querying only for that record, it is coming out.
102 00:08:38.600 ⇒ 00:08:46.609 Ashwini Sharma: Okay. So maybe, you know, we might have to reach out to these guys, and then ask Spins guys, and then ask, like, what exactly is going on.
103 00:08:49.550 ⇒ 00:08:54.060 Uttam Kumaran: Okay, so… Maybe what I’m gonna do here is also put, like, next steps.
104 00:08:54.620 ⇒ 00:08:59.250 Uttam Kumaran: No action. Well, I’ll just basically… we can discuss the next steps for them.
105 00:08:59.250 ⇒ 00:09:00.590 Ashwini Sharma: Yeah, yeah, yeah.
106 00:09:05.290 ⇒ 00:09:07.770 Uttam Kumaran: Platform data was pulled a week before…
107 00:09:10.180 ⇒ 00:09:15.259 Uttam Kumaran: Next steps? Potentially do a re-pull today.
108 00:09:15.940 ⇒ 00:09:21.799 Uttam Kumaran: See if it’s… see… to see if there’s a closer alignment.
109 00:09:22.790 ⇒ 00:09:24.400 Ashwini Sharma: Yeah, we can do that.
110 00:09:24.920 ⇒ 00:09:28.730 Uttam Kumaran: So, then I’m gonna put here, I’m gonna say QA dataset…
111 00:09:30.150 ⇒ 00:09:44.529 Uttam Kumaran: Cool. So, channel outlet, geo, geography, product level, category, subcat, department, blah blah blah. I changed this to have platform, so platform dollars, spins, API dollars, dollar spend. Okay.
112 00:09:44.920 ⇒ 00:09:53.590 Uttam Kumaran: One thing I’m also gonna do here is I’m gonna put, a bar,
113 00:09:53.690 ⇒ 00:09:59.870 Uttam Kumaran: Here, and I’m gonna make these… I’ll highlight in green.
114 00:10:00.230 ⇒ 00:10:02.540 Uttam Kumaran: Or maybe lighter green?
115 00:10:05.330 ⇒ 00:10:07.759 Uttam Kumaran: And then I’m gonna highlight these.
116 00:10:08.190 ⇒ 00:10:10.599 Uttam Kumaran: In a lighter orange.
117 00:10:12.450 ⇒ 00:10:13.560 Ashwini Sharma: Okay, yeah.
118 00:10:13.560 ⇒ 00:10:14.160 Uttam Kumaran: Great.
119 00:10:14.370 ⇒ 00:10:19.609 Uttam Kumaran: So, platform dollars, and then what I’m gonna do here is I’m just gonna make these all the same.
120 00:10:20.090 ⇒ 00:10:24.700 Uttam Kumaran: Size… Oh, I’m gonna go smaller.
121 00:10:30.500 ⇒ 00:10:33.770 Uttam Kumaran: So, platform dollars, spins, API dollars, dollars, diff.
122 00:10:34.970 ⇒ 00:10:36.859 Uttam Kumaran: And then what I’m gonna do here…
123 00:10:44.090 ⇒ 00:10:47.010 Uttam Kumaran: I can’t really see it, but let’s see.
124 00:10:50.070 ⇒ 00:10:53.139 Uttam Kumaran: So that way, it gives some visual exceptioning.
125 00:10:53.450 ⇒ 00:10:57.100 Uttam Kumaran: So, platform TDP spins API TDP,
126 00:10:58.320 ⇒ 00:11:03.219 Uttam Kumaran: So, this is gonna, like… I guess, what do we think about these?
127 00:11:05.170 ⇒ 00:11:11.969 Ashwini Sharma: Yeah, so these values are rounded up, like, they are already aggregated for 4 weeks, right? So it is bound to differ.
128 00:11:13.310 ⇒ 00:11:21.059 Ashwini Sharma: If you look at the platform API, it just ranges between 4 weeks at 252 weeks, right? It is not there at the week level.
129 00:11:23.040 ⇒ 00:11:31.179 Ashwini Sharma: Whereas the data that we got from spins is at the weak level. So when I calculate that 34, it is spins, line number something.
130 00:11:31.580 ⇒ 00:11:33.070 Ashwini Sharma: 2751.
131 00:11:33.310 ⇒ 00:11:41.749 Ashwini Sharma: Whatever value you are seeing in column T is an aggregated over 4 weeks period, or 12 weeks, or 24, or 52 weeks.
132 00:11:41.750 ⇒ 00:11:43.609 Uttam Kumaran: And we’re doing the aggregation?
133 00:11:43.970 ⇒ 00:11:45.769 Ashwini Sharma: We are doing the aggregation, yes.
134 00:11:45.950 ⇒ 00:11:51.890 Uttam Kumaran: But that’s, I guess, my question is, like, are we aggregating it Like.
135 00:11:51.890 ⇒ 00:11:52.420 Ashwini Sharma: it.
136 00:11:53.170 ⇒ 00:11:55.660 Uttam Kumaran: But see, that’s the thing, I don’t know whether, like…
137 00:11:55.880 ⇒ 00:11:57.909 Uttam Kumaran: I think we should do… we do an average.
138 00:12:01.130 ⇒ 00:12:06.959 Uttam Kumaran: Right? Because, like, it doesn’t make sense we should sum, because, look, if you do this, If you do…
139 00:12:09.430 ⇒ 00:12:15.349 Uttam Kumaran: It’s closer. Because this… this is the ace, this is the… this is just a number for that week.
140 00:12:16.400 ⇒ 00:12:21.570 Uttam Kumaran: And then this is the average over that aggregate for every unit, for every record in that aggregate.
141 00:12:21.730 ⇒ 00:12:25.300 Uttam Kumaran: So, sum is not… Correct here, in my opinion.
142 00:12:26.440 ⇒ 00:12:30.889 Ashwini Sharma: Right, yeah, but average will also be not be correct, right? Average will also not be correct.
143 00:12:32.470 ⇒ 00:12:35.439 Uttam Kumaran: No, average should be correct, right? Because you have 4 weeks.
144 00:12:35.600 ⇒ 00:12:40.960 Uttam Kumaran: You have 24 weeks, right? So you should take the average of the record over the 24 weeks.
145 00:12:42.460 ⇒ 00:12:45.789 Ashwini Sharma: No, I just did that calculation, it’s not coming correct.
146 00:12:46.210 ⇒ 00:12:47.500 Uttam Kumaran: What does it give you?
147 00:12:47.500 ⇒ 00:12:53.489 Ashwini Sharma: So, if you go to record number 1, can you remove that spins API dollars filter?
148 00:12:56.910 ⇒ 00:12:57.970 Ashwini Sharma: No, it’s…
149 00:13:03.110 ⇒ 00:13:05.900 Ashwini Sharma: Okay, I’m checking for, record number 1.
150 00:13:06.390 ⇒ 00:13:12.639 Ashwini Sharma: The average… for 4 weeks period, right? What is it showing over here?
151 00:13:13.070 ⇒ 00:13:16.109 Ashwini Sharma: It’s 52 weeks, right? Record number 1 is 52 weeks.
152 00:13:18.130 ⇒ 00:13:18.740 Uttam Kumaran: Yeah.
153 00:13:19.650 ⇒ 00:13:21.110 Ashwini Sharma: Don’t worry, okay.
154 00:13:24.520 ⇒ 00:13:27.510 Ashwini Sharma: 52 weeks ACV.
155 00:13:28.230 ⇒ 00:13:30.370 Ashwini Sharma: Total 53 here.
156 00:13:32.390 ⇒ 00:13:35.359 Ashwini Sharma: 52.7 is the average that I’m getting.
157 00:13:39.830 ⇒ 00:13:41.230 Uttam Kumaran: I guess, like…
158 00:13:41.940 ⇒ 00:13:46.409 Uttam Kumaran: But I’m… but I guess, like… okay, so one is, for some of these, we have no values, right?
159 00:13:46.410 ⇒ 00:13:53.719 Ashwini Sharma: Oh, yeah, no, ignore that, where there is no values, right? I have separate values that I extracted today.
160 00:13:54.180 ⇒ 00:13:55.770 Ashwini Sharma: Now, this is coming.
161 00:13:55.770 ⇒ 00:14:01.860 Uttam Kumaran: Wait, wait, wait, wait. But, like, I just, like, I need to understand… I basically need to understand everything at this point, so…
162 00:14:01.860 ⇒ 00:14:02.420 Ashwini Sharma: Yep.
163 00:14:02.420 ⇒ 00:14:06.979 Uttam Kumaran: What… why aren’t… like, why do we not have values for this at this point?
164 00:14:07.370 ⇒ 00:14:09.120 Ashwini Sharma: Issue with the filters, right?
165 00:14:09.500 ⇒ 00:14:11.999 Uttam Kumaran: Yeah, our filters are too much.
166 00:14:13.300 ⇒ 00:14:18.489 Ashwini Sharma: Yeah, when we are applying filters, it’s not returning the entire set of records.
167 00:14:21.030 ⇒ 00:14:33.700 Ashwini Sharma: when we are doing a bulk extract, it’s not returning entire set of records. But if I apply specific filters, make it, like, geography equals to New York something, and category equals to this thing, and…
168 00:14:34.240 ⇒ 00:14:35.079 Ashwini Sharma: In a bag.
169 00:14:35.080 ⇒ 00:14:40.170 Uttam Kumaran: I think, like, that’s what I want to say here. It’s… what I want to say is, like.
170 00:14:40.540 ⇒ 00:14:42.060 Uttam Kumaran: like, when…
171 00:14:42.900 ⇒ 00:14:50.919 Uttam Kumaran: when we’re querying… and what do you mean by bulk? Like, I guess I want to be very clear with, like, are we saying no filters?
172 00:14:51.040 ⇒ 00:14:54.740 Uttam Kumaran: Are we saying, like, when you actually put in three filters?
173 00:14:55.610 ⇒ 00:15:01.020 Ashwini Sharma: When filters are very generic, Right?
174 00:15:01.530 ⇒ 00:15:05.820 Ashwini Sharma: So, we applied filters for geographies.
175 00:15:05.820 ⇒ 00:15:07.369 Uttam Kumaran: But then again, if… what I’m asking
176 00:15:07.790 ⇒ 00:15:11.870 Uttam Kumaran: What I’m trying to get is, is it because of the… the amount of records?
177 00:15:12.270 ⇒ 00:15:16.360 Uttam Kumaran: Yes, that is what I… And is that, like, 10,000, or, like, what is the number cutoff?
178 00:15:16.680 ⇒ 00:15:22.130 Ashwini Sharma: A lot, a lot more, like, every, every batch returns about 100K records, right?
179 00:15:22.410 ⇒ 00:15:26.110 Uttam Kumaran: So after… so, after 100K, it’s breaking.
180 00:15:27.260 ⇒ 00:15:32.590 Ashwini Sharma: Well, I can’t really see if it is 100K that… that causes.
181 00:15:32.590 ⇒ 00:15:39.199 Uttam Kumaran: But, like, what’s, like, what is your guess? Like, what is it working for? Like, what size data sets is it working for?
182 00:15:39.200 ⇒ 00:15:46.429 Ashwini Sharma: So, when I’m querying for, like, only specific to that brand and category and all those things, it is returning me.
183 00:15:46.550 ⇒ 00:15:53.329 Ashwini Sharma: the records, right? But when the number of records returned is huge, It’s… it’s.
184 00:15:53.330 ⇒ 00:15:56.219 Uttam Kumaran: But then tell me what huge means. Like, what is huge?
185 00:15:56.980 ⇒ 00:15:58.490 Ashwini Sharma: Okay, let me…
186 00:15:58.490 ⇒ 00:16:04.829 Uttam Kumaran: That’s what I’m trying to… that’s what I’m trying to… I’m just trying to… because we… because I… I think I know what you’re… I know what you’re saying, but…
187 00:16:05.560 ⇒ 00:16:11.669 Uttam Kumaran: Just give me a sense of, like, hey, when we don’t have more than, like… anytime the dataset’s more than 100,000 rows.
188 00:16:12.270 ⇒ 00:16:14.129 Uttam Kumaran: We’re getting a corrupted file.
189 00:16:16.500 ⇒ 00:16:17.970 Ashwini Sharma: Give me a second.
190 00:16:17.970 ⇒ 00:16:18.600 Uttam Kumaran: Yeah.
191 00:16:33.940 ⇒ 00:16:36.090 Uttam Kumaran: And that’s from the API itself.
192 00:16:36.440 ⇒ 00:16:37.749 Ashwini Sharma: That’s from the API, yeah.
193 00:16:37.990 ⇒ 00:16:46.209 Uttam Kumaran: So the solution there is, like, we need to basically… Batch, ingest with heavier filters.
194 00:16:46.550 ⇒ 00:16:47.330 Ashwini Sharma: Yes.
195 00:16:47.840 ⇒ 00:16:50.159 Uttam Kumaran: And we just haven’t done that yet, basically?
196 00:16:50.160 ⇒ 00:16:51.849 Ashwini Sharma: No, we haven’t had that.
197 00:16:52.010 ⇒ 00:16:52.630 Uttam Kumaran: Okay.
198 00:16:54.080 ⇒ 00:16:57.980 Ashwini Sharma: Maybe narrow it… narrow down the result search that is returned, right?
199 00:16:57.980 ⇒ 00:17:01.080 Uttam Kumaran: But I guess, for example, like, 52 weeks…
200 00:17:01.500 ⇒ 00:17:07.449 Uttam Kumaran: So, like, what… given this, what combination of filters would work?
201 00:17:07.450 ⇒ 00:17:11.719 Ashwini Sharma: Like, so are you saying anything 52 weeks isn’t working? No, no, not.
202 00:17:11.720 ⇒ 00:17:14.400 Uttam Kumaran: There’s some 24 weeks, there’s some 12 weeks here, right?
203 00:17:14.560 ⇒ 00:17:31.050 Ashwini Sharma: Yeah, so, for example, like, I gave a filter where geography is something, right? Product level is something, category is something, right? Brand is something, and product universe is also something, right? When I apply these filters, right?
204 00:17:31.240 ⇒ 00:17:38.109 Ashwini Sharma: it narrows it down to one particular… not one, but 52 particular records, right? Because I’m asking for 52.
205 00:17:38.310 ⇒ 00:17:40.880 Ashwini Sharma: Week’s time frame, yeah.
206 00:17:41.290 ⇒ 00:17:44.560 Ashwini Sharma: So it returns me 52 records for a single product.
207 00:17:45.570 ⇒ 00:17:56.070 Ashwini Sharma: category and subcategory are sort of a, you know, it represents one product, let us say. It might not… there might be multiple products under that category, but
208 00:17:56.200 ⇒ 00:17:59.960 Ashwini Sharma: I mean, it gives the overall sale revenue.
209 00:18:00.100 ⇒ 00:18:03.869 Ashwini Sharma: For, products that fall under this category and subcategory.
210 00:18:06.050 ⇒ 00:18:06.660 Uttam Kumaran: Yeah.
211 00:18:08.130 ⇒ 00:18:09.460 Uttam Kumaran: Okay, but I mean, I.
212 00:18:09.460 ⇒ 00:18:19.779 Ashwini Sharma: So when I do that, I’m getting the records. But when I did, like, you know, geography is a bunch of places, and there is no filter for category, there’s no filter for subcategory.
213 00:18:20.050 ⇒ 00:18:29.299 Ashwini Sharma: And they’ll just filter for brand and product universe. When that happened, I didn’t get it. And, you see, I’ll just…
214 00:18:29.300 ⇒ 00:18:32.119 Uttam Kumaran: So, like… Give me an example of, like, why did Guest One work, then?
215 00:18:32.120 ⇒ 00:18:35.859 Ashwini Sharma: For example, BrandCat, there were about 17,000 records.
216 00:18:36.460 ⇒ 00:18:44.860 Ashwini Sharma: For UPC, there was a large amount. For brand, again, there was… 25K record.
217 00:18:47.330 ⇒ 00:18:49.960 Ashwini Sharma: Volume is not such huge.
218 00:18:50.900 ⇒ 00:18:52.360 Ashwini Sharma: That’s really weird.
219 00:18:52.360 ⇒ 00:18:54.280 Uttam Kumaran: But I guess, like, why did this one work?
220 00:18:54.920 ⇒ 00:18:55.600 Ashwini Sharma: Which one?
221 00:18:56.480 ⇒ 00:18:57.120 Uttam Kumaran: That’s wrong.
222 00:18:57.120 ⇒ 00:19:01.850 Ashwini Sharma: Yeah, yeah, I don’t have an answer to that. Why did it work?
223 00:19:02.770 ⇒ 00:19:03.750 Uttam Kumaran: But, like…
224 00:19:03.750 ⇒ 00:19:09.300 Ashwini Sharma: So it just didn’t show up in the… It didn’t show… it didn’t show up in the extract.
225 00:19:13.170 ⇒ 00:19:13.890 Uttam Kumaran: Okay.
226 00:19:14.050 ⇒ 00:19:18.499 Uttam Kumaran: Well… This is the… this is the majority of the spend, though.
227 00:19:18.830 ⇒ 00:19:22.319 Uttam Kumaran: Like, so, they’re not gonna… like, how do we…
228 00:19:22.790 ⇒ 00:19:29.640 Uttam Kumaran: The problem is, all the stuff for the data we did get is all, like, such a minority of spend, right? Like…
229 00:19:33.000 ⇒ 00:19:37.949 Uttam Kumaran: Is that… like, is that fair? Or, I don’t know, like, let’s… we can even calculate, like.
230 00:19:38.380 ⇒ 00:19:39.740 Uttam Kumaran: How much spend?
231 00:19:40.090 ⇒ 00:19:45.100 Uttam Kumaran: So… Looks like… There’s 2 billion here.
232 00:19:45.360 ⇒ 00:19:49.269 Uttam Kumaran: And then… I mean, it’s like, yeah, there’s overlap, of course.
233 00:19:49.270 ⇒ 00:19:51.050 Ashwini Sharma: A lot of the missing ones, yeah.
234 00:19:51.050 ⇒ 00:19:52.899 Uttam Kumaran: So this is for the ones that are there.
235 00:19:53.220 ⇒ 00:19:57.440 Ashwini Sharma: And then let’s just, like, try to get a rough sense of the missing ones, right? So…
236 00:19:57.550 ⇒ 00:19:59.239 Uttam Kumaran: Probably starts here.
237 00:20:00.130 ⇒ 00:20:01.560 Uttam Kumaran: Go all the way off…
238 00:20:06.110 ⇒ 00:20:07.960 Uttam Kumaran: Okay, I mean, it’s less.
239 00:20:08.390 ⇒ 00:20:09.380 Uttam Kumaran: For sure.
240 00:20:14.170 ⇒ 00:20:16.579 Uttam Kumaran: Alright, so that’s, like, around 300 million.
241 00:20:16.820 ⇒ 00:20:17.540 Ashwini Sharma: Yeah.
242 00:20:19.220 ⇒ 00:20:27.139 Uttam Kumaran: I guess I’m still, like, I just don’t know what I can tell them about this. Like, it’s still not clear to me, like… I guess my ask would be.
243 00:20:27.470 ⇒ 00:20:32.820 Uttam Kumaran: can you just fix it today, and, like, re-pull, or can I go re-pull, or what can we do?
244 00:20:32.820 ⇒ 00:20:35.369 Ashwini Sharma: Repul will take a lot of time.
245 00:20:35.710 ⇒ 00:20:42.029 Ashwini Sharma: The method generally is, like, even for small amounts of data, it takes a lot of time.
246 00:20:42.360 ⇒ 00:20:47.810 Ashwini Sharma: I mean, for… if I have to do a re-pull, probably it’s going to take 2 hours of running this code.
247 00:20:49.900 ⇒ 00:20:56.159 Uttam Kumaran: But I, like… but, like, what do… I just, like… I mean, like, I don’t know what to tell… I just don’t know what to tell you, because I’m not…
248 00:20:56.830 ⇒ 00:21:00.800 Uttam Kumaran: I’m not in… I don’t know what the code we wrote is to pull the data, but…
249 00:21:01.330 ⇒ 00:21:08.659 Uttam Kumaran: If we’re not able to get the 52-week or 24-week, Things for this, like…
250 00:21:09.580 ⇒ 00:21:13.759 Uttam Kumaran: I guess I’m, like, I’m not really clear on, like, what’s the next step here.
251 00:21:14.120 ⇒ 00:21:15.890 Ashwini Sharma: Okay, let’s.
252 00:21:15.890 ⇒ 00:21:19.360 Uttam Kumaran: Like, what is, what is the, what is your suggested, like, next step here?
253 00:21:19.360 ⇒ 00:21:24.600 Ashwini Sharma: I have the payload for some of these things, right? Department, magic spoon formats.
254 00:21:24.600 ⇒ 00:21:36.269 Uttam Kumaran: No, no, but I wanna… I wanna… I wanna send something out, I need to… I need to send something out right now. It’s already 2PM Eastern, like, I have to send something out right now, so, like, I can’t spend more time right now. What, like…
255 00:21:37.160 ⇒ 00:21:41.209 Uttam Kumaran: What can we say to this client about the fact that these are empty?
256 00:21:41.440 ⇒ 00:21:43.200 Uttam Kumaran: Like, So…
257 00:21:43.200 ⇒ 00:21:56.960 Ashwini Sharma: I mean, the only thing that I can say right now is let’s reduce… let’s make multiple calls to the API with reduced filters. In that case, it probably will return the data.
258 00:21:57.190 ⇒ 00:21:59.680 Uttam Kumaran: So, can you send me your code?
259 00:22:00.700 ⇒ 00:22:01.350 Ashwini Sharma: Yep.
260 00:22:01.350 ⇒ 00:22:02.989 Uttam Kumaran: For the script.
261 00:22:07.970 ⇒ 00:22:12.040 Ashwini Sharma: Let me push it as a PR, and then… should I send it to you?
262 00:22:12.730 ⇒ 00:22:18.290 Uttam Kumaran: Yeah, if you can just make a PR, anything so far that’s been done, if you just put it there, that’d be great.
263 00:23:04.760 ⇒ 00:23:06.849 Ashwini Sharma: Alright, I just dropped it in the channel.
264 00:23:19.230 ⇒ 00:23:20.509 Uttam Kumaran: You push a PR?
265 00:23:22.400 ⇒ 00:23:22.970 Ashwini Sharma: Yep.
266 00:23:23.700 ⇒ 00:23:26.720 Ashwini Sharma: I pushed a branch, not exactly a PR.
267 00:23:26.720 ⇒ 00:23:28.529 Uttam Kumaran: Can you, can you just create a PR?
268 00:23:29.940 ⇒ 00:23:33.969 Ashwini Sharma: You can pull it from the branch, right? We are… will be visible to those guys.
269 00:23:34.180 ⇒ 00:23:35.990 Uttam Kumaran: Yeah, I want it to be visible.
270 00:23:36.360 ⇒ 00:23:36.940 Ashwini Sharma: Oh, God.
271 00:23:36.940 ⇒ 00:23:44.339 Uttam Kumaran: Like, we’re… dude, we’re… they’re… they’re about to tell us on Friday that they don’t want us to work with them longer.
272 00:23:44.340 ⇒ 00:23:45.000 Ashwini Sharma: Okay.
273 00:23:45.000 ⇒ 00:23:53.910 Uttam Kumaran: So, it’s very serious that we, like, wrap this up. So, I’m really running out of, like, patience, because I don’t know how much else to say this, that, like.
274 00:23:54.050 ⇒ 00:23:58.949 Uttam Kumaran: The fact that we weren’t able to get them an answer on, like, the status here is really costing us.
275 00:23:59.060 ⇒ 00:24:04.309 Uttam Kumaran: So, we really need to just, like, get this done. So push everything you have.
276 00:24:04.470 ⇒ 00:24:08.419 Uttam Kumaran: So then I can take a look, I can try to come up with a strategy to communicate with them.
277 00:24:24.050 ⇒ 00:24:26.659 Uttam Kumaran: I’m not… I don’t even think I’m in there…
278 00:24:30.640 ⇒ 00:24:31.580 Uttam Kumaran: GitHub.
279 00:24:32.100 ⇒ 00:24:34.809 Uttam Kumaran: Oh, like, I have to log in with the thing?
280 00:24:34.810 ⇒ 00:24:37.660 Ashwini Sharma: With a different idea in this list account.
281 00:24:38.350 ⇒ 00:24:39.330 Uttam Kumaran: Okay, okay.
282 00:24:43.450 ⇒ 00:24:44.750 Uttam Kumaran: Okay, okay, I got it.
283 00:24:48.870 ⇒ 00:24:51.939 Uttam Kumaran: So this is on… into a Magic Spoon Prefect.
284 00:24:53.020 ⇒ 00:24:53.630 Ashwini Sharma: Yes.
285 00:24:53.810 ⇒ 00:24:54.350 Uttam Kumaran: Okay.
286 00:24:54.770 ⇒ 00:24:55.910 Uttam Kumaran: Cool, yeah.
287 00:25:01.320 ⇒ 00:25:03.480 Ashwini Sharma: Alright, needed a PR.
288 00:25:03.860 ⇒ 00:25:05.570 Uttam Kumaran: Yeah, I see the PR now, so…
289 00:25:05.570 ⇒ 00:25:06.180 Ashwini Sharma: Okay.
290 00:25:07.870 ⇒ 00:25:09.890 Uttam Kumaran: Okay, cool, so let’s just take a look.
291 00:25:17.210 ⇒ 00:25:23.869 Uttam Kumaran: Okay, so this… This is the core process.
292 00:25:24.650 ⇒ 00:25:26.420 Ashwini Sharma: Hold on a second,
293 00:25:26.880 ⇒ 00:25:38.399 Ashwini Sharma: Spins, this, Spins is, is the client that interacts with the API to send a request and receive the response. This does not have much code within it, you can skip this one.
294 00:25:38.930 ⇒ 00:25:40.020 Uttam Kumaran: Okay.
295 00:25:40.020 ⇒ 00:25:46.669 Ashwini Sharma: You can go directly to X… go to this one, marketinsights.py.
296 00:25:48.750 ⇒ 00:25:52.740 Ashwini Sharma: Yeah, so this is the flow, right? This is where the code starts.
297 00:25:53.590 ⇒ 00:25:58.130 Ashwini Sharma: When, when the pipeline runs, It comes down, and then…
298 00:25:58.300 ⇒ 00:26:06.500 Ashwini Sharma: it checks whether it’s an incremental extract or a full extract. Normally, right now, we are just doing full extract, so I’ve commented out some of the code.
299 00:26:06.730 ⇒ 00:26:07.130 Uttam Kumaran: Okay.
300 00:26:11.680 ⇒ 00:26:15.450 Uttam Kumaran: Okay, connect to Redshift, go through… okay, it’s…
301 00:26:16.310 ⇒ 00:26:23.749 Ashwini Sharma: Right, so basically what it does is it sends a bunch of requests, it sends out a request, receives a bunch of URLs, right?
302 00:26:23.950 ⇒ 00:26:28.640 Ashwini Sharma: when it produces an output, right, this export API,
303 00:26:28.930 ⇒ 00:26:42.210 Ashwini Sharma: it gives you an identifier, and using that identifier, you’ll have to check the status of that identifier. And when the status is done, it will present you with lots of different URLs for a single extract.
304 00:26:42.610 ⇒ 00:26:49.639 Ashwini Sharma: And then you loop through those URLs, you read out of them those URLs, one at a time, and then you load it into Redshift.
305 00:26:49.640 ⇒ 00:26:51.399 Uttam Kumaran: And then where are the filters?
306 00:26:52.110 ⇒ 00:26:55.160 Ashwini Sharma: Oh, it will be there in extract.py.
307 00:26:59.090 ⇒ 00:26:59.850 Uttam Kumaran: Okay.
308 00:27:00.260 ⇒ 00:27:02.779 Ashwini Sharma: Okay, so if you go here,
309 00:27:04.060 ⇒ 00:27:07.200 Ashwini Sharma: I’m building the filters somewhere down,
310 00:27:07.960 ⇒ 00:27:10.859 Ashwini Sharma: Let me… let me show it over here.
311 00:27:11.860 ⇒ 00:27:13.269 Ashwini Sharma: Hold on a second…
312 00:27:15.130 ⇒ 00:27:17.759 Uttam Kumaran: Okay, so these are the date time frames.
313 00:27:18.200 ⇒ 00:27:21.169 Uttam Kumaran: And then, get items from file.
314 00:27:21.420 ⇒ 00:27:25.600 Uttam Kumaran: Okay, build filters with the reporting level, okay.
315 00:27:35.970 ⇒ 00:27:39.949 Ashwini Sharma: So, yeah, in line number 193 to 198,
316 00:27:44.060 ⇒ 00:28:00.859 Ashwini Sharma: Yeah, this is where I’m building the filters, right? So Product Universe is there in three… there are three records for product universe. I mean, three array elements, right? In geographies, there is something called geographyMap.csv. Okay. So it will read a bunch of geographies, and then
317 00:28:01.150 ⇒ 00:28:04.809 Ashwini Sharma: put it in an array, right? Reporting level filter, that’s…
318 00:28:05.220 ⇒ 00:28:12.949 Ashwini Sharma: that’s, sort of an input parameter that I received from, somewhere else, right? I just put it into this one, and then…
319 00:28:13.120 ⇒ 00:28:17.399 Ashwini Sharma: pull the records for it, and brand is just Magic Spoon, this is hard-coded for now.
320 00:28:17.400 ⇒ 00:28:20.290 Uttam Kumaran: Okay. Because we just wanted to narrow it down, right?
321 00:28:20.590 ⇒ 00:28:25.340 Uttam Kumaran: And then this, this, this reporting level list, is that all coming from.
322 00:28:25.340 ⇒ 00:28:29.670 Ashwini Sharma: No, reporting level comes from, higher level up, marketing insights.
323 00:28:30.360 ⇒ 00:28:34.540 Uttam Kumaran: But then what are… what are these? This is building the geographies list.
324 00:28:34.540 ⇒ 00:28:39.559 Ashwini Sharma: geographies list, yes. This is sort of hard-coded in a CSV value.
325 00:28:40.100 ⇒ 00:28:41.320 Ashwini Sharma: Geography map…
326 00:28:41.320 ⇒ 00:28:48.290 Uttam Kumaran: And then when you hit… so then when you hit the endpoint, you’re giving it all of these filters.
327 00:28:48.660 ⇒ 00:28:49.180 Ashwini Sharma: Yes.
328 00:28:49.180 ⇒ 00:28:56.050 Uttam Kumaran: And then you’re basically… so then are you doing… are you running through with… you’re running through with every combination of filter?
329 00:28:56.170 ⇒ 00:28:56.980 Uttam Kumaran: Or you’re running.
330 00:28:56.980 ⇒ 00:28:57.330 Ashwini Sharma: I agree.
331 00:28:58.550 ⇒ 00:29:04.180 Ashwini Sharma: Not every combination. This reporting level can only be one at a time, right?
332 00:29:04.180 ⇒ 00:29:07.180 Uttam Kumaran: Yeah, so reporting level and brand is one.
333 00:29:07.460 ⇒ 00:29:08.859 Ashwini Sharma: Yes, and…
334 00:29:08.860 ⇒ 00:29:13.000 Uttam Kumaran: So then with… so these… it’s these two, and then these two crossed.
335 00:29:13.700 ⇒ 00:29:22.269 Ashwini Sharma: No, so, it’s reporting level across the rest of them.
336 00:29:23.190 ⇒ 00:29:27.420 Ashwini Sharma: Oh, okay. So, for each reporting level, we’ll have.
337 00:29:28.000 ⇒ 00:29:32.450 Uttam Kumaran: Every product universe crossed with every geography. I mean, why not…
338 00:29:33.030 ⇒ 00:29:35.519 Uttam Kumaran: Yeah, why not doing the subcategories right now?
339 00:29:37.030 ⇒ 00:29:40.029 Ashwini Sharma: No, we didn’t want to filter it by subcategory, right?
340 00:29:41.920 ⇒ 00:29:46.069 Ashwini Sharma: Filter will always reduce the data volume that we are getting, right?
341 00:29:47.480 ⇒ 00:29:54.840 Uttam Kumaran: So, if I was to look at our Google Sheet again, what we’re saying, essentially, is that
342 00:29:54.960 ⇒ 00:30:03.569 Uttam Kumaran: When we call… like, when we’re… if we’re looking at this row, when we called the API for…
343 00:30:05.130 ⇒ 00:30:09.409 Uttam Kumaran: So you’re… but you’re… you don’t select the… you’re not filtering on any channels, you’re taking in all channels?
344 00:30:09.930 ⇒ 00:30:13.789 Ashwini Sharma: Yeah, it’s not filtered on channels, it’s only for geography.
345 00:30:14.120 ⇒ 00:30:17.570 Uttam Kumaran: Okay, and then reporting level is time period?
346 00:30:17.940 ⇒ 00:30:19.930 Ashwini Sharma: No, reporting level is product level.
347 00:30:20.960 ⇒ 00:30:22.510 Uttam Kumaran: Oh, okay, okay.
348 00:30:23.000 ⇒ 00:30:28.550 Uttam Kumaran: So… When you’re… so you’re… you’re doing product level…
349 00:30:29.790 ⇒ 00:30:33.640 Ashwini Sharma: I’ll show you a payload that goes out.
350 00:30:33.640 ⇒ 00:30:37.499 Uttam Kumaran: So you’re doing product level, geography level.
351 00:30:39.090 ⇒ 00:30:44.250 Uttam Kumaran: So you’re doing product level, geography, the universe…
352 00:30:47.320 ⇒ 00:30:53.260 Uttam Kumaran: Okay… And then, you’re gonna get a report with…
353 00:30:53.940 ⇒ 00:30:57.270 Uttam Kumaran: Time period, and all of these metrics.
354 00:30:57.490 ⇒ 00:31:00.240 Ashwini Sharma: Give me one second, I’ll just share the picture.
355 00:31:00.240 ⇒ 00:31:00.850 Uttam Kumaran: Okay, I understand.
356 00:31:00.850 ⇒ 00:31:02.260 Ashwini Sharma: It will become super clear, yeah.
357 00:31:02.260 ⇒ 00:31:03.279 Uttam Kumaran: Okay, okay, okay.
358 00:31:03.280 ⇒ 00:31:04.250 Ashwini Sharma: This is the summer.
359 00:31:15.470 ⇒ 00:31:17.650 Ashwini Sharma: I’ll just drop it in Slack directly to.
360 00:31:17.650 ⇒ 00:31:18.330 Uttam Kumaran: Okay.
361 00:31:26.380 ⇒ 00:31:30.480 Ashwini Sharma: Oh man, this is… Not of too much, too long message.
362 00:31:31.580 ⇒ 00:31:33.199 Uttam Kumaran: No, just send me the file.
363 00:31:35.200 ⇒ 00:31:36.700 Ashwini Sharma: Payload file, okay.
364 00:31:36.900 ⇒ 00:31:37.640 Uttam Kumaran: Yeah.
365 00:31:37.640 ⇒ 00:31:39.030 Ashwini Sharma: Don’t it?
366 00:32:38.320 ⇒ 00:32:39.760 Uttam Kumaran: Okay, he sent it in.
367 00:32:39.760 ⇒ 00:32:41.849 Ashwini Sharma: Once again, I’m sending you.
368 00:32:43.310 ⇒ 00:32:45.309 Uttam Kumaran: Or you can just throw it into Google Drive.
369 00:32:47.050 ⇒ 00:32:53.150 Ashwini Sharma: Hold on a second, work documents… Oh, God.
370 00:32:53.670 ⇒ 00:32:57.500 Ashwini Sharma: It’s CDA… Yeah.
371 00:33:00.970 ⇒ 00:33:03.229 Ashwini Sharma: Okay, it’s, it’s in the Slack.
372 00:33:16.800 ⇒ 00:33:23.589 Ashwini Sharma: So, if you go towards the… oh, okay, see it in full, yeah. Do a see it in full, and then…
373 00:33:43.050 ⇒ 00:33:45.769 Uttam Kumaran: Wait, but what… oh, this is like a tax payload.
374 00:33:46.570 ⇒ 00:33:47.510 Ashwini Sharma: Yeah, yeah.
375 00:33:56.920 ⇒ 00:33:59.030 Uttam Kumaran: Okay, man, it’s hard, I, I mean, it’s…
376 00:33:59.820 ⇒ 00:34:01.850 Uttam Kumaran: Tough for me to see, like, it’s not.
377 00:34:01.850 ⇒ 00:34:10.479 Ashwini Sharma: Yeah, if you… can you open it and share it? I’ll explain, yeah. No, no, just open and share it, I’ll explain what exactly is where.
378 00:34:10.480 ⇒ 00:34:11.050 Uttam Kumaran: Okay.
379 00:34:39.080 ⇒ 00:34:43.780 Ashwini Sharma: Alright, now, if you see here, this, variables, right?
380 00:34:43.940 ⇒ 00:34:47.190 Ashwini Sharma: Line number 3, somewhere in the middle.
381 00:34:50.860 ⇒ 00:34:53.279 Ashwini Sharma: Wait, let me annotate.
382 00:34:54.320 ⇒ 00:34:55.660 Ashwini Sharma: Where is the annotate?
383 00:35:00.090 ⇒ 00:35:01.430 Ashwini Sharma: How do you annotate?
384 00:35:04.680 ⇒ 00:35:09.770 Uttam Kumaran: I think you just have to click… the drawing?
385 00:35:19.380 ⇒ 00:35:20.880 Ashwini Sharma: No, I don’t have that.
386 00:35:24.580 ⇒ 00:35:29.100 Uttam Kumaran: Okay, well, okay, I just, like, we just need to keep moving, dude, like…
387 00:35:29.100 ⇒ 00:35:35.660 Ashwini Sharma: Yeah, yeah, yeah, okay. Yeah, line number… line number 3, right? Line number three, if you see in that file, there is something called variables.
388 00:35:36.480 ⇒ 00:35:38.060 Uttam Kumaran: Yeah.
389 00:35:39.580 ⇒ 00:35:48.689 Ashwini Sharma: Third line, third, third line, yeah, yeah, yeah. You see there, right? This is where you’re specifying the end time frame. End date is 2025-128.
390 00:35:49.000 ⇒ 00:35:58.729 Ashwini Sharma: Right? Length is 1 week, and then you scroll the slide, number of time frames, 52. This is where you’re saying that this is… this is the time frame for which I need the data, right?
391 00:35:58.730 ⇒ 00:35:59.100 Uttam Kumaran: Okay.
392 00:35:59.100 ⇒ 00:36:04.739 Ashwini Sharma: And then the next is select, right? This is selecting the attributes and measures.
393 00:36:04.990 ⇒ 00:36:15.899 Ashwini Sharma: Okay? So, there’s a bunch of different attributes and measures, so TDP, ACV are part of this thing, right? And if you come down now, where the cap starts.
394 00:36:18.090 ⇒ 00:36:23.190 Ashwini Sharma: Come down, you’ll see a bunch of text in all caps.
395 00:36:28.280 ⇒ 00:36:35.290 Ashwini Sharma: Scroll down, scroll down, scroll down, 15, 20 lines, scroll down, scroll down, further, further, further, further, further, further, further, further, further.
396 00:36:35.290 ⇒ 00:36:35.849 Uttam Kumaran: Oh, yeah.
397 00:36:35.850 ⇒ 00:36:42.120 Ashwini Sharma: Yeah, yeah, yeah, you see, right? Now, if you see, go a little up, little up before, this thing.
398 00:36:42.630 ⇒ 00:36:52.180 Ashwini Sharma: Hold on a second, where is name? Is this thing, this thing, this thing? Temporary, where? If you see where, right, this is,
399 00:36:52.980 ⇒ 00:36:54.439 Ashwini Sharma: Just search for where?
400 00:36:57.670 ⇒ 00:37:00.610 Ashwini Sharma: Yeah, right over there. That’s where the filter starts.
401 00:37:01.490 ⇒ 00:37:04.309 Ashwini Sharma: No, no, go next, next, next, next.
402 00:37:04.930 ⇒ 00:37:05.730 Uttam Kumaran: Yeah, here.
403 00:37:05.860 ⇒ 00:37:08.410 Ashwini Sharma: Yeah, that’s where the filter starts, right?
404 00:37:08.740 ⇒ 00:37:11.630 Ashwini Sharma: So product universe is one of the filters.
405 00:37:11.860 ⇒ 00:37:20.630 Ashwini Sharma: And priority universe should be in TPL, HWA, and PI. Name. Again, geography is another filter, and geography is all these gaps characters.
406 00:37:29.630 ⇒ 00:37:33.740 Uttam Kumaran: Okay, well, but, like, I still am not, like, understanding…
407 00:37:34.170 ⇒ 00:37:38.610 Uttam Kumaran: How this… what this has anything to do with why we couldn’t get this in the spreadsheet.
408 00:37:39.200 ⇒ 00:37:42.960 Uttam Kumaran: Like… So, what, like, are you getting an error?
409 00:37:43.460 ⇒ 00:37:49.489 Ashwini Sharma: No, I’m not getting an error. I get a bunch of data, right? And I load it, and that particular record is not there.
410 00:37:50.340 ⇒ 00:37:53.369 Uttam Kumaran: And it’s… okay. And so…
411 00:37:54.450 ⇒ 00:38:02.800 Uttam Kumaran: And what we’re gonna say is that we need to work with the Spins team to understand why this isn’t coming in.
412 00:38:02.970 ⇒ 00:38:04.040 Ashwini Sharma: Right, yeah.
413 00:38:04.370 ⇒ 00:38:10.540 Ashwini Sharma: Because I have seen some records where the geography is also there in the filter, right? The brand matches.
414 00:38:10.750 ⇒ 00:38:13.020 Ashwini Sharma: The product universe matches.
415 00:38:13.360 ⇒ 00:38:17.399 Ashwini Sharma: So what else is there? Reporting level matches.
416 00:38:18.530 ⇒ 00:38:24.990 Ashwini Sharma: But the record itself is not there. And that is what I checked over here, right? The very first record, New York something.
417 00:38:25.420 ⇒ 00:38:25.810 Uttam Kumaran: Yeah.
418 00:38:25.810 ⇒ 00:38:26.890 Ashwini Sharma: Exist over here.
419 00:38:27.080 ⇒ 00:38:30.520 Ashwini Sharma: Filter is there, the brand is there, everything else is there.
420 00:38:30.980 ⇒ 00:38:31.330 Uttam Kumaran: Okay.
421 00:38:31.330 ⇒ 00:38:34.239 Ashwini Sharma: This is the… this is the actual request that I’ve sent out.
422 00:38:42.940 ⇒ 00:38:51.530 Uttam Kumaran: Okay, so… what I’m gonna tell them is that it’s actually not… the discrepancy you mentioned is not correct.
423 00:38:52.100 ⇒ 00:38:57.660 Uttam Kumaran: Right? Like… This is… so this is not correct. Instead, it’s like…
424 00:38:58.880 ⇒ 00:39:08.089 Uttam Kumaran: We are not seeing these records come in from SPIN’s API payload.
425 00:39:08.240 ⇒ 00:39:25.400 Uttam Kumaran: We need to meet… with the… Spins, support, Dean… To have them… QA, our… Our query to understand
426 00:39:26.640 ⇒ 00:39:28.720 Uttam Kumaran: Where we’re missing, right?
427 00:39:28.720 ⇒ 00:39:30.090 Ashwini Sharma: Yeah, yeah, we could do that.
428 00:39:30.450 ⇒ 00:39:33.190 Uttam Kumaran: So that’s what I want to know. That’s all I care about.
429 00:39:33.390 ⇒ 00:39:37.349 Uttam Kumaran: That’s all I care about.
430 00:39:39.490 ⇒ 00:39:45.670 Uttam Kumaran: And then, what did we say about this, like, corrupted file? Like… So…
431 00:39:46.050 ⇒ 00:39:48.629 Uttam Kumaran: Do we want to say anything about, like, what we tried?
432 00:39:49.870 ⇒ 00:39:56.810 Ashwini Sharma: Yeah, so when we are narrowing down this filter, right, basically adding more filters, or restricting the
433 00:39:57.500 ⇒ 00:40:09.249 Ashwini Sharma: the volume of data that is produced by this request. Then we are getting these records. But when the filter is very generic and it produces lots of records, it’s just not there.
434 00:40:09.730 ⇒ 00:40:16.119 Ashwini Sharma: So, yeah, we have tried narrowing down the filter. Records come when it is very specific.
435 00:40:20.080 ⇒ 00:40:25.120 Ashwini Sharma: But other than that, I don’t have… I don’t know.
436 00:40:25.360 ⇒ 00:40:27.180 Ashwini Sharma: What we can put over there.
437 00:41:39.450 ⇒ 00:41:45.140 Uttam Kumaran: Okay, so, I mean, I guess I’m just gonna leave this blank. We… Come on.
438 00:41:47.120 ⇒ 00:41:50.459 Uttam Kumaran: Like, I’m just not gonna say anything on further information.
439 00:42:15.960 ⇒ 00:42:20.160 Uttam Kumaran: Okay, so, can we also finish up, like, these definitions?
440 00:42:20.600 ⇒ 00:42:23.360 Uttam Kumaran: So, can we just add definitions for these?
441 00:42:26.350 ⇒ 00:42:27.990 Ashwini Sharma: Are you shooting something?
442 00:42:28.140 ⇒ 00:42:30.500 Ashwini Sharma: I’m unable to see the payload.
443 00:42:31.080 ⇒ 00:42:37.239 Uttam Kumaran: Yeah, like… Didn’t we add the definitions for these?
444 00:42:38.250 ⇒ 00:42:42.209 Ashwini Sharma: For the remaining items, right? Department,
445 00:42:43.170 ⇒ 00:42:46.239 Ashwini Sharma: Yeah, it’s not given anywhere, but I can just…
446 00:42:46.240 ⇒ 00:42:49.819 Uttam Kumaran: Oh, yeah, he just writes, just write, yeah, just write literally anything.
447 00:42:50.330 ⇒ 00:42:50.860 Ashwini Sharma: Gosh.
448 00:44:43.110 ⇒ 00:44:43.760 Uttam Kumaran: Okay.
449 00:44:44.730 ⇒ 00:44:46.899 Uttam Kumaran: So I’m gonna… I’m just gonna shoot this…
450 00:44:51.040 ⇒ 00:44:51.680 Ashwini Sharma: Okay.
451 00:44:51.860 ⇒ 00:44:57.180 Uttam Kumaran: I’m just gonna shoot this over, I’m gonna just put in our discrepancies, and I’m gonna just see if they can just hop on a call.
452 00:44:57.970 ⇒ 00:44:58.620 Ashwini Sharma: Sure.
453 00:44:58.850 ⇒ 00:45:02.359 Uttam Kumaran: Okay. Okay, cool. Alright, I’ll talk to you in Slack.
454 00:45:02.800 ⇒ 00:45:03.529 Ashwini Sharma: Alright, yeah.
455 00:45:03.530 ⇒ 00:45:04.680 Uttam Kumaran: Okay, thanks, Sue.