Meeting Title: Brainforge x CTA: Weekly! Date: 2026-03-06 Meeting participants: Awaish Kumar, Chi Quinn, Ashwini Sharma, Kyle Wandel, Uttam Kumaran, Katherine Bayless
WEBVTT
1 00:02:06.830 ⇒ 00:02:10.099 Chi Quinn: Hi, good morning, or good evening.
2 00:02:13.410 ⇒ 00:02:16.790 Awaish Kumar: Hi, good evening, yeah.
3 00:02:18.110 ⇒ 00:02:19.079 Awaish Kumar: How are you?
4 00:02:19.080 ⇒ 00:02:20.920 Chi Quinn: I’m good, how are you?
5 00:02:21.180 ⇒ 00:02:22.280 Awaish Kumar: I’m good as well.
6 00:02:23.010 ⇒ 00:02:23.820 Chi Quinn: Yes.
7 00:02:24.090 ⇒ 00:02:30.750 Chi Quinn: Yes, I’ve got to wait for everyone else. I see…
8 00:02:31.020 ⇒ 00:02:36.150 Chi Quinn: Catherine and Kyle, they’re online, so they should be coming in a few minutes.
9 00:02:38.970 ⇒ 00:02:39.990 Awaish Kumar: Hi, Kyle.
10 00:02:39.990 ⇒ 00:02:43.100 Kyle Wandel: Good morning, how you guys doing? Or afternoon for you guys.
11 00:02:44.000 ⇒ 00:02:45.269 Awaish Kumar: Yeah, funny.
12 00:02:51.700 ⇒ 00:02:52.630 Uttam Kumaran: Hello.
13 00:02:53.850 ⇒ 00:02:55.059 Kyle Wandel: Morning, Tom, how you doing?
14 00:02:55.420 ⇒ 00:02:57.090 Uttam Kumaran: Hey, good morning. How are you?
15 00:02:57.870 ⇒ 00:02:59.039 Kyle Wandel: Not too bad.
16 00:03:04.130 ⇒ 00:03:05.390 Uttam Kumaran: How was the week?
17 00:03:06.710 ⇒ 00:03:14.089 Kyle Wandel: My perspective, not too terrible. A lot more of, like, ad hoc stuff, and actually doing some more data science stuff, so that’s been…
18 00:03:14.210 ⇒ 00:03:17.220 Kyle Wandel: Actually kind of nice, so… Boom.
19 00:03:17.590 ⇒ 00:03:26.950 Kyle Wandel: Yeah, it’s, letting you guys kind of handle more of the engineering pipeline building right now, and then, just kind of helping, basically, ad hoc analysis where we can.
20 00:03:27.860 ⇒ 00:03:28.890 Uttam Kumaran: Yeah, definitely.
21 00:03:30.610 ⇒ 00:03:31.720 Uttam Kumaran: Hello.
22 00:03:31.720 ⇒ 00:03:33.279 Katherine Bayless: Morning. How’s everybody doing?
23 00:03:33.280 ⇒ 00:03:33.870 Uttam Kumaran: ain’.
24 00:03:34.870 ⇒ 00:03:35.720 Kyle Wandel: Good, good.
25 00:03:37.840 ⇒ 00:03:45.719 Katherine Bayless: A small anecdote, I just got off a call with Jackie, who is the VP, kind of under our conferences team.
26 00:03:45.910 ⇒ 00:04:05.580 Katherine Bayless: And Kyle, she was very blown away by what you put together, so thank you again for that. And she had a really interesting question that was, like, one of those things where I’m like, again, fascinating that this has not been possible historically. She would like to do some analysis on people who pay for the conference tracks, whether or not they go to the sessions they paid for.
27 00:04:06.340 ⇒ 00:04:08.080 Kyle Wandel: Interesting, okay.
28 00:04:08.080 ⇒ 00:04:20.790 Katherine Bayless: Yeah, and I was like, actually, I really… I like that as a question, and she’s got a whole bunch of places she wants to go with it, but I was, like, fascinating that that’s not something she’s actually been able to get at before. Yeah, yeah.
29 00:04:21.160 ⇒ 00:04:22.070 Uttam Kumaran: Interesting.
30 00:04:22.070 ⇒ 00:04:31.090 Katherine Bayless: She’s gonna send a whole thing that she’s got, like, a bunch of different, like, pieces of the question, but I don’t know. Anyway, just… we’re doing the right work. Yep.
31 00:04:31.090 ⇒ 00:04:42.660 Kyle Wandel: I was just telling… you were telling that, that’s mainly what we’ve been doing, which is, like, the actual in-depth analysis, or getting started on some of that, showcasing what we can do, and then letting them kind of do the back end and creating the structure for it, so…
32 00:04:43.020 ⇒ 00:04:43.720 Katherine Bayless: Yeah.
33 00:04:43.960 ⇒ 00:04:44.770 Katherine Bayless: Damn.
34 00:04:45.100 ⇒ 00:05:01.410 Katherine Bayless: She did also have a use case that is similar to those, like, dossiers that the membership and sales teams put together for Gary when he goes to, like, meet with somebody. I think it’s, like, a similar sort of thing, but more of a prospectus around track engagement for potential, track sponsors.
35 00:05:01.420 ⇒ 00:05:16.060 Katherine Bayless: And so I think if we start… I mean, not right now, obviously, but, like, as we start to kind of build out a pipeline that’ll take the data and create, like, some of those more PDF-y or Word doc-y kind of artifacts around it, that’s another good one to put in the queue, but…
36 00:05:16.430 ⇒ 00:05:18.669 Katherine Bayless: Anyway, I digress. What shall we talk about?
37 00:05:20.350 ⇒ 00:05:26.830 Uttam Kumaran: Yeah, I mean, I think today we could probably spend time just in Snowflake, and just talk through…
38 00:05:26.950 ⇒ 00:05:36.089 Uttam Kumaran: kind of the models we pushed, and then, you know, path towards QA. So I think we’ve… maybe, Awash, do you want to share Snowflake, and then we can talk through…
39 00:05:36.200 ⇒ 00:05:46.719 Uttam Kumaran: Both, like, what the structure in Snowflake looks like, so where we’re gonna be doing QA, how things are gonna move to production, and then let’s talk a little bit about just the models that
40 00:05:46.900 ⇒ 00:05:49.130 Uttam Kumaran: You know, we need some feedback on.
41 00:05:49.440 ⇒ 00:05:50.840 Katherine Bayless: Okay. Okay, sounds good.
42 00:05:50.840 ⇒ 00:05:51.390 Awaish Kumar: ship.
43 00:05:52.190 ⇒ 00:05:52.850 Uttam Kumaran: Cool.
44 00:05:53.170 ⇒ 00:06:00.980 Awaish Kumar: Yeah, it should be in… Hi, home… Bye.
45 00:06:00.980 ⇒ 00:06:05.730 Uttam Kumaran: And we have, like… I can take some notes if there’s, like, questions per table, and then I can…
46 00:06:06.870 ⇒ 00:06:08.629 Uttam Kumaran: I’ll send those after, so…
47 00:06:09.820 ⇒ 00:06:10.970 Katherine Bayless: Okay, yeah.
48 00:06:11.380 ⇒ 00:06:18.060 Katherine Bayless: Yeah, actually, I’m realizing the Zoom AI Companion’s kind of a great fit for, like, doing a QA meeting, right?
49 00:06:18.060 ⇒ 00:06:20.870 Uttam Kumaran: Yeah, it’s like, okay, this table needs this, this table needs this.
50 00:06:21.190 ⇒ 00:06:32.469 Katherine Bayless: Yeah. Like, part of me just is thinking, to be honest right now, I’m like, hmm, maybe I should do that, even if I’m, like, solo QAing? Like, I could totally, like, start a Zoom and, like, record myself… Just talk.
51 00:06:33.480 ⇒ 00:06:34.180 Katherine Bayless: I don’t know.
52 00:06:34.180 ⇒ 00:06:46.640 Uttam Kumaran: I’m not… I’m not like… I think I was talking to someone today, like, this week, and I don’t talk to my… I’m, like, in my head, so… but some people, I think, talk out loud and get through problems. They’re probably more suited.
53 00:06:46.640 ⇒ 00:06:49.620 Katherine Bayless: Yeah, because you had recommended that Whisper,
54 00:06:49.620 ⇒ 00:06:49.950 Uttam Kumaran: Yeah.
55 00:06:49.950 ⇒ 00:06:55.109 Katherine Bayless: really good AI voice-to-text, but the same thing, it’s like, I’m just not in the habit of talking out loud.
56 00:06:55.110 ⇒ 00:06:55.780 Uttam Kumaran: Kinda weird.
57 00:06:55.780 ⇒ 00:06:58.500 Katherine Bayless: It’s like, I say different things, for sure.
58 00:06:58.500 ⇒ 00:06:59.270 Uttam Kumaran: Yeah.
59 00:07:00.140 ⇒ 00:07:01.699 Katherine Bayless: My brain is odd.
60 00:07:02.120 ⇒ 00:07:09.069 Awaish Kumar: Okay, we can… Look at the catalog to basically see all these databases and tables.
61 00:07:09.230 ⇒ 00:07:20.689 Awaish Kumar: So, the last changes we made, basically, we just have now three environments. One is dev, and one is QA, and then there’s one called prod.
62 00:07:20.800 ⇒ 00:07:33.739 Awaish Kumar: So… and for each environment, we have multiple layers in dbt, where we split the databases. So we have a staging layer, intermediate layer, and the March layer.
63 00:07:34.240 ⇒ 00:07:38.079 Awaish Kumar: And Dev is more, like, for the ones where we are doing development.
64 00:07:38.730 ⇒ 00:07:53.029 Awaish Kumar: on our local machine, we want to push some changes and want to test out, then it goes to… when we create a PR, it goes to the QA, so it’s when we are going to basically, like, have all our changes, in a PR,
65 00:07:53.120 ⇒ 00:08:03.039 Awaish Kumar: And then it’s going to generate some models based on those changes, and we… we are basically using that for two purposes, basically, to validate the PRs that they are executing.
66 00:08:03.240 ⇒ 00:08:08.880 Awaish Kumar: Correctly, and also for the… doing the data QA at that level, that the data looks good.
67 00:08:09.040 ⇒ 00:08:15.430 Awaish Kumar: And then once it passes both these checks, we can merge it. Once merged, it will trigger the…
68 00:08:16.390 ⇒ 00:08:22.089 Awaish Kumar: the jobs in Florida, so we will be have basically same tables in the production.
69 00:08:22.530 ⇒ 00:08:34.299 Awaish Kumar: So in prod, we trigger at two levels. Basically, if you… one is when anything is new is merged in the main branch, then we are going to trigger.
70 00:08:35.360 ⇒ 00:08:40.949 Awaish Kumar: To recreate these models, but also… and then also it works, like, once a day.
71 00:08:41.179 ⇒ 00:08:43.190 Awaish Kumar: Basically, to refresh the data.
72 00:08:44.000 ⇒ 00:08:44.880 Katherine Bayless: Okay, okay.
73 00:08:44.920 ⇒ 00:08:53.000 Awaish Kumar: And… For each of these table and databases, then the second subfolder basically becomes a schema.
74 00:08:53.250 ⇒ 00:08:58.290 Awaish Kumar: So we are dividing, for example, in… if we look at prod marchs, we are dividing it and to…
75 00:08:58.510 ⇒ 00:09:04.030 Awaish Kumar: multiple layers, like CES, CRM, move reports.
76 00:09:04.190 ⇒ 00:09:05.290 Awaish Kumar: And,
77 00:09:05.510 ⇒ 00:09:12.539 Awaish Kumar: that’s how, like, the CS… all the tables related to CS are going to be there, and inside CS, we have…
78 00:09:13.000 ⇒ 00:09:15.989 Awaish Kumar: These tables, basically.
79 00:09:16.630 ⇒ 00:09:23.350 Awaish Kumar: So the latest changes which we have made are basically in QA, so we can maybe look at that.
80 00:09:23.490 ⇒ 00:09:24.569 Awaish Kumar: For example…
81 00:09:24.570 ⇒ 00:09:26.569 Uttam Kumaran: Show the PR, Awash, too?
82 00:09:26.770 ⇒ 00:09:31.510 Uttam Kumaran: And that way, I think that’ll just help show, like, when the PR gets created.
83 00:09:31.950 ⇒ 00:09:33.759 Uttam Kumaran: How it ends up landing here.
84 00:09:35.170 ⇒ 00:09:41.519 Awaish Kumar: Yeah, like, here’s my PR, which has, like, around 95s, 19 files there.
85 00:09:41.950 ⇒ 00:09:47.670 Awaish Kumar: And, it says… So, yeah, like, if I show…
86 00:09:48.730 ⇒ 00:09:51.739 Awaish Kumar: Basically, I think I can show the…
87 00:09:53.250 ⇒ 00:09:56.909 Awaish Kumar: The workflows where we explicitly define, like.
88 00:09:57.050 ⇒ 00:10:05.429 Awaish Kumar: what happens on each PR. So, when a pollicus is open against main, then we are basically going to…
89 00:10:06.330 ⇒ 00:10:15.849 Awaish Kumar: start to run. So this is how we have defined our workflows in GitHub Action. So this is for QA, which basically triggers on each commit on a PR, whenever we
90 00:10:16.130 ⇒ 00:10:22.649 Awaish Kumar: add a new commit, or create a PR, that’s when it is going to execute, these reads from.
91 00:10:23.210 ⇒ 00:10:27.859 Awaish Kumar: environment variables, the secrets, and then basically runs the dbt command.
92 00:10:28.060 ⇒ 00:10:32.350 Awaish Kumar: And similarly, second view is… this is for prod. For prod, it’s basically…
93 00:10:32.450 ⇒ 00:10:37.700 Awaish Kumar: When anything is pushed on branch main, and then also on a ground schedule.
94 00:10:38.140 ⇒ 00:10:45.390 Awaish Kumar: Then, going back to the pull request, we… this is where we are. We have added some checks, so it is, like.
95 00:10:45.530 ⇒ 00:10:54.460 Awaish Kumar: kind of… we have some checks before it gets merged. One, it needs to be approved by a reviewer, and also it needs to pass this,
96 00:10:55.150 ⇒ 00:11:03.759 Awaish Kumar: QA check that I was talking about. So once everything gets executed successfully, then we are… then, only then, we are able to merge this PR.
97 00:11:04.350 ⇒ 00:11:07.949 Awaish Kumar: There are some changes in here. It’s basically…
98 00:11:08.400 ⇒ 00:11:16.679 Awaish Kumar: all those models, which I have created in the semantic layer, these are… I have named them as… I’ve created them as views for now.
99 00:11:17.270 ⇒ 00:11:32.690 Awaish Kumar: That we can discuss if we just want to keep them as views, because this is all, coming from, if we go back here, in the semantic layer and the views. So this is all originating from a base model, which is basically the…
100 00:11:33.700 ⇒ 00:11:43.060 Awaish Kumar: the over… registration attendance overview table, which has the data at per attendee level. So it has, like, for this attendee, what is the data company name, what is the…
101 00:11:43.240 ⇒ 00:11:46.349 Awaish Kumar: Program codes and all these things, but…
102 00:11:46.510 ⇒ 00:11:53.270 Awaish Kumar: The tables, the data we required in the format in our… in our audit report, that depends, like, the…
103 00:11:53.420 ⇒ 00:11:58.559 Awaish Kumar: just queries on top of it. So, it is not a single model, which, like, we don’t need a…
104 00:11:58.940 ⇒ 00:12:09.160 Awaish Kumar: extra models for that. So we have base model, but we actually need some queries, and for each use case, there is a different query. So, the only thing which
105 00:12:09.380 ⇒ 00:12:21.109 Awaish Kumar: made sense, like, according to me, was to create some views where we basically define the logic, for example, which standardizes the calculation, like, the registration.
106 00:12:21.470 ⇒ 00:12:23.859 Awaish Kumar: By industry, for example.
107 00:12:24.100 ⇒ 00:12:26.760 Awaish Kumar: And we have, for example, this one.
108 00:12:26.990 ⇒ 00:12:32.960 Awaish Kumar: If we can look at… It says, Industry Attendees Department, so it’s like,
109 00:12:33.920 ⇒ 00:12:42.909 Awaish Kumar: All the attendees in the industry, reach, like, the section, and then we have the…
110 00:12:43.410 ⇒ 00:12:55.759 Awaish Kumar: segmented by the attendees of the departments. So this is, like, if there is any logic required, any filtering required, that happens inside of that. So anyone then needs this same
111 00:12:56.080 ⇒ 00:13:03.999 Awaish Kumar: table, anywhere in the CS report or anywhere else. I’m just going to reference this view instead of writing the query again from the base table.
112 00:13:04.190 ⇒ 00:13:19.409 Awaish Kumar: So that way, we try and standardize all our calculations. Like, the… the few things that we mentioned in the last call, that, okay, well, I need to calculate something, and the call… so we… and when we write the carries from base, then we might miss out on some filters, or…
113 00:13:19.500 ⇒ 00:13:28.989 Awaish Kumar: miss out on something and numbers become different. So this is where we are going to standardize all our logic, so if anyone needs attendance.
114 00:13:29.520 ⇒ 00:13:38.659 Awaish Kumar: By program code, it’s just going to come in and use this view, instead of writing a query by himself again on the base table.
115 00:13:40.040 ⇒ 00:13:41.340 Katherine Bayless: Nice, okay.
116 00:13:41.690 ⇒ 00:13:52.610 Katherine Bayless: Cool, so this is kind of where we’re, you know, sort of parking the right answer, so to speak. Yeah. Nice, okay, okay. I do, I just, going back to the, like.
117 00:13:52.860 ⇒ 00:14:17.699 Katherine Bayless: environments and dbt layer database cleanup. I do really appreciate it. I went in there to, like, look at this stuff, and I was just like, oh my god, I’m, like, so many things, I can’t… what… where do I go? So this is, honestly, this is much, much easier. I… I see a future where maybe that level of complexity makes sense, but for the moment, I really appreciate the kind of, you know, narrow consolidation. So, yeah, my tiny little brain will appreciate, being able to make
118 00:14:17.700 ⇒ 00:14:18.299 Katherine Bayless: Move more fluid.
119 00:14:18.740 ⇒ 00:14:30.589 Katherine Bayless: through this. But that’s cool. Okay, so those QA marts will ultimately wind up existing in prod marts as well. Correct. Okay, okay, okay, okay, cool.
120 00:14:30.590 ⇒ 00:14:33.099 Uttam Kumaran: So what it basically allows us to do is, like.
121 00:14:33.540 ⇒ 00:14:44.970 Uttam Kumaran: now, because nobody is, like, depending on anything in ProdMarts, it’s sort of safe, like, we could have pushed it there, but as things get hooked into other systems, or are available through Cortex.
122 00:14:45.060 ⇒ 00:14:59.459 Uttam Kumaran: we just don’t want to push without getting a QA. And then QA on our side, right, like, so I’ll be reviewing that, I’m looking at the logic, I’m looking… I’m gonna go look at the tables and make sure, like, there’s no duplication, like, the data exists.
123 00:14:59.460 ⇒ 00:15:11.269 Uttam Kumaran: And then, but it’s really, I think, the, like, is this the right number aggregation that we kind of need QA from the CTA team on? And then, yeah, I think this view layer is actually…
124 00:15:11.550 ⇒ 00:15:16.580 Uttam Kumaran: One step further, like, Hey, like, the tables for the report are, like.
125 00:15:16.720 ⇒ 00:15:34.380 Uttam Kumaran: all these joins, instead of just creating tables for each of those, we can just create views, and then eventually, like, we’ll just have this flexible model anyways. So we’re not, like, fixing the whole model just for this outcome. As we talked about, like, we’re creating the foundation models, and then we’ll just, like, create the joins as needed.
126 00:15:34.530 ⇒ 00:15:40.470 Uttam Kumaran: And then I think that’s really where we kind of need QA, too, like, did we do the proper joins, and is the output sort of matching, but…
127 00:15:40.990 ⇒ 00:15:54.589 Katherine Bayless: Yeah, I think that’s perfect. I think, yeah. And to your point, too, like, we are still so early that I think it’s part of why it’s good to be kind of figuring out exactly what we want this CES data to be like as the foundation, because
128 00:15:54.590 ⇒ 00:16:02.350 Katherine Bayless: once we start bringing more people in, and there’s more things built, to your point, right, that means more refactoring and stuff like that, but I think this is…
129 00:16:02.380 ⇒ 00:16:07.449 Katherine Bayless: I think this is absolutely the foundation that we need to be building, so I…
130 00:16:07.920 ⇒ 00:16:09.759 Katherine Bayless: This is cool. This is really cool.
131 00:16:09.760 ⇒ 00:16:12.799 Awaish Kumar: Yeah, based on these models, I actually just prepared this.
132 00:16:12.990 ⇒ 00:16:16.640 Awaish Kumar: this document, that will actually speed up the QA process.
133 00:16:17.430 ⇒ 00:16:23.410 Awaish Kumar: If we wanna do… so, like, this is the something, the snapshot from the audit report.
134 00:16:23.680 ⇒ 00:16:27.960 Awaish Kumar: And I’m running my queries on top of the view I’ve created.
135 00:16:28.160 ⇒ 00:16:32.019 Awaish Kumar: And then we have these actual results from that table.
136 00:16:32.250 ⇒ 00:16:33.520 Katherine Bayless: Yeah.
137 00:16:33.920 ⇒ 00:16:41.170 Awaish Kumar: If we compare that, like, we can see, for example, industry 2026, we have 86,687.
138 00:16:41.480 ⇒ 00:16:44.679 Katherine Bayless: And here, in the reform report, we have 6 and…
139 00:16:44.680 ⇒ 00:17:02.169 Awaish Kumar: 86,679, which basically, there’s a mismatch of few attendees, but it still looks much closer to the number. So, that way, I’ve been queuing this, and I have queued for all the, the tables we have in that audit report.
140 00:17:03.980 ⇒ 00:17:12.789 Awaish Kumar: And for most of them, it makes sense. The number does match what is coming from that order report, like registrations, attendance.
141 00:17:13.520 ⇒ 00:17:17.669 Awaish Kumar: But there are a few like, the open questions on,
142 00:17:18.329 ⇒ 00:17:21.519 Awaish Kumar: Like, the task number 3, where we are seeing the
143 00:17:22.450 ⇒ 00:17:33.970 Awaish Kumar: a pre-show, like, which… this logic is dependent on on-site flag, and for 2026, we don’t have that data yet, so it says, all as maybe a pre-show, because
144 00:17:34.160 ⇒ 00:17:35.780 Awaish Kumar: We don’t know on-site.
145 00:17:35.880 ⇒ 00:17:38.670 Awaish Kumar: And our flag just uses the default values.
146 00:17:38.910 ⇒ 00:17:40.849 Awaish Kumar: So it marks all of them.
147 00:17:40.960 ⇒ 00:17:50.800 Awaish Kumar: As such, yeah, so, but these are the actual numbers versus report numbers, and, like, we can actually…
148 00:17:50.900 ⇒ 00:17:55.959 Awaish Kumar: QA, the task, if you want to go in details, here on this, in this call.
149 00:18:00.530 ⇒ 00:18:11.449 Katherine Bayless: I admit, I’m very tempted to say yes. I’m also thinking it could be a deep rabbit hole, probably makes more sense to do asynchronously, but maybe we could work through, like, one, kind of, together?
150 00:18:11.450 ⇒ 00:18:12.690 Uttam Kumaran: Yeah, let’s do maybe one.
151 00:18:13.510 ⇒ 00:18:28.069 Katherine Bayless: For the… for the verified piece, I was curious, did we kind of decide if we wanted to, using the logic for verified, like, make it a flag on the data, similar to how it was in the past, or is it…
152 00:18:28.090 ⇒ 00:18:34.120 Katherine Bayless: We’re kind of just keeping the definition of verified and then using that, like, in a view, like, in a calculated field.
153 00:18:35.240 ⇒ 00:18:47.430 Awaish Kumar: That, like, the attendance field is basically a flag in the model, so it’s something… I received that logic from the Kyle regarding how he’s calculating
154 00:18:47.770 ⇒ 00:18:53.890 Awaish Kumar: 2025, the data from PIN26 and the data for the years before that.
155 00:18:54.090 ⇒ 00:18:54.630 Katherine Bayless: Yeah, yeah.
156 00:18:54.630 ⇒ 00:19:06.369 Awaish Kumar: Yeah, that exists in the model, because that is depend… that is tied to an individual, right? Our model is, like, per resistant, level.
157 00:19:06.370 ⇒ 00:19:16.019 Awaish Kumar: So, at that level, we can actually say, like, for this event in this year, this person, if he’s attended or not, so that kind of logic can just go in the model.
158 00:19:16.110 ⇒ 00:19:21.280 Awaish Kumar: And then we… but all the aggregations which we are doing here, these are happening in the view.
159 00:19:21.710 ⇒ 00:19:23.740 Katherine Bayless: Okay, gotcha. Cool.
160 00:19:24.060 ⇒ 00:19:42.550 Awaish Kumar: So, yeah, we are not… now we don’t depend on the verified flag, this is… this is something logic came from the car, it’s in the model, and then this view is just using that model. Like, this is basically just the carry on top of the view, and this view has the… the logic defined.
161 00:19:42.710 ⇒ 00:19:43.940 Awaish Kumar: Basically.
162 00:19:44.720 ⇒ 00:19:51.120 Awaish Kumar: If we can just… Go here, and in the gearbox, semantically…
163 00:19:57.050 ⇒ 00:20:13.869 Kyle Wandel: So it’s, yeah, it starts with the intermittent layer that you guys bit… created with the CES attendee base layer, and then it goes down to, I believe, the, overview, or the wide or something, and then it goes… then eventually goes to the overview, and then eventually goes to these semantic, layers.
164 00:20:17.040 ⇒ 00:20:19.140 Uttam Kumaran: It’s because…
165 00:20:19.140 ⇒ 00:20:20.170 Awaish Kumar: the table…
166 00:20:24.890 ⇒ 00:20:26.400 Awaish Kumar: Got this one, Megan.
167 00:20:31.180 ⇒ 00:20:34.580 Awaish Kumar: This is, like, basically the… the logic.
168 00:20:35.030 ⇒ 00:20:41.739 Awaish Kumar: For this view, so this we can see queries here directly, that… what I’m saying, I’m, like, just…
169 00:20:42.230 ⇒ 00:20:46.620 Awaish Kumar: Based on the report, I want to try this for years.
170 00:20:46.780 ⇒ 00:20:51.150 Awaish Kumar: 12456, and these, registration types.
171 00:20:51.660 ⇒ 00:20:54.269 Awaish Kumar: And then this utter dead flag.
172 00:20:54.390 ⇒ 00:21:04.680 Awaish Kumar: So here, we actually don’t have a logic for any flag, it just says flag, true, false, if the person attended or not. And the logic for that, basically, is in the…
173 00:21:05.390 ⇒ 00:21:07.969 Awaish Kumar: Model itself, for each year.
174 00:21:08.480 ⇒ 00:21:11.070 Katherine Bayless: Yeah, yeah, that makes sense. That makes sense.
175 00:21:12.010 ⇒ 00:21:13.280 Awaish Kumar: Yeah, so…
176 00:21:14.250 ⇒ 00:21:22.360 Awaish Kumar: And yeah, you can verify here if the query for the view looks fine, if we need to modify it, and then we can do that in dbt, and then…
177 00:21:22.720 ⇒ 00:21:28.569 Awaish Kumar: And this, yeah, this is just reading from the view, so this very simple thing.
178 00:21:29.450 ⇒ 00:21:29.970 Katherine Bayless: Yep.
179 00:21:31.390 ⇒ 00:21:36.890 Katherine Bayless: Yeah, I mean, if you want to pick one, and we can go through the QA on it,
180 00:21:37.070 ⇒ 00:21:42.049 Katherine Bayless: I don’t know if there’s any one that’s better than others to pick, but I’ll let you,
181 00:21:42.430 ⇒ 00:21:43.760 Katherine Bayless: I’ll let you decide.
182 00:21:43.760 ⇒ 00:21:44.360 Awaish Kumar: I don’t know.
183 00:21:45.410 ⇒ 00:21:47.349 Awaish Kumar: when I click?
184 00:21:47.850 ⇒ 00:21:58.999 Kyle Wandel: The one thing that I saw was, slightly off, I think, was Fortune 500, just looking at, that, and I think it might have been because we’re… I’m not looking at it now, but the number’s really high.
185 00:22:00.370 ⇒ 00:22:11.179 Kyle Wandel: Like, 2024, there was, like, 800 companies, so, like, I mean, unless it’s looking at the Fortune… Global 5… Global 1000, it’s per… it’s probably… it might be right, but, it can’t be 800.
186 00:22:12.260 ⇒ 00:22:20.649 Katherine Bayless: Yeah, I think the Fortune 500 stuff, that’s why I think we’re just gonna have to take it away from the, like, being a flag on the individual registrant, and instead, like.
187 00:22:20.650 ⇒ 00:22:21.310 Uttam Kumaran: Yeah.
188 00:22:21.670 ⇒ 00:22:22.000 Katherine Bayless: Tag it.
189 00:22:22.000 ⇒ 00:22:22.690 Uttam Kumaran: Give it ourselves.
190 00:22:22.850 ⇒ 00:22:32.270 Katherine Bayless: Right, and then use the identity stitching to find attendees that were part of that Fortune 500 company, because yeah, it’s just going to be too messy otherwise.
191 00:22:32.650 ⇒ 00:22:36.310 Kyle Wandel: And maybe… let me look at the overview…
192 00:22:38.360 ⇒ 00:22:42.129 Kyle Wandel: The Fortune 500 flag is there, from the wide.
193 00:22:42.130 ⇒ 00:22:44.690 Uttam Kumaran: Oh, which table is the Fortune 500?
194 00:22:45.500 ⇒ 00:22:57.220 Awaish Kumar: Yeah, right now, the logic is that we have Fortune 500 table, in the Snowflake, so from the attendees, we just get the company name, and then we
195 00:22:58.100 ⇒ 00:23:00.549 Awaish Kumar: compare with the Fortune 500 table.
196 00:23:00.760 ⇒ 00:23:05.410 Awaish Kumar: the company’s at that Fortune 500 table, and if the name matches, then we basically say.
197 00:23:06.130 ⇒ 00:23:11.110 Awaish Kumar: But we just flagged it as true that Attendant is from Fortune 500 company.
198 00:23:11.770 ⇒ 00:23:24.030 Katherine Bayless: Yeah, see, the trick is that we have, I mean, obviously, we know the company data is messy at the attendee level. We also have some situations where Fortune 500 companies
199 00:23:24.090 ⇒ 00:23:36.760 Katherine Bayless: we decided that they were there because, like, somebody attended from a subsidiary company, and so, like, the name will never match, but we’ve said that that company was in attendance at CES.
200 00:23:36.760 ⇒ 00:23:59.640 Katherine Bayless: And so that’s why it’s like, we really are gonna have to have the Fortune 500 list and the companies, like, canonically parked somewhere, and then the join out to the attendees can be a little shakier, because we’ll have the correct answer to how many Fortune 500 companies were at CES each year, and then if we’re using it to, like, find those people, it’s okay if we miss a few or get a false positive here and there, but that…
201 00:23:59.640 ⇒ 00:24:03.500 Katherine Bayless: The number that gets published will be correct, because it’s… Protected.
202 00:24:04.610 ⇒ 00:24:05.740 Awaish Kumar: Okay.
203 00:24:06.410 ⇒ 00:24:18.019 Awaish Kumar: Yeah, we can take this one. Maybe this is more, like, on a product category. It requires a little bit of logic in there, comparing with product code mappings.
204 00:24:19.600 ⇒ 00:24:25.550 Awaish Kumar: And we can go in here, and actually, I can… Run this…
205 00:24:41.760 ⇒ 00:24:44.740 Awaish Kumar: Okay, and basically it says…
206 00:24:44.960 ⇒ 00:24:57.759 Awaish Kumar: order category artificial intelligence, and this total number is for 2026 only, because, like, that’s what I saw in the document, and these are the percentages per year.
207 00:24:57.890 ⇒ 00:25:03.220 Awaish Kumar: So this is… number for artificial intelligence is 46,677.
208 00:25:03.410 ⇒ 00:25:06.709 Awaish Kumar: F, if we look at this report,
209 00:25:18.950 ⇒ 00:25:26.150 Awaish Kumar: Okay, so in the support it says, 43, 672.
210 00:25:26.900 ⇒ 00:25:30.790 Awaish Kumar: So there is a little bit of variation in the numbers.
211 00:25:30.990 ⇒ 00:25:35.440 Awaish Kumar: And that’s… I don’t know, like, it’s because… The…
212 00:25:35.700 ⇒ 00:25:39.649 Awaish Kumar: The report is, is from past, or is, like, we need to…
213 00:25:40.980 ⇒ 00:25:46.279 Katherine Bayless: Yeah, I mean, they should match exactly.
214 00:25:47.440 ⇒ 00:25:50.610 Awaish Kumar: And we can basically also look at the…
215 00:25:52.570 ⇒ 00:25:58.549 Katherine Bayless: I’m trying to remember… well, okay, actually, though, let me think about that, because…
216 00:25:59.960 ⇒ 00:26:10.970 Katherine Bayless: They should match exactly, however, we know that there were a little bit of truncation happening on the product codes.
217 00:26:10.990 ⇒ 00:26:24.059 Katherine Bayless: So it could be that there were a few people that are now, like, now that that field’s been kind of restored are coming through, although I’m assuming those product codes are listed in alpha order, so you wouldn’t expect
218 00:26:24.360 ⇒ 00:26:28.399 Katherine Bayless: artificial intelligence to be the one getting truncated.
219 00:26:29.440 ⇒ 00:26:30.970 Awaish Kumar: Okay, yeah, like…
220 00:26:31.170 ⇒ 00:26:36.990 Awaish Kumar: Yeah, thing is that if… if that… the string is long, right? If there are multiple codes attached to the same…
221 00:26:37.320 ⇒ 00:26:47.440 Katherine Bayless: Right, right, right. But I’m like, if they’re always in alphabetical order, artificial intelligence should never be the one at the very end getting trimmed, right? Because it begins with an A.
222 00:26:49.290 ⇒ 00:26:57.739 Katherine Bayless: But they might not be in alphabetical order, I’m actually not sure if they come through that way, or if they come through in, like, a totally random order somehow.
223 00:27:00.340 ⇒ 00:27:05.579 Awaish Kumar: And this is the, yeah, logic for calculating that. We can see it here.
224 00:27:07.150 ⇒ 00:27:10.649 Awaish Kumar: So this is… this is coming from both industry and media.
225 00:27:12.620 ⇒ 00:27:16.229 Awaish Kumar: And, then we are just using product category.
226 00:27:16.660 ⇒ 00:27:18.970 Awaish Kumar: I’m getting product-liness from here.
227 00:27:19.100 ⇒ 00:27:24.200 Awaish Kumar: And then it is being mapped to the… CS product interest codes.
228 00:27:24.680 ⇒ 00:27:31.069 Awaish Kumar: on the ID, Column, and so that it will now have map to the names.
229 00:27:31.210 ⇒ 00:27:34.440 Awaish Kumar: And for Indian… yeah, we are using this name.
230 00:27:34.970 ⇒ 00:27:35.690 Awaish Kumar: Thank you.
231 00:27:36.330 ⇒ 00:27:37.400 Awaish Kumar: Corresponding TMA.
232 00:27:37.400 ⇒ 00:27:38.240 Katherine Bayless: Have you heard that?
233 00:27:38.240 ⇒ 00:27:42.280 Awaish Kumar: We should use, or… There’s the other one, field value.
234 00:27:43.750 ⇒ 00:27:44.980 Katherine Bayless: Yes, yeah.
235 00:27:44.980 ⇒ 00:27:50.129 Awaish Kumar: there is a name, this one, corresponding TMA name, and there’s one called
236 00:27:50.530 ⇒ 00:27:53.640 Awaish Kumar: Field value, like, into the sport.
237 00:27:53.850 ⇒ 00:27:55.710 Katherine Bayless: We should probably use him.
238 00:27:55.770 ⇒ 00:28:19.190 Katherine Bayless: field value… like, let me take a look at it real quick, just so I make sure I say the right thing. While I pull it up. The TMA name thing is… so we have a… the TMA stands for Targeted Marketing Alert, and we have a small number of these product categories that we do send, like, specific marketing pieces around, and so the corresponding TMA name is populated for the ones that are kind of, like, most important.
239 00:28:19.430 ⇒ 00:28:25.270 Katherine Bayless: And, and have that, additional marketing happening around them. I think…
240 00:28:25.380 ⇒ 00:28:30.890 Katherine Bayless: Field value is the… yeah, it’s kind of like the raw ones.
241 00:28:31.170 ⇒ 00:28:35.670 Katherine Bayless: Year by year. So you could probably use corresponding TMA name.
242 00:28:38.070 ⇒ 00:28:41.890 Katherine Bayless: it just… We should probably rename it, is all, because…
243 00:28:43.190 ⇒ 00:28:46.599 Katherine Bayless: Yeah. Like, maybe if we just called it something like, you know.
244 00:28:47.490 ⇒ 00:28:54.769 Katherine Bayless: standardized code, I guess, or something like that, because there is the variation against the years, but not necessarily…
245 00:28:55.370 ⇒ 00:29:06.409 Katherine Bayless: Like, we don’t need to make sure we report it as exactly as it was then, we just need to make sure we can consistently tie it together historically. So we can use the TMA name one, we probably just want to rename that field.
246 00:29:07.040 ⇒ 00:29:12.449 Awaish Kumar: Yeah, that field, just, like, in the end, in our view, it’s called product category, so…
247 00:29:14.040 ⇒ 00:29:19.439 Awaish Kumar: We are just then running it on the product interscode, and then we use count on the…
248 00:29:19.460 ⇒ 00:29:20.700 Katherine Bayless: emails.
249 00:29:20.700 ⇒ 00:29:25.549 Awaish Kumar: We can also count on registered ID, but then maybe the user email here.
250 00:29:27.850 ⇒ 00:29:28.560 Katherine Bayless: Yeah, I should note.
251 00:29:28.560 ⇒ 00:29:29.410 Awaish Kumar: She’s registered?
252 00:29:29.410 ⇒ 00:29:40.810 Katherine Bayless: Pattern ID and email should both aggregate the same, 99.9% of the time, when I’ve randomly decided to check and see if that’s still true. It has been true.
253 00:29:40.810 ⇒ 00:29:49.680 Katherine Bayless: there were a few brief days where there were somehow duplicate emails with different registrant IDs, but then those have gotten cleaned up, and so I think…
254 00:29:49.710 ⇒ 00:29:57.769 Katherine Bayless: for the most part, it’s totally equivalent to count distinct email versus count distinct registrant ID. Like, it should be safe to use either.
255 00:30:00.260 ⇒ 00:30:06.869 Awaish Kumar: Okay, and yeah, this is just the logic for calculation of percentages and the sum for each year.
256 00:30:07.550 ⇒ 00:30:13.400 Awaish Kumar: And that’s how… I got it, so…
257 00:30:15.050 ⇒ 00:30:32.870 Katherine Bayless: Okay, so if we wanted to pin down the delta of the, like, 5 records for artificial intelligence for 2026 between the Word doc report and this, we would go to the attendee level, I’m guessing? So, like, making sure that…
258 00:30:33.110 ⇒ 00:30:36.410 Katherine Bayless: We’re not… missing anybody…
259 00:30:37.910 ⇒ 00:30:44.650 Katherine Bayless: which number was higher? Sorry, I already forget. Snowflake had the higher number, right? And the report was 5 more? Yeah. Yeah. Okay.
260 00:30:45.150 ⇒ 00:30:49.080 Katherine Bayless: So yeah, so then I guess what we would want to do is make sure that…
261 00:30:53.070 ⇒ 00:31:11.329 Katherine Bayless: whoever was considered industry or media and interested in AI in the query I used for the Word doc is coming through in this here. And so we could look at this, like, instead of the aggregated view, we could pull the, like, raw row by row and compare them. Like, that’s how we could QA this, I’m guessing? Am I going in the right direction?
262 00:31:12.020 ⇒ 00:31:20.749 Awaish Kumar: if you have the query for the Word doc, like, you can just send us a… send us that, and I can verify, like, what’s the difference between.
263 00:31:21.020 ⇒ 00:31:23.460 Katherine Bayless: Okay, yeah.
264 00:31:35.070 ⇒ 00:31:39.710 Uttam Kumaran: Yeah, so I feel like this… this… this QA piece is really, like, the primary…
265 00:31:40.190 ⇒ 00:31:40.620 Katherine Bayless: Yeah.
266 00:31:40.620 ⇒ 00:31:44.840 Uttam Kumaran: we kind of need some iteration on. So, like, what do we think is the best, like.
267 00:31:45.320 ⇒ 00:31:48.220 Uttam Kumaran: Cadence, you’d think, like, more working sessions?
268 00:31:48.480 ⇒ 00:31:53.730 Uttam Kumaran: Like, what do you think, Catherine, to, like, kind of get through it?
269 00:31:54.670 ⇒ 00:32:01.699 Katherine Bayless: I… I mean, I… I’m totally guilty as charged. I love a good working session.
270 00:32:02.460 ⇒ 00:32:14.469 Katherine Bayless: Woof, my calendar next week looks terrible. I want to unsee all of this. Okay, I mean, there are some spots in here, mostly in the afternoons, it looks like. But yeah, I think…
271 00:32:14.910 ⇒ 00:32:29.430 Katherine Bayless: probably what we can do is, like, maybe me and Kai and Kyle can divide and conquer and, like, do a first pass at, like, okay, what do we have in Snowflake? What’s the logic? What did we have in the Word doc? I mean, also, just to be clear.
272 00:32:29.560 ⇒ 00:32:45.160 Katherine Bayless: possible the Word doc is wrong, in my case, with the 2026 numbers. I did a very good job, I think, but, you know, we might… that might ultimately wind up being what we find, too. But yeah, so if we take, like, a first pass to go through these, and then, yeah, book some time to kind of, like, dig in on…
273 00:32:45.190 ⇒ 00:33:01.590 Katherine Bayless: anything that either we can’t figure out, or things where we do identify, like, this is the tweak that needs to happen. But if we want to do that, like, as a big group, that’s okay, too. I’m just not sure if everybody wants to sit on a Zoom call and pore over numbers. I might be the only nerd who thinks that sounds like a fun time.
274 00:33:02.720 ⇒ 00:33:04.330 Kyle Wandel: No, I think that sounds good to me.
275 00:33:04.330 ⇒ 00:33:04.990 Chi Quinn: No.
276 00:33:05.350 ⇒ 00:33:06.719 Katherine Bayless: Yeah. Okay.
277 00:33:07.210 ⇒ 00:33:09.940 Katherine Bayless: In that case.
278 00:33:11.200 ⇒ 00:33:12.039 Awaish Kumar: Yeah, so maybe we can…
279 00:33:12.040 ⇒ 00:33:14.430 Uttam Kumaran: find… Time next week.
280 00:33:14.640 ⇒ 00:33:18.590 Uttam Kumaran: And that way, at least it’s on the calendar, so we can keep Driving, and then…
281 00:33:18.590 ⇒ 00:33:20.780 Katherine Bayless: We’ll have something to talk about regardless.
282 00:33:20.780 ⇒ 00:33:30.400 Uttam Kumaran: Yeah, and I’m now using some of these models to test Cortex. Basically, these kind of came out in QA yesterday, so I’m kind of starting to use them, so…
283 00:33:32.650 ⇒ 00:33:44.489 Katherine Bayless: Okay, well, how about… Monday, I think, is kind of… we could use some of the time in planning, if we get through planning fast, and then… Okay. But apart from that, I could do,
284 00:33:44.960 ⇒ 00:33:49.560 Katherine Bayless: 3 o’clock on Tuesday? I could do an hour or two there.
285 00:33:49.710 ⇒ 00:33:59.809 Katherine Bayless: And then third Wednesday, I could do 1 o’clock. Actually, Wednesday we could do any time in the afternoon, because we could move the meeting with membership and data if we needed to.
286 00:34:00.260 ⇒ 00:34:01.809 Uttam Kumaran: Yeah, Wednesday could be good.
287 00:34:02.320 ⇒ 00:34:03.010 Katherine Bayless: Yeah.
288 00:34:03.260 ⇒ 00:34:08.170 Katherine Bayless: I could book, like, like, 1 to 3 local time. Yeah. Yeah? Okay.
289 00:34:08.179 ⇒ 00:34:08.889 Uttam Kumaran: Let’s do that.
290 00:34:09.110 ⇒ 00:34:10.120 Katherine Bayless: Okay, cool, cool.
291 00:34:14.290 ⇒ 00:34:19.669 Awaish Kumar: Okay, yeah, I actually also wanted to bump the Asana request, and
292 00:34:19.840 ⇒ 00:34:26.099 Awaish Kumar: I’ve added these 5 things here, which I need to create sonar tickets for, like, we need to work on these things.
293 00:34:26.350 ⇒ 00:34:28.110 Awaish Kumar: And, like, interbrand flag?
294 00:34:28.600 ⇒ 00:34:29.420 Awaish Kumar: Yes, you do.
295 00:34:29.880 ⇒ 00:34:46.880 Katherine Bayless: Yeah, Interbrand you can ignore. The twice is its own sort of list that, we probably can… we at least have the historical list, and then I… we probably need to pull the one for 2025. But Interbrand, I’ve been told, nobody cares about, we can ditch that.
296 00:34:49.109 ⇒ 00:34:55.609 Awaish Kumar: Then we have Fortune 500, like, we already have a table, but I think you mentioned that we need a devised version of that.
297 00:34:55.969 ⇒ 00:34:57.160 Katherine Bayless: Yeah.
298 00:34:57.160 ⇒ 00:35:00.310 Awaish Kumar: One, and then, yeah, this one that just come up.
299 00:35:00.440 ⇒ 00:35:01.570 Awaish Kumar: We want to…
300 00:35:01.820 ⇒ 00:35:08.500 Awaish Kumar: have industry and media by department that… like the query, so that I can just go and also QA.
301 00:35:09.310 ⇒ 00:35:10.520 Awaish Kumar: Asynchronously.
302 00:35:15.180 ⇒ 00:35:27.809 Katherine Bayless: For the Fortune 500 thing, I mean, I think it probably basically just becomes a seed file, honestly. Like, we’ll just have, like, this was the answer for these years, it doesn’t get refreshed apart from generating the New Year’s file.
303 00:35:28.010 ⇒ 00:35:40.130 Katherine Bayless: I don’t know if I have a strong opinion one way or the other, like, does it make sense for the file to contain all 500 companies, or just the ones that were at CES?
304 00:35:40.250 ⇒ 00:35:49.680 Katherine Bayless: probably, ultimately, kind of Kyle comes down to, like, do we ever get asked, like, who wasn’t there, where it would be convenient to have the rest of the Fortune 500 list already in there? .
305 00:35:50.480 ⇒ 00:35:54.020 Kyle Wandel: I mean, I think we definitely keep it in Snowflake, for sure.
306 00:35:54.150 ⇒ 00:36:03.710 Kyle Wandel: Yeah, it definitely will be used as, like, a prospecting… I mean, should be probably used as a prospecting list, potentially, so anybody that’s not in there, I think it would make sense, to keep them.
307 00:36:04.120 ⇒ 00:36:11.699 Katherine Bayless: Okay, yeah, so maybe we do one seed per year with the Fortune 500 list, and just a Boolean for present at CES, true-false.
308 00:36:12.110 ⇒ 00:36:16.400 Kyle Wandel: Yeah, I think so. The one annoying thing with the Fortune 500, well…
309 00:36:17.230 ⇒ 00:36:34.870 Kyle Wandel: I can’t remember if I had to pay to get the previous data, or just the current year data, I can’t remember what, but it was, like, you had to pay, like, I think I did, like, a trial, so I just scraped, like, literally the past, like, 10 years’ data, and then took it all, and then, whatever, unsubscribed. But I don’t know if you have to do that for every year or not, is one thing I don’t know.
310 00:36:35.410 ⇒ 00:36:48.890 Katherine Bayless: Yeah, we can take a look at that. That’s funny. I definitely asked, like, Claude to bring me the Fortune 500 list for this year, and it came back with one, that matched the website. So yeah, now I’m like, I wonder how it got it.
311 00:36:49.570 ⇒ 00:36:54.050 Katherine Bayless: Is it back my credit card. No. But yeah, yeah, yeah, I think that makes sense.
312 00:36:54.050 ⇒ 00:37:10.790 Kyle Wandel: So, I understand, like, the pre-audit report a little bit more. You guys are going from, because looking at the SQL, it looks like you’re going from the raw versions of the registration into this audit-wide version, this intermediate, and then going down into the various, like, schematic reports, basically, is that correct?
313 00:37:12.040 ⇒ 00:37:16.010 Awaish Kumar: Yeah, from raw to intermediate, then it goes to the marts as well.
314 00:37:17.070 ⇒ 00:37:21.529 Awaish Kumar: the overview table, that is the cleaner version of intermediate.
315 00:37:22.110 ⇒ 00:37:23.800 Awaish Kumar: Then we have SantiClear.
316 00:37:24.080 ⇒ 00:37:33.029 Awaish Kumar: So, yeah, so all these, joining, like, for example, joins with Fortune 500, product interest code, these,
317 00:37:33.260 ⇒ 00:37:42.500 Awaish Kumar: happen in, like, right now, I’m doing it in intermediate, but they can also happen. If we have a clean table, we can just do in mods as well.
318 00:37:42.980 ⇒ 00:37:43.510 Awaish Kumar: Understood.
319 00:37:43.510 ⇒ 00:37:58.799 Kyle Wandel: Yeah, I think… yeah, I guess that would be my biggest question. I think I know Catherine and I talked a little bit about trying to clean it up a little bit. I just kind of want to have, and I don’t know if this is, like, best… it’s probably not best practices from a developer standpoint, but, like, have, like, one… we need to have, like, one clean table of, like.
320 00:37:58.940 ⇒ 00:38:10.339 Kyle Wandel: registration history, I guess, and that’s the way I would start, basically. And then all the semantic views would go off that, is what I was thinking. I don’t know if that’s correct, what you were thinking, Catherine, but…
321 00:38:10.540 ⇒ 00:38:12.760 Kyle Wandel: If there… we want to do, like.
322 00:38:12.940 ⇒ 00:38:28.269 Kyle Wandel: is it really necessary to create a whole new model for just this pre-auto report, I guess is my question. Because the schematic model… most of these pre-order reports would be… are generic questions that we get asked anyways, so it’s almost just like it’s part of just registration
323 00:38:28.320 ⇒ 00:38:39.759 Kyle Wandel: data itself. So, like, right now, we have two registration pipelines, the one that you created and the one I created. And so if there’s a way we can, like, make that just one, I think that’s what… I guess I’m where I’m getting at.
324 00:38:40.820 ⇒ 00:38:48.329 Awaish Kumar: Yeah, we can work it, basically. So, I also saw that while working on it, so it’s just,
325 00:38:48.550 ⇒ 00:38:52.670 Awaish Kumar: I didn’t know, like, what is there yet, so I just went from…
326 00:38:52.910 ⇒ 00:39:08.529 Awaish Kumar: So, yeah, but I think if I can QA, that if both models return the same data, then we can just maybe stick to the one, maybe, and then do the joining with, like, product interests, or joining with Virtual 500, that we can do on top of that.
327 00:39:08.940 ⇒ 00:39:17.169 Kyle Wandel: Yeah. I don’t know, Catherine, was that your thinking, too, or were you actually thinking to do, like, a different model, almost, or different…
328 00:39:17.320 ⇒ 00:39:21.520 Kyle Wandel: Schema per, like, major, like, report, I guess.
329 00:39:22.220 ⇒ 00:39:28.270 Katherine Bayless: Yeah, so I think… Mmm, so I think, kind of.
330 00:39:28.620 ⇒ 00:39:35.299 Katherine Bayless: it’s a little bit of a both, but I think the… So, because we…
331 00:39:35.600 ⇒ 00:39:47.929 Katherine Bayless: Because we get that reg data as just, like, a giant flat file that is not… like, it… it’s coming from a bunch of different components that are then getting put together, but it…
332 00:39:48.610 ⇒ 00:39:57.489 Katherine Bayless: I want to not have a giant fat file, like, behind the numbers, because the risk in my mind is the aggregation, so, like.
333 00:39:57.520 ⇒ 00:40:22.480 Katherine Bayless: the Fortune 500 flag, I mean, I know we’re gonna move that anyways, kind of a bad example, but, like, things that are about a company but getting repeated at each individual registrant level, like, that’s where I see a lot of, like, risk for, like, you know, numbers getting a little squirrely, or things not making the right aggregation out of the jump, with the Cortex Code Analyst and things like that. So, like, separating anything that’s in that reg data and about a company from
334 00:40:22.480 ⇒ 00:40:25.759 Katherine Bayless: Anything that’s in that reg data and about an individual.
335 00:40:25.820 ⇒ 00:40:37.279 Katherine Bayless: And then, for the individual ones, the next sort of break is what are the things that are persistent about the individual, like their name and their phone number, and what are the things that are
336 00:40:37.280 ⇒ 00:40:49.400 Katherine Bayless: time-sensor, or, like, time-bound, like, the responses to the demographic questions that year, where we do want to have, like, you know, this was the selection they made for CES 2023, and this was the one for CES 2026.
337 00:40:49.470 ⇒ 00:40:54.220 Katherine Bayless: We wouldn’t want to collapse those into just one, like, most current value.
338 00:40:54.220 ⇒ 00:41:12.560 Katherine Bayless: necessarily. I mean, sometimes we also might want that, but we’d have the structure behind it to be able to see the granularity. And so that’s… that’s really kind of where my brain is going. Like, I just want to have the CES registration data be modeled at the right granularity for what people are looking for. And so I think having the,
339 00:41:13.340 ⇒ 00:41:19.089 Katherine Bayless: The semantic layer piece that’s got these sort of, like, correct answers to common questions.
340 00:41:19.590 ⇒ 00:41:38.109 Katherine Bayless: it’s kind of, like, neither here nor there with the way that we want to model the CES data, but I think it’ll help reinforce and help with the QA to make sure that, okay, the way that we’ve modeled this data does still aggregate correctly, and this is kind of the, like, the answers in the back of the book that we need to make sure it’s always matching to.
341 00:41:39.480 ⇒ 00:41:55.490 Kyle Wandel: So then this might be, I mean, stupid question, but… so, like, I guess the… going forward, so raw would be, like, main… like, obviously ingest, obviously, and then staging, is this more of just, like, a… I guess, cleaning and coming to, like, a one type of cleaned-up version of the registration data?
342 00:41:55.490 ⇒ 00:41:59.670 Kyle Wandel: So what I mean by that is, like, obviously there’s gonna be, like, there’s, like, 4 or 5 different types of
343 00:41:59.690 ⇒ 00:42:14.859 Kyle Wandel: email column names. Should that be whittled down to one, basically, in the staging layer? Or is it like… like that, basically? And that’s the staging, and then the inter… and then the INT makes sense. The INT layer makes sense. You basically just break out reg ID by year.
344 00:42:14.860 ⇒ 00:42:34.020 Kyle Wandel: And then use… then create the different tables off that, and then reconnect, for the… for marts. I think that makes sense for me. But is that… so, like, the staging layer should be more of, like, having, like, a designated set of, like, 20 columns, basically, like they have… like, you guys have for the audit-wide, pre-audit. Is that correct?
345 00:42:35.870 ⇒ 00:42:36.870 Awaish Kumar: Yes, so…
346 00:42:38.230 ⇒ 00:42:44.640 Katherine Bayless: I think… well, no, I was just gonna say, like, I think I’m realizing that, like, we’re probably talking about two different pieces of the same thing, because…
347 00:42:44.900 ⇒ 00:42:46.769 Katherine Bayless: Kyle, to your point.
348 00:42:47.230 ⇒ 00:43:03.690 Katherine Bayless: we’ve already, yeah, done a lot of that work to get all of the data harmonized across those column names, and we don’t want to lose that. So it does make sense to start from that giant consolidated registry file before we do the thing I was talking about, where we’re breaking it into the right granularities.
349 00:43:03.690 ⇒ 00:43:07.450 Katherine Bayless: I don’t think we need to, like, reinvent the wheel creating that file.
350 00:43:07.950 ⇒ 00:43:24.749 Katherine Bayless: However, at the same time, we’ll probably also still see column name changes in the future, so, like, I think we can use that file for everything up to 2026, but then when we get the 2027 file, it has also some slightly different column names. Then the question becomes.
351 00:43:24.750 ⇒ 00:43:36.369 Katherine Bayless: do we force that file to fit into the giant history file, or do we just, like, keep tabs on what this was called in this year, what this was called in this year? Which I know you’ve also done that work, so…
352 00:43:36.370 ⇒ 00:43:37.050 Kyle Wandel: Yep.
353 00:43:37.050 ⇒ 00:43:39.099 Katherine Bayless: Could go either way, but you’re right.
354 00:43:39.100 ⇒ 00:43:39.550 Kyle Wandel: You got a great day.
355 00:43:39.550 ⇒ 00:43:42.999 Katherine Bayless: You don’t lose the work you’ve done to harmonize the files historically.
356 00:43:43.000 ⇒ 00:44:07.319 Kyle Wandel: Yeah, right now, the way I’ve done it is I did the individual years, and then combined them all in staging, and then cleaned up a little bit more in intermediate, but it sounds like the next step would be, instead of doing, like, the attendee base table that I have created, which has, like, 100, like, all columns, it’d be better to do what they have done, what you guys have done, basically, and limit it down to, like, 20-something columns, or 26 columns, or maybe even less.
357 00:44:07.320 ⇒ 00:44:09.699 Kyle Wandel: And that’s, like, the base table.
358 00:44:10.030 ⇒ 00:44:22.070 Awaish Kumar: Yeah, it, it, like, it depends, like, on… because in the transformation layer, we want to minimize the data we want to process. That’s also one of the things.
359 00:44:22.650 ⇒ 00:44:40.699 Awaish Kumar: like, so that’s… that is one of the reasons we… we keep the raw layer in dbt, that is just ephemeral, that just lists the column names for… for the readability, but then in the staging intermediate layers, we just keep the columns that are needed, because we don’t…
360 00:44:40.870 ⇒ 00:44:50.800 Awaish Kumar: Like, if we are using only 20 fields, there’s no reason to keep 100 in the table, so it becomes, like, it shouldn’t confuse the… the…
361 00:44:50.930 ⇒ 00:44:53.830 Awaish Kumar: Like, other developers and users of that table.
362 00:44:54.040 ⇒ 00:45:07.839 Awaish Kumar: So, this is, like, one of the things where, like, the… when… as we move up to the layer, we just try to minimize and filter out and pre-aggregate things as soon as possible in the process. At the end, we just have the things that we need.
363 00:45:08.280 ⇒ 00:45:23.070 Kyle Wandel: So that it sounds like, okay, so it sounds like staging’s a little bit of both, cleaning and, like, removing and parsing slash cleaning data, and then intermediate is what you were talking about, Catherine, where we create all the separate tables. So, like, one table would be just reg ID and year, and that’s, like, the standard
364 00:45:23.550 ⇒ 00:45:32.519 Kyle Wandel: reg… whatever, reg base table, and then one table would be just the reg ID, year, and product code. That would be, like, the product interest table, is that correct?
365 00:45:32.520 ⇒ 00:45:33.050 Katherine Bayless: Okay.
366 00:45:33.050 ⇒ 00:45:33.590 Kyle Wandel: Okay.
367 00:45:34.420 ⇒ 00:45:35.290 Katherine Bayless: Exactly.
368 00:45:35.750 ⇒ 00:45:41.710 Kyle Wandel: Okay, so then we’ll need to, the biggest thing we’ll do is need to move,
369 00:45:41.820 ⇒ 00:45:44.340 Kyle Wandel: Okay, I got it. Alright, I understand.
370 00:45:45.350 ⇒ 00:45:49.770 Katherine Bayless: Yeah, I mean, it’s… like, honestly, the way I’ve kind of thought about it, too, is, like.
371 00:45:49.790 ⇒ 00:46:08.419 Katherine Bayless: since starting, like, because this organization didn’t have a CRM in the middle here, like, we kind of have to be a CRM almost, which is where we kind of ran into these sorts of things, where it’s like, I know we have a report, but we do need to break it into
372 00:46:08.420 ⇒ 00:46:18.049 Katherine Bayless: component pieces in order to actually be able to use it the way people expect us to, because we are functioning more like a makeshift CRM than a reporting engine in some ways.
373 00:46:19.170 ⇒ 00:46:19.760 Kyle Wandel: Yep.
374 00:46:21.380 ⇒ 00:46:25.479 Kyle Wandel: Okay, I’m gonna shine a little bit more into it, and maybe we can clean this up,
375 00:46:25.790 ⇒ 00:46:31.010 Kyle Wandel: Even better, if that makes sense. But the pre-audit makes sense, because my whole thinking was,
376 00:46:31.170 ⇒ 00:46:50.949 Kyle Wandel: the semantic… so, like, now we’ll have to change the semantic layers a little bit, because we’ll have to create the, like, the pre-audit-wide table, and again, maybe, correct me if I’m wrong, since it has 26 columns, we’ll want to break that out into the various different, potential DIMMs, basically. So, like, is Fortune 500 company… company-level data.
377 00:46:51.110 ⇒ 00:46:52.420 Kyle Wandel: Okay.
378 00:46:52.580 ⇒ 00:46:54.990 Katherine Bayless: So my thinking is right on that, okay.
379 00:46:55.180 ⇒ 00:46:57.079 Katherine Bayless: Yeah, yeah, yeah.
380 00:47:03.110 ⇒ 00:47:12.870 Katherine Bayless: Yeah. No, yeah, so I think if we take some time and kind of start going through these, and then, yeah, I’ll book the meeting for Wednesday for us to all get back together, because I think…
381 00:47:13.750 ⇒ 00:47:27.519 Katherine Bayless: I think everything that we have is gonna make it really easy to get through the QA. I know that maybe sounds kind of weird to say, but, like, I think we’re… we’re very close to what we need. There’s just a few pieces that’ll come through in QA that’ll be like, reshape this.
382 00:47:27.890 ⇒ 00:47:28.940 Katherine Bayless: So, yeah.
383 00:47:30.190 ⇒ 00:47:35.820 Awaish Kumar: Okay, then on this, I just had…
384 00:47:36.770 ⇒ 00:47:39.299 Awaish Kumar: One… yeah, the one more thought on…
385 00:47:40.000 ⇒ 00:47:43.359 Awaish Kumar: the… the models, like, I… I…
386 00:47:43.640 ⇒ 00:47:48.280 Awaish Kumar: I saw that model that Kyle is mentioning. I just had one question, like…
387 00:47:48.390 ⇒ 00:48:02.429 Awaish Kumar: Although, like, the way I’m doing it is just using few fields instead of all the fields that Kyle created. So, like, we want to keep them both for now in there, because it’s essentially for…
388 00:48:02.630 ⇒ 00:48:12.870 Awaish Kumar: for the recent years, it’s just, like, the kind of duplicate. I’m not sure if I just point to the ones that Kyle has created, or we’ll continue to build that.
389 00:48:14.210 ⇒ 00:48:25.739 Kyle Wandel: That’s a good question, Catherine. I think we’ll have to, like I said, I think we’ll have to change it up a little bit. So I think… I think the staging is good. So, I think the… the ones that I created in staging, so I think it’s prod staging…
390 00:48:25.860 ⇒ 00:48:44.919 Kyle Wandel: registration views, and then there’s the 2014-22, and then 2023-2026. We just need to union them, and then that might be good from a staging standpoint. And then for intermediate, we’ll need to do, like, what I was talking about, which is, like, create all of those, like.
391 00:48:44.940 ⇒ 00:48:53.420 Kyle Wandel: base table, so, like, a registration base. So, like, all the registration ID, the year of that registration ID, and that’s it.
392 00:48:53.500 ⇒ 00:48:58.519 Kyle Wandel: And then we have an attendee table, so attendee ID, or registration ID,
393 00:48:58.850 ⇒ 00:49:10.680 Kyle Wandel: and year, based on those metrics, or whatever those filters that we talked about earlier, or that I posted on there earlier. I think that’s… is that where your head’s at, Catherine?
394 00:49:10.990 ⇒ 00:49:19.359 Katherine Bayless: yeah. I mean, it’s, I guess, you know, similar to, like, comparing it to CRM, it’s, like, also star schema approaches, but yeah, I think…
395 00:49:21.100 ⇒ 00:49:21.900 Katherine Bayless: Yeah.
396 00:49:23.320 ⇒ 00:49:26.899 Katherine Bayless: If we harmonize two chunks of the historical data.
397 00:49:27.310 ⇒ 00:49:33.210 Katherine Bayless: Get rid of the 3, 4, 5, 6 on their own, and then we just have one…
398 00:49:33.890 ⇒ 00:49:36.550 Katherine Bayless: CES Reg up through 2026.
399 00:49:36.690 ⇒ 00:49:41.239 Katherine Bayless: Then we work off of that to put all the other pieces together, yeah.
400 00:49:41.590 ⇒ 00:49:44.249 Awaish Kumar: This is where we have them, this waste stream.
401 00:49:44.900 ⇒ 00:50:01.659 Kyle Wandel: Yeah, and so that attendee and that registration base table is all of the columns. It’s not filtered down to, like, the 20 columns like you guys have in the audit-wide, but I think Catherine even wants to go even more granular, which is, like, at least for the base table, just, it’ll only be 2 columns, which would be the badge number and then the year.
402 00:50:02.300 ⇒ 00:50:06.329 Kyle Wandel: And maybe email if we want, and that might actually not be bad. Maybe include email, I don’t know.
403 00:50:06.630 ⇒ 00:50:10.280 Katherine Bayless: Yeah, yeah, I would include an email, he could call, yeah, yeah.
404 00:50:11.730 ⇒ 00:50:18.639 Kyle Wandel: And then we’ll have another table that’s intermediate CES Company Base, and then you’ll have all the company identifiers, basically.
405 00:50:19.470 ⇒ 00:50:20.250 Katherine Bayless: Which…
406 00:50:20.250 ⇒ 00:50:24.490 Awaish Kumar: Oh, we are trying to… that we can do, like,
407 00:50:25.040 ⇒ 00:50:30.080 Awaish Kumar: That is, like, basically converting this single table into a star schema.
408 00:50:30.080 ⇒ 00:50:30.710 Katherine Bayless: Exactly.
409 00:50:30.710 ⇒ 00:50:31.140 Awaish Kumar: There you go.
410 00:50:31.140 ⇒ 00:50:32.700 Kyle Wandel: Yeah, I think that’s what she wants, yeah.
411 00:50:33.320 ⇒ 00:50:33.990 Katherine Bayless: Yeah.
412 00:50:33.990 ⇒ 00:50:41.839 Awaish Kumar: We can create, like, the attendees, dim companies, dimension for, for example, there is a…
413 00:50:42.300 ⇒ 00:50:50.759 Awaish Kumar: like, the department, like, the… the state, country, city, like, the DIM address, and then basically, something like,
414 00:50:51.010 ⇒ 00:50:52.840 Awaish Kumar: And one table, fact.
415 00:50:52.990 ⇒ 00:50:56.710 Awaish Kumar: events or something, which basically has
416 00:50:57.180 ⇒ 00:51:03.120 Awaish Kumar: attendees… attendee ID, event ID, and something like that, so… where we can actually…
417 00:51:03.420 ⇒ 00:51:09.429 Awaish Kumar: see all these connections. Yeah, we can move towards that as well. But this is, like,
418 00:51:09.810 ⇒ 00:51:20.680 Awaish Kumar: Pointing to that doc audit report, we are, like, trying to come up with the, actually, the numbers, which exactly match with the document. So now that we know, like.
419 00:51:20.960 ⇒ 00:51:23.999 Awaish Kumar: We are able to come up with that, we can actually try to
420 00:51:24.290 ⇒ 00:51:35.310 Awaish Kumar: Divide, basically, audit-wide, or this… these tables into a star, complete star schema, and then maybe rewrite our regulations on top of that.
421 00:51:35.910 ⇒ 00:51:38.209 Katherine Bayless: Yeah, exactly, exactly, yep.
422 00:51:39.510 ⇒ 00:51:45.630 Awaish Kumar: Yeah, because our aggregations right now just read from one, so then it will read from multiple tables, so…
423 00:51:45.810 ⇒ 00:51:49.740 Awaish Kumar: Right, it will… it will get from… start from event…
424 00:51:50.160 ⇒ 00:51:53.940 Awaish Kumar: Fact table, and then it get data from also from the…
425 00:51:54.760 ⇒ 00:51:58.800 Awaish Kumar: Departments, or people, or whatever, and then combine them.
426 00:51:59.540 ⇒ 00:51:59.880 Katherine Bayless: Yeah.
427 00:51:59.880 ⇒ 00:52:01.849 Awaish Kumar: We can move towards that.
428 00:52:02.630 ⇒ 00:52:19.220 Katherine Bayless: Yeah, and that’s what I mean, I’m like, I think the work that you’ve done is actually going to make it really easy to QA the star schema build-out, because we’ve got the logic, we’ve got the right answer thing, we’ll figure out the onesie-twosies that are missing, right? But then, moving it into the star schema being the under-the-hood piece…
429 00:52:19.790 ⇒ 00:52:23.039 Katherine Bayless: Should be pretty easy, I think. Like, yeah, yeah, yeah.
430 00:52:24.450 ⇒ 00:52:27.910 Awaish Kumar: Yeah, at least we’ll have something to compare. It will be easy.
431 00:52:28.240 ⇒ 00:52:39.490 Awaish Kumar: So, once we have this estimate, now we can create new views, compare the data, if it all looks good, then move forward and delete the old aggregations.
432 00:52:39.730 ⇒ 00:52:40.200 Katherine Bayless: Yep.
433 00:52:40.200 ⇒ 00:52:42.870 Awaish Kumar: That way, it’ll make us easier.
434 00:52:43.370 ⇒ 00:52:58.419 Kyle Wandel: I don’t know if I’m allowed to share this. I found the 2026 merits, like, flow doc again, from Adrian. I don’t know if I can share that. That actually might be helpful as well to create some of the star schemas, because it lists most of, like, all of, like.
435 00:52:58.560 ⇒ 00:53:07.509 Kyle Wandel: whatever the registrar can input on their user, on the UI, when they register. So it’ll break down of all the codes that they have listed for the 2026 year.
436 00:53:08.080 ⇒ 00:53:11.390 Katherine Bayless: Is that that master filtration draft, or master.
437 00:53:11.390 ⇒ 00:53:11.770 Kyle Wandel: Yeah.
438 00:53:11.960 ⇒ 00:53:21.860 Katherine Bayless: Yeah, I think I put that in the bucket, but let me actually see if it’s in there. Okay. And if not, you might also have a newer copy of it, potentially, I don’t know.
439 00:53:21.860 ⇒ 00:53:30.419 Kyle Wandel: I just saw the, I found the 2026, and that was, 2 weeks… I opened it two weeks ago, so I don’t know if that’s it or not.
440 00:53:30.930 ⇒ 00:53:36.299 Katherine Bayless: Yeah, so if it’s the CES2026masterfiltrationdraft.xlsx.
441 00:53:36.500 ⇒ 00:53:37.789 Kyle Wandel: Yeah, you got it, Ben.
442 00:53:37.790 ⇒ 00:53:52.760 Katherine Bayless: Okay, okay, yeah, so that’s in the data governance bucket, and Kyle’s right, like, that is actually, I think, gonna be really helpful to figure out, like, some of the star schema pieces, because yeah, it’ll tell you exactly which questions were asked for which groups, and what the codes are, and all that kind of stuff, so yeah.
443 00:53:52.770 ⇒ 00:53:58.120 Katherine Bayless: like, I don’t think we need to bring the file into Snowflake itself, but, like, all of the…
444 00:53:58.380 ⇒ 00:54:10.570 Katherine Bayless: information inside of it will be useful as we build this out, and probably something also for CHI to kind of think about how we capture some of that, like, metadata around reg and data collection.
445 00:54:11.290 ⇒ 00:54:28.529 Kyle Wandel: So, it looks like this… I don’t know, it might be looked different than what you sent, Catherine, but it’s just basically just this big, matrix of how our rules are broken out, what the master filtration is, and then it breaks it down sometimes by, like, research code, or by code per thing. So, like, this is department.
446 00:54:29.660 ⇒ 00:54:38.520 Kyle Wandel: job title, primary function. So, it’s probably going to be really helpful for developing those star schemas. We just haven’t had a chance to build it out ourselves yet, really.
447 00:54:41.940 ⇒ 00:54:50.980 Awaish Kumar: Okay, but I think that the work we have done until intermediate will be useful, because these marts going to be in the marts.
448 00:54:52.010 ⇒ 00:54:52.440 Awaish Kumar: Yeah.
449 00:54:52.640 ⇒ 00:54:53.320 Awaish Kumar: Exactly.
450 00:54:53.920 ⇒ 00:54:58.830 Awaish Kumar: And so, whatever we have in staging, it will be… all will be useful.
451 00:54:59.460 ⇒ 00:55:00.929 Awaish Kumar: Creating the response schema.
452 00:55:01.470 ⇒ 00:55:06.990 Katherine Bayless: Yeah, exactly. Like, I really, I think we’re pretty close. It’s just, like, the last little, little bits, yeah.
453 00:55:08.200 ⇒ 00:55:09.720 Awaish Kumar: And then we,
454 00:55:10.670 ⇒ 00:55:16.190 Awaish Kumar: And then we have semantic layer, and that’s where we put the aggregations. I think, that’s all.
455 00:55:16.890 ⇒ 00:55:22.119 Awaish Kumar: For the next week, what do you think we should prioritize? Like, getting this into the March, or…
456 00:55:22.740 ⇒ 00:55:25.680 Awaish Kumar: Like, should it be the first priority, or, like, how do you wanna…
457 00:55:26.420 ⇒ 00:55:30.610 Katherine Bayless: I think… Yes?
458 00:55:31.100 ⇒ 00:55:42.479 Katherine Bayless: Yeah, yeah, yeah. Yeah, I think so. I think, yeah, getting through this QA, getting it, moved into the marts, and then pushing on the star schema build, makes sense to me.
459 00:55:42.480 ⇒ 00:55:52.599 Katherine Bayless: I know we also have the Shopify data that we can start working with, but I think it makes sense to stay focused on the CES stuff and get that over the finish line, yeah.
460 00:55:53.650 ⇒ 00:56:12.359 Awaish Kumar: Okay, for the next week, we can focus on finalizing, like, we can work on moving this into the marts, and at the same time, we can, like, get some QA done. Yeah. So we can have a second pass, and that’s when we can close this CES, and then we can move to Shopify next.
461 00:56:12.590 ⇒ 00:56:16.990 Awaish Kumar: And yeah, we also have identity stitching in the… In the process.
462 00:56:17.150 ⇒ 00:56:17.730 Awaish Kumar: So…
463 00:56:17.730 ⇒ 00:56:24.129 Katherine Bayless: I mean, it kind of naturally is an outgrowth of working with the CES data, so I think…
464 00:56:24.760 ⇒ 00:56:26.899 Katherine Bayless: Yeah, getting this all modeled.
465 00:56:27.050 ⇒ 00:56:39.410 Katherine Bayless: then that is, like, the data we will want to be doing the identity stitching with, and using it against the remember stuff, and things like that. So, yeah, I think that’s what we can pick up as soon as we’ve got the CES stuff kind of done.
466 00:56:39.410 ⇒ 00:56:42.199 Awaish Kumar: Okay, CS modeling, then identity switching,
467 00:56:43.150 ⇒ 00:56:48.110 Awaish Kumar: And Shopify, in parallel, if we have time, and yeah.
468 00:56:48.580 ⇒ 00:56:51.090 Katherine Bayless: Yeah, yeah, exactly, exactly.
469 00:56:51.590 ⇒ 00:56:59.610 Kyle Wandel: The good news is, once you get CS in, pretty much that’s… and Shopify, that’s pretty much all of it, like, quite frankly, so… it’s most of it.
470 00:57:00.060 ⇒ 00:57:03.640 Awaish Kumar: Yeah, for Shopify, we’ve done a lot of modeling before, so…
471 00:57:05.580 ⇒ 00:57:11.419 Katherine Bayless: Nice. Okay. Yeah, it looks like it’s pretty easy to stitch together that data, too, which is good, but yeah.
472 00:57:12.510 ⇒ 00:57:13.850 Katherine Bayless: Yeah. Cool.
473 00:57:14.530 ⇒ 00:57:17.640 Awaish Kumar: Okay, yeah, that’s it from my side.
474 00:57:18.900 ⇒ 00:57:20.980 Katherine Bayless: Okay.
475 00:57:21.330 ⇒ 00:57:28.149 Awaish Kumar: Yeah, and just an update, Ashwini also has worked on… on some of the aggregations, and…
476 00:57:28.180 ⇒ 00:57:30.980 Katherine Bayless: Like, maybe by end of day, he will have…
477 00:57:31.010 ⇒ 00:57:37.389 Awaish Kumar: Also, push through… aggregations there, so you can QA all of them, like, the next week.
478 00:57:38.090 ⇒ 00:57:39.149 Katherine Bayless: Okay, cool.
479 00:57:39.760 ⇒ 00:57:44.410 Katherine Bayless: I’m trying to think other things that are on my brain…
480 00:57:46.880 ⇒ 00:57:50.050 Katherine Bayless: Hmm. No, I mean, I think that’s kind of…
481 00:57:51.280 ⇒ 00:57:54.280 Katherine Bayless: I think that covers it pretty well, yeah, honestly. Oh!
482 00:57:54.590 ⇒ 00:58:15.730 Katherine Bayless: Okay, I knew there was something. So the, the other tiny thing, and actually, I think I have a call with him after this, the, remembers Data Share, they wanted us to switch from using the one with the longer name to the data share V2, and so we weren’t sure if we wanted to just, like, hot swap by renaming them, or if we wanted to refactor.
483 00:58:16.070 ⇒ 00:58:26.730 Ashwini Sharma: Yeah, I have a PR ready for that. We are just changing the name, how we refer to that data set, right? We’ll not rename the databases, as such. We’ll just, yeah, change the reference.
484 00:58:27.380 ⇒ 00:58:40.809 Katherine Bayless: Okay, okay. Should I let him know that, like, we’ll have that PR merge by, like, Monday, and then we can give him a go-ahead on Monday to just turn off the, I don’t think he’ll do it right away, to be honest, but, like, so that he can turn off the old data share.
485 00:58:41.730 ⇒ 00:58:42.150 Awaish Kumar: Oh, yeah.
486 00:58:42.150 ⇒ 00:58:46.010 Ashwini Sharma: In fact, we can merge it today, but it’s a Friday evening, so let’s not merge.
487 00:58:47.010 ⇒ 00:58:49.980 Ashwini Sharma: We can merge it on Monday morning.
488 00:58:50.320 ⇒ 00:58:57.350 Katherine Bayless: Okay, that works. Cool. Then I’ll just… I’ll let him know that on Monday we’ll do the switch, and then we can do a little QA, and we should be fine.
489 00:58:58.650 ⇒ 00:58:59.680 Awaish Kumar: Okay, cool.
490 00:59:01.670 ⇒ 00:59:06.020 Katherine Bayless: Fabulous. Alright. Anything else, Kai? Kyle?
491 00:59:06.790 ⇒ 00:59:10.510 Kyle Wandel: Nope, Arisha, I just added you to Asana finally, so sorry that took so long.
492 00:59:11.110 ⇒ 00:59:12.450 Awaish Kumar: No, no worries.
493 00:59:12.810 ⇒ 00:59:13.940 Awaish Kumar: Thank you.
494 00:59:17.530 ⇒ 00:59:18.619 Kyle Wandel: But that’s it for me.
495 00:59:19.800 ⇒ 00:59:21.360 Awaish Kumar: Okay, thank you.
496 00:59:21.360 ⇒ 00:59:22.090 Katherine Bayless: little fun back.
497 00:59:22.090 ⇒ 00:59:23.990 Kyle Wandel: Thank you.