Meeting Title: MatterMore | internal Standup Date: 2025-07-03 Meeting participants: Luke Daque, Amber Lin


WEBVTT

1 00:02:48.610 00:02:49.640 Luke Daque: Hi amber.

2 00:02:51.700 00:02:52.550 Amber Lin: Hello.

3 00:02:55.620 00:03:02.650 Amber Lin: so quick. Update on, madam, more. So we’re done with this project. So right we don’t called Matthew

4 00:03:02.830 00:03:21.780 Amber Lin: yesterday, and they just confirmed on that. They’re just gonna use their in-house team moving forward. So right now, we’re just doing the final project closure. Handoff phase and I’m currently writing a few forms, and I would love your help

5 00:03:22.070 00:03:34.410 Amber Lin: to sort of guide me through what items we are going to need to hand off and like how we can create it, create instructions for their in-house team.

6 00:03:36.210 00:03:36.980 Luke Daque: Okay.

7 00:03:37.650 00:03:38.170 Amber Lin: Yeah.

8 00:03:39.400 00:03:44.310 Luke Daque: Yeah, if you can maybe share me that form or.

9 00:03:45.225 00:03:45.730 Amber Lin: Sure.

10 00:03:45.730 00:03:51.690 Luke Daque: Yeah, I’ll try to make up something. I guess we can also like, ask help from

11 00:03:51.810 00:03:53.690 Luke Daque: Chat Gpt, or whatever.

12 00:03:53.690 00:03:57.668 Amber Lin: Yeah, I think I we will. I I’m just gonna copy

13 00:04:00.690 00:04:02.190 Amber Lin: Let me.

14 00:04:02.320 00:04:14.279 Amber Lin: We. You can look at it together 1st and then just edit, if anything’s wrong, and then I’ll leave the rest to you. So it’s in notion.

15 00:04:14.660 00:04:16.399 Amber Lin: So right here.

16 00:04:20.370 00:04:24.289 Amber Lin: Sorry, client client is rather more.

17 00:04:25.020 00:04:25.554 Luke Daque: Okay.

18 00:04:27.500 00:04:33.920 Amber Lin: Or reinforced timeline page.

19 00:04:35.540 00:04:37.862 Luke Daque: Maybe. Also, yeah, before I forget

20 00:04:40.590 00:04:48.820 Luke Daque: Maybe before we close this we we will have to like probably Fork, the repository, because it’s there.

21 00:04:49.090 00:04:50.959 Luke Daque: Theirs right. It’s theirs.

22 00:04:51.380 00:04:51.800 Amber Lin: Okay.

23 00:04:51.800 00:04:52.200 Luke Daque: Nice.

24 00:04:52.260 00:04:56.759 Amber Lin: Yeah, let me write down a few things. So to

25 00:04:56.940 00:05:05.699 Amber Lin: for create cause, I think we would benefit from having that work because a lot of it. Repository.

26 00:05:06.370 00:05:07.230 Amber Lin: Okay.

27 00:05:08.061 00:05:11.370 Amber Lin: Brain forge to fork repository.

28 00:05:13.672 00:05:16.720 Amber Lin: I’m gonna share this.

29 00:05:17.850 00:05:20.009 Amber Lin: We don’t have that.

30 00:05:20.400 00:05:23.969 Luke Daque: Dashboard Navigation Guide. I think we should do that.

31 00:05:26.860 00:05:34.099 Amber Lin: Think, where was the okay data platform documentation?

32 00:05:35.990 00:05:42.170 Amber Lin: Think, do you think it’s necessary that we record a video to walk them through these.

33 00:05:42.880 00:05:46.559 Amber Lin: because it’s we do have a lot of different pages.

34 00:05:48.073 00:05:50.289 Luke Daque: Yeah, that’s a good question.

35 00:05:50.420 00:05:51.160 Luke Daque: And.

36 00:05:51.370 00:05:54.630 Amber Lin: Would you know all what these pages are? Oh, okay.

37 00:05:54.870 00:05:57.429 Luke Daque: I was. Gonna say, I’m not even like

38 00:05:57.560 00:06:06.560 Luke Daque: 100% sure about all of these, because, like, I believe, some of them were created by math or more. Some of them, I think, were created by Annie.

39 00:06:07.320 00:06:09.500 Luke Daque: Stuff like that. So like, yeah.

40 00:06:09.750 00:06:11.243 Amber Lin: I see.

41 00:06:14.970 00:06:20.930 Amber Lin: I mean it will be great if we can walk them through the at least the ones we know

42 00:06:21.560 00:06:26.960 Amber Lin: cause for, like for these ones. I I have no clue what they’re for, but

43 00:06:27.751 00:06:33.780 Amber Lin: maybe a video to walk them through would be great or just like a quick.

44 00:06:35.240 00:06:37.289 Amber Lin: Can we add descriptions?

45 00:06:38.130 00:06:39.890 Amber Lin: I don’t think so.

46 00:06:42.520 00:06:45.279 Amber Lin: So quick walkthrough of that. Maybe.

47 00:06:47.080 00:06:47.730 Luke Daque: Yeah.

48 00:06:48.235 00:06:56.229 Amber Lin: I’ll create a for everything that we discussed here that we need. An action item on. I’ll create a ticket and we can go. Look at that together.

49 00:06:56.720 00:07:02.359 Luke Daque: Okay, yeah, it sucks that we did all that work. And then they’re not gonna continue. Anyway.

50 00:07:02.360 00:07:03.610 Amber Lin: I know.

51 00:07:04.000 00:07:05.599 Luke Daque: What happens, I guess.

52 00:07:15.246 00:07:16.879 Amber Lin: Give me one quick second.

53 00:07:17.360 00:07:18.150 Luke Daque: Sure.

54 00:07:34.540 00:07:37.040 Amber Lin: Okay, I’m back. I just got my coffee

55 00:07:59.990 00:08:01.130 Amber Lin: Let’s see.

56 00:08:04.560 00:08:07.910 Amber Lin: Yes, I’ll make a ticket to

57 00:08:16.068 00:08:26.869 Amber Lin: I mean, look if if you pretend that you are a new analyst? What would you have questions about?

58 00:08:27.020 00:08:34.179 Amber Lin: Like? Maybe on how we did the modeling, where we can find different things like, what? What do you think we should include in a documentation.

59 00:08:37.841 00:08:41.129 Luke Daque: Yeah, that’s a good question. But yeah, the

60 00:08:42.669 00:08:47.769 Luke Daque: yeah, definitely like, where what the modeling is, what.

61 00:08:48.830 00:08:53.710 Luke Daque: The sources are and stuff like that. I don’t know what else.

62 00:08:53.840 00:08:57.319 Luke Daque: But yeah, we can think about that. We can add that to the documentation, for sure.

63 00:08:57.320 00:09:07.535 Amber Lin: Okay, a lot of the tabs are, yeah.

64 00:09:09.530 00:09:12.029 Amber Lin: guess we can just quickly start.

65 00:09:23.950 00:09:31.460 Amber Lin: What are the main things we’re using? Like all where all the modeling are, is in.

66 00:09:32.190 00:09:33.530 Luke Daque: In Dbt.

67 00:09:33.760 00:09:36.489 Amber Lin: Okay, do they have access to Dvt.

68 00:09:37.975 00:09:41.160 Luke Daque: Yeah, like, it’s in the repository. So.

69 00:09:41.160 00:09:41.730 Amber Lin: Oh, okay.

70 00:09:41.730 00:09:42.310 Luke Daque: Mistake.

71 00:09:43.723 00:09:45.090 Amber Lin: Power band.

72 00:09:47.530 00:09:56.250 Amber Lin: Okay? That is so.

73 00:09:57.400 00:10:02.330 Amber Lin: Data pipelines, isn’t. We’re not using anything for orchestration. Right?

74 00:10:06.560 00:10:10.809 Luke Daque: Well, not at the moment. Because, like, we’re just using synthetic data.

75 00:10:13.220 00:10:18.569 Luke Daque: And like we’re, we’re all. We’re also not like running Dbt jobs on schedules and stuff like that.

76 00:10:18.570 00:10:19.810 Amber Lin: Oh, okay.

77 00:10:20.530 00:10:21.150 Luke Daque: Yeah, we just.

78 00:10:21.500 00:10:22.900 Luke Daque: And then once.

79 00:10:23.210 00:10:30.570 Amber Lin: Could you send me? Actually, I’ll share this document with you. Can you just insert insert the link for

80 00:10:32.580 00:10:34.549 Amber Lin: Where is our chat?

81 00:10:35.090 00:10:37.400 Amber Lin: Huh? Oh, there’s our check.

82 00:10:38.093 00:10:41.720 Amber Lin: To have insert the link for the repository there.

83 00:10:42.680 00:10:43.530 Luke Daque: Yeah, sure.

84 00:11:10.360 00:11:15.840 Luke Daque: Where? Where do I put it? Says, maybe I see.

85 00:11:16.243 00:11:17.050 Amber Lin: Right here.

86 00:11:18.290 00:11:19.290 Luke Daque: It’s cool.

87 00:11:19.820 00:11:21.559 Luke Daque: Oh, let’s see what we have.

88 00:11:24.070 00:11:26.619 Luke Daque: Yep, just added it.

89 00:11:27.334 00:11:35.120 Amber Lin: Awesome. I’m gonna add in phase, one requirements.

90 00:11:47.430 00:11:59.060 Amber Lin: okay, final deliverables, dashboard link modeling link okay, key decisions.

91 00:12:04.590 00:12:09.999 Amber Lin: we didn’t do that productivity also didn’t do that.

92 00:12:17.640 00:12:21.869 Amber Lin: Well, do you recall if there’s any assumptions or key decisions that we made.

93 00:12:28.110 00:12:31.980 Luke Daque: yeah, I guess the synthetic data based on the Api documentation.

94 00:12:35.490 00:12:37.009 Luke Daque: What else do we do?

95 00:12:40.340 00:12:41.320 Amber Lin: I guess.

96 00:12:41.840 00:12:44.610 Luke Daque: I guess that’s like the the bulk of it, I guess.

97 00:12:44.610 00:12:45.310 Amber Lin: Okay.

98 00:12:47.800 00:12:49.000 Amber Lin: Source.

99 00:12:50.700 00:12:52.220 Amber Lin: Bigquery.

100 00:12:57.690 00:12:59.959 Amber Lin: Is that the right flow.

101 00:13:01.120 00:13:01.900 Luke Daque: Good evening.

102 00:13:02.814 00:13:06.959 Amber Lin: The the current data flow. So we started in Microsoft.

103 00:13:07.310 00:13:11.709 Amber Lin: And then does it go to azure? Does it go directly to Bigquery?

104 00:13:11.710 00:13:12.230 Luke Daque: It.

105 00:13:13.910 00:13:21.370 Luke Daque: Well, currently, we’re not doing anything related to data pipeline. So we’re just creating synthetic data. But

106 00:13:21.530 00:13:30.069 Luke Daque: yeah, that would the the ideally, it would go directly to bigquery. Somehow, I’m not sure they’ll be using

107 00:13:31.563 00:13:35.499 Luke Daque: some sort of data ingestion tool like 5 Tran, or

108 00:13:35.980 00:13:37.859 Luke Daque: or they’re gonna upgrade their own.

109 00:13:38.780 00:13:43.500 Luke Daque: Data pipeline. It depends. But yeah.

110 00:13:43.500 00:13:44.030 Amber Lin: So

111 00:13:45.400 00:13:53.140 Amber Lin: after that we do transformation. Should I call it transformations in Dvt, or what should I call this.

112 00:13:54.404 00:13:56.540 Luke Daque: Yeah, data modeling, maybe.

113 00:13:57.980 00:13:58.610 Luke Daque: Yeah.

114 00:14:03.680 00:14:07.279 Luke Daque: and then, yeah, data visualization would be power. Bi.

115 00:14:07.450 00:14:11.689 Amber Lin: Hmm, are we using SQL. Server, or just bigquery? Right?

116 00:14:12.060 00:14:13.130 Luke Daque: And just pick the.

117 00:14:13.130 00:14:16.230 Amber Lin: Query iphone.

118 00:14:17.060 00:14:20.180 Amber Lin: And then we’re using Dbts.

119 00:14:21.030 00:14:22.130 Luke Daque: Yeah.

120 00:14:26.570 00:14:28.499 Amber Lin: We have no diagram.

121 00:14:29.770 00:14:31.210 Luke Daque: Yeah, we don’t have it.

122 00:14:31.210 00:14:34.359 Amber Lin: Okay, access credentials.

123 00:14:42.270 00:14:43.460 Amber Lin: Yeah.

124 00:14:48.200 00:14:58.000 Amber Lin: Ray gorge so I could say, next, let’s say, July.

125 00:15:00.370 00:15:04.359 Amber Lin: we’ll clean all the access by July 11.th

126 00:15:05.760 00:15:06.680 Luke Daque: Okay.

127 00:15:06.900 00:15:07.910 Luke Daque: Next week.

128 00:15:22.020 00:15:26.219 Amber Lin: Bigquery as as you’re in power.

129 00:15:38.800 00:15:44.299 Amber Lin: I think they can just inactivate this right. Do we still have to share our credentials with them?

130 00:15:45.890 00:15:47.919 Luke Daque: No, they can do it on their own.

131 00:15:47.920 00:15:50.099 Amber Lin: Yeah, cause I think it’s all hosted.

132 00:15:54.140 00:15:54.900 Luke Daque: Yeah.

133 00:15:57.140 00:16:00.090 Amber Lin: Okay, ongoing and ownership.

134 00:16:01.630 00:16:08.300 Amber Lin: No dashboard refresh logic. Huh?

135 00:16:08.660 00:16:09.630 Luke Daque: Oh, we don’t.

136 00:16:09.880 00:16:14.900 Luke Daque: I don’t think we have any refresh schedules, for now, at the moment.

137 00:16:15.090 00:16:15.840 Amber Lin: Okay.

138 00:16:25.780 00:16:32.990 Amber Lin: data modeling. There’s also also no like schedules on Dbt, right?

139 00:16:33.460 00:16:36.630 Luke Daque: No, at the moment. No, but I guess when they

140 00:16:37.970 00:16:42.430 Luke Daque: migrate or like they do it under there, they should have schedules, basically. Yeah.

141 00:17:07.650 00:17:10.669 Amber Lin: Was there any issues when we were trying to build it up.

142 00:17:20.089 00:17:24.609 Luke Daque: I don’t know. Maybe the access stuff that we’re

143 00:17:24.839 00:17:28.599 Luke Daque: causing the delays. But I don’t know if we can call this issues.

144 00:17:29.570 00:17:39.720 Amber Lin: I see I’ll just document it, having issues with access, identify that it was caused.

145 00:17:40.190 00:17:41.220 Amber Lin: Bye?

146 00:17:42.410 00:17:43.470 Amber Lin: Why,

147 00:17:47.900 00:17:58.040 Amber Lin: Trying to use external out other than internal.

148 00:17:59.630 00:18:00.950 Amber Lin: I want to at least open.

149 00:18:06.270 00:18:18.250 Amber Lin: Gee, okay, that was the main issue documentation data platform, dashboard, navigation, walkthrough.

150 00:18:42.810 00:18:44.910 Amber Lin: yes, I can put the

151 00:18:49.060 00:18:51.830 Amber Lin: I’ll put the recommendation here.

152 00:18:57.910 00:18:58.600 Luke Daque: Sure.

153 00:18:59.070 00:19:00.370 Luke Daque: Yeah, that makes sense.

154 00:19:00.370 00:19:01.050 Amber Lin: Hmm.

155 00:19:02.250 00:19:03.460 Luke Daque: Modeling.

156 00:19:04.160 00:19:08.299 Luke Daque: Yeah, I guess the data ingestion as well.

157 00:19:08.667 00:19:11.609 Amber Lin: Well, what would it be about? Data ingestion.

158 00:19:12.850 00:19:18.859 Luke Daque: They will have to connect the data sources to bigquery.

159 00:19:21.530 00:19:27.049 Amber Lin: Okay, I see. I don’t know. I like I don’t know how they’re gonna use our current modeling.

160 00:19:27.470 00:19:30.200 Luke Daque: Yeah, I don’t know if they can use that, too, because.

161 00:19:30.200 00:19:31.120 Amber Lin: Yeah.

162 00:19:31.120 00:19:35.210 Luke Daque: Like, we can never like 100% replicate.

163 00:19:35.940 00:19:55.290 Amber Lin: It might look like something. But yeah, recommendations and risk considerations, i’ll say, like, please note that even

164 00:19:55.600 00:19:57.650 Amber Lin: so we

165 00:20:06.970 00:20:08.270 Amber Lin: God, b

166 00:20:15.590 00:20:18.160 Amber Lin: our data, Doc.

167 00:20:24.140 00:20:25.140 Amber Lin: you know.

168 00:20:27.730 00:20:28.600 Amber Lin: Okay.

169 00:20:32.535 00:20:33.690 Amber Lin: I’ve got nothing.

170 00:20:34.220 00:20:35.709 Amber Lin: There’s 2 separate issues.

171 00:20:41.540 00:20:43.120 Amber Lin: Rainforge.

172 00:20:53.730 00:20:55.370 Amber Lin: is there?

173 00:20:55.700 00:21:02.439 Amber Lin: Okay? So what do we have to do? I think we’re pretty good on this augmentation. I think we need to record.

174 00:21:02.570 00:21:09.420 Amber Lin: create documentation on but warm sheets.

175 00:21:12.300 00:21:16.790 Amber Lin: Would you be able to record a walkthrough of the dashboard or.

176 00:21:18.596 00:21:21.020 Luke Daque: Yeah, we can. I can do that.

177 00:21:21.020 00:21:21.700 Amber Lin: Hmm.

178 00:21:25.010 00:21:29.449 Luke Daque: But I think it’s probably best if Annie does this like, I know. But.

179 00:21:29.860 00:21:40.410 Amber Lin: I I don’t know if she’s gonna be here like we can record a 1st version and probably just take 2, 3, maybe 5 min, and then if she comes, we can have her record a new version.

180 00:21:41.290 00:21:42.349 Luke Daque: That makes sense.

181 00:21:42.350 00:21:51.080 Amber Lin: To do first, st I guess, for dashboards, they would want to know, like where to find data

182 00:21:51.950 00:21:56.090 Amber Lin: what the different pages are, what

183 00:21:57.330 00:22:02.760 Luke Daque: Although it’s I, I would say it’s pretty intuitive, though. But yeah, it’s still best if we can.

184 00:22:02.760 00:22:08.820 Amber Lin: Yeah, I guess I I guess a few things are just like the sides dimensions

185 00:22:09.250 00:22:14.829 Amber Lin: that we just created. Just a quick, quick walkthrough, that I think that should be good.

186 00:22:17.780 00:22:23.550 Amber Lin: What do you think they will be wondering about

187 00:22:24.210 00:22:26.620 Amber Lin: like? What else would they be?

188 00:22:28.010 00:22:30.670 Amber Lin: What else would would questions be.

189 00:22:34.580 00:22:36.020 Luke Daque: For the dashboard.

190 00:22:36.330 00:22:39.140 Amber Lin: No, the data on the data side.

191 00:22:39.320 00:22:50.200 Amber Lin: Yeah, maybe it’s also if you walk through of current Dvt setup, we’ve modeled.

192 00:22:51.040 00:22:53.460 Amber Lin: Like? Does that make sense at all?

193 00:22:56.140 00:22:58.430 Luke Daque: I don’t know if we need a walkthrough for that.

194 00:22:59.650 00:23:05.259 Luke Daque: Just like it’s already in in the reposit, in the code, the repository basically. So.

195 00:23:05.430 00:23:06.160 Amber Lin: Okay.

196 00:23:06.470 00:23:07.070 Luke Daque: Yeah.

197 00:23:10.710 00:23:15.149 Amber Lin: Do you think we need a video walkthrough of this data platform sheets?

198 00:23:19.240 00:23:24.110 Luke Daque: We can try. But yeah, yeah, we can do that.

199 00:23:24.290 00:23:37.569 Amber Lin: Okay. I mean that will. Also. I think you can do a walkthrough record, a loom, and then we can take the transcript and then convert that into this documentation. I think that will make your life a lot easier.

200 00:23:38.320 00:23:39.610 Luke Daque: Yeah, that makes sense.

201 00:23:39.610 00:23:40.260 Amber Lin: Okay,

202 00:23:50.270 00:23:53.339 Amber Lin: okay, okay, so

203 00:23:53.450 00:23:58.339 Amber Lin: we will fork. I guess we can fork the repository. Now, I don’t think we’re making anything.

204 00:23:59.140 00:23:59.810 Amber Lin: Okay.

205 00:23:59.810 00:24:00.310 Luke Daque: Yeah.

206 00:24:00.310 00:24:01.179 Amber Lin: Sounds good.

207 00:24:02.313 00:24:11.520 Amber Lin: I will. Okay, sounds good. I will make tickets for these.

208 00:24:13.795 00:24:14.550 Amber Lin: Podcast.

209 00:24:35.990 00:24:40.120 Amber Lin: Okay, yeah, I think that’s all. I’ll make the tickets and.

210 00:24:40.120 00:24:40.680 Luke Daque: So look.

211 00:24:41.246 00:24:52.320 Amber Lin: I don’t think we’re able to meet tomorrow, because there’s a holiday day off if you end up working tomorrow we’ll we’ll just text and slack, if not like. We still have next week.

212 00:24:53.140 00:24:54.000 Luke Daque: Sounds good.

213 00:24:54.000 00:24:55.730 Amber Lin: Yeah, awesome.

214 00:24:55.910 00:24:56.899 Amber Lin: Thank you so much.

215 00:24:56.900 00:24:57.909 Luke Daque: Thanks, thanks, Emily.

216 00:24:57.910 00:24:59.740 Amber Lin: Alrighty! Bye-bye.