Meeting Title: CTA Working session Date: 2026-04-30 Meeting participants: Awaish Kumar, Amber Lin, Ashwini Sharma
WEBVTT
1 00:06:17.670 ⇒ 00:06:18.710 Amber Lin: Hello!
2 00:06:19.090 ⇒ 00:06:20.770 Awaish Kumar: My number for you.
3 00:06:21.530 ⇒ 00:06:25.329 Amber Lin: I’m good, just had lunch.
4 00:06:25.690 ⇒ 00:06:28.650 Awaish Kumar: So, are you able to work right after lunch?
5 00:06:29.690 ⇒ 00:06:37.229 Amber Lin: I… no. I’m a little sleepy, but it’s okay. I wanted to work on sets for a while.
6 00:06:38.800 ⇒ 00:06:39.440 Awaish Kumar: Okay.
7 00:06:40.370 ⇒ 00:06:46.720 Amber Lin: Cool. I don’t think Ashwini’s coming, because I know he logs off in, like, 30 minutes.
8 00:06:49.200 ⇒ 00:07:00.119 Awaish Kumar: Okay, yeah, no worries, but, like, I wanted… what are the real challenges right now, in terms of, like, if you need any modeling help? I saw your escalations. I did create a…
9 00:07:00.550 ⇒ 00:07:03.079 Awaish Kumar: But it is under review, because in the CTA,
10 00:07:04.020 ⇒ 00:07:13.190 Awaish Kumar: Without approval, so if you can approve it, I can watch that. That basically creates the attendance table for Unveiled Vegas.
11 00:07:13.770 ⇒ 00:07:14.720 Awaish Kumar: Image.
12 00:07:15.370 ⇒ 00:07:30.209 Amber Lin: Hmm, sounds good. I was able to… so what you… you said yesterday to find attendance out of registration? I was able to do that, it just, I think, overall, the numbers don’t map
13 00:07:30.620 ⇒ 00:07:32.179 Amber Lin: One-to-one.
14 00:07:32.880 ⇒ 00:07:35.850 Awaish Kumar: what I… yesterday, I told was… That.
15 00:07:36.380 ⇒ 00:07:41.679 Awaish Kumar: So, like, the registration… if you… have you open Snowflake?
16 00:07:42.880 ⇒ 00:07:50.720 Amber Lin: Yeah. One sec… Yeah, keep going, keep going, I’ll… I’ll log in.
17 00:07:51.030 ⇒ 00:07:56.030 Awaish Kumar: Okay, in the snowflake, where we have this C-E-S star.
18 00:07:56.400 ⇒ 00:08:01.380 Awaish Kumar: And inside of that, Demo name?
19 00:08:03.440 ⇒ 00:08:05.420 Awaish Kumar: Yeah, fact registration.
20 00:08:07.540 ⇒ 00:08:10.859 Awaish Kumar: That table should be able to give you a tenants?
21 00:08:11.060 ⇒ 00:08:12.920 Awaish Kumar: for CES events.
22 00:08:13.230 ⇒ 00:08:17.009 Amber Lin: Yeah, I actually was able to do that from, like.
23 00:08:17.340 ⇒ 00:08:26.229 Amber Lin: for members, I was able to use the registration bridge table, and then I joined it and found, like, attendance per
24 00:08:26.370 ⇒ 00:08:34.320 Amber Lin: per person. So I was able to do that. I think the… the issue I’m running into is that
25 00:08:35.200 ⇒ 00:08:45.049 Amber Lin: even just by registration, the numbers don’t match up completely. So let me show you… I’m using this…
26 00:08:46.180 ⇒ 00:08:48.120 Amber Lin: Oh.
27 00:08:48.120 ⇒ 00:08:50.900 Awaish Kumar: Well, what… my point is…
28 00:08:51.060 ⇒ 00:08:51.780 Amber Lin: It’s a…
29 00:08:51.780 ⇒ 00:08:53.890 Awaish Kumar: twining the data, right? It’s about
30 00:08:54.220 ⇒ 00:08:58.859 Awaish Kumar: Joining… when you are trying to join the data from…
31 00:08:59.530 ⇒ 00:09:05.430 Awaish Kumar: CS2 remembers, right? That’s where the problem is. Not with the CES data itself.
32 00:09:07.420 ⇒ 00:09:11.159 Amber Lin: Guy, can you repeat that question?
33 00:09:11.610 ⇒ 00:09:19.440 Awaish Kumar: So if I go in and I see the attendance in the 2026 CEC experience.
34 00:09:20.570 ⇒ 00:09:28.019 Awaish Kumar: I can use just FETC as transit registration table to do that, and I… Yeah. And that number looks…
35 00:09:28.610 ⇒ 00:09:39.660 Amber Lin: Yeah, I agree. The star schema doesn’t have an issue. I think the main issue is between the bridge table and that join, some of it is missing. So.
36 00:09:40.070 ⇒ 00:09:43.959 Awaish Kumar: So, I’m still trying to understand what… what we are trying to…
37 00:09:44.120 ⇒ 00:09:47.979 Awaish Kumar: Why we are trying to generate with membership data?
38 00:09:49.360 ⇒ 00:10:01.349 Amber Lin: Oh, I’m not joining it with membership data. The bridge table has membership, I assume, because, like, whatever is in the bridge table is a member, right?
39 00:10:01.680 ⇒ 00:10:05.750 Awaish Kumar: Oh, so this resolved… I wrote a staging table, right?
40 00:10:06.760 ⇒ 00:10:10.489 Amber Lin: Yeah, I’m using the.
41 00:10:10.920 ⇒ 00:10:12.720 Awaish Kumar: Okay, so what is happening here is…
42 00:10:12.720 ⇒ 00:10:13.890 Amber Lin: Yeah.
43 00:10:14.170 ⇒ 00:10:14.840 Amber Lin: So much.
44 00:10:15.700 ⇒ 00:10:19.019 Awaish Kumar: So what is, happening here is,
45 00:10:20.350 ⇒ 00:10:25.910 Awaish Kumar: We are… this is… we are… this is how we are trying the company names.
46 00:10:27.250 ⇒ 00:10:30.440 Awaish Kumar: E… C-E-S… registration data.
47 00:10:30.800 ⇒ 00:10:33.019 Awaish Kumar: to the DIM organization table.
48 00:10:36.540 ⇒ 00:10:40.550 Awaish Kumar: So, if you want to do that.
49 00:10:42.660 ⇒ 00:10:46.039 Awaish Kumar: You’re saying if you try to do that, you are…
50 00:10:46.530 ⇒ 00:10:49.730 Awaish Kumar: You are not able to get the correct number.
51 00:10:49.730 ⇒ 00:11:02.869 Amber Lin: Yeah. I’m a little bit lower, because see here, I have this, and then right here, this is… I think this is this number. So it’s 1,100 something, so I’m missing, like…
52 00:11:03.060 ⇒ 00:11:08.040 Amber Lin: a few each year. Like, I’m just, like… Yeah.
53 00:11:08.040 ⇒ 00:11:14.480 Awaish Kumar: With the identity stitching method, we are not able to stitch 100% of the companies.
54 00:11:15.090 ⇒ 00:11:18.070 Awaish Kumar: So there were always some missing data.
55 00:11:19.370 ⇒ 00:11:20.010 Amber Lin: Yeah.
56 00:11:21.190 ⇒ 00:11:21.889 Awaish Kumar: So, yeah.
57 00:11:21.890 ⇒ 00:11:25.100 Amber Lin: I… I kind of… I kind of realized, so…
58 00:11:25.460 ⇒ 00:11:29.269 Amber Lin: I guess we just have to tell them that…
59 00:11:29.270 ⇒ 00:11:30.700 Awaish Kumar: They know already, right?
60 00:11:31.130 ⇒ 00:11:32.430 Amber Lin: Oh, sure. Oh, yeah.
61 00:11:32.430 ⇒ 00:11:40.809 Awaish Kumar: work. We already shared the approach that by doing that, we can match maybe up to 90% of the company’s data. Okay.
62 00:11:40.810 ⇒ 00:11:42.820 Amber Lin: Okay, yeah, that sounds good.
63 00:11:42.970 ⇒ 00:11:43.879 Awaish Kumar: So, yeah.
64 00:11:44.710 ⇒ 00:11:46.110 Amber Lin: Cool.
65 00:11:46.210 ⇒ 00:11:56.449 Amber Lin: So, last thing on this membership topic, I just want to confirm, like, I am doing the right thing, right? I’m just… I’m just doing… I don’t think I need to join this, but…
66 00:11:56.690 ⇒ 00:12:01.099 Awaish Kumar: Actually, this is the correct table she’s pointing, right? Paradent decision.
67 00:12:02.220 ⇒ 00:12:04.520 Ashwini Sharma: Show me the table name, yeah.
68 00:12:04.790 ⇒ 00:12:14.710 Ashwini Sharma: QA, intermediate, Identity Stitching, member engagement, yes, or resolution, yes, yes, the ones with org resolution in the end. Those are the…
69 00:12:14.930 ⇒ 00:12:16.680 Ashwini Sharma: R&D stretch tables.
70 00:12:17.120 ⇒ 00:12:23.460 Awaish Kumar: Ashwini, can you please go and approve this? Since I created this PR, I’m not able to approve it.
71 00:12:23.680 ⇒ 00:12:24.200 Awaish Kumar: If you’re gonna.
72 00:12:24.200 ⇒ 00:12:24.890 Ashwini Sharma: Ugh.
73 00:12:25.310 ⇒ 00:12:27.080 Ashwini Sharma: Yeah, one second.
74 00:12:27.550 ⇒ 00:12:28.180 Amber Lin: Cool.
75 00:12:29.140 ⇒ 00:12:44.210 Amber Lin: I mean, that’s all I have on the membership stuff, and then for attendance, I just joined it to, let’s see, the star schema, and then got the attendance, so I think we’re… I think we’re fine there.
76 00:12:44.380 ⇒ 00:12:48.240 Amber Lin: I have a list of to-dos, so I think this is done.
77 00:12:48.400 ⇒ 00:12:52.100 Amber Lin: Let’s… let me cross that out.
78 00:12:53.610 ⇒ 00:12:59.109 Ashwini Sharma: It looks like I’m not able to log into CTA when something happened.
79 00:13:02.240 ⇒ 00:13:04.130 Awaish Kumar: Login to GitHub.
80 00:13:04.940 ⇒ 00:13:05.930 Awaish Kumar: Procedure.
81 00:13:05.930 ⇒ 00:13:10.189 Ashwini Sharma: Yeah, I have to go through CTA. Oh, yeah, yeah, I’m able to log in now. Cool.
82 00:13:10.190 ⇒ 00:13:10.840 Amber Lin: Okay.
83 00:13:13.760 ⇒ 00:13:19.090 Amber Lin: Yeah, I think for us, the remaining two things are…
84 00:13:19.210 ⇒ 00:13:22.249 Amber Lin: Are here, so the first one is…
85 00:13:22.670 ⇒ 00:13:27.820 Amber Lin: this product code. I don’t… I don’t think this is a major issue.
86 00:13:27.820 ⇒ 00:13:29.070 Awaish Kumar: This is…
87 00:13:29.920 ⇒ 00:13:44.299 Awaish Kumar: should have this, right? It might not be the exact, like, the name, you… you might not see the exact names, the way they are being shown, but you can affect CS registration if you… if you join it with,
88 00:13:44.510 ⇒ 00:13:46.339 Awaish Kumar: Which table?
89 00:13:46.640 ⇒ 00:13:49.409 Awaish Kumar: And then get the product names from.
90 00:13:49.410 ⇒ 00:13:50.580 Amber Lin: Yeah.
91 00:13:50.790 ⇒ 00:13:51.629 Awaish Kumar: Yeah, didn’t worry.
92 00:13:51.630 ⇒ 00:13:55.379 Amber Lin: I have it, it’s just when… when I…
93 00:13:55.430 ⇒ 00:14:14.220 Amber Lin: right now, query it, it gives me the 2026 codes, which I think is what the bridge table is based off of, which is not directly… like, the first one is still AI, but the second one is not robotics. It gives me… like, the second one it gives me is…
94 00:14:14.770 ⇒ 00:14:15.880 Amber Lin: Let me see…
95 00:14:16.030 ⇒ 00:14:22.699 Amber Lin: So this is more of, like, a more minor thing, I think. Let me… let me see where it is.
96 00:14:22.850 ⇒ 00:14:23.680 Amber Lin: Yeah.
97 00:14:24.110 ⇒ 00:14:27.090 Amber Lin: I think the bridge table… .
98 00:14:27.470 ⇒ 00:14:27.819 Awaish Kumar: That’s probably…
99 00:14:27.820 ⇒ 00:14:42.840 Amber Lin: product codes right here, and the 2026 codes are these codes. So, it’s just a slight mismatch, and, like, I don’t really know why, when I query Cortex.
100 00:14:42.970 ⇒ 00:14:48.609 Amber Lin: The second in line is, like, audio, and not this.
101 00:14:50.840 ⇒ 00:14:54.590 Awaish Kumar: I… it’s… What do you mean by…
102 00:14:55.620 ⇒ 00:15:02.009 Amber Lin: Here, let me… let me show you… So, attendance by product…
103 00:15:02.010 ⇒ 00:15:06.510 Awaish Kumar: So, Amber, like, is it… it’s… when you are using this method.
104 00:15:06.670 ⇒ 00:15:07.790 Amber Lin: Chromebooks.
105 00:15:07.790 ⇒ 00:15:12.569 Awaish Kumar: It is possible it is maybe using some other table, because there is a lot of different
106 00:15:12.750 ⇒ 00:15:22.580 Awaish Kumar: Tables we are… in here. And some of it is using some old data. Like, for example, PD…
107 00:15:23.100 ⇒ 00:15:27.289 Awaish Kumar: like, the RPD table that you have been using previously?
108 00:15:27.290 ⇒ 00:15:32.009 Amber Lin: Yeah, I’m not using the RPT table, I’m using this.
109 00:15:32.260 ⇒ 00:15:36.599 Amber Lin: This one, and then the bridge table.
110 00:15:36.740 ⇒ 00:15:40.360 Awaish Kumar: Yeah, this is the correct approach. Like, if using these both.
111 00:15:40.820 ⇒ 00:15:45.000 Awaish Kumar: We should have the number, and that’s the only one way to get that.
112 00:15:45.460 ⇒ 00:15:46.440 Amber Lin: Yeah.
113 00:15:46.560 ⇒ 00:15:51.169 Amber Lin: Like, right now… like, see, the second,
114 00:15:55.120 ⇒ 00:15:59.770 Amber Lin: Let’s see, so it took from… yeah, it took from this…
115 00:16:00.290 ⇒ 00:16:06.210 Amber Lin: That’s registration, and then to come bridge. So, it’s using the right tables, it’s all from prod.
116 00:16:06.210 ⇒ 00:16:12.850 Awaish Kumar: Let’s see, from FactCS registration, Yeah. It takes,
117 00:16:16.250 ⇒ 00:16:16.950 Awaish Kumar: Boom.
118 00:16:17.100 ⇒ 00:16:18.349 Awaish Kumar: Where is the…
119 00:16:24.250 ⇒ 00:16:26.419 Awaish Kumar: Yeah, but it is not joining on…
120 00:16:26.720 ⇒ 00:16:29.600 Awaish Kumar: Correct CE registration, it is not…
121 00:16:29.840 ⇒ 00:16:32.939 Awaish Kumar: Why is… it’s not joining on product code with the…
122 00:16:36.650 ⇒ 00:16:38.900 Awaish Kumar: It’s doing outer joined.
123 00:16:39.140 ⇒ 00:16:45.320 Awaish Kumar: That it means it includes… the data… from…
124 00:16:46.550 ⇒ 00:16:48.120 Awaish Kumar: I don’t know, stable here it is.
125 00:16:48.120 ⇒ 00:16:51.240 Amber Lin: Let me try… Yeah, it’s still…
126 00:16:51.670 ⇒ 00:16:53.800 Amber Lin: It is saying an outer join.
127 00:16:55.790 ⇒ 00:16:56.650 Amber Lin: Okay.
128 00:16:56.950 ⇒ 00:17:09.140 Awaish Kumar: And, is it… Also… filtering out, the students canceled.
129 00:17:18.869 ⇒ 00:17:22.530 Awaish Kumar: But this filter isn’t being used anywhere in Kyrie.
130 00:17:24.890 ⇒ 00:17:26.919 Awaish Kumar: Audit scope, attended row.
131 00:17:27.490 ⇒ 00:17:29.820 Awaish Kumar: Is it… Okay, it is…
132 00:17:29.820 ⇒ 00:17:34.270 Amber Lin: Yeah, this… this one I already… this is, like, already filtered.
133 00:17:34.490 ⇒ 00:17:38.929 Amber Lin: I exclude, like, is canceled, and I exclude as student.
134 00:17:46.180 ⇒ 00:17:49.260 Awaish Kumar: So you’re… what you’re saying is… Cute that.
135 00:17:49.980 ⇒ 00:17:50.660 Awaish Kumar: It is true.
136 00:17:50.660 ⇒ 00:17:51.910 Amber Lin: Same… yeah.
137 00:17:52.200 ⇒ 00:17:55.159 Awaish Kumar: Is cancer false, and is stroke is false?
138 00:17:55.290 ⇒ 00:17:57.779 Awaish Kumar: Only then we take this. Okay.
139 00:17:58.250 ⇒ 00:18:09.660 Amber Lin: Yeah. It’s mainly the, the, like, the products that don’t match. Right here, they have, like, it’s AI, robotics, and computing.
140 00:18:09.660 ⇒ 00:18:13.449 Awaish Kumar: That’s God, that’s… that’s possible that it…
141 00:18:14.330 ⇒ 00:18:18.649 Awaish Kumar: Like, when they did the… did the exercise.
142 00:18:18.910 ⇒ 00:18:23.249 Awaish Kumar: There might not be the… enough data.
143 00:18:23.530 ⇒ 00:18:25.890 Awaish Kumar: We have the newer version of the data?
144 00:18:27.340 ⇒ 00:18:32.769 Awaish Kumar: And second thing, also, That, the approach they were taking.
145 00:18:33.080 ⇒ 00:18:38.800 Awaish Kumar: Doesn’t… does not mean that we’re right. That was right, Rich. They just tried to do that somehow.
146 00:18:39.310 ⇒ 00:18:40.400 Awaish Kumar: Andrew…
147 00:18:41.760 ⇒ 00:18:42.830 Amber Lin: Okay.
148 00:18:42.830 ⇒ 00:18:44.120 Awaish Kumar: Okay.
149 00:18:44.600 ⇒ 00:18:53.470 Awaish Kumar: Yeah, the only thing is that we can… we just can verify, like, fact registration gives us the correct numbers, and if we join it with product, like.
150 00:18:53.740 ⇒ 00:18:56.139 Awaish Kumar: Bridge table?
151 00:18:57.180 ⇒ 00:19:01.000 Awaish Kumar: And based on that, if our result is correct, then…
152 00:19:01.000 ⇒ 00:19:01.600 Amber Lin: Yeah.
153 00:19:01.620 ⇒ 00:19:03.130 Awaish Kumar: The… then it’s correct.
154 00:19:03.130 ⇒ 00:19:08.850 Amber Lin: Okay, yeah, I think… I think the query, maybe it’s the outer join, but, like.
155 00:19:09.550 ⇒ 00:19:23.620 Amber Lin: Okay, I think I have sign-off from you, like, this is, like, this is okay. The numbers overall are decent, it’s just these specific things are slightly different, like, robotics is much lower in our query, but we can leave it.
156 00:19:23.830 ⇒ 00:19:28.390 Awaish Kumar: So, I’m just concerned. If we’re gonna look back at the query.
157 00:19:28.710 ⇒ 00:19:30.310 Amber Lin: Yeah, I can…
158 00:19:30.590 ⇒ 00:19:38.940 Awaish Kumar: I’m just concerned, when it is auto-doiled, and we don’t have data from fact CES registration, it is possible that
159 00:19:39.090 ⇒ 00:19:44.240 Awaish Kumar: This row, audit score, attendant row becomes null, right?
160 00:19:44.910 ⇒ 00:19:45.590 Amber Lin: Hmm.
161 00:19:45.590 ⇒ 00:19:49.729 Awaish Kumar: personal, then what happens? Can you scroll right?
162 00:19:50.980 ⇒ 00:19:53.940 Awaish Kumar: Scroll right, this girly a little bit, okay.
163 00:19:54.890 ⇒ 00:19:57.440 Awaish Kumar: When it is null, then it’s null, and we don’t count.
164 00:19:58.320 ⇒ 00:20:03.579 Awaish Kumar: Right, the… Count on, count nulls.
165 00:20:04.010 ⇒ 00:20:05.099 Awaish Kumar: Am I correct?
166 00:20:06.460 ⇒ 00:20:12.310 Amber Lin: Huh, say that again, please. So you’re saying we count it as null when there’s no row there?
167 00:20:13.360 ⇒ 00:20:18.180 Awaish Kumar: So if there are 10 rows, and we are counting on a column that… that is all null.
168 00:20:18.360 ⇒ 00:20:22.359 Awaish Kumar: So, it should return 0. Count of that column should be 0.
169 00:20:24.870 ⇒ 00:20:31.240 Amber Lin: Yeah, let me try an inner join and… and see how it… see how it goes.
170 00:20:31.240 ⇒ 00:20:32.570 Awaish Kumar: Okay, let’s do that.
171 00:20:33.220 ⇒ 00:20:36.789 Awaish Kumar: But it’s… yeah, it was an SQL question, like, actually…
172 00:20:36.920 ⇒ 00:20:39.290 Awaish Kumar: I think it don’t count now.
173 00:20:45.410 ⇒ 00:20:47.660 Ashwini Sharma: Sorry, Avish, was that a question for me?
174 00:20:49.130 ⇒ 00:20:54.260 Awaish Kumar: Yeah, I, I, I’m just… Brainstorming, like, count…
175 00:20:54.380 ⇒ 00:20:57.970 Awaish Kumar: Does not count null row, null values, right?
176 00:20:58.840 ⇒ 00:21:01.869 Amber Lin: I think it gave us identical numbers.
177 00:21:02.080 ⇒ 00:21:08.480 Amber Lin: There’s probably no… No… Joins…
178 00:21:14.860 ⇒ 00:21:15.450 Awaish Kumar: Okay.
179 00:21:15.820 ⇒ 00:21:18.699 Amber Lin: Yeah, should be fine, but I can adjust it.
180 00:21:25.300 ⇒ 00:21:27.129 Amber Lin: Oh, wait, one sec.
181 00:21:29.140 ⇒ 00:21:30.020 Amber Lin: Oh.
182 00:21:30.870 ⇒ 00:21:32.989 Amber Lin: Here, yeah, you’re right.
183 00:21:33.290 ⇒ 00:21:37.689 Amber Lin: There’s null from… Oh, this is the other one.
184 00:21:38.820 ⇒ 00:21:51.110 Amber Lin: There’s no null product interest name, no null product codes. There’s just a bit from 2026 that doesn’t have…
185 00:21:51.780 ⇒ 00:21:53.100 Amber Lin: a match.
186 00:21:55.960 ⇒ 00:22:00.960 Awaish Kumar: Yeah, but that’s true, right? Registration is filtering on 2026.
187 00:22:01.150 ⇒ 00:22:03.020 Awaish Kumar: So, that’s why we…
188 00:22:03.620 ⇒ 00:22:04.750 Amber Lin: Mmm, okay.
189 00:22:05.660 ⇒ 00:22:10.829 Ashwini Sharma: Can you do a sum instead of COUNT, and then just mark 0 and 1 so that
190 00:22:14.060 ⇒ 00:22:17.180 Ashwini Sharma: I mean, we can be sure whether the nulls are included or not.
191 00:22:18.250 ⇒ 00:22:22.649 Amber Lin: Where should I… should I type here?
192 00:22:23.000 ⇒ 00:22:28.350 Ashwini Sharma: Lines 26 to… 30?
193 00:22:28.480 ⇒ 00:22:29.220 Ashwini Sharma: Right?
194 00:22:29.610 ⇒ 00:22:33.149 Ashwini Sharma: What you’re doing is you’re doing an aggregating on account.
195 00:22:33.320 ⇒ 00:22:37.140 Ashwini Sharma: And then inside count, you’re doing case when this thing is null.
196 00:22:38.740 ⇒ 00:22:39.600 Ashwini Sharma: Then 1?
197 00:22:39.600 ⇒ 00:22:40.190 Amber Lin: Cool.
198 00:22:40.600 ⇒ 00:22:44.130 Ashwini Sharma: Otherwise, it still will consider it as null, right? So…
199 00:22:44.130 ⇒ 00:22:44.700 Amber Lin: Hmm.
200 00:22:44.900 ⇒ 00:22:46.749 Ashwini Sharma: Baga explicit zero.
201 00:22:47.340 ⇒ 00:22:52.650 Ashwini Sharma: And… and then do a sum, so that it just counts the number of ones.
202 00:22:57.150 ⇒ 00:23:02.040 Ashwini Sharma: So, yeah, instead of count in the last three lines, right, do a sum.
203 00:23:04.590 ⇒ 00:23:06.150 Ashwini Sharma: Oh, yeah, yeah, okay.
204 00:23:16.000 ⇒ 00:23:16.889 Awaish Kumar: The same reason.
205 00:23:16.890 ⇒ 00:23:17.260 Ashwini Sharma: Yeah.
206 00:23:17.260 ⇒ 00:23:19.239 Amber Lin: After the sum, it says the same thing.
207 00:23:19.240 ⇒ 00:23:20.080 Ashwini Sharma: Same thing, okay.
208 00:23:20.730 ⇒ 00:23:21.570 Amber Lin: Yeah. Okay.
209 00:23:22.550 ⇒ 00:23:23.500 Awaish Kumar: That’s okay.
210 00:23:24.060 ⇒ 00:23:35.819 Amber Lin: Okay, I think this is… this is a minor issue. Okay, last thing on this company revenue data. My numbers are just… oh, here.
211 00:23:35.950 ⇒ 00:23:48.879 Amber Lin: Here I used… I tried to tape… two approaches. The RPT table is much closer than what I can get right now with the star schema, which is what we want to use. So…
212 00:23:49.090 ⇒ 00:23:52.639 Amber Lin: The RPD table, the report table, gives me this.
213 00:23:53.790 ⇒ 00:23:57.089 Amber Lin: This is, like, your num- this column is what…
214 00:23:57.290 ⇒ 00:24:01.430 Amber Lin: the pre-audit report has. As you can see, this is very, very close.
215 00:24:03.150 ⇒ 00:24:20.749 Amber Lin: But if I use my star schema, I am much further off. Like, for example, under 1 million is 6K, and under 1 million should be 7.5 or 7.6. So, like…
216 00:24:21.590 ⇒ 00:24:24.280 Amber Lin: I don’t know why the RPT table and this.
217 00:24:24.280 ⇒ 00:24:25.610 Awaish Kumar: Yeah, can you look at the curation?
218 00:24:25.610 ⇒ 00:24:26.340 Amber Lin: today.
219 00:24:26.570 ⇒ 00:24:30.709 Amber Lin: Yeah, let me… Let me grab…
220 00:24:30.990 ⇒ 00:24:33.300 Amber Lin: I think that… I think this is the query.
221 00:24:33.560 ⇒ 00:24:35.690 Amber Lin: This is from the RPT.
222 00:24:35.690 ⇒ 00:24:38.909 Awaish Kumar: No, no, no. This is… we just have to delete this.
223 00:24:40.060 ⇒ 00:24:40.600 Amber Lin: Cool.
224 00:24:40.630 ⇒ 00:24:41.650 Awaish Kumar: completed.
225 00:24:41.930 ⇒ 00:24:50.320 Amber Lin: Yeah, I’m… I was just trying to figure out, like, if this… what difference is between this and the star schema? I’ll have to…
226 00:24:50.320 ⇒ 00:24:56.010 Awaish Kumar: Sorry, can you go back to this table? I think it’s… this table is created on top of the…
227 00:24:56.760 ⇒ 00:24:57.790 Awaish Kumar: style schema.
228 00:24:57.790 ⇒ 00:25:00.400 Amber Lin: Yeah, yeah, one sec.
229 00:25:00.830 ⇒ 00:25:03.610 Amber Lin: Let’s go, let’s go there.
230 00:25:04.590 ⇒ 00:25:05.380 Awaish Kumar: Yeah, I…
231 00:25:05.380 ⇒ 00:25:06.970 Amber Lin: Alright. Thanks, Lisa.
232 00:25:06.970 ⇒ 00:25:09.340 Awaish Kumar: If it is created on top of a star schema, then…
233 00:25:10.440 ⇒ 00:25:13.789 Amber Lin: Yeah, which is why I’m confused. Maybe I missed something there.
234 00:25:13.790 ⇒ 00:25:14.769 Awaish Kumar: This is the correct one.
235 00:25:14.770 ⇒ 00:25:15.970 Amber Lin: boards…
236 00:25:19.590 ⇒ 00:25:20.980 Amber Lin: 416…
237 00:25:23.530 ⇒ 00:25:24.200 Awaish Kumar: No.
238 00:25:24.540 ⇒ 00:25:31.780 Awaish Kumar: Slot, okay. So… the… the one which is created on top of StarskyMayer, Has the name…
239 00:25:31.780 ⇒ 00:25:32.470 Amber Lin: swap.
240 00:25:32.470 ⇒ 00:25:33.690 Awaish Kumar: In the suffix. Yeah.
241 00:25:34.090 ⇒ 00:25:35.499 Awaish Kumar: So, that is the…
242 00:25:37.160 ⇒ 00:25:38.809 Amber Lin: So this one is not…
243 00:25:39.270 ⇒ 00:25:40.000 Awaish Kumar: Yeah, okay.
244 00:25:40.000 ⇒ 00:25:40.330 Amber Lin: Hmm…
245 00:25:40.660 ⇒ 00:25:42.290 Awaish Kumar: Move it, let’s hang up.
246 00:25:42.960 ⇒ 00:25:45.770 Awaish Kumar: This is not… we are not going to use this, it’s…
247 00:25:46.850 ⇒ 00:26:04.050 Amber Lin: Yeah, I don’t… in my semantic view, I don’t use this, it just… I asked it to find the reason, and this one’s just much closer. I can run… let me run the query right now to see what it comes up for the star schema.
248 00:26:07.490 ⇒ 00:26:10.900 Awaish Kumar: So, basically, let me also look at the code.
249 00:26:11.490 ⇒ 00:26:15.120 Awaish Kumar: CTA data ops, dbt project models.
250 00:26:15.410 ⇒ 00:26:17.170 Awaish Kumar: What’s up?
251 00:26:18.550 ⇒ 00:26:19.510 Awaish Kumar: See?
252 00:26:24.380 ⇒ 00:26:28.360 Awaish Kumar: Company revenue, it’s coming from here.
253 00:26:40.470 ⇒ 00:26:45.790 Amber Lin: So, it seems like it’s a difference between the DIMCES company and the
254 00:26:45.950 ⇒ 00:26:50.250 Amber Lin: whatever field is in the RPT table, I think.
255 00:26:50.900 ⇒ 00:26:57.719 Amber Lin: So, company revenue from DMCS company is different than company revenue band in the RPD table.
256 00:26:58.360 ⇒ 00:26:59.560 Amber Lin: It seems.
257 00:27:10.350 ⇒ 00:27:11.910 Awaish Kumar: It’s the same thing.
258 00:27:13.220 ⇒ 00:27:13.950 Amber Lin: Hmm.
259 00:27:25.780 ⇒ 00:27:27.330 Amber Lin: Oh, that’s weird.
260 00:27:31.960 ⇒ 00:27:32.800 Amber Lin: Okay.
261 00:27:33.720 ⇒ 00:27:36.340 Amber Lin: And this uses the RBT table.
262 00:27:37.260 ⇒ 00:27:38.860 Amber Lin: Interesting.
263 00:28:11.280 ⇒ 00:28:12.330 Awaish Kumar: revenue…
264 00:28:33.550 ⇒ 00:28:34.790 Amber Lin: Okay.
265 00:28:41.560 ⇒ 00:28:49.289 Amber Lin: Okay, let me dive deeper into this, and then come back to you guys. It seems like it’s getting… seems like it’s getting closer.
266 00:28:49.500 ⇒ 00:28:51.450 Amber Lin: So, I can go figure out…
267 00:28:51.450 ⇒ 00:28:54.870 Awaish Kumar: It is the same field we use, I just verified.
268 00:28:55.030 ⇒ 00:28:55.910 Amber Lin: Okay.
269 00:28:56.060 ⇒ 00:28:57.990 Awaish Kumar: In both the tables?
270 00:28:58.240 ⇒ 00:29:03.059 Awaish Kumar: the… the bi… the… The column that is being used is a store.
271 00:29:03.480 ⇒ 00:29:04.380 Awaish Kumar: So…
272 00:29:04.560 ⇒ 00:29:13.550 Awaish Kumar: Either it is because of updated data, because the old one is still, as I said, it depends on
273 00:29:13.820 ⇒ 00:29:17.790 Awaish Kumar: The earlier dump of data, and then we got Fair enough.
274 00:29:17.970 ⇒ 00:29:18.990 Awaish Kumar: Okay. And this… Okay.
275 00:29:19.650 ⇒ 00:29:23.679 Awaish Kumar: new… But the column that is being used is the same.
276 00:29:24.410 ⇒ 00:29:43.810 Amber Lin: Okay, sounds good. I think the new result is much closer, so I can mark this as resolved as well. Seems like we covered all three here. Let me just read really quick through these, just in case you guys also have answers.
277 00:29:44.330 ⇒ 00:29:49.440 Amber Lin: So these are things I’m currently asking to the CTA folks.
278 00:29:49.610 ⇒ 00:29:50.400 Amber Lin: So…
279 00:29:50.400 ⇒ 00:29:51.360 Awaish Kumar: Thank you, what’s up?
280 00:29:52.450 ⇒ 00:29:56.550 Awaish Kumar: So, for the biggest data, is…
281 00:29:57.450 ⇒ 00:30:00.820 Awaish Kumar: Like, are you not able to find this in the…
282 00:30:01.200 ⇒ 00:30:03.889 Awaish Kumar: Int… int table, like, that you were…
283 00:30:04.270 ⇒ 00:30:15.729 Amber Lin: Oh, I… I was able to find it. It’s just, I’m right now… I right now have 5 semantic views. I… I have one using the badge scans, and one using C event, so…
284 00:30:15.920 ⇒ 00:30:19.169 Amber Lin: Like, I’m… I’m able to find it, it’s just they have…
285 00:30:19.350 ⇒ 00:30:22.440 Amber Lin: They have different names, that’s all.
286 00:30:22.440 ⇒ 00:30:30.999 Awaish Kumar: But Catherine mentioned that And we’re Vegas data only exists In the badge skins.
287 00:30:31.000 ⇒ 00:30:31.460 Amber Lin: Goodbye.
288 00:30:31.460 ⇒ 00:30:34.189 Awaish Kumar: Does not exist in this event.
289 00:30:34.880 ⇒ 00:30:36.480 Amber Lin: I’m using badge skins.
290 00:30:36.680 ⇒ 00:30:39.619 Awaish Kumar: I have created a… also created a PR, if you…
291 00:30:39.930 ⇒ 00:30:44.660 Awaish Kumar: I want to use the, like, the mod table and intermediate table.
292 00:30:44.820 ⇒ 00:30:47.140 Awaish Kumar: It is in the PR, if.
293 00:30:47.930 ⇒ 00:30:52.829 Amber Lin: Yeah, Ashwini, can you approve that, and I can change it this afternoon?
294 00:30:53.080 ⇒ 00:30:55.019 Ashwini Sharma: Yeah, I… I approved the PR.
295 00:30:56.120 ⇒ 00:30:59.320 Awaish Kumar: Are… for this, I agree.
296 00:30:59.320 ⇒ 00:31:00.190 Ashwini Sharma: institane?
297 00:31:00.190 ⇒ 00:31:02.200 Awaish Kumar: Unveiled events, not Identistry.
298 00:31:02.570 ⇒ 00:31:05.479 Ashwini Sharma: Okay, can you send the link to the PR field?
299 00:31:09.180 ⇒ 00:31:13.269 Amber Lin: Does it only cover Unveiled, or is it just the whole badge scan table?
300 00:31:15.670 ⇒ 00:31:21.219 Awaish Kumar: Yeah, I think it’s mainly… I think it’s…
301 00:31:22.610 ⇒ 00:31:26.150 Awaish Kumar: The, sorry, the Unwielded Vegas.
302 00:31:26.460 ⇒ 00:31:27.030 Awaish Kumar: Tinker.
303 00:31:27.030 ⇒ 00:31:27.770 Ashwini Sharma: the author?
304 00:31:28.820 ⇒ 00:31:29.890 Awaish Kumar: I’m not there.
305 00:31:31.080 ⇒ 00:31:33.320 Ashwini Sharma: Star badge scan, this one?
306 00:31:33.440 ⇒ 00:31:34.469 Awaish Kumar: Yes, yes.
307 00:31:35.240 ⇒ 00:31:35.890 Amber Lin: Cool.
308 00:31:37.110 ⇒ 00:31:37.900 Amber Lin: Yeah.
309 00:31:38.020 ⇒ 00:31:47.189 Amber Lin: The… I think the only thing I… I’m… this is more of something I’m confirming with them, is that there are different unveiled events, and historically, they have different…
310 00:31:47.330 ⇒ 00:31:52.399 Amber Lin: categories and session types, so I don’t think this is something we can do, but…
311 00:31:52.890 ⇒ 00:31:56.179 Amber Lin: Once they confirm, we can maybe clean it up in a model.
312 00:31:56.430 ⇒ 00:32:01.290 Ashwini Sharma: I’m not sure if this PR is the correct one, I’m looking at a badgecan.sql.
313 00:32:02.490 ⇒ 00:32:06.340 Amber Lin: Sounds correct to me. Do you want to share screen so we actually can confirm?
314 00:32:06.800 ⇒ 00:32:09.960 Awaish Kumar: Yeah, this is the PR I sent in the chat.
315 00:32:14.560 ⇒ 00:32:15.350 Ashwini Sharma: Okay.
316 00:32:18.540 ⇒ 00:32:23.650 Awaish Kumar: So, the… Scans, media…
317 00:32:26.860 ⇒ 00:32:27.500 Awaish Kumar: You know.
318 00:32:27.500 ⇒ 00:32:28.980 Ashwini Sharma: Approved… approved it.
319 00:32:29.580 ⇒ 00:32:30.280 Awaish Kumar: Okay.
320 00:32:31.010 ⇒ 00:32:33.429 Awaish Kumar: So… scans that…
321 00:32:43.480 ⇒ 00:32:44.030 Amber Lin: Cool.
322 00:32:45.050 ⇒ 00:32:46.080 Awaish Kumar: So… Yeah.
323 00:32:46.580 ⇒ 00:32:49.720 Awaish Kumar: And we’re… okay, can we see… look at the…
324 00:32:50.440 ⇒ 00:32:53.249 Awaish Kumar: Permission issues that you were talking about?
325 00:32:53.560 ⇒ 00:33:05.910 Amber Lin: Oh, sure, yes. Let me pull up… so I was able to create… In… Broadmarts.
326 00:33:06.510 ⇒ 00:33:18.670 Amber Lin: I was able to create my Samantha views, so I’m good there. It’s just I cannot edit, at least as of yesterday, I cannot edit the agent yet, so this is…
327 00:33:18.840 ⇒ 00:33:19.700 Amber Lin: B.
328 00:33:20.310 ⇒ 00:33:21.700 Amber Lin: That’s the agent.
329 00:33:21.860 ⇒ 00:33:29.149 Amber Lin: Oh, yeah, role developer has only usage, so when I go in…
330 00:33:29.600 ⇒ 00:33:35.639 Awaish Kumar: Can you click on… Can you click on your name at the bottom left?
331 00:33:35.960 ⇒ 00:33:37.319 Awaish Kumar: And change the role.
332 00:33:45.190 ⇒ 00:33:51.070 Awaish Kumar: Okay, so I gave you… Access to…
333 00:33:52.870 ⇒ 00:33:57.330 Awaish Kumar: Okay, you need… okay, I gave you access to create the agents.
334 00:33:57.890 ⇒ 00:34:00.330 Amber Lin: Mmm, but not to edit this one, right?
335 00:34:00.330 ⇒ 00:34:01.609 Awaish Kumar: Yeah, like…
336 00:34:01.610 ⇒ 00:34:02.210 Amber Lin: Okay.
337 00:34:02.210 ⇒ 00:34:08.000 Awaish Kumar: You… you can’t… you can do that from the code, right? Okay, let’s go…
338 00:34:08.280 ⇒ 00:34:10.960 Awaish Kumar: There’s also a PR for that.
339 00:34:12.639 ⇒ 00:34:14.559 Awaish Kumar: Where is the…
340 00:34:14.610 ⇒ 00:34:19.230 Amber Lin: Yeah, I was trying to find that in, like, when I was in Cursor yesterday.
341 00:34:19.239 ⇒ 00:34:21.009 Awaish Kumar: No, exactly, no.
342 00:34:21.569 ⇒ 00:34:23.689 Awaish Kumar: If you can also approve this.
343 00:34:27.519 ⇒ 00:34:31.149 Awaish Kumar: Yeah, Ashwini, Amber, any one of you can actually prove this behind?
344 00:34:34.509 ⇒ 00:34:35.889 Awaish Kumar: I sent in the.
345 00:34:36.340 ⇒ 00:34:39.260 Amber Lin: Okay, Ashwini, can you help me with that, since you already logged in?
346 00:34:39.260 ⇒ 00:34:41.389 Ashwini Sharma: One second, doing it.
347 00:34:43.150 ⇒ 00:34:45.539 Ashwini Sharma: Pre-ordered semantic view, alright.
348 00:34:57.290 ⇒ 00:34:58.330 Ashwini Sharma: Alright, approved.
349 00:34:58.750 ⇒ 00:34:59.090 Awaish Kumar: Okay.
350 00:34:59.090 ⇒ 00:34:59.700 Amber Lin: Okay.
351 00:34:59.700 ⇒ 00:35:08.569 Awaish Kumar: Thank you. So, Amber, what you can do right now, you can’t… okay, you can’t use your own account.
352 00:35:09.410 ⇒ 00:35:12.940 Awaish Kumar: But for… For these upgrades, because…
353 00:35:13.050 ⇒ 00:35:15.420 Awaish Kumar: You only have access to role developer?
354 00:35:16.330 ⇒ 00:35:21.050 Awaish Kumar: And an old developer don’t have access to edit, like, it should…
355 00:35:22.600 ⇒ 00:35:28.350 Awaish Kumar: like, it don’t have edit access, you might not be able to edit it from UI.
356 00:35:28.460 ⇒ 00:35:30.550 Awaish Kumar: But let’s try from,
357 00:35:30.800 ⇒ 00:35:41.210 Awaish Kumar: from the code, I have given you the access to create, so basically what the code does is, is it runs the command create or replace.
358 00:35:41.490 ⇒ 00:35:42.570 Amber Lin: Hmm.
359 00:35:42.570 ⇒ 00:35:46.259 Awaish Kumar: Edit any, like, update anything, it will just replace the agent.
360 00:35:46.550 ⇒ 00:35:50.020 Amber Lin: Cool, awesome. It’s under…
361 00:35:51.960 ⇒ 00:35:54.969 Awaish Kumar: So… Where is it? That was.
362 00:35:56.510 ⇒ 00:35:58.280 Amber Lin: Sorry, let me… let me share a screen.
363 00:35:58.280 ⇒ 00:35:58.700 Awaish Kumar: Nope.
364 00:35:58.700 ⇒ 00:35:59.550 Amber Lin: Real quick.
365 00:36:00.870 ⇒ 00:36:05.349 Amber Lin: I am in my cursor. I am trying to find…
366 00:36:05.350 ⇒ 00:36:05.760 Awaish Kumar: Nope.
367 00:36:05.760 ⇒ 00:36:09.525 Amber Lin: I’m trying to find that… .
368 00:36:10.300 ⇒ 00:36:14.539 Awaish Kumar: Try to pull the latest changes.
369 00:36:15.140 ⇒ 00:36:16.080 Awaish Kumar: Oh, okay.
370 00:36:16.370 ⇒ 00:36:17.080 Amber Lin: Cool.
371 00:36:17.680 ⇒ 00:36:20.829 Amber Lin: Oh, it is changing…
372 00:36:25.210 ⇒ 00:36:27.969 Awaish Kumar: You can just pull the latest main, right?
373 00:36:28.170 ⇒ 00:36:29.139 Awaish Kumar: Main robot.
374 00:36:29.790 ⇒ 00:36:34.679 Awaish Kumar: It will have all the changes, whatever we… We just merged.
375 00:36:35.770 ⇒ 00:36:41.629 Amber Lin: Cool, yeah, I just fetched… the… I just fetched orange in, but…
376 00:36:42.220 ⇒ 00:36:44.720 Awaish Kumar: Yeah, but it says agent-related parts, so you’ll…
377 00:36:44.880 ⇒ 00:36:48.640 Awaish Kumar: You just, like, check out the main and pull everything.
378 00:36:52.160 ⇒ 00:36:53.870 Awaish Kumar: Stop for Ghost Tom, guys.
379 00:36:55.920 ⇒ 00:36:56.580 Awaish Kumar: Yeah, Oscar.
380 00:36:56.580 ⇒ 00:36:57.006 Amber Lin: Thank you.
381 00:36:57.220 ⇒ 00:37:05.719 Awaish Kumar: Ask the cursor to pull… Yeah, check out through main branch, and pull the latest changes.
382 00:37:39.080 ⇒ 00:37:40.870 Amber Lin: Cool. I am there.
383 00:37:40.870 ⇒ 00:37:44.109 Awaish Kumar: Also, Click on the scripts.
384 00:37:45.470 ⇒ 00:37:47.689 Awaish Kumar: Folder, under there, Cortex user.
385 00:37:47.890 ⇒ 00:37:50.589 Awaish Kumar: Rob, CS agent, and… Awesome.
386 00:37:50.590 ⇒ 00:37:51.559 Amber Lin: And then…
387 00:37:51.560 ⇒ 00:37:53.370 Awaish Kumar: This is all the things.
388 00:37:54.140 ⇒ 00:37:57.270 Awaish Kumar: Basically, this is everything here.
389 00:37:57.480 ⇒ 00:37:59.929 Awaish Kumar: It creates the semantic view.
390 00:38:00.270 ⇒ 00:38:04.970 Awaish Kumar: It creates the table, it creates the stage, it creates the…
391 00:38:05.670 ⇒ 00:38:15.470 Awaish Kumar: purpose, finally creates the agent and deploys it. So it does everything, and you have separate scripts for everything, but you can use… look at create agent
392 00:38:15.650 ⇒ 00:38:17.290 Awaish Kumar: Script, 05.
393 00:38:17.580 ⇒ 00:38:22.969 Awaish Kumar: And you… it will… it will just replace your agent. You can write your own stalls.
394 00:38:22.970 ⇒ 00:38:23.870 Amber Lin: Awesome, awesome.
395 00:38:23.870 ⇒ 00:38:30.840 Awaish Kumar: Yeah, you can… And in the… in the tool resource, you can just point to your semantic view.
396 00:38:31.080 ⇒ 00:38:38.420 Awaish Kumar: instead of this one, it will, like, in the row number 40… 8.
397 00:38:39.000 ⇒ 00:38:42.760 Awaish Kumar: Rule number 48. Here, you can update your…
398 00:38:43.050 ⇒ 00:38:46.460 Awaish Kumar: Okay. Like, give the path to your semantic view.
399 00:38:46.840 ⇒ 00:38:50.499 Amber Lin: Yeah, sounds good. I can… I can do that. That works for me.
400 00:38:50.500 ⇒ 00:38:57.460 Awaish Kumar: Then one more thing, if you have any other specific view, regarding any other…
401 00:38:59.220 ⇒ 00:39:07.779 Awaish Kumar: like, business domain? Like, if you have created any other analyst for the adoption or anything, we can even include that in here.
402 00:39:07.810 ⇒ 00:39:14.189 Amber Lin: Yeah, I will… I will add that, and then, like, I think today I’ll tune the…
403 00:39:14.570 ⇒ 00:39:17.090 Amber Lin: the AI prompt, so if route.
404 00:39:17.090 ⇒ 00:39:21.909 Awaish Kumar: Same agent can… Help us carry multiple different kinds of data.
405 00:39:22.140 ⇒ 00:39:23.580 Awaish Kumar: This way, okay.
406 00:39:23.840 ⇒ 00:39:30.820 Amber Lin: Awesome. What else do I have? Oh, one last question I had was on the…
407 00:39:31.000 ⇒ 00:39:35.209 Amber Lin: Cortex agent usage data, because I’m trying to…
408 00:39:35.210 ⇒ 00:39:38.149 Awaish Kumar: I haven’t looked into that, so…
409 00:39:38.150 ⇒ 00:39:38.760 Amber Lin: Okay.
410 00:39:38.930 ⇒ 00:39:40.850 Awaish Kumar: I… I will let you know.
411 00:39:41.070 ⇒ 00:39:42.130 Awaish Kumar: by tomorrow.
412 00:39:42.410 ⇒ 00:39:50.509 Amber Lin: Okay, sounds good. Yeah, that’s all, because I think we will use that usage instead of the semantic view usage.
413 00:39:50.880 ⇒ 00:39:51.470 Amber Lin: Alright.
414 00:39:52.230 ⇒ 00:39:54.300 Awaish Kumar: Okay, thank you.
415 00:39:54.300 ⇒ 00:39:57.290 Amber Lin: That’s all my questions. Thank you both. It was really helpful.
416 00:39:57.870 ⇒ 00:39:59.820 Amber Lin: Alrighty. Bye, guys.