Meeting Title: MatterMore | internal Standup Date: 2025-06-13 Meeting participants: Awaish Kumar, Fireflies.ai Notetaker Awaish, Annie Yu, Amber Lin, Luke Daque
WEBVTT
1 00:04:26.230 ⇒ 00:04:27.670 Amber Lin: Hi.
2 00:04:36.020 ⇒ 00:04:36.810 Awaish Kumar: Hello!
3 00:04:37.590 ⇒ 00:04:38.650 Amber Lin: Hello,
4 00:04:40.380 ⇒ 00:04:51.519 Amber Lin: I know you guys probably have a meeting later. I’m gonna try and keep this short. Let me just go around in a quick update and then let me ping Luke. That we’re in a meeting.
5 00:04:55.390 ⇒ 00:05:02.010 Amber Lin: And then I guess first, st Annie, were you able to get into power Bi and get things started.
6 00:05:03.710 ⇒ 00:05:08.379 Annie Yu: I don’t have problem accessing it, but I still can’t create anything.
7 00:05:09.380 ⇒ 00:05:17.479 Annie Yu: And I also can create like semantic model. So I I think the flow similar to tableau is.
8 00:05:17.730 ⇒ 00:05:25.970 Annie Yu: I have to get get it connected to bigquery and then bring a model into power Bi cloud.
9 00:05:26.410 ⇒ 00:05:29.610 Amber Lin: Well, I think Trevor already connected it to bigquery.
10 00:05:29.900 ⇒ 00:05:31.750 Annie Yu: Yeah. Then I don’t see that.
11 00:05:31.950 ⇒ 00:05:34.569 Annie Yu: At least I can’t do anything on my end.
12 00:05:35.310 ⇒ 00:05:41.360 Amber Lin: Can you share your screen, and maybe a way she can look at it also a wish, Trevor added you to
13 00:05:41.855 ⇒ 00:05:45.870 Amber Lin: I’m a big carrier. So maybe you can help Annie to
14 00:05:46.100 ⇒ 00:05:48.489 Amber Lin: see if there’s anything we need to add.
15 00:05:49.700 ⇒ 00:05:54.060 Awaish Kumar: Yeah, I just got the access. So I gonna need some time to.
16 00:05:54.310 ⇒ 00:05:54.720 Amber Lin: Hmm.
17 00:05:54.720 ⇒ 00:05:55.720 Awaish Kumar: Actually going.
18 00:06:01.230 ⇒ 00:06:06.079 Awaish Kumar: Oh, yeah, action time range
19 00:06:13.840 ⇒ 00:06:14.700 Awaish Kumar: shouldn’t.
20 00:06:15.340 ⇒ 00:06:18.179 Awaish Kumar: Apart from that, is there anything we are blocked on.
21 00:06:20.920 ⇒ 00:06:41.290 Amber Lin: I guess. And wish. Would you work with Annie later to just go through this anything we need? If there’s anything we need, Trevor to help on I need to send it to him before the end of today. He’s also very busy. So if we could consolidate the list of items we need. He? That would be ideal.
22 00:06:41.933 ⇒ 00:06:49.369 Amber Lin: So we can. Maybe you guys can find a time to just call shortly after this, or you can use this meeting room.
23 00:06:52.040 ⇒ 00:06:52.820 Awaish Kumar: Really.
24 00:06:53.210 ⇒ 00:06:53.760 Amber Lin: Okay.
25 00:06:53.760 ⇒ 00:06:55.150 Awaish Kumar: How did you got invite?
26 00:06:57.660 ⇒ 00:06:58.530 Amber Lin: Pardon me.
27 00:06:59.680 ⇒ 00:07:03.320 Awaish Kumar: Okay, when you except the accepted.
28 00:07:03.840 ⇒ 00:07:06.479 Amber Lin: To find the invite go.
29 00:07:06.480 ⇒ 00:07:07.349 Awaish Kumar: I have the invite.
30 00:07:08.270 ⇒ 00:07:09.189 Amber Lin: In.
31 00:07:09.190 ⇒ 00:07:17.089 Awaish Kumar: I have the I have the invite. I clicked on. Accept invitation. Now it it is asking me for password
32 00:07:18.050 ⇒ 00:07:21.399 Awaish Kumar: like asking me for Brainforge Microsoft account.
33 00:07:22.010 ⇒ 00:07:24.970 Awaish Kumar: and I don’t know. We don’t have the microphone.
34 00:07:24.970 ⇒ 00:07:27.999 Amber Lin: Can you share your screen and we can look at it.
35 00:07:28.910 ⇒ 00:07:29.900 Awaish Kumar: Yeah. Sure.
36 00:07:31.430 ⇒ 00:07:32.750 Awaish Kumar: Hello.
37 00:07:44.570 ⇒ 00:07:45.860 Awaish Kumar: This one.
38 00:07:50.440 ⇒ 00:07:52.009 Amber Lin: Wait! Is this.
39 00:07:53.440 ⇒ 00:07:56.159 Amber Lin: Is this the link from the invite.
40 00:07:58.050 ⇒ 00:07:59.970 Awaish Kumar: Like this is the link.
41 00:08:01.480 ⇒ 00:08:01.820 Amber Lin: Okay.
42 00:08:01.820 ⇒ 00:08:03.570 Awaish Kumar: I accepted the invitation.
43 00:08:04.310 ⇒ 00:08:10.650 Awaish Kumar: And now it brings me to this screen, which is like this one.
44 00:08:13.170 ⇒ 00:08:14.590 Amber Lin: Oh!
45 00:08:16.080 ⇒ 00:08:18.470 Amber Lin: Does your usual password work.
46 00:08:22.060 ⇒ 00:08:25.219 Awaish Kumar: Arjun like, when for this email is not
47 00:08:26.190 ⇒ 00:08:29.130 Awaish Kumar: we just start with any Microsoft conflict.
48 00:08:35.480 ⇒ 00:08:36.250 Awaish Kumar: Come up.
49 00:08:37.159 ⇒ 00:08:38.799 Amber Lin: Oh, it’s so weird!
50 00:08:39.579 ⇒ 00:08:48.189 Amber Lin: Did you guys have to look in any? Did you guys have to do this when you were go like logging in. I don’t think I had to do this.
51 00:08:51.090 ⇒ 00:09:00.699 Annie Yu: I’m gonna sign out because I also went through this. But I don’t remember having to create or type in my password.
52 00:09:00.930 ⇒ 00:09:06.020 Amber Lin: Right. I don’t remember that. I think I just connected to my Google account. And that was it.
53 00:09:06.430 ⇒ 00:09:08.340 Annie Yu: Oh, really, that’s what you did.
54 00:09:08.570 ⇒ 00:09:13.629 Amber Lin: I think I think so. I’m not sure if I did not have to deal with this.
55 00:09:16.620 ⇒ 00:09:18.719 Annie Yu: I think I did have.
56 00:09:18.720 ⇒ 00:09:19.580 Amber Lin: Options.
57 00:09:19.580 ⇒ 00:09:20.710 Annie Yu: Yeah. Signing option.
58 00:09:20.710 ⇒ 00:09:23.240 Amber Lin: Oh and no, never mind.
59 00:09:24.110 ⇒ 00:09:25.400 Amber Lin: Oh, that’s so weird!
60 00:09:27.180 ⇒ 00:09:32.790 Awaish Kumar: Is not asking me for any organization or anything.
61 00:09:36.280 ⇒ 00:09:40.799 Luke Daque: Yeah, I can’t remember going through that as well. I just click the accept, invite, and then.
62 00:09:42.210 ⇒ 00:09:42.950 Amber Lin: Can I.
63 00:09:42.950 ⇒ 00:09:43.630 Luke Daque: Yes.
64 00:09:55.670 ⇒ 00:09:58.640 Annie Yu: So weird. Yeah, there’s no sign into an organization.
65 00:09:58.640 ⇒ 00:10:04.990 Amber Lin: Can we see the email face interface again, the email he sent you?
66 00:10:08.070 ⇒ 00:10:12.204 Amber Lin: Okay.
67 00:10:20.610 ⇒ 00:10:25.060 Amber Lin: yeah. Their stuff is so weird. Every time we need access. There’s a problem.
68 00:10:26.570 ⇒ 00:10:31.779 Annie Yu: And even how I got it and how Amber got it were different.
69 00:10:32.180 ⇒ 00:10:44.800 Amber Lin: It’s so weird. What if we do go directly to power bi like, maybe it’s added already, and you can just go directly to power. Bi, let me go find the power bi link, and then I can send it to you.
70 00:10:46.595 ⇒ 00:10:50.710 Amber Lin: Add a more linear power. Bi
71 00:10:58.380 ⇒ 00:11:01.650 Amber Lin: Right here.
72 00:11:07.850 ⇒ 00:11:13.169 Amber Lin: I think that was the power Bi account. And I sent you a link in the chat.
73 00:11:14.180 ⇒ 00:11:23.309 Amber Lin: Think if we click oh, God, okay, if we click on that.
74 00:11:23.720 ⇒ 00:11:25.060 Awaish Kumar: This is the same.
75 00:11:25.060 ⇒ 00:11:27.540 Amber Lin: Yeah, click on sign into the organization.
76 00:11:29.320 ⇒ 00:11:30.939 Amber Lin: mattermore.ai.
77 00:11:40.850 ⇒ 00:11:46.410 Amber Lin: Oh, damn it! Oh, that’s so annoying.
78 00:11:46.940 ⇒ 00:11:47.860 Amber Lin: Okay.
79 00:11:51.763 ⇒ 00:11:55.380 Amber Lin: that did not work. Okay.
80 00:12:01.620 ⇒ 00:12:02.910 Awaish Kumar: Hmm sure.
81 00:12:04.290 ⇒ 00:12:04.640 Amber Lin: Okay.
82 00:12:04.640 ⇒ 00:12:05.030 Awaish Kumar: It doesn’t.
83 00:12:05.030 ⇒ 00:12:06.519 Amber Lin: Back to square one.
84 00:12:06.520 ⇒ 00:12:11.289 Awaish Kumar: Is, is any of you have like had to create the account using.
85 00:12:11.770 ⇒ 00:12:12.430 Amber Lin: Oh!
86 00:12:12.430 ⇒ 00:12:14.010 Awaish Kumar: My voice is a colleague.
87 00:12:14.010 ⇒ 00:12:16.050 Amber Lin: No, I didn’t have to.
88 00:12:22.600 ⇒ 00:12:24.160 Amber Lin: Let me check.
89 00:12:24.720 ⇒ 00:12:31.789 Amber Lin: Okay, we’ll figure this out in a bit, I guess, Luke, any updates on your side.
90 00:12:33.500 ⇒ 00:12:38.249 Luke Daque: Yeah, on my end. I also have like access issues. Again, it looks like
91 00:12:38.540 ⇒ 00:12:41.400 Luke Daque: I can’t create a data set
92 00:12:41.600 ⇒ 00:12:50.519 Luke Daque: in bigquery. But I already did create the models in Dbt, I just didn’t create a Pr yet, because I can’t run them. I can share my screen if you like.
93 00:12:50.520 ⇒ 00:12:53.240 Amber Lin: I see. Yeah, let’s see your screen.
94 00:12:57.230 ⇒ 00:12:58.829 Luke Daque: Can you see my screen now?
95 00:13:01.780 ⇒ 00:13:02.470 Annie Yu: Yes.
96 00:13:02.760 ⇒ 00:13:08.810 Luke Daque: Yeah. So yes, I already created the models here, and also added the
97 00:13:09.600 ⇒ 00:13:17.637 Luke Daque: like missing fields that we were discussing in the I mean that that were in the sheet. This one right? Like,
98 00:13:18.390 ⇒ 00:13:21.859 Luke Daque: yeah. The post office mandate, the location.
99 00:13:23.010 ⇒ 00:13:23.540 Amber Lin: Yay!
100 00:13:23.540 ⇒ 00:13:25.819 Luke Daque: Yeah. But yeah, I just didn’t
101 00:13:26.180 ⇒ 00:13:29.489 Luke Daque: do this yet, because we don’t have that. And like the focus time.
102 00:13:29.490 ⇒ 00:13:33.139 Amber Lin: Don’t need to do, we don’t do comply. So that’s okay.
103 00:13:33.710 ⇒ 00:13:36.300 Luke Daque: And then for the focus time, it’s still.
104 00:13:36.890 ⇒ 00:13:41.015 Amber Lin: And we also don’t need to do that. It was not required. So whatever.
105 00:13:41.310 ⇒ 00:13:50.599 Luke Daque: So. Yeah. But anyway, going back, we did like Trevor and I had a call the other day, I believe, and we were able to figure out like the
106 00:13:50.760 ⇒ 00:13:56.499 Luke Daque: connection to dB to bigquery. So if I I do a Dbt debug.
107 00:13:57.030 ⇒ 00:14:01.120 Luke Daque: it’s already like, everything’s already okay, all checks past.
108 00:14:01.700 ⇒ 00:14:05.999 Luke Daque: But like as soon as after I created all the models, and then more like updated
109 00:14:06.470 ⇒ 00:14:10.149 Luke Daque: the models when I do a Dbt. Run well yesterday.
110 00:14:10.640 ⇒ 00:14:14.780 Luke Daque: by the way, I wasn’t able to do anything. It was like a big period. I mean, Google.
111 00:14:14.780 ⇒ 00:14:15.110 Amber Lin: Yeah.
112 00:14:15.110 ⇒ 00:14:19.170 Luke Daque: Down and like I was always getting like time out issues.
113 00:14:19.440 ⇒ 00:14:19.960 Amber Lin: But.
114 00:14:21.900 ⇒ 00:14:24.259 Amber Lin: Do we try to happen today.
115 00:14:24.620 ⇒ 00:14:32.210 Luke Daque: Try to do a DVD run, and it looks like it’s getting this error.
116 00:14:32.460 ⇒ 00:14:33.560 Amber Lin: Oh!
117 00:14:33.910 ⇒ 00:14:34.970 Luke Daque: It’s correct.
118 00:14:35.760 ⇒ 00:14:38.140 Luke Daque: Oh, yeah. So this one access denied
119 00:14:38.270 ⇒ 00:14:44.250 Luke Daque: user does not have this create data set permission. So.
120 00:14:44.250 ⇒ 00:14:49.809 Amber Lin: I thought I thought that was that was granted yesterday by Trevor.
121 00:14:49.960 ⇒ 00:14:53.199 Luke Daque: Yeah, I did already send him.
122 00:14:54.523 ⇒ 00:14:55.730 Luke Daque: The issue.
123 00:14:55.920 ⇒ 00:15:02.840 Luke Daque: So even in bigquery, I can’t create a data set. So I go here, even if I do a manual creation.
124 00:15:02.950 ⇒ 00:15:04.840 Luke Daque: So let’s just do a test.
125 00:15:05.450 ⇒ 00:15:08.000 Luke Daque: Yeah, I I get this issue. So it’s the same.
126 00:15:08.000 ⇒ 00:15:08.349 Amber Lin: I know.
127 00:15:08.480 ⇒ 00:15:13.370 Luke Daque: Basically the reason why it can’t create a data set. So.
128 00:15:13.370 ⇒ 00:15:13.770 Amber Lin: Okay.
129 00:15:13.770 ⇒ 00:15:21.509 Luke Daque: But maybe one thing I can do for now is to add everything in just this specific data set.
130 00:15:24.300 ⇒ 00:15:25.450 Luke Daque: But yeah, it would.
131 00:15:25.570 ⇒ 00:15:31.899 Luke Daque: It’d be quite like messy to have everything in just one.
132 00:15:32.357 ⇒ 00:15:35.100 Amber Lin: Under that under that data set.
133 00:15:35.520 ⇒ 00:15:41.189 Luke Daque: No, that’s it’s not like Snowflake, where there’s a database, and there’s a schema.
134 00:15:41.190 ⇒ 00:15:41.960 Amber Lin: So.
135 00:15:41.960 ⇒ 00:15:44.260 Luke Daque: In bigquery. It’s just a data set.
136 00:15:44.260 ⇒ 00:15:44.700 Amber Lin: Flat.
137 00:15:44.700 ⇒ 00:15:48.620 Luke Daque: Okay, yeah, I can.
138 00:15:48.910 ⇒ 00:15:53.099 Luke Daque: I guess I can go ahead and put everything here in just the synthetic data.
139 00:15:53.100 ⇒ 00:15:56.610 Amber Lin: Will that let you run the tests? If we add it? There.
140 00:15:57.550 ⇒ 00:16:02.110 Luke Daque: It should. But let me just try it.
141 00:16:05.330 ⇒ 00:16:18.159 Amber Lin: Yeah, I mean, we can change the original naming of the either these data sets or the original ones in the synthetic one. I just want us to run the test and not be completely blocked by the matter. More team.
142 00:16:18.480 ⇒ 00:16:20.979 Luke Daque: Yeah, so, yeah, but.
143 00:16:20.980 ⇒ 00:16:21.740 Amber Lin: Yay!
144 00:16:21.740 ⇒ 00:16:26.350 Luke Daque: Yeah, aside from that, well, it’s the same option I can’t.
145 00:16:26.780 ⇒ 00:16:28.009 Luke Daque: It still gets into this.
146 00:16:28.010 ⇒ 00:16:31.169 Amber Lin: Oh, we can’t! We can’t even add it to the synthetic.
147 00:16:31.350 ⇒ 00:16:32.040 Luke Daque: Yeah.
148 00:16:33.330 ⇒ 00:16:33.920 Amber Lin: Oh!
149 00:16:33.920 ⇒ 00:16:34.820 Luke Daque: Oh, my God!
150 00:16:36.150 ⇒ 00:16:37.329 Luke Daque: This found it
151 00:16:45.990 ⇒ 00:16:47.239 Luke Daque: next I’m sorry.
152 00:16:48.010 ⇒ 00:16:51.560 Amber Lin: Okay, so we can’t even add it to the synthetic data set.
153 00:16:52.000 ⇒ 00:16:54.630 Luke Daque: Look, okay.
154 00:16:55.560 ⇒ 00:16:57.299 Amber Lin: Okay, that’s an issue.
155 00:16:58.756 ⇒ 00:17:04.417 Amber Lin: What else? Okay? So that’s blocked with them. Oh, gosh!
156 00:17:05.880 ⇒ 00:17:17.410 Amber Lin: I don’t know what it is with their access. But anyways, let me let me also share my screen. And then I just want to update any tickets. No doubt anything. I I need to ask them.
157 00:17:17.869 ⇒ 00:17:26.959 Amber Lin: and then talk a little bit about tool usage. So I think these 3 things.
158 00:17:27.349 ⇒ 00:17:30.880 Amber Lin: these 2 things are already done right.
159 00:17:36.620 ⇒ 00:17:37.069 Amber Lin: See, my.
160 00:17:37.070 ⇒ 00:17:42.279 Luke Daque: Well, it should be in my model. But yeah, I can’t verify it.
161 00:17:42.280 ⇒ 00:17:44.650 Amber Lin: Oh, I see we can’t test it yet.
162 00:17:44.650 ⇒ 00:17:45.310 Luke Daque: Yeah.
163 00:17:45.767 ⇒ 00:17:51.259 Amber Lin: Okay, let me mark that as done right. This is done right.
164 00:17:52.000 ⇒ 00:17:55.550 Amber Lin: I’ll just add another testing ticket, because it’s so blocked.
165 00:17:56.480 ⇒ 00:17:57.090 Luke Daque: Well.
166 00:17:58.870 ⇒ 00:18:00.120 Amber Lin: Is this one done.
167 00:18:01.150 ⇒ 00:18:05.290 Luke Daque: I don’t know if we can call it done. If we didn’t run we weren’t able to run
168 00:18:05.980 ⇒ 00:18:07.119 Luke Daque: the Dbt models.
169 00:18:07.120 ⇒ 00:18:09.700 Amber Lin: Testing for the.
170 00:18:09.700 ⇒ 00:18:10.520 Luke Daque: Yeah.
171 00:18:10.550 ⇒ 00:18:13.250 Amber Lin: Testing as well.
172 00:18:13.450 ⇒ 00:18:19.090 Luke Daque: You know, and I don’t think like maybe testing is the right we’re
173 00:18:19.200 ⇒ 00:18:25.750 Luke Daque: to use. Maybe just running the models or materializing the models would be.
174 00:18:27.440 ⇒ 00:18:30.360 Luke Daque: We, we can’t materialize the models. Basically.
175 00:18:30.600 ⇒ 00:18:31.180 Amber Lin: Us.
176 00:18:31.180 ⇒ 00:18:32.480 Luke Daque: From Dbt.
177 00:18:32.800 ⇒ 00:18:38.730 Amber Lin: Never mind in progress. It goes needing.
178 00:18:41.110 ⇒ 00:18:44.180 Luke Daque: Maybe we can even put it to block or something.
179 00:18:44.180 ⇒ 00:18:47.009 Awaish Kumar: What is the authentication issue right now?
180 00:18:47.740 ⇒ 00:18:53.590 Luke Daque: The data set permission bigquery data set create permission is missing.
181 00:18:54.280 ⇒ 00:18:58.429 Awaish Kumar: So like in. If you go in the ui, are you able to create it?
182 00:18:59.420 ⇒ 00:19:00.110 Luke Daque: No, I can’t.
183 00:19:00.110 ⇒ 00:19:03.950 Luke Daque: I can create a data set. I can create a view in the same.
184 00:19:04.320 ⇒ 00:19:06.610 Luke Daque: Maybe I can do like a
185 00:19:06.960 ⇒ 00:19:13.199 Luke Daque: Dbt compile and then create it there one by one. But yeah, we can.
186 00:19:13.200 ⇒ 00:19:14.929 Awaish Kumar: No, no! What I meant is like
187 00:19:15.310 ⇒ 00:19:19.560 Awaish Kumar: instead of dbt like. If you go in through the ui.
188 00:19:19.700 ⇒ 00:19:23.050 Luke Daque: Are you able to create the views and the tables?
189 00:19:24.340 ⇒ 00:19:30.990 Luke Daque: I can create a view and table in just one data set. But I can’t create them in a different data set.
190 00:19:31.910 ⇒ 00:19:39.600 Awaish Kumar: Okay? And what DVD is trying to do like through Dbt, we are trying to create tables in another data set.
191 00:19:40.980 ⇒ 00:19:42.830 Luke Daque: Yeah, it looks like it’s the
192 00:19:43.610 ⇒ 00:19:48.629 Luke Daque: yeah. Even if I add, I, even if I let it create in the same data set, it’s still
193 00:19:48.980 ⇒ 00:19:50.670 Luke Daque: goes into that error.
194 00:19:53.170 ⇒ 00:19:58.270 Awaish Kumar: Okay. So we are not able to create the table from Dbt.
195 00:19:58.650 ⇒ 00:19:59.770 Luke Daque: Yeah, yeah.
196 00:19:59.770 ⇒ 00:20:04.640 Awaish Kumar: Able to create it when you log in to the Ui right.
197 00:20:05.140 ⇒ 00:20:08.060 Luke Daque: Yeah, but only yeah, but only for one data set. Yeah.
198 00:20:08.060 ⇒ 00:20:13.639 Awaish Kumar: Yeah. So in the so like, let’s figure out, what is the missing permission?
199 00:20:13.790 ⇒ 00:20:16.710 Awaish Kumar: Like, do we have like a job user.
200 00:20:17.440 ⇒ 00:20:20.809 Luke Daque: It’s that bigquery data set create permission. It’s what.
201 00:20:21.850 ⇒ 00:20:22.170 Awaish Kumar: Yeah.
202 00:20:22.170 ⇒ 00:20:22.960 Luke Daque: The errors.
203 00:20:24.490 ⇒ 00:20:31.282 Awaish Kumar: The like underline, like victory, is not able to create the data sets, but, like
204 00:20:33.130 ⇒ 00:20:38.909 Awaish Kumar: like what we like, what kind of if they go in, and they want to assign some
205 00:20:39.730 ⇒ 00:20:46.030 Awaish Kumar: more permission to your account. What that would be? Is it like job user?
206 00:20:49.130 ⇒ 00:20:50.920 Awaish Kumar: Like, I know, like, when we are trying.
207 00:20:50.920 ⇒ 00:20:51.360 Luke Daque: Yeah.
208 00:20:51.360 ⇒ 00:20:54.980 Awaish Kumar: Try to create something using using SDK,
209 00:20:55.480 ⇒ 00:21:01.409 Awaish Kumar: or through the rest of guys. Then we need a little bit more permissions. Then then
210 00:21:02.090 ⇒ 00:21:04.019 Awaish Kumar: the ones we already have.
211 00:21:06.300 ⇒ 00:21:07.070 Luke Daque: If so.
212 00:21:08.410 ⇒ 00:21:10.620 Awaish Kumar: It might be like something like
213 00:21:12.220 ⇒ 00:21:23.840 Awaish Kumar: related to job user. And like, session creation, like one or 2 more
214 00:21:24.280 ⇒ 00:21:26.640 Awaish Kumar: things you will need and maybe like.
215 00:21:26.770 ⇒ 00:21:31.990 Awaish Kumar: do some Google search on on it, and then maybe share it.
216 00:21:32.570 ⇒ 00:21:35.930 Awaish Kumar: Share that with the travel. That okay, I I need
217 00:21:36.240 ⇒ 00:21:41.529 Awaish Kumar: you to give give more permission to my account, and then we can see.
218 00:21:45.050 ⇒ 00:21:46.399 Luke Daque: This is what I got.
219 00:21:46.550 ⇒ 00:21:48.160 Luke Daque: Let me just bring it here
220 00:21:51.870 ⇒ 00:21:52.940 Luke Daque: in the chat.
221 00:21:53.090 ⇒ 00:22:00.289 Luke Daque: So it’s if Trevor gives us bigquery data editor, for example, then we can create
222 00:22:01.200 ⇒ 00:22:06.540 Luke Daque: data sets. But if he doesn’t want that generic role
223 00:22:07.229 ⇒ 00:22:12.989 Luke Daque: he can always add the specific custom role which is just the bigquery data set create
224 00:22:13.270 ⇒ 00:22:22.420 Luke Daque: permission, or the and the data set you get this late. That’s also combined.
225 00:22:22.420 ⇒ 00:22:32.609 Awaish Kumar: But we carry data admin of data editor, like, I think data editor can also be
226 00:22:32.860 ⇒ 00:22:38.610 Awaish Kumar: given to to a specific data set. It doesn’t need to be on a all the.
227 00:22:38.610 ⇒ 00:22:39.050 Luke Daque: Yes.
228 00:22:39.550 ⇒ 00:22:40.030 Awaish Kumar: So.
229 00:22:40.030 ⇒ 00:22:47.649 Luke Daque: It’s those are predefined roles that they can, Trevor can add, but I don’t think he has. He is like adding predefined roles.
230 00:22:47.650 ⇒ 00:22:50.029 Awaish Kumar: But he hasn’t added them like.
231 00:22:51.010 ⇒ 00:22:55.319 Luke Daque: From what I understand, he’s not like adding the predefined roles.
232 00:22:55.890 ⇒ 00:23:01.180 Awaish Kumar: Yeah. But if you are able to go in in the ui, and you are able to create a table
233 00:23:01.320 ⇒ 00:23:06.210 Awaish Kumar: inside of a inside of a like single data set.
234 00:23:06.530 ⇒ 00:23:09.650 Awaish Kumar: Then he has given you the access
235 00:23:10.450 ⇒ 00:23:13.440 Awaish Kumar: to create right some tables inside.
236 00:23:13.440 ⇒ 00:23:13.830 Luke Daque: Yeah.
237 00:23:13.830 ⇒ 00:23:14.420 Awaish Kumar: He doesn’t.
238 00:23:14.420 ⇒ 00:23:15.050 Luke Daque: Yeah.
239 00:23:16.180 ⇒ 00:23:22.469 Awaish Kumar: But you are saying that you are not able to create that data. The table in the same data set
240 00:23:22.880 ⇒ 00:23:31.690 Awaish Kumar: through the DVD, yes, that means there is something else like something else is missing
241 00:23:34.780 ⇒ 00:23:36.759 Awaish Kumar: because you are using the same
242 00:23:37.190 ⇒ 00:23:41.610 Awaish Kumar: email to authenticate yourself in the Dbt as well.
243 00:23:44.460 ⇒ 00:23:49.730 Awaish Kumar: and your your email should have the same permissions. But there are something.
244 00:23:50.980 ⇒ 00:23:59.679 Awaish Kumar: maybe some things which are missing is because you are not in the Ui, but you are trying to create those tables using some SDK,
245 00:23:59.840 ⇒ 00:24:03.229 Awaish Kumar: so Dbt is in the back end. Uses some SDK
246 00:24:04.700 ⇒ 00:24:07.700 Awaish Kumar: to connect with bigquery and create form tables.
247 00:24:08.180 ⇒ 00:24:11.759 Awaish Kumar: So let’s let’s like, do some Google search on on this.
248 00:24:11.890 ⇒ 00:24:18.720 Awaish Kumar: What exactly other like apart from these 2, what other custom
249 00:24:21.000 ⇒ 00:24:23.350 Awaish Kumar: role or permissions you will need
250 00:24:23.630 ⇒ 00:24:26.679 Awaish Kumar: compile that, and then send it to Trevor.
251 00:24:28.080 ⇒ 00:24:28.850 Luke Daque: Okay.
252 00:24:29.770 ⇒ 00:24:30.470 Awaish Kumar: Okay.
253 00:24:33.110 ⇒ 00:24:35.379 Awaish Kumar: So I have to drop off actually.
254 00:24:35.780 ⇒ 00:24:38.527 Awaish Kumar: So I brought in another meeting. But
255 00:24:39.150 ⇒ 00:24:44.230 Awaish Kumar: I will try to sign into power Bi and I will let you know how it goes.
256 00:24:44.860 ⇒ 00:24:56.380 Amber Lin: Okay. I wish one last thing, we probably we can talk later. I do want us to look at the tool usage
257 00:24:56.690 ⇒ 00:25:01.400 Amber Lin: so how we can add that we probably need a synthetic data set for that.
258 00:25:01.859 ⇒ 00:25:16.699 Amber Lin: I wanted to talk about how we can. I gathered what sources we need, and I think we can get started on a few of them to generate a synthetic data set. I wanted to see from you wish on how you think we should best approach this?
259 00:25:17.706 ⇒ 00:25:23.340 Amber Lin: And then one last thing here.
260 00:25:23.340 ⇒ 00:25:23.690 Awaish Kumar: Seamless.
261 00:25:23.690 ⇒ 00:25:40.079 Amber Lin: I added the list of questions that we were talking about. Oh, great! And you commented of what we want to ask for each of these metrics, and it will be great if you guys can just add comments or type things in so that I can send a complete list of the clients to get them answered.
262 00:25:41.020 ⇒ 00:25:42.419 Amber Lin: it’s in this, Doc.
263 00:25:42.530 ⇒ 00:25:43.559 Amber Lin: It’s right here.
264 00:25:45.980 ⇒ 00:25:46.830 Awaish Kumar: Okay.
265 00:25:47.300 ⇒ 00:25:49.419 Amber Lin: Okay, thank you. All.
266 00:25:49.923 ⇒ 00:25:53.450 Annie Yu: I I just did a quick research.
267 00:25:53.990 ⇒ 00:26:09.795 Annie Yu: So I think we are in that workspace as a guest user, right? And even if we are assigned contributor access, it looks like we still won’t be able to create model there.
268 00:26:11.970 ⇒ 00:26:13.470 Annie Yu: So that’s 1 thing.
269 00:26:13.470 ⇒ 00:26:20.509 Amber Lin: Oh, so does that mean that we should just ask them to give the brain forwards? Not a more account access.
270 00:26:22.310 ⇒ 00:26:29.840 Annie Yu: I I don’t know, because they own this workspace right? And we are just here as focus.
271 00:26:30.200 ⇒ 00:26:39.699 Amber Lin: Yeah, I mean, if they if they add our brain forge matter more account, then it will be from inside their organization. So we’ll we won’t be guests anymore.
272 00:26:40.060 ⇒ 00:26:43.159 Annie Yu: That’s probably better.
273 00:26:45.155 ⇒ 00:26:52.309 Amber Lin: Would you be able to drop the screenshot in the channel, and then we can write up what we need from them.
274 00:26:52.750 ⇒ 00:26:53.629 Annie Yu: Yeah, I, thought.
275 00:26:53.630 ⇒ 00:26:54.220 Amber Lin: 50 back.
276 00:26:54.220 ⇒ 00:26:55.269 Amber Lin: I don’t need.
277 00:26:55.440 ⇒ 00:27:03.770 Annie Yu: This from the microsoft.com, though I’m just using like Google Research AI, and then chat Gpt, and both of them say, this.
278 00:27:04.080 ⇒ 00:27:17.719 Amber Lin: Okay, there should be links from them. Just drop in anything, and then I know to drop, and then we’ll summarize a list of things we need from them, because I think they’re getting tired of constant troubleshooting. But this is what we need.
279 00:27:19.160 ⇒ 00:27:19.740 Annie Yu: Yeah.
280 00:27:20.540 ⇒ 00:27:22.400 Amber Lin: Okay, thank you. All.
281 00:27:25.420 ⇒ 00:27:31.460 Amber Lin: Okay, I guess.
282 00:27:33.382 ⇒ 00:27:39.189 Amber Lin: Luke, do you have something to work on? Or is everything for you blocked right now?
283 00:27:40.730 ⇒ 00:27:50.660 Luke Daque: I can continue working on this, maybe, for now, just manually create these models in bigquery. Since I do have, I mean, I can manually create
284 00:27:50.870 ⇒ 00:27:54.589 Luke Daque: tables in the same synthetic data. Set that way. I can.
285 00:27:54.590 ⇒ 00:27:56.380 Amber Lin: It’s necessary.
286 00:27:56.610 ⇒ 00:28:02.339 Luke Daque: I can continue work like testing, making sure the models are working and stuff like that.
287 00:28:04.205 ⇒ 00:28:05.060 Amber Lin: Like.
288 00:28:05.350 ⇒ 00:28:14.253 Amber Lin: Hopefully, he just gives you access, and we can add all of them. If you want, we can start creating the synthetic data set for
289 00:28:14.820 ⇒ 00:28:18.099 Amber Lin: tool usage. That’s something that we can work on.
290 00:28:21.680 ⇒ 00:28:23.469 Luke Daque: Showing tool usage.
291 00:28:24.320 ⇒ 00:28:31.519 Amber Lin: Yeah. So they wanted, you know, when we created synthetic data sets for emails and chats and all that.
292 00:28:31.980 ⇒ 00:28:44.109 Amber Lin: There’s they also want to see tool usage. And it’s about like it’s in the document they sent us, and it’s example of office, 3, 60, or co-pilot.
293 00:28:45.826 ⇒ 00:28:54.839 Annie Yu: If I’m not wrong. I think we talk about this when Tom was also involved. I and it’s kind of blurry now, but I think
294 00:28:55.360 ⇒ 00:29:02.640 Annie Yu: having getting those information will require like more charge, like paying more.
295 00:29:02.940 ⇒ 00:29:06.730 Annie Yu: So I’m not sure if they ended up going with that route.
296 00:29:07.190 ⇒ 00:29:19.139 Annie Yu: because right now with the Apis, we we plan to get they are free. If I’m not wrong, but then to have those, I think they’re called audit logs.
297 00:29:19.880 ⇒ 00:29:22.320 Annie Yu: Then that means like paying more.
298 00:29:23.020 ⇒ 00:29:25.650 Annie Yu: But I’m not sure if that’s the case.
299 00:29:28.950 ⇒ 00:29:37.029 Amber Lin: I guess, Kim, I guess the 1st step we can do is to make sure where they where we can find those Api documents.
300 00:29:37.370 ⇒ 00:29:40.269 Luke Daque: I guess that I don’t think we have the
301 00:29:40.850 ⇒ 00:29:43.520 Luke Daque: synthetic data set for tool usage. Yeah.
302 00:29:43.890 ⇒ 00:29:44.530 Amber Lin: Right.
303 00:29:44.530 ⇒ 00:29:45.180 Annie Yu: No.
304 00:29:45.180 ⇒ 00:29:52.179 Amber Lin: Well, that’s kind of what we’re trying, what we’ll what we’ll try to do. Let me just share my screen. We can walk through this.
305 00:29:52.370 ⇒ 00:30:02.530 Amber Lin: So what they want, they want co-pilot, they want office. And then these are like other things that they brought up. But these are not the rest here. This is Non Microsoft.
306 00:30:03.190 ⇒ 00:30:04.969 Amber Lin: if I’m not mistaken
307 00:30:06.880 ⇒ 00:30:11.369 Amber Lin: And so these 2 should be Microsoft, and I guess the 1st step.
308 00:30:13.290 ⇒ 00:30:20.650 Amber Lin: I do think the graph Api, which is the one we’re using, provides information about
309 00:30:20.890 ⇒ 00:30:25.610 Amber Lin: this. But Prob, I don’t know if it provides co-pilot usage.
310 00:30:25.970 ⇒ 00:30:27.190 Amber Lin: I guess.
311 00:30:28.413 ⇒ 00:30:39.330 Amber Lin: We can 1st step is to identify Api for each source.
312 00:30:41.710 ⇒ 00:30:52.229 Amber Lin: Like, where? Where will we get that data from the Api and then outline fields for each source
313 00:30:53.020 ⇒ 00:31:04.170 Amber Lin: based on api documentation, great Cynthia data and send, how is this?
314 00:31:04.780 ⇒ 00:31:07.590 Amber Lin: Okay, I think we can do this step today.
315 00:31:15.170 ⇒ 00:31:21.610 Amber Lin: Look, do you think this is doable for today?
316 00:31:26.490 ⇒ 00:31:28.920 Luke Daque: Just the identify Api.
317 00:31:29.850 ⇒ 00:31:37.899 Amber Lin: Yeah, I mean, if if that goes successfully, I think outlining the fields for each source would be relatively easy.
318 00:31:39.790 ⇒ 00:31:43.219 Luke Daque: Yeah, I can try to do some research on that. Sure.
319 00:31:43.220 ⇒ 00:31:53.550 Amber Lin: Okay, okay, sounds good. I will make a make a ticket. I’ll assign it to you, and then
320 00:31:57.520 ⇒ 00:32:02.710 Amber Lin: then I will list the tools here.
321 00:32:12.470 ⇒ 00:32:17.949 Amber Lin: Maybe we can add it to the spreadsheet we currently have. So just have an Api
322 00:32:18.180 ⇒ 00:32:32.010 Amber Lin: documentation link for all each of these. And if you have extra time. We can start with the Microsoft ones. I think these 2 are very important just outlining the fields
323 00:32:32.980 ⇒ 00:32:37.910 Amber Lin: for these 2. If you have extra time today.
324 00:32:39.460 ⇒ 00:32:39.920 Luke Daque: Sounds good.
325 00:32:39.920 ⇒ 00:32:40.580 Amber Lin: How does
326 00:32:40.880 ⇒ 00:32:53.020 Amber Lin: okay sounds good, because I know you’re very blocked, and I know there’s nothing we can do about that. I don’t want you to have to do duplicate work, and so this would be a good a good point. And, Annie, I will.
327 00:32:53.520 ⇒ 00:33:00.309 Amber Lin: I will try to see. Let me do some research on the access issue. And I hope I hope I can get you unblocked
328 00:33:00.500 ⇒ 00:33:02.169 Amber Lin: for this today.
329 00:33:08.360 ⇒ 00:33:09.440 Amber Lin: Okay,
330 00:33:13.600 ⇒ 00:33:22.009 Amber Lin: alright, thanks. Guys. Send me any specifics in terms of your access issues in our internal channel, and I’ll try to relay that to Trevor.
331 00:33:23.690 ⇒ 00:33:25.050 Annie Yu: Yeah, sounds good.
332 00:33:25.050 ⇒ 00:33:26.480 Amber Lin: Okay. Thank you.