Meeting Title: MatterMore x Brainforge | Standup Date: 2025-05-05 Meeting participants: Annie Yu, Luke Daque, Trevor Cohen, Amber Lin
WEBVTT
1 00:00:09.510 ⇒ 00:00:10.900 Amber Lin: Hi! There!
2 00:00:15.150 ⇒ 00:00:15.890 Luke Daque: Hi! Everyone.
3 00:00:18.300 ⇒ 00:00:19.050 Trevor Cohen: Hey!
4 00:00:19.790 ⇒ 00:00:20.830 Amber Lin: Hello bye!
5 00:00:21.160 ⇒ 00:00:22.860 Trevor Cohen: Hello. Happy Monday.
6 00:00:23.450 ⇒ 00:00:24.390 Luke Daque: Happy. Monday.
7 00:00:24.390 ⇒ 00:00:26.563 Amber Lin: Happy. Monday.
8 00:00:28.670 ⇒ 00:00:30.330 Trevor Cohen: How is everyone’s weekend.
9 00:00:32.772 ⇒ 00:00:37.529 Amber Lin: I completely just did nothing. I was very happy about that.
10 00:00:37.530 ⇒ 00:00:39.210 Trevor Cohen: Didn’t fully support that.
11 00:00:39.934 ⇒ 00:00:55.580 Amber Lin: And my girlfriend was watching the like. Do you guys know, apex, the game is like a battle royal. There was like a national final. So we were watching that I had
12 00:00:55.580 ⇒ 00:00:55.930 Amber Lin: cool.
13 00:00:58.490 ⇒ 00:01:00.339 Trevor Cohen: Was it entertaining, though.
14 00:01:00.630 ⇒ 00:01:12.260 Amber Lin: It was entertaining. I think they designed the scoring system really. Well, so even if you’re at last place, there’s still something you can work for. So people were still trying, and that was pretty fun.
15 00:01:12.260 ⇒ 00:01:15.009 Trevor Cohen: As it stays. Competitive. Yeah.
16 00:01:16.320 ⇒ 00:01:16.990 Trevor Cohen: Cool.
17 00:01:17.230 ⇒ 00:01:18.350 Amber Lin: What about you?
18 00:01:18.840 ⇒ 00:01:27.759 Trevor Cohen: I did the in New York. There’s this thing called the 5 Borough Bike Tour, that, like goes through all 5 boroughs of New York. And so I did that.
19 00:01:27.950 ⇒ 00:01:30.220 Amber Lin: Oh, so I’m all for now.
20 00:01:32.230 ⇒ 00:01:37.150 Trevor Cohen: It’s like 40 miles or something. It’s not. It’s not like egregious, but it’s long.
21 00:01:37.150 ⇒ 00:01:41.070 Amber Lin: Okay, cause I run. So when I hear 40 miles, I’m like.
22 00:01:42.540 ⇒ 00:01:44.319 Trevor Cohen: Completely different, completely different.
23 00:01:46.230 ⇒ 00:01:46.810 Amber Lin: Boy.
24 00:01:46.810 ⇒ 00:01:49.510 Trevor Cohen: Run marathons, or like, what kind of running do you do.
25 00:01:49.600 ⇒ 00:01:51.879 Amber Lin: Trying to get running a marathon.
26 00:01:51.880 ⇒ 00:01:52.320 Trevor Cohen: That’s true.
27 00:01:52.320 ⇒ 00:01:57.700 Amber Lin: Very far away, but I went from couch to.
28 00:01:58.500 ⇒ 00:02:02.660 Trevor Cohen: 5 miles. I think that’s awesome. That’s great.
29 00:02:02.790 ⇒ 00:02:05.689 Trevor Cohen: Yeah, cool. Okay? So you’re you’re new runner.
30 00:02:06.170 ⇒ 00:02:11.694 Amber Lin: Yeah, I’m new. I used to do body building, and I could not run a single mile.
31 00:02:12.270 ⇒ 00:02:15.419 Trevor Cohen: Yeah, it’s good to do both. It’s good to get some cardio in there.
32 00:02:15.420 ⇒ 00:02:30.629 Amber Lin: Yeah, totally makes me feel a lot better. Okay, trying to keep this meeting short for you. What are we gonna do this week. I know we have 2 synthetic data sets we’re trying to figure out joins.
33 00:02:30.820 ⇒ 00:02:34.080 Amber Lin: How’s that progress? Luke and Annie.
34 00:02:37.730 ⇒ 00:02:41.708 Luke Daque: That’s still a working progress. So in terms of like
35 00:02:42.410 ⇒ 00:03:02.279 Luke Daque: getting the data for each of the table the same like, for example, there’s the use a specific user id exist in the other table. Basically, that’s what we are currently working at at the moment. Once we get that then we should be able to like join the data. So we’re gonna we’re going to update the script so that will
36 00:03:02.490 ⇒ 00:03:03.280 Luke Daque: haven’t.
37 00:03:03.390 ⇒ 00:03:09.080 Amber Lin: Like user ids across all the tables that we have. So I see it so.
38 00:03:09.080 ⇒ 00:03:10.270 Annie Yu: To my knowledge.
39 00:03:11.425 ⇒ 00:03:19.110 Annie Yu: Keep me honest. But I think utam last week said he was working on that right. That’s how I understood it.
40 00:03:19.500 ⇒ 00:03:25.450 Annie Yu: cause he’s making another kind of intermediate
41 00:03:25.790 ⇒ 00:03:31.190 Annie Yu: mapping table. I think that’s what he said, but
42 00:03:31.760 ⇒ 00:03:35.569 Annie Yu: it’s all kind of baked out. So correct me if I’m wrong.
43 00:03:36.000 ⇒ 00:03:41.639 Luke Daque: Yeah, we’ll we’ll check with Utah. So we make sure we’re not like duplicate duplicating efforts.
44 00:03:41.640 ⇒ 00:03:46.599 Amber Lin: Okay, sounds good. Is there anything you guys would like Trevor to review
45 00:03:47.440 ⇒ 00:03:54.060 Amber Lin: like any of these data sets that we need comments on? Or is this more of a utop thing like, what do you guys think.
46 00:03:55.510 ⇒ 00:03:56.600 Annie Yu: I think we’re
47 00:03:58.030 ⇒ 00:04:06.020 Annie Yu: we’re all set I think we don’t need any review on Trevor’s end, as of now.
48 00:04:06.020 ⇒ 00:04:06.730 Trevor Cohen: Cool.
49 00:04:06.730 ⇒ 00:04:12.209 Amber Lin: Oh, okay, when is like, at what point should do? We need Trevor to review
50 00:04:12.590 ⇒ 00:04:14.429 Amber Lin: like, what else do we need to do?
51 00:04:18.649 ⇒ 00:04:23.830 Amber Lin: Because I imagine we wanna confirm that the data sets are what we need.
52 00:04:24.170 ⇒ 00:04:36.759 Trevor Cohen: I was. Gonna say, maybe it would make sense. Once we start getting to actually building the analytics off of the data sets, because then it’ll become clear like if we’re missing anything. If, like the data seems reasonable. Things like that like that’ll just be a good way to go check it.
53 00:04:38.270 ⇒ 00:04:39.590 Amber Lin: Okay. Sounds good.
54 00:04:41.110 ⇒ 00:04:46.391 Annie Yu: Trevor, does that mean? You’ll set up the what’s that?
55 00:04:46.920 ⇒ 00:04:47.810 Trevor Cohen: You need to.
56 00:04:48.190 ⇒ 00:04:53.128 Annie Yu: What’s that called? Wait, what’s going on with me? A power bi.
57 00:04:53.980 ⇒ 00:04:54.420 Trevor Cohen: Bi.
58 00:04:54.420 ⇒ 00:04:55.850 Annie Yu: Yeah. Would you send.
59 00:04:55.850 ⇒ 00:04:56.190 Amber Lin: Okay.
60 00:04:57.980 ⇒ 00:05:18.890 Trevor Cohen: That’s a good question. I guess that is the question. I mean, before we start doing power bi stuff, I think there’s still work to be done just within bigquery, where we want to. Just like make the join tables figure out like what intermediate models we want, and then figure out what the queries are to pull the kinds of analytics we want before we start talking about visualizing them. I think.
61 00:05:18.890 ⇒ 00:05:19.420 Annie Yu: Yeah.
62 00:05:19.620 ⇒ 00:05:20.560 Amber Lin: Yeah,
63 00:05:21.500 ⇒ 00:05:31.120 Trevor Cohen: It probably does make sense eventually to set up a power bi instance and actually start like creating the visualization. So let’s just like stay in touch about that about when to do that.
64 00:05:31.770 ⇒ 00:05:41.479 Amber Lin: So what I hear here is that is question, is this staging, or like the intermediate model? Is that in bigquery, or is that in dvt like. I’m confused.
65 00:05:41.480 ⇒ 00:05:43.509 Trevor Cohen: It’s all in bigquery. Dbt. Operates.
66 00:05:43.510 ⇒ 00:05:46.849 Amber Lin: Okay, okay, sorry. Okay, it’s weird.
67 00:05:46.850 ⇒ 00:05:47.240 Trevor Cohen: Cool.
68 00:05:50.650 ⇒ 00:06:00.430 Trevor Cohen: Too many apps running around. I know. So that’s what I’m working on now, like, I I just deployed it to or I’m like just about to deploy to Google.
69 00:06:00.880 ⇒ 00:06:13.229 Trevor Cohen: and get it all set up to like auto deploy from Github. But anyway, the point is, the Dvc stuff is kind of like that’s that’s like the last step, you know, like, once we have all the stuff we need to do. Then.
70 00:06:13.230 ⇒ 00:06:14.220 Amber Lin: So.
71 00:06:14.220 ⇒ 00:06:16.860 Trevor Cohen: It automates the pipeline by putting them into Dbc.
72 00:06:16.860 ⇒ 00:06:19.353 Amber Lin: I see. So this is actually bigquery.
73 00:06:19.950 ⇒ 00:06:20.749 Trevor Cohen: Yeah, yeah.
74 00:06:22.450 ⇒ 00:06:27.609 Amber Lin: I see. Cool models.
75 00:06:27.990 ⇒ 00:06:33.500 Amber Lin: Great join logic, big group. That’s then that’s the same thing.
76 00:06:34.450 ⇒ 00:06:42.989 Luke Daque: Just a quick question, though, like, for the actual source data, are we like using any like 3rd party integration tools? Or are we like directly.
77 00:06:43.110 ⇒ 00:06:47.289 Luke Daque: Are you integrating it to bigquery through Api, or something.
78 00:06:48.320 ⇒ 00:06:51.300 Trevor Cohen: Like, how are we getting the source data into bigquery?
79 00:06:51.300 ⇒ 00:06:52.120 Luke Daque: Yeah, yeah.
80 00:06:52.430 ⇒ 00:07:13.309 Trevor Cohen: Yeah, so we so that’s we have an app that is basically gonna connect to all the source data sources and pull the data and anonymize it and then dump it into a dedicated bigquery. Data sets for that client. So this, you know, for, as far as you guys are concerned. Just assume everything’s in bigquery and ready to go just like the synthetic data will be.
81 00:07:13.620 ⇒ 00:07:14.730 Luke Daque: Okay. Cool.
82 00:07:18.180 ⇒ 00:07:23.829 Amber Lin: Does it make sense? If I say, define the analytics because we need
83 00:07:24.040 ⇒ 00:07:33.830 Amber Lin: to figure out the joins, we can figure out bigquery. And then we kind of need to define, like what kind of analytics is needed before we even do the dashboard mockups.
84 00:07:33.830 ⇒ 00:07:36.199 Trevor Cohen: Agreed. Yeah. So you can tag me with that.
85 00:07:37.110 ⇒ 00:07:37.930 Amber Lin: Okay.
86 00:07:44.250 ⇒ 00:07:55.989 Amber Lin: yeah. And then we’ll either do a sync, maybe on Wednesday to check on this progress. And if we can get started because once we have this, I think, and it will be pretty straightforward for Annie to figure out
87 00:07:56.150 ⇒ 00:07:58.789 Amber Lin: what kind of visualizations we need.
88 00:07:59.890 ⇒ 00:08:00.780 Trevor Cohen: I agree.
89 00:08:01.010 ⇒ 00:08:06.780 Amber Lin: Okay, what is what is it about this staging models? Again, that’s part of the
90 00:08:07.160 ⇒ 00:08:09.149 Amber Lin: the stuff we’re doing right now.
91 00:08:11.920 ⇒ 00:08:20.400 Luke Daque: Let’s just basically get for the staging models that’s just basically trying to. That’s still Dbt related. And bigquery.
92 00:08:20.400 ⇒ 00:08:20.810 Amber Lin: Oh!
93 00:08:20.810 ⇒ 00:08:22.700 Luke Daque: So, yeah, once we get this
94 00:08:23.200 ⇒ 00:08:26.419 Luke Daque: models, those Csv files, or like the
95 00:08:27.097 ⇒ 00:08:32.099 Luke Daque: Synthetic data into bigquery, then we can create staging models and intermediate models, and
96 00:08:32.530 ⇒ 00:08:34.920 Luke Daque: the March models for the joints and stuff.
97 00:08:36.380 ⇒ 00:08:38.980 Trevor Cohen: It kind of overlaps with his other tasks. Probably.
98 00:08:39.169 ⇒ 00:08:39.859 Luke Daque: Yeah.
99 00:08:43.260 ⇒ 00:08:47.500 Amber Lin: Cool, awesome anything else that we need. I think we covered everything.
100 00:08:48.160 ⇒ 00:08:51.964 Luke Daque: I think it’s we still not are not seeing the
101 00:08:53.240 ⇒ 00:08:55.909 Luke Daque: bigquery stuff. Let me share my screen real quick.
102 00:08:56.440 ⇒ 00:08:57.759 Trevor Cohen: Unless it’s you.
103 00:08:57.760 ⇒ 00:08:59.749 Luke Daque: I can show you like what it looks like.
104 00:08:59.970 ⇒ 00:09:01.149 Trevor Cohen: What it looks like.
105 00:09:04.740 ⇒ 00:09:06.280 Luke Daque: Can you see my screen.
106 00:09:07.080 ⇒ 00:09:07.700 Trevor Cohen: Yeah.
107 00:09:08.900 ⇒ 00:09:17.230 Luke Daque: So this is this is using the Brainforge user at matter more. And if I go to bigquery, basically.
108 00:09:17.520 ⇒ 00:09:19.680 Luke Daque: yeah, there’s like, it’s not.
109 00:09:19.680 ⇒ 00:09:22.379 Trevor Cohen: We’ll go click where it says, madam, or.ai.
110 00:09:23.780 ⇒ 00:09:29.840 Trevor Cohen: Okay, what about wait. Hold on. Stay there, me one sec.
111 00:09:32.010 ⇒ 00:09:35.840 Trevor Cohen: Because blue. I’m not sure if I’ve actually given you access through your
112 00:09:36.333 ⇒ 00:09:38.150 Trevor Cohen: let me see. Hold on.
113 00:09:41.430 ⇒ 00:09:44.200 Trevor Cohen: Andy. Do you see the same thing that Luke does.
114 00:09:44.200 ⇒ 00:09:50.360 Annie Yu: Yeah, both account or my personal account.
115 00:10:14.380 ⇒ 00:10:19.560 Trevor Cohen: can I? Can I see your screen, Annie, just like the same thing as I’m gonna try it.
116 00:10:19.740 ⇒ 00:10:20.790 Trevor Cohen: Something.
117 00:10:21.250 ⇒ 00:10:23.000 Annie Yu: Yeah, I can pull up.
118 00:10:38.090 ⇒ 00:10:40.872 Annie Yu: Yeah. So this is with my personal one.
119 00:10:41.560 ⇒ 00:10:42.230 Trevor Cohen: Oh!
120 00:10:44.420 ⇒ 00:10:46.239 Annie Yu: Yeah, this is all I have.
121 00:10:47.550 ⇒ 00:10:49.367 Trevor Cohen: What happens if you go to
122 00:10:50.450 ⇒ 00:10:51.440 Trevor Cohen: Hold on.
123 00:10:52.500 ⇒ 00:10:54.459 Trevor Cohen: I’m just gonna slack you a link.
124 00:11:15.330 ⇒ 00:11:17.999 Trevor Cohen: What what happens if you click on that link. I just slacked you.
125 00:11:20.390 ⇒ 00:11:22.460 Annie Yu: The whoop!
126 00:11:23.360 ⇒ 00:11:24.610 Annie Yu: 3!
127 00:11:26.670 ⇒ 00:11:29.630 Luke Daque: Access denied. It looks like it shows.
128 00:11:31.260 ⇒ 00:11:32.010 Annie Yu: Wait!
129 00:11:35.630 ⇒ 00:11:39.779 Trevor Cohen: Got it just like copy and paste that link again.
130 00:11:40.660 ⇒ 00:11:42.440 Annie Yu: Oh, see!
131 00:11:50.940 ⇒ 00:11:53.030 Annie Yu: Oh, it’s annoying like it.
132 00:11:53.330 ⇒ 00:11:54.940 Trevor Cohen: No, it keeps going, boy.
133 00:11:57.100 ⇒ 00:12:00.850 Amber Lin: Maybe try a incognito tab.
134 00:12:04.400 ⇒ 00:12:05.720 Annie Yu: Oh!
135 00:12:09.190 ⇒ 00:12:10.060 Trevor Cohen: Smart.
136 00:12:10.960 ⇒ 00:12:11.610 Amber Lin: And.
137 00:12:20.690 ⇒ 00:12:21.470 Annie Yu: Oh!
138 00:12:21.890 ⇒ 00:12:30.210 Amber Lin: Oh, what did so much more? Trevor? Okay.
139 00:12:30.210 ⇒ 00:12:31.740 Trevor Cohen: Show up there what the heck.
140 00:12:33.350 ⇒ 00:12:34.450 Luke Daque: Check there!
141 00:12:34.450 ⇒ 00:12:40.160 Trevor Cohen: Maybe I need to give you like a, because what I had done last time was give you like a viewer role. And so
142 00:12:40.838 ⇒ 00:12:42.950 Trevor Cohen: hold on one second.
143 00:12:43.260 ⇒ 00:12:46.799 Amber Lin: Okay. So it’s not in any of the organizations.
144 00:12:47.190 ⇒ 00:12:48.810 Amber Lin: It’s just it’s a.
145 00:12:48.810 ⇒ 00:12:49.530 Luke Daque: Gotcha.
146 00:12:50.920 ⇒ 00:12:58.870 Luke Daque: So like a a check the dropdown. Yeah, there’s it’s not yeah.
147 00:12:59.510 ⇒ 00:13:02.840 Annie Yu: But are we in this one already or not?
148 00:13:02.970 ⇒ 00:13:03.500 Annie Yu: I don’t.
149 00:13:03.500 ⇒ 00:13:08.509 Luke Daque: Maybe maybe just start that specific project in the
150 00:13:09.280 ⇒ 00:13:12.449 Luke Daque: in the what do you call that in the Explorer Tab.
151 00:13:12.870 ⇒ 00:13:17.020 Luke Daque: because you can’t see it in the dropdown. For some reason it’s weird.
152 00:13:18.290 ⇒ 00:13:21.289 Annie Yu: Oh, you mean there’s no like a matter more. AI!
153 00:13:22.301 ⇒ 00:13:29.940 Luke Daque: Yeah. Yeah. But it is showing in your explorer at the left, like in the where the data sets are.
154 00:13:31.810 ⇒ 00:13:38.409 Luke Daque: The matter more at the analytics. You can just click on the star that way. You should be able to see it all the time. The star.
155 00:13:38.410 ⇒ 00:13:39.805 Trevor Cohen: Where.
156 00:13:42.617 ⇒ 00:13:44.889 Annie Yu: Is this, okay? Wait.
157 00:13:44.890 ⇒ 00:13:45.859 Luke Daque: The other the other one.
158 00:13:45.860 ⇒ 00:13:48.369 Trevor Cohen: Next up next to matter, where analytics.
159 00:13:48.370 ⇒ 00:13:52.920 Annie Yu: Okay? And then so does that mean, okay.
160 00:13:52.920 ⇒ 00:13:54.339 Trevor Cohen: Try to refresh the page.
161 00:13:55.060 ⇒ 00:13:55.780 Annie Yu: Okay.
162 00:13:56.790 ⇒ 00:14:03.320 Luke Daque: Yeah, that’s that’s just so. It like bookmarks. The the project there in your explorer.
163 00:14:03.580 ⇒ 00:14:05.530 Annie Yu: Star, okay, start.
164 00:14:06.700 ⇒ 00:14:09.339 Amber Lin: Where is the explorer?
165 00:14:11.370 ⇒ 00:14:12.940 Amber Lin: Oh, there we go!
166 00:14:14.730 ⇒ 00:14:23.729 Luke Daque: But yeah, we should be able. Well, that’s your can you do the same thing? For to the brain forge it matter more account, Trevor, like whatever you did to.
167 00:14:23.730 ⇒ 00:14:30.769 Trevor Cohen: Yeah. Well, Luke, try to try. Try to do it now see if you can. Or here, hold on. I’ll just paste that link that I gave Annie also.
168 00:14:31.020 ⇒ 00:14:31.270 Annie Yu: Yeah.
169 00:14:31.270 ⇒ 00:14:32.420 Luke Daque: It’s blues.
170 00:14:32.420 ⇒ 00:14:40.479 Trevor Cohen: I’ll paste in the yeah. Let’s see if that works. Now for your for your email.
171 00:14:41.500 ⇒ 00:14:43.699 Amber Lin: Yeah, I’m checking for mine as well.
172 00:14:44.130 ⇒ 00:14:44.820 Trevor Cohen: Okay.
173 00:14:46.070 ⇒ 00:14:51.520 Annie Yu: So should I, I guess, save this one.
174 00:14:51.520 ⇒ 00:14:53.359 Trevor Cohen: Yeah, you can bookmark that page.
175 00:14:53.360 ⇒ 00:14:53.810 Annie Yu: Yeah.
176 00:14:54.329 ⇒ 00:14:56.409 Trevor Cohen: Organization that’s really weird.
177 00:14:59.830 ⇒ 00:15:01.680 Luke Daque: Yeah, let’s see. But.
178 00:15:01.820 ⇒ 00:15:02.380 Amber Lin: It works.
179 00:15:02.380 ⇒ 00:15:04.039 Luke Daque: For the brain for experience. Yeah.
180 00:15:04.630 ⇒ 00:15:09.039 Amber Lin: Yeah, I’m in the I’m in there. I can see what Annie sees now. So.
181 00:15:09.040 ⇒ 00:15:11.739 Trevor Cohen: Okay, wait for amber for your email.
182 00:15:11.960 ⇒ 00:15:20.370 Amber Lin: Yeah, for my wait for it should be for my Brainforge email. Let me see, I think I keep going to my own.
183 00:15:20.830 ⇒ 00:15:22.239 Trevor Cohen: Like my own account.
184 00:15:22.240 ⇒ 00:15:26.789 Trevor Cohen: Wait, Luke, can you see, are you looking at the brain forge email or your brain forge email?
185 00:15:27.240 ⇒ 00:15:34.190 Luke Daque: It’s it’s the brain forge of matter. More. I can’t see it using my, I can’t.
186 00:15:34.960 ⇒ 00:15:36.850 Trevor Cohen: Oh, you wait, can, or cannot.
187 00:15:36.850 ⇒ 00:15:38.170 Luke Daque: Cannot so.
188 00:15:38.170 ⇒ 00:15:42.010 Trevor Cohen: Hi, did you get an email that showed that you were added to a Google group.
189 00:15:42.300 ⇒ 00:15:43.499 Luke Daque: Let me check.
190 00:15:45.260 ⇒ 00:15:47.249 Luke Daque: Did you do it? Just do that now.
191 00:15:47.510 ⇒ 00:15:49.589 Trevor Cohen: I did it like 5 min ago. Yeah.
192 00:15:50.340 ⇒ 00:15:50.890 Amber Lin: M.
193 00:15:56.970 ⇒ 00:15:59.569 Luke Daque: I did not receive anything.
194 00:16:00.700 ⇒ 00:16:03.790 Luke Daque: Let me check, spam. It might be in spam.
195 00:16:03.970 ⇒ 00:16:09.057 Trevor Cohen: It might take. It takes a little bit to to do. But let me try one more thing.
196 00:16:11.560 ⇒ 00:16:14.530 Luke Daque: Yeah. But this is like what it looks like.
197 00:16:15.280 ⇒ 00:16:21.410 Luke Daque: You see my email. But if I go to the Brainforge, it matter more shows.
198 00:16:21.860 ⇒ 00:16:26.130 Trevor Cohen: I see, okay, weird.
199 00:16:30.660 ⇒ 00:16:33.570 Amber Lin: Annie, was it for your Brainforge email.
200 00:16:33.570 ⇒ 00:16:34.280 Annie Yu: Yes.
201 00:16:34.970 ⇒ 00:16:36.510 Amber Lin: Oh, okay, so is.
202 00:16:36.510 ⇒ 00:16:36.850 Trevor Cohen: So.
203 00:16:36.850 ⇒ 00:16:37.400 Amber Lin: Sure.
204 00:16:37.900 ⇒ 00:16:53.090 Trevor Cohen: It should work. So look, I, just the thing is that, yeah. So what I did was, I added, like, I create a brain for it to matter more group, and gave it all the necessary permissions. And Annie’s been in it. But I just added you so it’s possible. Just need some time for that to refresh. But
205 00:16:53.800 ⇒ 00:16:59.649 Trevor Cohen: okay, yeah. So you should be able to do it through your own email hopefully. Soon.
206 00:16:59.830 ⇒ 00:17:01.310 Luke Daque: Yeah, sounds good.
207 00:17:01.660 ⇒ 00:17:02.280 Trevor Cohen: Okay?
208 00:17:02.390 ⇒ 00:17:07.840 Trevor Cohen: And then I’ll just get rid of that brain forge user 1 1. Just ping me once if and when you get access, Luke.
209 00:17:08.230 ⇒ 00:17:08.940 Luke Daque: Okay.
210 00:17:09.270 ⇒ 00:17:13.210 Trevor Cohen: Alright cool. Glad we got that sort of figured out.
211 00:17:13.589 ⇒ 00:17:14.099 Luke Daque: Nice.
212 00:17:14.109 ⇒ 00:17:14.729 Amber Lin: So.
213 00:17:15.200 ⇒ 00:17:15.859 Trevor Cohen: Yeah.
214 00:17:17.540 ⇒ 00:17:19.620 Trevor Cohen: Okay. Anything. Else.
215 00:17:24.980 ⇒ 00:17:27.310 Luke Daque: I think that should be it, for now
216 00:17:27.420 ⇒ 00:17:35.920 Luke Daque: we can start working on adding the synthetic data in bigquery, since we already have access once we get the user ids.
217 00:17:36.040 ⇒ 00:17:36.860 Luke Daque: awesome.
218 00:17:37.810 ⇒ 00:17:46.189 Trevor Cohen: And you said, you guys are also, we’re we’re reviewing a Pr to, or whatever. Yeah, pr, to add it to the repo. And Github.
219 00:17:46.440 ⇒ 00:17:49.459 Luke Daque: Right? Yeah, I think there’s a note on opinion. If you are there.
220 00:17:49.460 ⇒ 00:17:51.630 Trevor Cohen: Cool. Yeah, no rush on that, of course.
221 00:17:53.090 ⇒ 00:17:54.270 Trevor Cohen: Just good to have.
222 00:17:55.700 ⇒ 00:18:02.119 Trevor Cohen: Alright sweet. Well, let me know if anything else for me, I’m gonna keep working on the Dbt stuff. And yeah.
223 00:18:03.380 ⇒ 00:18:06.230 Luke Daque: Did you already create the Bbt project? By the way.
224 00:18:06.770 ⇒ 00:18:07.789 Trevor Cohen: Say it again.
225 00:18:07.790 ⇒ 00:18:09.669 Luke Daque: Did you create the Dbt Project.
226 00:18:10.935 ⇒ 00:18:17.154 Trevor Cohen: Yeah, I created a project, and I am just doing all the infrastructure stuff now and then.
227 00:18:17.890 ⇒ 00:18:22.610 Trevor Cohen: you know, I’ll like add all the internals of the Dbt. Project itself.
228 00:18:23.060 ⇒ 00:18:26.470 Luke Daque: Nice, sounds good.
229 00:18:27.330 ⇒ 00:18:30.550 Trevor Cohen: Cool. Well, have a good week. I’ll chat with you guys later.
230 00:18:31.120 ⇒ 00:18:31.780 Luke Daque: See? How many.
231 00:18:31.780 ⇒ 00:18:32.430 Annie Yu: Then.
232 00:18:32.950 ⇒ 00:18:33.570 Trevor Cohen: Bye.
233 00:18:33.730 ⇒ 00:18:34.870 Amber Lin: Bye, bye, bye.