Meeting Title: CTA Identity Stitching and Data Load Date: 2026-03-11 Meeting participants: Awaish Kumar, Ashwini Sharma
WEBVTT
1 00:00:04.750 ⇒ 00:00:05.570 Awaish Kumar: Finally.
2 00:00:19.620 ⇒ 00:00:20.930 Awaish Kumar: Oh, my truck.
3 00:00:20.930 ⇒ 00:00:22.250 Ashwini Sharma: Hey, hey, Wish.
4 00:00:22.670 ⇒ 00:00:23.620 Awaish Kumar: How’s it doing?
5 00:00:24.050 ⇒ 00:00:24.900 Ashwini Sharma: I’m okay.
6 00:00:25.400 ⇒ 00:00:25.929 Ashwini Sharma: How are you?
7 00:00:28.430 ⇒ 00:00:31.409 Awaish Kumar: So, how it has been going.
8 00:00:31.980 ⇒ 00:00:32.750 Awaish Kumar: With the…
9 00:00:32.750 ⇒ 00:00:36.220 Ashwini Sharma: Going good. Yesterday, I didn’t work,
10 00:00:36.680 ⇒ 00:00:40.460 Ashwini Sharma: Monday, the data was uploaded for this one, Spence.
11 00:00:41.510 ⇒ 00:00:42.230 Awaish Kumar: Hmm.
12 00:00:42.250 ⇒ 00:00:48.409 Ashwini Sharma: Yeah, so now Michael will do an initial set of QA on that, and then we can
13 00:00:48.910 ⇒ 00:00:52.080 Ashwini Sharma: He will comment whether he needs something extra, or…
14 00:00:52.260 ⇒ 00:00:55.760 Ashwini Sharma: whether what he saw in the QA was sufficient, right?
15 00:00:57.680 ⇒ 00:01:03.760 Awaish Kumar: Yeah, but, like, I have… like, few reservations. I already…
16 00:01:04.099 ⇒ 00:01:12.540 Awaish Kumar: Mike talked to you once on, like, the… The… perfect pipeline.
17 00:01:12.730 ⇒ 00:01:14.109 Ashwini Sharma: And.
18 00:01:14.620 ⇒ 00:01:18.050 Awaish Kumar: Like, although I know that you have been doing
19 00:01:18.690 ⇒ 00:01:21.949 Awaish Kumar: Some manual work, and then the deployment.
20 00:01:22.170 ⇒ 00:01:25.850 Awaish Kumar: But when you’ve made a change, we can run it for one.
21 00:01:26.560 ⇒ 00:01:28.000 Awaish Kumar: a big, or one…
22 00:01:29.180 ⇒ 00:01:34.849 Awaish Kumar: So, just to test it out, if it works, it works, right? Then we should just deploy it.
23 00:01:37.070 ⇒ 00:01:39.190 Ashwini Sharma: Yeah, I can deploy it.
24 00:01:39.370 ⇒ 00:01:46.519 Ashwini Sharma: on any day. The thing is, once it is deployed, it will start working, right? And then I will not be able to do this manual work after that.
25 00:01:47.120 ⇒ 00:01:48.280 Ashwini Sharma: So…
26 00:01:48.980 ⇒ 00:01:50.040 Awaish Kumar: What do you mean?
27 00:01:50.040 ⇒ 00:01:56.230 Ashwini Sharma: Yeah. Like, see, after… once it deploys, right, it has to go in an incremental mode.
28 00:01:56.820 ⇒ 00:01:57.880 Awaish Kumar: Yeah, okay.
29 00:01:57.880 ⇒ 00:02:02.830 Ashwini Sharma: Yeah, so if I have to do, again, manual work, then it doesn’t make any sense, right?
30 00:02:05.160 ⇒ 00:02:09.430 Awaish Kumar: No, but it… it can just go in incremental mode.
31 00:02:09.979 ⇒ 00:02:12.269 Awaish Kumar: And run for future deeds.
32 00:02:12.740 ⇒ 00:02:16.109 Awaish Kumar: And while… You are still working on…
33 00:02:16.300 ⇒ 00:02:19.540 Awaish Kumar: The past dates, and you can have a config which
34 00:02:20.010 ⇒ 00:02:25.860 Awaish Kumar: Which… which gets an input from, for example, Because somewhere, primal fire.
35 00:02:25.860 ⇒ 00:02:34.999 Ashwini Sharma: No, it goes back up to 26 weeks, right? So, when you run an incremental, it goes back to 26 weeks, and then,
36 00:02:35.550 ⇒ 00:02:46.400 Ashwini Sharma: like, the next incremental is one month of data, right? And then the overlapping incremental is back up to 26 weeks, right? That is how it works.
37 00:02:47.170 ⇒ 00:02:49.200 Awaish Kumar: No, for example, it runs today.
38 00:02:50.040 ⇒ 00:02:54.010 Awaish Kumar: it goes back 26 weeks. If it runs tomorrow, what happens?
39 00:02:54.440 ⇒ 00:02:55.459 Ashwini Sharma: It will not run.
40 00:02:57.010 ⇒ 00:02:58.309 Awaish Kumar: It will not run tomorrow.
41 00:02:58.310 ⇒ 00:02:59.339 Ashwini Sharma: Yeah, no.
42 00:02:59.730 ⇒ 00:03:01.239 Ashwini Sharma: It runs once a month.
43 00:03:01.540 ⇒ 00:03:04.239 Ashwini Sharma: Like, it will just check whether it has to run or not.
44 00:03:04.390 ⇒ 00:03:08.660 Ashwini Sharma: And it will find out the data has already been loaded, it will not run.
45 00:03:09.990 ⇒ 00:03:18.580 Awaish Kumar: Yeah, but that you know, right? Even if I put my… push my pipeline out, I know it needs to start from, let’s say, next month.
46 00:03:18.750 ⇒ 00:03:22.609 Awaish Kumar: So I can deploy it and see it done to the task.
47 00:03:22.780 ⇒ 00:03:23.720 Awaish Kumar: Kind.
48 00:03:25.140 ⇒ 00:03:28.960 Ashwini Sharma: Okay, so you just want me to, you know, make it as done, and then finish it off?
49 00:03:30.560 ⇒ 00:03:37.059 Awaish Kumar: We should test it. If the pipeline is working, we should just deploy it and mark it as done. That’s the…
50 00:03:37.060 ⇒ 00:03:43.419 Ashwini Sharma: The pipeline is working, okay, okay. Yeah, I’ll get it done tomorrow. Today, I’ll be working on CTA identity thing.
51 00:03:45.190 ⇒ 00:03:46.860 Awaish Kumar: That’s all.
52 00:03:46.860 ⇒ 00:03:48.180 Ashwini Sharma: Yep.
53 00:03:48.180 ⇒ 00:03:52.029 Awaish Kumar: Like, we don’t need to… there are two, three things. Number one.
54 00:03:52.670 ⇒ 00:03:58.499 Awaish Kumar: I know that you are working on something, and someone comes in and changes the requirements.
55 00:03:58.760 ⇒ 00:04:01.689 Awaish Kumar: Right? And… or maybe ask for something.
56 00:04:01.830 ⇒ 00:04:10.530 Awaish Kumar: Then it’s your responsibility to make it clear. One way to make it clear is, Close that ticket.
57 00:04:10.900 ⇒ 00:04:16.970 Awaish Kumar: If the scope of that ticket is complete, just close this, and so, like, let everybody know that.
58 00:04:17.440 ⇒ 00:04:26.069 Awaish Kumar: the scope of this ticket was this, this, this, and it is done. Now I’m getting new requirements from them, and it will… I need to create a new ticket for that.
59 00:04:26.830 ⇒ 00:04:28.640 Awaish Kumar: Because it will take some extra time.
60 00:04:29.070 ⇒ 00:04:31.549 Awaish Kumar: So that way, it’s visible to everyone.
61 00:04:31.900 ⇒ 00:04:37.239 Awaish Kumar: That, what you are… doing. If it just one ticket is there.
62 00:04:37.390 ⇒ 00:04:42.770 Awaish Kumar: And you keep saying something, like, then it’s… everybody thinks that you’re stupid.
63 00:04:44.260 ⇒ 00:04:49.719 Awaish Kumar: The thing’s some, like, it’s not being closed within a month or something. So we need to…
64 00:04:50.890 ⇒ 00:04:53.960 Awaish Kumar: Define the scope of a ticket clearly, and close it.
65 00:04:54.140 ⇒ 00:04:55.209 Awaish Kumar: When did you stop?
66 00:04:56.010 ⇒ 00:05:08.480 Ashwini Sharma: Yeah, because, see, the last load for QA that we did was only for 1 year, okay? They would need data for 3 years, right? So again, I’ll have to… yeah, again, I’ll be involved in this manual load again, right?
67 00:05:08.480 ⇒ 00:05:11.099 Awaish Kumar: It was a new request, that’s all I’m saying.
68 00:05:11.770 ⇒ 00:05:13.659 Ashwini Sharma: Yeah, it was a new request, right?
69 00:05:13.660 ⇒ 00:05:23.900 Awaish Kumar: That’s… that’s all I’m saying, that when it’s a new request, make it clear to everybody that you are not… when everybody hears, like, Space API, Space API, and…
70 00:05:24.100 ⇒ 00:05:26.880 Awaish Kumar: Everyone thinks that you are working on the same thing.
71 00:05:28.350 ⇒ 00:05:31.970 Awaish Kumar: Right? And we have to just make it clear that it’s not…
72 00:05:32.830 ⇒ 00:05:44.090 Awaish Kumar: if you are doing something, if they are asking for you to do something more, do QA, write the ticket that way. I’m doing QA for this, and that, and…
73 00:05:44.470 ⇒ 00:05:50.609 Awaish Kumar: And the requirements has changed, and things… make it clear for everybody to see that you are spending time
74 00:05:50.780 ⇒ 00:05:53.939 Awaish Kumar: Doing something that is asked just now.
75 00:05:54.560 ⇒ 00:06:05.020 Awaish Kumar: And, and the, yeah, previous tickets are closed. Number one… Second thing is, Try to also,
76 00:06:05.680 ⇒ 00:06:08.180 Awaish Kumar: Split your time between clients in a day.
77 00:06:08.630 ⇒ 00:06:14.110 Awaish Kumar: So… What, like… Everybody needs true time.
78 00:06:15.500 ⇒ 00:06:19.290 Awaish Kumar: And we can’t see in my time, and also Tammy’s time.
79 00:06:19.410 ⇒ 00:06:23.390 Awaish Kumar: And we can’t say that I’m working today on Element, and I can’t touch.
80 00:06:23.520 ⇒ 00:06:24.430 Awaish Kumar: So dear.
81 00:06:24.820 ⇒ 00:06:28.960 Awaish Kumar: Or I can’t touch Amazing City, and I can’t touch Magic Spoon.
82 00:06:29.850 ⇒ 00:06:39.250 Awaish Kumar: So, what is the normal way? Is that you split your time in a way that, okay, I will give 4 hours today on CTA, 4 hours on Magic Sport.
83 00:06:39.740 ⇒ 00:06:42.629 Awaish Kumar: Or, if CTA needs more attention, okay, let’s…
84 00:06:42.810 ⇒ 00:06:46.100 Awaish Kumar: Give 5 hours on CT and 3 hours on magical phones.
85 00:06:46.390 ⇒ 00:06:49.739 Awaish Kumar: So it… so that way, you are making…
86 00:06:50.120 ⇒ 00:06:52.730 Awaish Kumar: Little, like, some progress on both.
87 00:06:52.860 ⇒ 00:07:08.160 Awaish Kumar: the clients. That you can share in each Slack channel, in each team’s… the client team. So they will be like, okay, Ashwini did work today on this, right? Instead of… because client doesn’t care if you’re working with someone else.
88 00:07:14.160 ⇒ 00:07:15.569 Awaish Kumar: I don’t care if you’re…
89 00:07:15.570 ⇒ 00:07:22.630 Ashwini Sharma: See, when working with Magic Spoon, I could not work with any other client, right? To be honest. It was simply not feasible.
90 00:07:24.410 ⇒ 00:07:32.710 Ashwini Sharma: Yeah, but when the workload is lesser, right? I mean, workload is smaller, I would say, then I can definitely work with multiple clients, not an issue.
91 00:07:32.710 ⇒ 00:07:34.249 Awaish Kumar: Let’s say, let’s say…
92 00:07:34.790 ⇒ 00:07:41.140 Awaish Kumar: Magic Spoon is taking a lot of time, let’s say, like, maybe you can call it out, like… Whoa.
93 00:07:42.130 ⇒ 00:07:49.459 Awaish Kumar: do QA for, like, don’t do QA for everything, like, let’s divide it. Like, I did QA for these dates.
94 00:07:49.580 ⇒ 00:07:51.849 Awaish Kumar: Or for these time frames today.
95 00:07:52.070 ⇒ 00:07:57.580 Awaish Kumar: And try to, like, split your time that way, like, And instead of…
96 00:07:58.640 ⇒ 00:08:03.010 Awaish Kumar: I’m doing full QA for everything, and spending one or two days.
97 00:08:03.250 ⇒ 00:08:04.079 Awaish Kumar: Just a minute.
98 00:08:04.080 ⇒ 00:08:07.119 Ashwini Sharma: It’s not a QA that I’m doing, right? I’m doing data load.
99 00:08:07.380 ⇒ 00:08:08.169 Ashwini Sharma: And.
100 00:08:08.170 ⇒ 00:08:09.440 Awaish Kumar: Yep, in a note…
101 00:08:11.010 ⇒ 00:08:16.379 Awaish Kumar: What I’m trying to say, whatever it is you are doing, I’m taking an example of QA.
102 00:08:16.700 ⇒ 00:08:23.150 Awaish Kumar: it’s not exactly like you are doing QA. My point is, whatever you are doing, try to…
103 00:08:23.670 ⇒ 00:08:28.009 Awaish Kumar: scope it out, like, if it takes you 8 hours to do something.
104 00:08:28.120 ⇒ 00:08:32.140 Awaish Kumar: We… we are able to, like, we are… experience, you know, to…
105 00:08:32.270 ⇒ 00:08:34.729 Awaish Kumar: To split that ticket into multiples.
106 00:08:35.250 ⇒ 00:08:38.299 Ashwini Sharma: and then say, okay, I will do these 3 tickets today.
107 00:08:38.299 ⇒ 00:08:43.000 Awaish Kumar: these two tickets tomorrow, I will spend 5… Hours today, 3 hours tomorrow.
108 00:08:43.549 ⇒ 00:08:44.290 Awaish Kumar: Right.
109 00:08:44.290 ⇒ 00:08:49.600 Ashwini Sharma: Aish, I’ll give you an example, right? The Magic Spoon load on Monday, I started early at 6 AM.
110 00:08:49.760 ⇒ 00:08:52.770 Ashwini Sharma: Right? I completed at 10pm in the evening.
111 00:08:53.130 ⇒ 00:08:57.760 Ashwini Sharma: India time I’m talking about, right? If I had to split it, it would take 4 days to load.
112 00:08:58.390 ⇒ 00:08:59.750 Awaish Kumar: But the… but…
113 00:08:59.890 ⇒ 00:09:03.560 Ashwini Sharma: I could split it, right? But is that agreeable?
114 00:09:03.910 ⇒ 00:09:04.340 Awaish Kumar: Yeah, bye.
115 00:09:04.340 ⇒ 00:09:07.790 Ashwini Sharma: I can do that, no. But it would take 4 days to load, simply.
116 00:09:07.790 ⇒ 00:09:10.260 Awaish Kumar: Okay, yeah, but when you see that load.
117 00:09:10.490 ⇒ 00:09:13.320 Awaish Kumar: Does it require you to be…
118 00:09:13.320 ⇒ 00:09:28.290 Ashwini Sharma: I have to be there, I have to be running the commands over there, doing the extraction, monitoring if it fails, and then if it fails, then redo it, modify the code, make changes for a different extraction, run the extraction again. I was there sitting in the decks throughout the day.
119 00:09:28.440 ⇒ 00:09:29.460 Ashwini Sharma: We’re doing it.
120 00:09:30.660 ⇒ 00:09:36.800 Awaish Kumar: Yeah, that… that is not clear to me, right? Maybe write it down, what we were doing, I don’t know, like…
121 00:09:36.900 ⇒ 00:09:44.189 Awaish Kumar: if I have to… I have done similar, like, the extracts before. I can tell, okay, let’s…
122 00:09:45.070 ⇒ 00:09:51.109 Awaish Kumar: If I write a script, For a four… one time frame, for a four-week time frame, for example.
123 00:09:51.530 ⇒ 00:09:53.890 Awaish Kumar: And I can run it for multiple
124 00:09:54.440 ⇒ 00:09:59.720 Awaish Kumar: start dates, and it can run, right? Without me being there.
125 00:10:00.150 ⇒ 00:10:03.080 Awaish Kumar: Like, looking at that script for…
126 00:10:03.510 ⇒ 00:10:06.310 Awaish Kumar: some hours. I can just see when this fails.
127 00:10:06.420 ⇒ 00:10:08.159 Awaish Kumar: But apart from that, it just…
128 00:10:08.570 ⇒ 00:10:12.340 Awaish Kumar: It should just work, right? Without me interfering.
129 00:10:22.820 ⇒ 00:10:23.420 Ashwini Sharma: Okay.
130 00:10:29.740 ⇒ 00:10:32.450 Awaish Kumar: And then again, it’s the same,
131 00:10:32.860 ⇒ 00:10:35.749 Awaish Kumar: It takes 4 days, then it takes 4 days, like…
132 00:10:35.990 ⇒ 00:10:39.009 Awaish Kumar: There are a few things we can do, right? Number one.
133 00:10:39.540 ⇒ 00:10:44.470 Awaish Kumar: Are… like, is it… number one is… is there… are any ways?
134 00:10:44.730 ⇒ 00:10:50.670 Awaish Kumar: to… automated? Are there any ways, to discuss? Like, I don’t know what…
135 00:10:50.990 ⇒ 00:11:00.530 Awaish Kumar: the situation, the thing, like, I don’t know what exactly you have been doing until you show me on a loom, or you write down
136 00:11:01.420 ⇒ 00:11:03.850 Awaish Kumar: in a… in a Notion page that…
137 00:11:04.770 ⇒ 00:11:07.619 Awaish Kumar: that I’m doing this, and it’s taking me a lot of time.
138 00:11:07.940 ⇒ 00:11:16.149 Awaish Kumar: then maybe somebody can give you idea, I can give you idea, or Utam can give you some idea if he has… if we all agree that, okay, it’s…
139 00:11:16.510 ⇒ 00:11:23.049 Awaish Kumar: Not possible to… Oh… To optimize it further.
140 00:11:23.150 ⇒ 00:11:28.820 Awaish Kumar: then… That’s 4 days of work, right? Then everybody will just say yes.
141 00:11:29.040 ⇒ 00:11:33.629 Awaish Kumar: And you… the… the… Then it’s not on your shoulders.
142 00:11:35.970 ⇒ 00:11:46.159 Awaish Kumar: To justify it. Everybody knows, okay, it’s a lot of work, and there’s no way we can optimize it. Let’s do it the way Ashwini’s doing, and it’s okay.
143 00:11:59.670 ⇒ 00:12:00.580 Awaish Kumar: Okay.
144 00:12:05.590 ⇒ 00:12:06.590 Awaish Kumar: Are you gonna do?
145 00:12:07.030 ⇒ 00:12:08.140 Ashwini Sharma: Yeah, yeah, I’m here.
146 00:12:08.140 ⇒ 00:12:14.730 Awaish Kumar: Yeah, so that’s just… One thing, like, Just raise it,
147 00:12:15.430 ⇒ 00:12:19.480 Awaish Kumar: Maybe, like, you can ask for… feedback.
148 00:12:19.610 ⇒ 00:12:21.309 Awaish Kumar: Doesn’t necessarily that…
149 00:12:21.540 ⇒ 00:12:29.470 Awaish Kumar: like, it’s not always that you don’t know, that’s why you’re asking the feedback. It’s always possible that somebody can give you an idea.
150 00:12:31.750 ⇒ 00:12:36.509 Awaish Kumar: like, I call you sometimes, also, for CTA when I need something.
151 00:12:36.770 ⇒ 00:12:40.610 Awaish Kumar: I need context or anything, so that’s… that’s how it works, right?
152 00:12:40.800 ⇒ 00:12:44.289 Awaish Kumar: I, like, my mind won’t work,
153 00:12:44.590 ⇒ 00:13:00.089 Awaish Kumar: this, same, every, every time I might have to ask for feedbacks, look for different opinions, so it’s the same here. You ask for some, like, just write a loom, send, record a loom and send it, and…
154 00:13:00.220 ⇒ 00:13:04.870 Awaish Kumar: and Magic Spoon internal channel, and ask Debbie, Utah, me, or anyone.
155 00:13:05.160 ⇒ 00:13:11.790 Awaish Kumar: Now, okay, I’m doing this. It is taking me this time. Is there any… Any ideas to optimize it?
156 00:13:12.590 ⇒ 00:13:21.480 Awaish Kumar: This way, it gives you an opportunity to become transparent and let everybody know that it is taking time. Second thing is,
157 00:13:22.190 ⇒ 00:13:26.790 Awaish Kumar: Bucketing your time, that’s really needed here.
158 00:13:27.150 ⇒ 00:13:29.310 Awaish Kumar: It’s not just for you, it’s for…
159 00:13:29.560 ⇒ 00:13:39.349 Awaish Kumar: like, we all had this problem, we have already discussed with Utam. Okay, there’s a lot of context switching, there’s a lot of, like, we are working on this, and this, and then…
160 00:13:39.470 ⇒ 00:13:43.749 Awaish Kumar: There’s 1, 2, 3 clients, and, like, just,
161 00:13:44.920 ⇒ 00:13:49.379 Awaish Kumar: It becomes sometimes hard to context switch, but that’s the reality.
162 00:13:49.580 ⇒ 00:13:55.490 Awaish Kumar: Of being here, everybody has to contact switch between multiple Price, and
163 00:13:57.260 ⇒ 00:14:01.159 Awaish Kumar: And that’s how Thomas recommended me, also.
164 00:14:01.320 ⇒ 00:14:08.140 Awaish Kumar: that I should spend my time based on, like, Buckets, like, okay, let’s give…
165 00:14:08.680 ⇒ 00:14:12.219 Awaish Kumar: 2 hours on CDA, and make some progress.
166 00:14:13.410 ⇒ 00:14:18.040 Awaish Kumar: Close this thing… out, and move on to other one.
167 00:14:18.290 ⇒ 00:14:22.680 Awaish Kumar: Instead of… Completing the… everything for one client, and…
168 00:14:23.030 ⇒ 00:14:28.490 Awaish Kumar: I’m not doing anything for the rest, so… That’s all I wanted, whiskers.
169 00:14:31.070 ⇒ 00:14:35.460 Awaish Kumar: So, what’s your take on this? Do you agree with what I’m saying, or…
170 00:14:36.350 ⇒ 00:14:37.229 Ashwini Sharma: Yeah, yeah, yeah.
171 00:14:39.740 ⇒ 00:14:40.370 Awaish Kumar: slowly.
172 00:14:42.380 ⇒ 00:14:43.220 Awaish Kumar: Okay.
173 00:14:47.830 ⇒ 00:14:55.420 Awaish Kumar: Okay, now we can dive in into… the… exact tickets,
174 00:14:59.660 ⇒ 00:15:03.060 Awaish Kumar: So I… yeah, you want to discuss something regarding identity.
175 00:15:03.990 ⇒ 00:15:08.030 Ashwini Sharma: I just wanted to know one thing. For CTA, what is the scope for identity stitching?
176 00:15:09.650 ⇒ 00:15:17.000 Awaish Kumar: scope, right, and from… so there are… so there is a CES… data.
177 00:15:18.240 ⇒ 00:15:28.979 Awaish Kumar: And, preES modeling, In the CES data, we need to create, I wonder what…
178 00:15:29.240 ⇒ 00:15:36.620 Awaish Kumar: Stitch the identity for the… For the companies, so that we can.
179 00:15:38.210 ⇒ 00:15:39.670 Ashwini Sharma: For all the CS data?
180 00:15:40.030 ⇒ 00:15:44.620 Awaish Kumar: Yes, but the way I’m thinking is.
181 00:15:46.010 ⇒ 00:15:51.410 Awaish Kumar: So, we are, like, the way we are trying to connect it to the impacts ID, right?
182 00:15:52.200 ⇒ 00:15:54.460 Awaish Kumar: all the organization ID.
183 00:15:54.610 ⇒ 00:15:57.560 Awaish Kumar: That is coming from remembers data, right?
184 00:15:59.210 ⇒ 00:16:00.179 Ashwini Sharma: Yes.
185 00:16:00.800 ⇒ 00:16:08.319 Awaish Kumar: So… and yeah, that’s… and the approach I took in that document was similar, like, I took one table.
186 00:16:08.560 ⇒ 00:16:15.819 Awaish Kumar: And then for each one, for that table, I’m trying to give the… companies, like, the few fields.
187 00:16:15.940 ⇒ 00:16:19.520 Awaish Kumar: The company information, domain, website, and these things.
188 00:16:19.900 ⇒ 00:16:24.830 Awaish Kumar: And, then, trying to get an,
189 00:16:27.760 ⇒ 00:16:29.770 Awaish Kumar: Try to connect it with any of the…
190 00:16:30.160 ⇒ 00:16:33.690 Awaish Kumar: company and DIM organization table that you have created.
191 00:16:34.510 ⇒ 00:16:39.380 Awaish Kumar: That includes both the organization ID and… Impacts your mighty.
192 00:16:40.200 ⇒ 00:16:45.810 Awaish Kumar: So, the scope is hold the CS data, Like, wherever you have the…
193 00:16:45.960 ⇒ 00:16:56.600 Awaish Kumar: company tables, like, company data. So, like, for example, if you have our 10 CS tables, where we have, company data,
194 00:16:57.100 ⇒ 00:16:59.749 Awaish Kumar: One way is to create dim,
195 00:17:00.380 ⇒ 00:17:03.310 Awaish Kumar: organization table for CES as well.
196 00:17:03.470 ⇒ 00:17:09.470 Awaish Kumar: So… We, first of all, get all the… unique companies.
197 00:17:09.760 ⇒ 00:17:15.250 Awaish Kumar: in the CES data, And then stitch it, to the members.
198 00:17:17.280 ⇒ 00:17:24.939 Ashwini Sharma: No, no, I get that, I just wanted to understand, like, for which data sets do we need to stitch it? Because there’s a bunch of archive tables, right?
199 00:17:24.940 ⇒ 00:17:27.619 Awaish Kumar: Yes, Jess, I know, and I just…
200 00:17:27.760 ⇒ 00:17:31.349 Awaish Kumar: Used one as an example for the… for showing the approach.
201 00:17:31.530 ⇒ 00:17:35.149 Awaish Kumar: But we have to do it for all, wherever the company information is.
202 00:17:35.570 ⇒ 00:17:37.320 Ashwini Sharma: Okay, alright, alright, yeah.
203 00:17:37.320 ⇒ 00:17:38.889 Awaish Kumar: So, yeah, but…
204 00:17:39.250 ⇒ 00:17:48.869 Awaish Kumar: I’m discussing an approach. What do you think is a good approach? Like, the way we are doing it right now, just take one table, get company information, try to connect it with the…
205 00:17:49.110 ⇒ 00:17:55.760 Awaish Kumar: Team organization, and then take one… another table, do the same, then third table, do the same.
206 00:17:57.120 ⇒ 00:17:59.840 Ashwini Sharma: Yeah, that is what we’ll have to do, right, eventually, because…
207 00:18:01.300 ⇒ 00:18:04.060 Ashwini Sharma: I didn’t see any other way to do it.
208 00:18:04.560 ⇒ 00:18:12.689 Awaish Kumar: So, the other way is that, for E in all of these tables, And grab the company information.
209 00:18:13.760 ⇒ 00:18:17.290 Awaish Kumar: from all the CES tables, and build a new table.
210 00:18:20.700 ⇒ 00:18:21.720 Awaish Kumar: with the…
211 00:18:22.340 ⇒ 00:18:30.140 Ashwini Sharma: Yeah, in the staging, that identity will be resolved, right? When we are creating a staging table out of the raw tables, we’ll have a resolved identity.
212 00:18:38.320 ⇒ 00:18:40.570 Awaish Kumar: Right now, it is not happening, right?
213 00:18:42.730 ⇒ 00:18:47.100 Ashwini Sharma: Right now, it’s not happening, no. It’s just a cleanup from…
214 00:18:47.100 ⇒ 00:18:47.870 Awaish Kumar: My, my bundle.
215 00:18:47.870 ⇒ 00:18:48.390 Ashwini Sharma: Beautiful.
216 00:18:49.480 ⇒ 00:18:54.780 Awaish Kumar: If we go, like, the way for each table.
217 00:18:55.420 ⇒ 00:19:00.810 Awaish Kumar: then we are able to connect. Like, for each retrieval, you have to… you can give one more column.
218 00:19:02.050 ⇒ 00:19:03.810 Awaish Kumar: Impex MID, for example.
219 00:19:04.200 ⇒ 00:19:06.709 Awaish Kumar: And you can say, okay, this row is now…
220 00:19:09.830 ⇒ 00:19:12.190 Ashwini Sharma: Yeah, after resolution, yes.
221 00:19:12.190 ⇒ 00:19:19.949 Awaish Kumar: the resolution. My… what I’m trying to say here… is that, can we build a…
222 00:19:20.260 ⇒ 00:19:23.370 Awaish Kumar: DIM organization table for CS as well.
223 00:19:26.180 ⇒ 00:19:31.399 Ashwini Sharma: No, demodernization is a clean table, right? You don’t need to build anything on top of that.
224 00:19:31.400 ⇒ 00:19:36.599 Awaish Kumar: No, no, I mean one more table called… you can call it, maybe, CES democratization.
225 00:19:36.900 ⇒ 00:19:40.680 Awaish Kumar: So, all the companies in the CES data, that’s what I’m talking about.
226 00:19:52.060 ⇒ 00:19:54.560 Ashwini Sharma: I don’t know, is there any advantage of doing that?
227 00:19:57.120 ⇒ 00:20:00.080 Awaish Kumar: Like, she mentioned that we need to move it
228 00:20:00.770 ⇒ 00:20:04.420 Awaish Kumar: like, I’m already working on CES model, remodeling, right?
229 00:20:04.570 ⇒ 00:20:05.580 Awaish Kumar: Freedom.
230 00:20:05.690 ⇒ 00:20:07.420 Awaish Kumar: We have to convert this.
231 00:20:07.960 ⇒ 00:20:10.550 Awaish Kumar: to, restata schema.
232 00:20:13.100 ⇒ 00:20:14.280 Awaish Kumar: Okay.
233 00:20:14.510 ⇒ 00:20:24.619 Awaish Kumar: So, we have the, like, CES data, where first off, first, first, ever table is, we have CES registrations data.
234 00:20:25.490 ⇒ 00:20:32.880 Awaish Kumar: for using that, I will create, like, different organizations, the…
235 00:20:33.770 ⇒ 00:20:36.850 Awaish Kumar: of people, like, and
236 00:20:37.280 ⇒ 00:20:45.140 Awaish Kumar: And the events, when the person attend, attended or registered, registered, and,
237 00:20:46.320 ⇒ 00:20:53.069 Awaish Kumar: To an event, and then also, you know, we will have some tables, like, Dream Fortune 500.
238 00:20:53.420 ⇒ 00:20:58.329 Awaish Kumar: Something like that, because she don’t want a flag, she wants all the company names to be there.
239 00:20:58.740 ⇒ 00:21:04.300 Awaish Kumar: And then… for each event, we maybe… we have to connect to DIMP for 200, because
240 00:21:04.580 ⇒ 00:21:08.479 Awaish Kumar: That will give us a flag. If it is… the attendee first from…
241 00:21:08.610 ⇒ 00:21:11.470 Awaish Kumar: One of the Fortune 500 companies.
242 00:21:11.740 ⇒ 00:21:15.090 Awaish Kumar: Similarly, I will be creating that flow.
243 00:21:15.550 ⇒ 00:21:16.830 Awaish Kumar: All these tables.
244 00:21:17.380 ⇒ 00:21:18.220 Awaish Kumar: So…
245 00:21:18.220 ⇒ 00:21:23.919 Ashwini Sharma: No, whatever companies are there in the CES data should be there in the remembers data also, right?
246 00:21:27.350 ⇒ 00:21:33.529 Awaish Kumar: I don’t know, I’m just… When we are doing the stitching, we… figure that out, but I…
247 00:21:35.670 ⇒ 00:21:42.380 Awaish Kumar: So, yeah, the point is that using that, the DIM organization, since we are moving to an,
248 00:21:42.810 ⇒ 00:21:44.379 Awaish Kumar: Model, like that.
249 00:21:44.680 ⇒ 00:21:50.780 Awaish Kumar: Anyway, the… whatever approach you take, we will have the same… process that you…
250 00:21:50.980 ⇒ 00:21:54.610 Awaish Kumar: If you implement, same… if it’s a macro, whatever.
251 00:21:55.540 ⇒ 00:22:04.029 Awaish Kumar: We have to, basically, come up with any ID that can join the data to remember data.
252 00:22:04.170 ⇒ 00:22:10.879 Awaish Kumar: And then, I can create a CS demolition after that, that’s okay.
253 00:22:11.390 ⇒ 00:22:13.590 Awaish Kumar: But the thing is,
254 00:22:14.300 ⇒ 00:22:21.140 Awaish Kumar: there are two things. Number one, identity testing. Number two, CE as data modeling, having a star schema.
255 00:22:21.300 ⇒ 00:22:27.180 Awaish Kumar: And then… We have to come up with a set of companies where
256 00:22:27.420 ⇒ 00:22:31.320 Awaish Kumar: we can’t do identity sitting from our CES data.
257 00:22:31.530 ⇒ 00:22:38.530 Awaish Kumar: So, for example, using our approach, maybe we can stretch, 80% of the companies?
258 00:22:39.050 ⇒ 00:22:46.059 Awaish Kumar: And 20% where we call stitch, we have to, like… we should be able to get that list and give it to the…
259 00:22:46.500 ⇒ 00:22:49.599 Awaish Kumar: Catherine, Catherine, to… to minorities.
260 00:22:49.850 ⇒ 00:22:51.020 Awaish Kumar: Stitch it down.
261 00:22:51.440 ⇒ 00:22:56.130 Awaish Kumar: Fire, like… Find someone in her team to actually go through the…
262 00:22:57.060 ⇒ 00:23:02.250 Awaish Kumar: Completing the standards, and then switch it to the… Do you remember status.
263 00:23:05.100 ⇒ 00:23:11.859 Ashwini Sharma: Okay, let’s take one step at a time, Avish. I think we’re just discussing a lot of stuff, right?
264 00:23:12.790 ⇒ 00:23:18.780 Awaish Kumar: Yes, so my point number one, I’m going through all of this because I need to come up with a plan.
265 00:23:18.890 ⇒ 00:23:20.129 Awaish Kumar: How to attack it.
266 00:23:22.500 ⇒ 00:23:24.579 Awaish Kumar: So, number one thing is,
267 00:23:25.340 ⇒ 00:23:30.609 Awaish Kumar: I think let’s… like, if you’d go from table to table, what is your…
268 00:23:31.550 ⇒ 00:23:35.400 Awaish Kumar: take on it. Okay, let’s maybe start with the registration data, then.
269 00:23:36.990 ⇒ 00:23:39.419 Awaish Kumar: That’s the most important registration data.
270 00:23:39.920 ⇒ 00:23:40.580 Ashwini Sharma: Yeah.
271 00:23:40.580 ⇒ 00:23:45.280 Awaish Kumar: For each year, for 2023, 2024, 25, 25, and 26.
272 00:23:45.530 ⇒ 00:23:47.220 Awaish Kumar: These are the ones.
273 00:23:47.430 ⇒ 00:23:49.429 Awaish Kumar: Or maybe you can use,
274 00:23:50.020 ⇒ 00:23:54.489 Awaish Kumar: Kyle’s table, he has much more historical data.
275 00:23:56.090 ⇒ 00:24:03.400 Ashwini Sharma: No, I’ll do it in the staging table itself, right? From the staging table, and then create one layer above after resolution, you know.
276 00:24:03.400 ⇒ 00:24:04.080 Awaish Kumar: No, but…
277 00:24:04.080 ⇒ 00:24:04.610 Ashwini Sharma: District.
278 00:24:04.610 ⇒ 00:24:12.160 Awaish Kumar: We already have a… Tables and staging for each of the years, from 2013 till… 2026.
279 00:24:16.030 ⇒ 00:24:20.069 Ashwini Sharma: Yeah, he has done some intermediate things, right? .
280 00:24:20.390 ⇒ 00:24:26.069 Awaish Kumar: I’m just saying the registration data, you can use just that, instead of going directly to raw.
281 00:24:26.600 ⇒ 00:24:29.910 Awaish Kumar: Just get data from his tables.
282 00:24:30.180 ⇒ 00:24:35.170 Awaish Kumar: for the registration data, and stretch… Try to…
283 00:24:35.170 ⇒ 00:24:40.069 Ashwini Sharma: The raw data has more columns than what Kyle has picked up, I think.
284 00:24:45.180 ⇒ 00:24:45.960 Awaish Kumar: Okay.
285 00:24:48.280 ⇒ 00:24:54.910 Ashwini Sharma: So I’ll look into all the columns, and then see if Kyle does not have those columns, or maybe if we can incorporate those columns into Kyle’s model.
286 00:24:54.910 ⇒ 00:24:56.680 Awaish Kumar: Right.
287 00:24:56.680 ⇒ 00:24:58.399 Ashwini Sharma: Then work on top of that, yeah?
288 00:25:01.200 ⇒ 00:25:08.319 Ashwini Sharma: I mean, I wanted the identity stitching to be in one single place, right? Not scattered across different layers of the models.
289 00:25:10.890 ⇒ 00:25:13.709 Awaish Kumar: Identity stitching at one place, but for that.
290 00:25:15.170 ⇒ 00:25:23.670 Awaish Kumar: That can only happen if you can collect all the company information from all the tables in CES, and then do the identity stitching.
291 00:25:24.310 ⇒ 00:25:26.789 Awaish Kumar: That way, you will have it in one place.
292 00:25:36.410 ⇒ 00:25:41.809 Ashwini Sharma: Let me go through this, Savesh, and know, like, what to comment right now on this thing.
293 00:25:45.160 ⇒ 00:25:48.810 Awaish Kumar: That… that should make it, like, according to my…
294 00:25:49.540 ⇒ 00:25:52.380 Awaish Kumar: what I’m thinking, based on that, it should be…
295 00:25:52.530 ⇒ 00:25:54.389 Awaish Kumar: This would make it easier, because
296 00:25:54.900 ⇒ 00:26:01.969 Awaish Kumar: You will have just… you can create just one table with all the company names, whatever the… whatever fields you want to use in identity state.
297 00:26:02.270 ⇒ 00:26:06.130 Awaish Kumar: And then, based on that, we can… Stitch to the…
298 00:26:07.760 ⇒ 00:26:16.530 Awaish Kumar: Identities to the remembers data, but then we have to… Have to… create a…
299 00:26:17.330 ⇒ 00:26:22.059 Awaish Kumar: like, ID column somewhere to… to make… to keep the references to individual tables.
300 00:26:23.330 ⇒ 00:26:23.980 Ashwini Sharma: Yeah.
301 00:26:24.200 ⇒ 00:26:24.840 Awaish Kumar: Nope.
302 00:26:24.990 ⇒ 00:26:31.270 Awaish Kumar: Okay, yeah, let’s… Let’s… okay, let’s see, let’s… we can discuss tomorrow, whatever you come up with.
303 00:26:31.730 ⇒ 00:26:32.330 Ashwini Sharma: Okay.
304 00:26:32.410 ⇒ 00:26:35.200 Awaish Kumar: And also, yeah, I’m also working on CTA.
305 00:26:35.400 ⇒ 00:26:39.359 Awaish Kumar: for CES modeling, and I will… while doing that, if I come across anything, I will…
306 00:26:39.490 ⇒ 00:26:40.549 Awaish Kumar: I’ve let you know.
307 00:26:42.940 ⇒ 00:26:44.190 Ashwini Sharma: Honor, honor it, man.
308 00:26:44.390 ⇒ 00:26:45.469 Awaish Kumar: Alright, thank you.
309 00:26:46.400 ⇒ 00:26:47.170 Ashwini Sharma: Yeah, thanks.