Meeting Title: ABC | Standup Date: 2025-06-17 Meeting participants: Mustafa Raja, Amber Lin, Casie Aviles, Annie Yu, Awaish Kumar
WEBVTT
1 00:01:10.900 ⇒ 00:01:11.370 Casie Aviles: Hey, guys.
2 00:01:11.370 ⇒ 00:01:12.090 Mustafa Raja: Hey?
3 00:01:12.090 ⇒ 00:01:12.560 Mustafa Raja: Okay.
4 00:01:13.690 ⇒ 00:01:20.469 Amber Lin: And waiting for Aish and Annie.
5 00:01:20.930 ⇒ 00:01:23.880 Amber Lin: But we can get started, and once they join in.
6 00:01:24.270 ⇒ 00:01:24.950 Mustafa Raja: Yep.
7 00:01:25.450 ⇒ 00:01:26.090 Amber Lin: Okay.
8 00:01:27.130 ⇒ 00:01:28.616 Amber Lin: Hi, Abby.
9 00:01:29.770 ⇒ 00:01:30.740 Annie Yu: Hello guys.
10 00:01:31.580 ⇒ 00:01:34.581 Amber Lin: Hi! Welcome back! It’s been a while.
11 00:01:35.840 ⇒ 00:01:36.500 Annie Yu: Yeah.
12 00:01:37.510 ⇒ 00:01:48.959 Amber Lin: Yeah, but we got the Api. So you don’t have to ask Brian for every single time now. Let me share my screen, and that can go through what we want.
13 00:01:54.520 ⇒ 00:01:56.490 Amber Lin: Can someone help me ping a wish.
14 00:01:59.370 ⇒ 00:02:00.210 Casie Aviles: Sure.
15 00:02:00.780 ⇒ 00:02:02.287 Amber Lin: Okay, thank you.
16 00:02:05.020 ⇒ 00:02:05.860 Amber Lin: Okay.
17 00:02:06.060 ⇒ 00:02:23.409 Amber Lin: so we just started this cycle. And I’m gonna start with the data tasks to just fill Annie in. So we just we just got the Apis from Tim. And currently we’re about to
18 00:02:23.883 ⇒ 00:02:36.270 Amber Lin: pull the data from the Apis, and we will get a wish to help us with that. And, Annie, I think, since you are the one that’s going to build a dashboard, I included here
19 00:02:36.780 ⇒ 00:02:39.449 Amber Lin: in this one of all the
20 00:02:40.370 ⇒ 00:02:44.190 Amber Lin: all the fields. Let me share my full screen
21 00:02:45.100 ⇒ 00:02:50.179 Amber Lin: of all the fields that you would. That the Api would give us.
22 00:02:50.490 ⇒ 00:02:53.580 Amber Lin: Oh, yay! You were already filling in. Thank you.
23 00:02:54.250 ⇒ 00:03:00.280 Amber Lin: And then, based on this, a wish will pull the data that you need and build a pipeline to
24 00:03:01.720 ⇒ 00:03:05.389 Amber Lin: constantly feed that. So I’m gonna put this in progress.
25 00:03:06.329 ⇒ 00:03:06.729 Awaish Kumar: Hello!
26 00:03:06.730 ⇒ 00:03:08.730 Amber Lin: It’s Hi!
27 00:03:09.190 ⇒ 00:03:10.250 Amber Lin: Hi! Aish!
28 00:03:10.710 ⇒ 00:03:22.140 Amber Lin: I was just filling Annie, and so we we have great progress on the Apis. We got the Apis from Tim. And so right now, Annie is gonna help
29 00:03:22.748 ⇒ 00:03:45.650 Amber Lin: build a dashboard. And she’s gonna 1st she’s gonna tell you what data she needs. We’ll put everything in this spreadsheet. This is all the data that that Api endpoint will give us. And then Annie’s gonna mark which ones we need. And would you be able to help us build a pipeline that pulls data from
30 00:03:45.930 ⇒ 00:03:47.219 Amber Lin: bad source.
31 00:03:50.280 ⇒ 00:03:54.700 Awaish Kumar: We are looking to get the data from 8 and 8 to.
32 00:03:55.590 ⇒ 00:03:57.339 Amber Lin: Yeah, by 8 by 8. Yeah.
33 00:04:00.580 ⇒ 00:04:11.130 Amber Lin: And then we want to pull it from 8 by 8, and then put it into our database and then feed it into our dashboards.
34 00:04:12.180 ⇒ 00:04:13.529 Awaish Kumar: And what database
35 00:04:18.459 ⇒ 00:04:23.729 Awaish Kumar: like, how we’re looking to put.
36 00:04:23.730 ⇒ 00:04:24.340 Amber Lin: Right.
37 00:04:24.900 ⇒ 00:04:25.360 Casie Aviles: Yeah, it’s.
38 00:04:26.066 ⇒ 00:04:27.479 Awaish Kumar: Or Snowflake.
39 00:04:28.390 ⇒ 00:04:29.449 Casie Aviles: Yes, no, thank you.
40 00:04:30.240 ⇒ 00:04:30.870 Awaish Kumar: Sorry.
41 00:04:31.530 ⇒ 00:04:32.490 Casie Aviles: Snowflake.
42 00:04:34.350 ⇒ 00:04:39.300 Awaish Kumar: And then we are going to move it again to supervise, or we will be directly reading from super.
43 00:04:41.510 ⇒ 00:04:46.710 Annie Yu: From snowflake. We then need to move it to real data.
44 00:04:49.790 ⇒ 00:04:50.720 Annie Yu: Yeah, real.
45 00:04:51.630 ⇒ 00:04:59.430 Awaish Kumar: Okay, okay, so it goes to Snowflake. Not superbase.
46 00:04:59.950 ⇒ 00:05:07.050 Amber Lin: Yeah. Cause, I think the data that’s coming from 8 by 8 is not our AI related data. It’s all
47 00:05:07.050 ⇒ 00:05:08.270 Amber Lin: from home data.
48 00:05:09.390 ⇒ 00:05:10.830 Awaish Kumar: Okay. Sure.
49 00:05:11.530 ⇒ 00:05:17.719 Amber Lin: And is this a good point estimate? And what due date should I put down?
50 00:05:18.030 ⇒ 00:05:21.309 Amber Lin: Is this a good due date? I feel like that’s too close, isn’t it?
51 00:05:21.680 ⇒ 00:05:25.400 Awaish Kumar: Like, how, when will be? Is it urgent, or when do you need it?
52 00:05:26.003 ⇒ 00:05:26.789 Amber Lin: It’s not.
53 00:05:26.790 ⇒ 00:05:27.879 Awaish Kumar: Should I be broken.
54 00:05:27.880 ⇒ 00:05:28.220 Amber Lin: It’s not.
55 00:05:28.220 ⇒ 00:05:28.900 Awaish Kumar: Anyone.
56 00:05:29.697 ⇒ 00:05:50.919 Amber Lin: This blocks, Annie, from doing the dashboards, that’s all. But currently let me show you the current cycle. We just started this cycle so as long as Annie’s able to play with this has a week to look through, the data has time to do her tasks. This is this is good. So I would say that as long as we have the data by end of this week
57 00:05:51.230 ⇒ 00:05:54.790 Amber Lin: it should, it would be fine. Would that be a good time for you?
58 00:05:55.643 ⇒ 00:05:56.429 Awaish Kumar: Yeah, sure.
59 00:05:56.820 ⇒ 00:06:02.249 Amber Lin: Okay. So I’m gonna say, this is by end of this week.
60 00:06:02.640 ⇒ 00:06:06.510 Amber Lin: and is 5 point estimate good. Or should I change that.
61 00:06:07.530 ⇒ 00:06:12.310 Awaish Kumar: I I’m not. I haven’t looked at like, what data do you need, and how many endpoints.
62 00:06:12.310 ⇒ 00:06:19.010 Amber Lin: Okay, okay, okay, I’m gonna put it as 8. And then, if you can like, we can flesh out this ticket.
63 00:06:19.645 ⇒ 00:06:23.739 Annie Yu: Amber and the spreadsheet that you share are like
64 00:06:24.310 ⇒ 00:06:26.709 Annie Yu: the raw data. But then, if.
65 00:06:29.250 ⇒ 00:06:38.420 Annie Yu: From the raw data I need other aggregated fields. Is that something that I wish will also be be helping with.
66 00:06:40.390 ⇒ 00:06:49.419 Amber Lin: I believe that will be best if we do the modeling directly. Right? I guess currently, we don’t have any Dbt models involved in ABC, right?
67 00:06:49.590 ⇒ 00:06:53.299 Annie Yu: Yeah. But right now I do some modeling using real, which I’m not.
68 00:06:53.633 ⇒ 00:06:54.299 Amber Lin: Super! Common.
69 00:06:54.623 ⇒ 00:06:59.470 Annie Yu: Think, like some of the fields that I built are not a hundred percent accurate.
70 00:07:00.080 ⇒ 00:07:12.139 Amber Lin: I see. I can make another ticket for transferring all the models that you hard coded into real in. Maybe. Say, Dvt, how’s that?
71 00:07:13.260 ⇒ 00:07:16.070 Amber Lin: Okay? Okay, so I’ll make a ticket.
72 00:07:16.070 ⇒ 00:07:18.769 Amber Lin: and then we’ll define all the fields that you need.
73 00:07:18.770 ⇒ 00:07:20.149 Annie Yu: Okay. Awesome. Awesome.
74 00:07:21.500 ⇒ 00:07:27.871 Amber Lin: Yeah, let’s get all the data in. And then we can worry about any models that we need to build
75 00:07:28.530 ⇒ 00:07:29.240 Amber Lin: phone number.
76 00:07:29.650 ⇒ 00:07:30.380 Amber Lin: And
77 00:07:39.230 ⇒ 00:07:43.469 Amber Lin: I guess a wish I want to ask you, is it best that we transfer it to Dbt.
78 00:07:44.070 ⇒ 00:07:48.449 Amber Lin: The the hard-coded models that Annie has in Braille? Is that the right path.
79 00:07:49.590 ⇒ 00:07:54.700 Awaish Kumar: Like, are you writing? S. 12.
80 00:07:56.250 ⇒ 00:07:59.529 Annie Yu: Yeah, as of now, cause that’s all we have.
81 00:08:01.290 ⇒ 00:08:04.340 Awaish Kumar: Okay, we do. We have the Dbt project.
82 00:08:06.130 ⇒ 00:08:09.860 Amber Lin: Nope, not to my recollection. No.
83 00:08:12.450 ⇒ 00:08:12.825 Awaish Kumar: Okay?
84 00:08:14.530 ⇒ 00:08:16.660 Awaish Kumar: So in like, 1st of all, you have to
85 00:08:17.190 ⇒ 00:08:22.210 Awaish Kumar: define that like, do we? Are we going to have a separate ebt project or not?
86 00:08:22.640 ⇒ 00:08:27.830 Awaish Kumar: Is it is this a big client, or are there too many models we need to maintain
87 00:08:28.570 ⇒ 00:08:36.540 Awaish Kumar: and like? Is this, is this going to scale? If yes, then we can create like, we need someone to work on limiting project right?
88 00:08:36.909 ⇒ 00:08:39.780 Awaish Kumar: We don’t have any analytics engineer here.
89 00:08:40.240 ⇒ 00:08:43.079 Awaish Kumar: so we’ll need one more person. Then if we meant we.
90 00:08:46.040 ⇒ 00:08:46.860 Amber Lin: I see.
91 00:08:47.480 ⇒ 00:09:00.339 Amber Lin: Thank you. That’s that’s I didn’t think of that. So I, this client will be scaling because we’re scaling to another of their departments soon. So we will have more data. So that is, that is yes, and
92 00:09:02.280 ⇒ 00:09:28.660 Amber Lin: I do think, in my perspective, I think it’s better that we transfer it. Let me, we should confirm with on if we can get another analytics engineer. My my hunch is that he’s gonna say, yes, I’m gonna define these tickets, and I will go ask, but I think it’s it. And if you have time we can define all these hard coded models. So that like.
93 00:09:29.190 ⇒ 00:09:33.800 Amber Lin: like, we have a like record of them. So that would be better.
94 00:09:35.040 ⇒ 00:09:38.770 Annie Yu: Yeah, I’ll still start with the raw data spreadsheet, and then.
95 00:09:38.770 ⇒ 00:09:47.439 Amber Lin: Yeah, yeah, definitely, definitely. I can put this as end of this week. That should be enough time for you. Right?
96 00:09:48.470 ⇒ 00:09:49.140 Annie Yu: Yeah.
97 00:09:49.140 ⇒ 00:09:55.390 Amber Lin: It’s okay, sounds good. And then I’m gonna transgender.
98 00:09:55.670 ⇒ 00:09:56.480 Amber Lin: Oh.
99 00:10:00.190 ⇒ 00:10:01.829 Amber Lin: I’m gonna put this.
100 00:10:04.640 ⇒ 00:10:08.200 Amber Lin: I’m just gonna put there. I’ll go ask Utam on that.
101 00:10:10.204 ⇒ 00:10:18.809 Amber Lin: Okay, that’s good. And then quickly, any updates Casey and Mustafa from your side.
102 00:10:20.220 ⇒ 00:10:23.150 Amber Lin: Progress is still in progress. Let me know.
103 00:10:24.231 ⇒ 00:10:26.990 Casie Aviles: Yeah, for the trainer. But
104 00:10:27.110 ⇒ 00:10:29.940 Casie Aviles: Tim has. Yeah, it’s live now on.
105 00:10:30.260 ⇒ 00:10:30.750 Amber Lin: Yeah.
106 00:10:30.750 ⇒ 00:10:31.720 Casie Aviles: Client, side.
107 00:10:32.100 ⇒ 00:10:32.450 Amber Lin: Okay.
108 00:10:32.450 ⇒ 00:10:35.080 Casie Aviles: And also the the code changes that we have so.
109 00:10:37.750 ⇒ 00:10:43.610 Amber Lin: Okay, yeah, I’ll I’ll take a I’ll go try these out, and then we can move this.
110 00:10:45.901 ⇒ 00:10:58.108 Mustafa Raja: For for the updates on update to a central dot thing. I have added tools, the train trainer. But so now it has a context of our super base.
111 00:10:58.490 ⇒ 00:10:58.820 Amber Lin: Hmm.
112 00:10:58.820 ⇒ 00:11:03.630 Mustafa Raja: Knows about sections and brief descriptions on the sections.
113 00:11:04.373 ⇒ 00:11:20.916 Mustafa Raja: And it. It can update an existing section. And I have added, a spreadsheet for human in the loop thing next steps would be to
114 00:11:21.790 ⇒ 00:11:26.869 Mustafa Raja: Listen to the changes in the spreadsheet. If a human has approved a change.
115 00:11:28.080 ⇒ 00:11:32.070 Mustafa Raja: And then make those changes into Google docs.
116 00:11:32.590 ⇒ 00:11:39.570 Mustafa Raja: And then I have to add a tool to create an entirely new section from scratch.
117 00:11:40.350 ⇒ 00:11:41.779 Amber Lin: What would that be?
118 00:11:44.650 ⇒ 00:11:47.299 Amber Lin: Well, what what are we creating? Sorry I didn’t catch you last.
119 00:11:48.389 ⇒ 00:11:52.749 Mustafa Raja: to create a entirely new section from scratch.
120 00:11:53.090 ⇒ 00:11:54.747 Amber Lin: Oh, I see.
121 00:11:58.480 ⇒ 00:12:07.519 Amber Lin: awesome. I think that this is really great progress. We already have the updates down, and then we’re tackling the next part of creating a new section.
122 00:12:07.660 ⇒ 00:12:09.859 Amber Lin: I think this is really on track.
123 00:12:10.050 ⇒ 00:12:18.420 Amber Lin: I don’t need to worry about anything. I’m very happy about that. Just checking on smart tasks.
124 00:12:19.640 ⇒ 00:12:26.279 Amber Lin: Okay, yeah. In Casey, I pinged, I emailed him again on these.
125 00:12:26.680 ⇒ 00:12:30.340 Amber Lin: So if he responds, that’s good.
126 00:12:30.650 ⇒ 00:12:34.109 Amber Lin: Is that all? Your that’s all your tickets in this cycle right?
127 00:12:35.550 ⇒ 00:12:36.830 Casie Aviles: Yeah, that’s about it.
128 00:12:36.830 ⇒ 00:12:37.490 Amber Lin: Okay.
129 00:12:37.630 ⇒ 00:12:58.900 Amber Lin: okay. Sounds good. I’ll let you know if there’s any like ad hoc tickets that pop up once I do the feedback section with them. But if not, I think Mustafa is already we’re doing a lot of hours, and mostly most of this on a data side. And Mustafa, and then next cycle. I think you’ll have a lot more tickets available.
130 00:12:59.150 ⇒ 00:13:03.210 Amber Lin: Okay, okay, sounds good. Thank you. Everyone.
131 00:13:03.810 ⇒ 00:13:05.200 Casie Aviles: Thank you. Bye-bye.
132 00:13:05.570 ⇒ 00:13:08.890 Amber Lin: Okay, bye-bye, bye, it’s.