Meeting Title: Dataplatform Kickoff Date: 2025-07-30 Meeting participants: Awaish Kumar, Amber Lin, Annie Yu, Vashdev Heerani
WEBVTT
1 00:03:13.860 ⇒ 00:03:15.040 Amber Lin: Hi.
2 00:03:17.650 ⇒ 00:03:18.260 Awaish Kumar: Hello!
3 00:03:20.210 ⇒ 00:03:27.500 Amber Lin: Hello, wait! Let’s see, where are all the current tickets and data platforms?
4 00:03:31.850 ⇒ 00:03:35.760 Amber Lin: Oh, let me check data.
5 00:03:37.500 ⇒ 00:03:39.330 Amber Lin: Wait! Where is it?
6 00:03:46.070 ⇒ 00:03:48.960 Amber Lin: Oh, oh, there it is!
7 00:03:58.080 ⇒ 00:03:58.920 Amber Lin: Hi! Annie!
8 00:04:01.830 ⇒ 00:04:02.770 Annie Yu: Hello!
9 00:04:03.770 ⇒ 00:04:10.500 Amber Lin: Hi! I added, fast up, and I have a feeling. Oh, no, I added his Brainforce email so he should be here soon.
10 00:04:11.227 ⇒ 00:04:16.599 Amber Lin: So we were talking in the Allocations meeting yesterday, and
11 00:04:16.950 ⇒ 00:04:24.489 Amber Lin: now that the Ltv project closed. I think so for Eden you probably spend
12 00:04:25.190 ⇒ 00:04:33.490 Amber Lin: between 10 to 20 h, and then for ABC. Is like 5 h, and so we have some time, and then we have some time left.
13 00:04:34.272 ⇒ 00:04:39.470 Amber Lin: Fan Stick hasn’t signed yet. So that’s not something to that
14 00:04:39.800 ⇒ 00:04:48.440 Amber Lin: that we’re gonna start. So one of your help on the data platform stuff. So it’s mostly internal dashboards.
15 00:04:53.470 ⇒ 00:04:58.540 Amber Lin: I wish. Do you want to give a little bit of context of this project?
16 00:05:00.800 ⇒ 00:05:07.750 Awaish Kumar: Yeah. So on the data platform, dashboarding, we just want to measure the
17 00:05:08.960 ⇒ 00:05:16.730 Awaish Kumar: teams productivity along with. How like.
18 00:05:17.200 ⇒ 00:05:21.629 Awaish Kumar: how we, how much efforts we are, how much, how many
19 00:05:21.750 ⇒ 00:05:24.320 Awaish Kumar: resources we are putting for a client.
20 00:05:24.580 ⇒ 00:05:28.289 Awaish Kumar: So we want to see, like the for each
21 00:05:29.820 ⇒ 00:05:32.470 Awaish Kumar: like for each day or week, like
22 00:05:33.600 ⇒ 00:05:42.019 Awaish Kumar: how many tickets we we have, and how many are resolved like. For example, if I if I want to see a back sprint. Then
23 00:05:43.230 ⇒ 00:05:49.489 Awaish Kumar: kind of want to measure the the overall tickets for the client in this sprint.
24 00:05:49.710 ⇒ 00:05:55.149 Awaish Kumar: How many story points they are, and then how each team, each team member is being
25 00:05:55.990 ⇒ 00:06:00.310 Awaish Kumar: utilizes like for those tickets at that.
26 00:06:02.140 ⇒ 00:06:03.280 Awaish Kumar: Oh.
27 00:06:04.418 ⇒ 00:06:28.820 Awaish Kumar: yeah, how many hours are, for example, we have, we predicted, like we estimated initially for the client work. And at the end, how? How it it end up, how much efforts we ended up putting in doing that work. So it’s like kind of productivity dashboard, combining all 3 linear, slack and clockifying.
28 00:06:32.270 ⇒ 00:06:36.900 Annie Yu: Okay, got it. So will someone support the ingestion part.
29 00:06:37.760 ⇒ 00:06:40.920 Awaish Kumar: So most of the data is already in the snowflake.
30 00:06:41.040 ⇒ 00:06:49.530 Awaish Kumar: like the linear. This is in Snowflake clock. If I is in Snowflake, and also
31 00:06:55.090 ⇒ 00:07:01.440 Awaish Kumar: what else, like linear clockify and operating.
32 00:07:01.580 ⇒ 00:07:04.389 Awaish Kumar: So linear clock, if I is already in snowflake.
33 00:07:04.620 ⇒ 00:07:07.489 Amber Lin: For operating, we.
34 00:07:07.650 ⇒ 00:07:11.389 Awaish Kumar: We have some Prs ready, like like wastem is going to
35 00:07:11.980 ⇒ 00:07:18.120 Awaish Kumar: take over all those to to bring all that data in the in the snowflake.
36 00:07:18.320 ⇒ 00:07:22.910 Awaish Kumar: And then you can connect, like real with snowflake and like.
37 00:07:23.340 ⇒ 00:07:27.479 Awaish Kumar: like, basically start building the charts and maybe
38 00:07:27.962 ⇒ 00:07:52.180 Awaish Kumar: collaborate with like for your modeling requirements. For example, if if you want to join like, there are multiple tables for clockify multiple tables for linear, and then we want to join them together, and then how you want to join them, to build, to support your charts like the that will be like modeling requirements, and you can ask last name for that.
39 00:07:53.610 ⇒ 00:08:00.319 Annie Yu: So you said in Snowflake there’s already linear and clockify. What’s the 3rd one that you’re.
40 00:08:00.320 ⇒ 00:08:00.650 Awaish Kumar: Plenty.
41 00:08:02.850 ⇒ 00:08:06.300 Awaish Kumar: Corporate is a separate platform like similar to
42 00:08:06.790 ⇒ 00:08:09.724 Awaish Kumar: like as Block 5 is a different platform as
43 00:08:10.540 ⇒ 00:08:14.450 Awaish Kumar: a linear. And similarly, we have a platform called operating.
44 00:08:15.040 ⇒ 00:08:18.310 Awaish Kumar: So we will have some data coming from there.
45 00:08:18.590 ⇒ 00:08:24.810 Awaish Kumar: And what what is that is? Basically the estimation of each questions.
46 00:08:25.030 ⇒ 00:08:25.990 Awaish Kumar: God.
47 00:08:27.210 ⇒ 00:08:32.309 Awaish Kumar: And I’m like hours for each client.
48 00:08:34.010 ⇒ 00:08:35.350 Annie Yu: Okay, and.
49 00:08:35.350 ⇒ 00:08:47.549 Awaish Kumar: So that’s why, when I when I for example, I estimate where, like I, I would need any 10 h for even in the next week, and then we actually work on some of the tickets. And how many
50 00:08:47.690 ⇒ 00:08:52.230 Awaish Kumar: story the tickets are assigned. So you we are going to see
51 00:08:52.820 ⇒ 00:08:56.020 Awaish Kumar: for a film for a like for a past week.
52 00:08:56.410 ⇒ 00:09:06.870 Awaish Kumar: What I estimated, how many story points were actually assigned, and how many like hours you logged and clock, if I. So a view of all 3. So we
53 00:09:07.010 ⇒ 00:09:10.699 Awaish Kumar: so we can improve our estimations basically.
54 00:09:11.940 ⇒ 00:09:16.410 Annie Yu: Okay? And so what? Visualization tool? All.
55 00:09:16.410 ⇒ 00:09:17.010 Awaish Kumar: Real data.
56 00:09:17.940 ⇒ 00:09:18.970 Annie Yu: Real.
57 00:09:19.560 ⇒ 00:09:27.360 Awaish Kumar: Rail. Yeah, for internal work. We are going to use rail so you connect with Snowflake and any modeling requirements.
58 00:09:27.690 ⇒ 00:09:28.920 Awaish Kumar: Oh, wow!
59 00:09:29.020 ⇒ 00:09:32.129 Awaish Kumar: Like you need help with, like what Steve can handle that.
60 00:09:32.520 ⇒ 00:09:37.099 Awaish Kumar: And ingestion part also, like, Gosh, maybe it’s going to handle that.
61 00:09:37.270 ⇒ 00:09:39.460 Awaish Kumar: And basically you are going to have
62 00:09:40.104 ⇒ 00:09:51.840 Awaish Kumar: help with the dashboarding, plus like. What like. You already work with kind of metamor like. They were also measuring the productivity in different contexts. But
63 00:09:51.980 ⇒ 00:09:55.000 Awaish Kumar: like for us, like.
64 00:09:55.640 ⇒ 00:10:04.939 Awaish Kumar: what you are going to do is like what kind of different charts we need like. For example, if I want to. I have described some, some situations.
65 00:10:05.140 ⇒ 00:10:17.050 Awaish Kumar: and then you can see, okay, what different charts I can put in together for this, and to to support those charts. If you need any modeling requirement, if you have any modeling requirement, you can ask them.
66 00:10:17.360 ⇒ 00:10:22.090 Awaish Kumar: and he can build those models for you.
67 00:10:23.240 ⇒ 00:10:23.910 Annie Yu: Okay?
68 00:10:25.760 ⇒ 00:10:32.619 Annie Yu: And then there’s no footage. Should I be able to see that already? Is there.
69 00:10:32.620 ⇒ 00:10:34.630 Awaish Kumar: I don’t know if you have access to Snowflake.
70 00:10:35.860 ⇒ 00:10:41.140 Annie Yu: I do, but which which I think my stuff like that is.
71 00:10:41.140 ⇒ 00:10:41.480 Awaish Kumar: Would be.
72 00:10:43.900 ⇒ 00:10:50.600 Awaish Kumar: I know, like if you if you search for blockify, you can see where it is.
73 00:10:52.140 ⇒ 00:10:58.539 Annie Yu: But I thought my snowflake is exclusive to like ABC. Isn’t that.
74 00:10:58.540 ⇒ 00:11:07.760 Awaish Kumar: ABC is basically, I don’t know if what accesses you have. But ABC is using basically our internal snowflake instance.
75 00:11:08.280 ⇒ 00:11:14.969 Awaish Kumar: So yeah, you will be staying on the same account for Snowflake.
76 00:11:15.120 ⇒ 00:11:21.269 Awaish Kumar: But I don’t know what access you have right now. If you can let me know I can like
77 00:11:21.750 ⇒ 00:11:24.340 Awaish Kumar: we can. I can give you more access if you can.
78 00:11:25.180 ⇒ 00:11:28.020 Annie Yu: Okay, I’ll I’ll go find it and.
79 00:11:28.020 ⇒ 00:11:31.399 Awaish Kumar: I like. I just give you an overview here like.
80 00:11:31.940 ⇒ 00:11:34.080 Awaish Kumar: like kind of tickets we have here.
81 00:11:35.720 ⇒ 00:11:38.165 Awaish Kumar: You just have to like like.
82 00:11:41.340 ⇒ 00:11:42.270 Awaish Kumar: oh.
83 00:11:47.860 ⇒ 00:11:54.179 Amber Lin: Yeah, I guess I can look at the tickets together. So for.
84 00:11:54.180 ⇒ 00:12:09.760 Awaish Kumar: So like some of the sorry humble, like some of the things which are already in the in the in the Prs. I just want to give some overview to Ashley so he can take over like they are already built, built by by Vishnu. But he only needs to like
85 00:12:10.395 ⇒ 00:12:16.960 Awaish Kumar: that. Basically, the get of action is failing. And he might need to like fix few things there. So.
86 00:12:17.630 ⇒ 00:12:21.890 Awaish Kumar: like all these 3 tickets operating, I/O to Snowflake, lockify to Snowflake
87 00:12:22.010 ⇒ 00:12:27.549 Awaish Kumar: and building a Dbt project for our data modeling work. Basically was there, like
88 00:12:28.810 ⇒ 00:12:35.170 Awaish Kumar: we already have Prs in print, extra pipelines project.
89 00:12:35.583 ⇒ 00:12:36.409 Amber Lin: I see.
90 00:12:36.410 ⇒ 00:12:37.130 Awaish Kumar: So if you needed one.
91 00:12:37.130 ⇒ 00:12:42.370 Amber Lin: Sorry. One question before we proceed. Are we doing the marketing dashboard?
92 00:12:42.590 ⇒ 00:12:43.180 Amber Lin: Still.
93 00:12:44.280 ⇒ 00:12:47.069 Awaish Kumar: So, for for now we are only working on productivity.
94 00:12:47.070 ⇒ 00:12:48.429 Amber Lin: Okay. Sounds good.
95 00:12:48.430 ⇒ 00:12:54.820 Awaish Kumar: Like this Dbt project will be like a single project where we put models for both of them.
96 00:12:55.230 ⇒ 00:12:57.590 Amber Lin: Okay. Okay. Sounds good.
97 00:12:57.590 ⇒ 00:12:59.990 Annie Yu: Work. Dbt, not real.
98 00:13:02.130 ⇒ 00:13:02.900 Awaish Kumar: Sorry.
99 00:13:03.220 ⇒ 00:13:10.079 Annie Yu: So the modeling work should be done in Dbt. Instead of real right. But the dashboard will be.
100 00:13:10.080 ⇒ 00:13:14.616 Awaish Kumar: Yeah, like, that is like, I would like, I think,
101 00:13:17.400 ⇒ 00:13:22.260 Awaish Kumar: yeah, I think DVD would be nicer, because if we grow.
102 00:13:24.992 ⇒ 00:13:35.989 Awaish Kumar: we can do that individually. So basically, dB, 1, 0 8 and dB, 1, 0 9. There, these are 2 tickets. We already have Prs and extra pipeline.
103 00:13:36.510 ⇒ 00:13:46.730 Awaish Kumar: What I would like is that you just test those brs and see if they’re working as expected, what both of them are doing basically getting the data from
104 00:13:47.425 ⇒ 00:13:53.100 Awaish Kumar: Glockify, and and a tool called operating. And they’re putting them through the snowflake.
105 00:13:54.340 ⇒ 00:13:59.579 Awaish Kumar: So once that is done, we can create a DVD project and start working on modeling
106 00:14:00.350 ⇒ 00:14:06.190 Awaish Kumar: of these, this data and the linear data is already in this. In this sort of link.
107 00:14:08.590 ⇒ 00:14:16.160 Amber Lin: Okay, let me, can I share screen? I just want to clarify a few tickets on what to do. This cycle.
108 00:14:17.334 ⇒ 00:14:20.350 Amber Lin: Let’s check over here.
109 00:14:21.040 ⇒ 00:14:27.260 Amber Lin: Okay, so oh, all right.
110 00:14:28.666 ⇒ 00:14:32.420 Amber Lin: I’ll probably start a new cycle later.
111 00:14:32.640 ⇒ 00:14:36.650 Amber Lin: So for this, and no.
112 00:14:38.000 ⇒ 00:14:39.430 Awaish Kumar: What? I? Yeah.
113 00:14:39.860 ⇒ 00:14:42.840 Amber Lin: Connect real data will with no.
114 00:14:42.840 ⇒ 00:14:44.980 Awaish Kumar: This one we should assign to any.
115 00:14:45.240 ⇒ 00:14:46.010 Amber Lin: Okay.
116 00:14:47.200 ⇒ 00:14:48.339 Amber Lin: Sounds good.
117 00:14:48.340 ⇒ 00:14:48.970 Awaish Kumar: No.
118 00:14:49.270 ⇒ 00:14:50.180 Amber Lin: Alrighty!
119 00:14:52.970 ⇒ 00:14:54.829 Annie Yu: Can you? Can you clarify that.
120 00:14:54.830 ⇒ 00:14:55.739 Awaish Kumar: I’ll show the.
121 00:14:58.360 ⇒ 00:14:58.920 Amber Lin: Hmm.
122 00:14:59.380 ⇒ 00:15:06.159 Annie Yu: Can you go back to that ticket and and just spend some time because I haven’t read through? And I’m not sure if I have enough context.
123 00:15:07.030 ⇒ 00:15:14.259 Awaish Kumar: Yeah, it’s just like connecting real data with our snowflake account and then exploring the linear tickets data which is already in the snowflake.
124 00:15:14.880 ⇒ 00:15:18.970 Annie Yu: Okay, exploring exploration means that like, as I explained.
125 00:15:19.090 ⇒ 00:15:26.349 Awaish Kumar: We want to build a productivity productivity dashboard and productivity of each employee
126 00:15:27.150 ⇒ 00:15:35.010 Awaish Kumar: and want to see like how many tickets are perform like.
127 00:15:36.050 ⇒ 00:15:51.370 Awaish Kumar: and also like in terms of productivity, overall productivity on the client level. For example, maybe we have. We want to view that if there are any tickets which are open for more than 30 days. There are some tickets which are
128 00:15:52.053 ⇒ 00:16:01.049 Awaish Kumar: which are OP. Open which are in backlog for like 3, if 3 months things like that. So it like, I, we just want to view
129 00:16:02.418 ⇒ 00:16:05.690 Awaish Kumar: these kind of things for linear tickets data.
130 00:16:07.090 ⇒ 00:16:16.930 Awaish Kumar: And then there is like employee. Specific productivity is that we want to see like for each client, the
131 00:16:17.130 ⇒ 00:16:27.319 Awaish Kumar: people who are working on tickets and what they are assigned in a specific week, and how much story points it is things like that.
132 00:16:29.270 ⇒ 00:16:34.309 Annie Yu: Okay, then, is there any table names you can provide to me?
133 00:16:34.690 ⇒ 00:16:37.539 Awaish Kumar: No, I I haven’t explored the data. So like it’s.
134 00:16:37.700 ⇒ 00:16:41.109 Awaish Kumar: there are like tickets. There are like users
135 00:16:41.547 ⇒ 00:16:45.600 Awaish Kumar: issues like different table names like you, you can explore the data.
136 00:16:47.120 ⇒ 00:16:49.930 Awaish Kumar: Okay? And then, if I know.
137 00:16:50.700 ⇒ 00:16:52.409 Annie Yu: Let’s say I, I need to.
138 00:16:52.870 ⇒ 00:16:59.559 Annie Yu: I need a model that’s combining some of them. That’s something I can pass on to right.
139 00:16:59.560 ⇒ 00:17:01.539 Awaish Kumar: Yeah. Like, for example, if
140 00:17:01.900 ⇒ 00:17:09.059 Awaish Kumar: if you think like you have to join 3 tables, and then you have to perform some transformation. How.
141 00:17:09.349 ⇒ 00:17:14.430 Awaish Kumar: then just let let Vashev know that what he needs to do.
142 00:17:16.440 ⇒ 00:17:17.140 Annie Yu: Okay.
143 00:17:17.670 ⇒ 00:17:29.150 Awaish Kumar: And it is just for like linear tickets. So like, we need maybe need multiple views. What I just described for linear it. It was just for linear data. But as soon as those Prs are
144 00:17:29.400 ⇒ 00:17:36.500 Awaish Kumar: merge we will have clock if I and also the operating data in there. So we want to build it like.
145 00:17:36.720 ⇒ 00:17:39.709 Awaish Kumar: so just create one view for linear data
146 00:17:40.440 ⇒ 00:17:43.721 Awaish Kumar: and then maybe for clockify. And then
147 00:17:44.470 ⇒ 00:17:50.780 Awaish Kumar: operating. So you have like understanding of data, and we can see something. And then we can create a
148 00:17:51.020 ⇒ 00:17:53.629 Awaish Kumar: final view which will be basically
149 00:17:53.810 ⇒ 00:17:56.890 Awaish Kumar: kind of combining all 3 source resources.
150 00:17:57.020 ⇒ 00:18:01.910 Awaish Kumar: So it so we can see it of one place like in a single view I want to see
151 00:18:02.360 ⇒ 00:18:07.230 Awaish Kumar: for Eden annie is assigned, like
152 00:18:08.160 ⇒ 00:18:16.445 Awaish Kumar: 50, like 15 story points in a in a sprint. Webinar is assigned, for example, 20 story points, and that
153 00:18:17.450 ⇒ 00:18:23.119 Awaish Kumar: How many hours are logged and clock? If I for Eden in that timeframe. And similarly.
154 00:18:23.832 ⇒ 00:18:29.250 Awaish Kumar: how many, how much we estimated initially based on operating data.
155 00:18:29.380 ⇒ 00:18:32.429 Awaish Kumar: So that basically will be a unified view at the end.
156 00:18:42.130 ⇒ 00:18:44.039 Amber Lin: Yeah, I will.
157 00:18:44.940 ⇒ 00:18:45.880 Amber Lin: Okay.
158 00:18:47.100 ⇒ 00:18:54.909 Awaish Kumar: And I think, like maybe amber like the Utam. The meeting he had with interns will have much more information
159 00:18:55.100 ⇒ 00:18:57.880 Awaish Kumar: if we can get something from the task
160 00:18:58.190 ⇒ 00:19:01.339 Awaish Kumar: transcript of those meetings in these, in these tickets.
161 00:19:01.520 ⇒ 00:19:03.049 Awaish Kumar: That would be nice.
162 00:19:03.050 ⇒ 00:19:04.059 Amber Lin: Can you specify.
163 00:19:04.060 ⇒ 00:19:12.640 Awaish Kumar: Like like I was not part of those, but it was like just like stakeholder meetings between Uptam and, for example, Vishnu and
164 00:19:12.760 ⇒ 00:19:13.700 Awaish Kumar: Epican.
165 00:19:17.910 ⇒ 00:19:18.480 Amber Lin: Okay.
166 00:19:22.370 ⇒ 00:19:23.170 Amber Lin: okay?
167 00:19:23.890 ⇒ 00:19:25.150 Amber Lin: Oh.
168 00:19:28.620 ⇒ 00:19:33.659 Amber Lin: yeah. Any right now, just worry about connecting the
169 00:19:34.150 ⇒ 00:19:45.189 Amber Lin: data snowflake to real. I think. Let’s focus on that first.st Then, once you do that, the next one for you would be to Eda to do the Eda.
170 00:19:45.420 ⇒ 00:19:54.010 Amber Lin: and then so for the I will do the gather requirements.
171 00:19:54.860 ⇒ 00:20:01.760 Amber Lin: Let a wish know if you need any more tickets, I can do a
172 00:20:03.830 ⇒ 00:20:08.090 Amber Lin: we can do like a quick stand up.
173 00:20:09.140 ⇒ 00:20:10.360 Amber Lin: How’s that?
174 00:20:11.620 ⇒ 00:20:12.190 Awaish Kumar: No.
175 00:20:12.740 ⇒ 00:20:16.570 Amber Lin: Okay, I’m gonna put it
176 00:20:17.510 ⇒ 00:20:23.220 Amber Lin: right after we’ll combine it right after with the ABC. Stand up.
177 00:20:23.600 ⇒ 00:20:24.749 Amber Lin: Can I do that?
178 00:20:27.820 ⇒ 00:20:30.629 Amber Lin: Okay, I I will do that.
179 00:20:36.010 ⇒ 00:20:44.830 Amber Lin: Oh, sorry, Russia. I still have your personal email there. ABC,
180 00:20:48.400 ⇒ 00:20:49.070 Amber Lin: so
181 00:21:05.110 ⇒ 00:21:12.809 Amber Lin: oh, I don’t want it to be so long. Okay, 20 min still following events.
182 00:21:15.940 ⇒ 00:21:16.890 Amber Lin: Okay.
183 00:21:17.630 ⇒ 00:21:25.909 Amber Lin: so we’ll meet tomorrow at the ABC. Stand up. Time to go over anything. Rush stuff. Do you have any questions on what you need to do.
184 00:21:27.210 ⇒ 00:21:33.070 Vashdev Heerani: Nope, I just wanted to say that I need a snowflake credential for that.
185 00:21:34.680 ⇒ 00:21:35.400 Amber Lin: Okay.
186 00:21:35.750 ⇒ 00:21:36.760 Amber Lin: Did that? I asked.
187 00:21:39.180 ⇒ 00:21:42.070 Awaish Kumar: Yeah, I can create your account just right now.
188 00:21:44.620 ⇒ 00:21:46.989 Amber Lin: All right. I will sign into you a wish.
189 00:21:47.400 ⇒ 00:21:52.769 Amber Lin: and, Annie, when you explore it, let wish know if you need more. Access.
190 00:21:52.950 ⇒ 00:21:53.790 Vashdev Heerani: Okay.
191 00:21:54.750 ⇒ 00:21:55.480 Amber Lin: Okay.
192 00:21:56.750 ⇒ 00:21:58.609 Amber Lin: Sounds good.
193 00:21:59.710 ⇒ 00:22:02.829 Amber Lin: Alright, I think that’s all for this meeting.
194 00:22:03.550 ⇒ 00:22:04.730 Annie Yu: Alright. Thank you.
195 00:22:05.400 ⇒ 00:22:07.949 Amber Lin: Yeah, thanks. I’ll get more requirements for you guys.
196 00:22:09.540 ⇒ 00:22:10.600 Amber Lin: Bye-bye.
197 00:22:11.110 ⇒ 00:22:11.920 Vashdev Heerani: My best.