Meeting Title: Awaish <> Annie 1:1 Date: 2025-05-20 Meeting participants: Annie Yu, Awaish Kumar
WEBVTT
1 00:00:28.460 ⇒ 00:00:29.660 Annie Yu: Hello! Weish!
2 00:00:37.820 ⇒ 00:00:38.910 Annie Yu: Hey?
3 00:00:38.910 ⇒ 00:00:39.870 Awaish Kumar: How are you doing.
4 00:00:40.380 ⇒ 00:00:42.730 Annie Yu: Good, good! Can you hear me?
5 00:00:43.650 ⇒ 00:00:45.981 Awaish Kumar: I can hear you, but I can’t see you.
6 00:00:47.200 ⇒ 00:00:52.637 Annie Yu: Yeah, I I just showered. So I’m probably gonna stay off camera today.
7 00:00:54.560 ⇒ 00:00:56.750 Awaish Kumar: Okay, no, no worries. And how?
8 00:00:58.140 ⇒ 00:01:02.230 Awaish Kumar: How’s going? How’s the week going so far in the last week.
9 00:01:03.623 ⇒ 00:01:06.516 Annie Yu: Last week was busy.
10 00:01:08.460 ⇒ 00:01:17.766 Annie Yu: Let me reflect on it. I think last week was probably the one of the bus busiest week I’ve got so far
11 00:01:18.600 ⇒ 00:01:22.925 Annie Yu: but I think it was good.
12 00:01:27.410 ⇒ 00:01:47.561 Annie Yu: yeah, I I don’t know how much you know about like matter, more project. But we are doing. We’re using like synthetic synthetic data, and then do some analysis. And then, once our client actually got their actual data from their client, we’ll be able to.
13 00:01:48.520 ⇒ 00:01:56.622 Annie Yu: at least, like right now, we can showcase what we can do. And then, when we get the actual data will be probably easier.
14 00:01:57.530 ⇒ 00:02:02.650 Annie Yu: throughout each step, and from there I was able to use
15 00:02:04.870 ⇒ 00:02:11.670 Annie Yu: like python. And then there’s some not only like visualization, but also some statistics
16 00:02:11.770 ⇒ 00:02:16.140 Annie Yu: like correlations and things of that sort. So that’s that’s.
17 00:02:16.350 ⇒ 00:02:19.569 Annie Yu: I think that’s like, kind of in line of what I’m
18 00:02:19.760 ⇒ 00:02:30.519 Annie Yu: looking for. So I think that was great. But that also like took me a good while, because I will not. I was not like super familiar with the the whole process, but I think it was good.
19 00:02:37.130 ⇒ 00:02:38.100 Awaish Kumar: Oh, wow!
20 00:02:40.640 ⇒ 00:02:42.100 Awaish Kumar: It goes to the spam.
21 00:02:47.750 ⇒ 00:02:49.490 Awaish Kumar: Sorry. Yeah. So.
22 00:02:53.390 ⇒ 00:02:59.430 Awaish Kumar: okay, so how in terms of workload are you happy with the in workload?
23 00:03:00.873 ⇒ 00:03:06.769 Annie Yu: Yeah, yeah. So far, so good. I remember a few weeks ago I
24 00:03:07.230 ⇒ 00:03:22.480 Annie Yu: amber, and I had a a meeting. I think she was asking if I wanted to get more projects. I told her like, if there is any like need, if there’s no one i i’m always happy to step in. But also.
25 00:03:22.710 ⇒ 00:03:24.310 Annie Yu: I think, with the
26 00:03:25.230 ⇒ 00:03:31.820 Annie Yu: work. Now I think sometimes it’s challenging. On. Some week I feel lighter, like I feel. Oh, I could
27 00:03:31.970 ⇒ 00:03:33.850 Annie Yu: probably get more projects.
28 00:03:33.990 ⇒ 00:03:37.832 Annie Yu: But then on some week it’s it’s it’s
29 00:03:38.670 ⇒ 00:03:42.590 Annie Yu: it’s challenging. So yeah, that’s that’s
30 00:03:42.810 ⇒ 00:03:49.310 Annie Yu: like, that’s 1 thing that I I’m still trying to find the balance.
31 00:03:50.410 ⇒ 00:03:58.579 Awaish Kumar: Okay, like you mentioned to me about getting full time. So I don’t know if you are moved to salary, or is it still hourly.
32 00:03:59.780 ⇒ 00:04:04.209 Annie Yu: Well, no, no salary for me. It’s salary.
33 00:04:04.850 ⇒ 00:04:05.510 Awaish Kumar: Okay?
34 00:04:07.900 ⇒ 00:04:11.500 Awaish Kumar: So like, in that case, you have 40 h of work here. Right.
35 00:04:12.100 ⇒ 00:04:16.180 Annie Yu: Yeah, yeah, I think that’s the idea.
36 00:04:16.180 ⇒ 00:04:29.020 Awaish Kumar: Ideal ideal case. Yeah, but like, yeah, I I actually pushed, pushed forward. About your, oh, yeah.
37 00:04:29.680 ⇒ 00:04:39.970 Awaish Kumar: like about this context switching thing. But I think you mentioned that you are okay with learning, like, I’m also mentioned that you are okay with working on multiple projects.
38 00:04:40.340 ⇒ 00:04:42.500 Awaish Kumar: As long as you.
39 00:04:43.320 ⇒ 00:04:45.590 Awaish Kumar: We are not contact switching now
40 00:04:46.080 ⇒ 00:04:49.130 Awaish Kumar: a lot on the same day, but otherwise it’s okay.
41 00:04:49.650 ⇒ 00:04:51.980 Annie Yu: Yeah, yeah, that’s that’s why I think.
42 00:04:53.000 ⇒ 00:05:03.490 Awaish Kumar: Okay, and apart from that.
43 00:05:04.264 ⇒ 00:05:10.469 Awaish Kumar: so things are going good. And what about DVD certification
44 00:05:11.190 ⇒ 00:05:13.599 Awaish Kumar: you mentioned? You want to do it. But did that one.
45 00:05:13.600 ⇒ 00:05:17.362 Annie Yu: Yeah, I I have some traveling
46 00:05:18.260 ⇒ 00:05:25.360 Annie Yu: happening. I think it’s gonna be the last 2 weeks of the the Dbt kind of timeline.
47 00:05:26.040 ⇒ 00:05:28.359 Annie Yu: So I don’t think I can.
48 00:05:29.710 ⇒ 00:05:30.480 Awaish Kumar: That’s okay.
49 00:05:30.907 ⇒ 00:05:38.180 Annie Yu: Yeah, I think if there’s a a another chance I would. I would want to sign up.
50 00:05:38.350 ⇒ 00:05:40.429 Annie Yu: And I’m I’m talking about, maybe like
51 00:05:40.560 ⇒ 00:05:46.170 Annie Yu: the week. No, the month after you do it, or like it, could be
52 00:05:46.390 ⇒ 00:05:48.470 Annie Yu: like it could be soon, but.
53 00:05:52.070 ⇒ 00:05:57.930 Awaish Kumar: But, like like whatever you are comfortable with, like you, just let let me know
54 00:05:59.320 ⇒ 00:06:00.480 Awaish Kumar: And I think in
55 00:06:00.670 ⇒ 00:06:07.749 Awaish Kumar: like, I know, like some other people also they they want to do it. But they are not comfortable with this timeline.
56 00:06:07.980 ⇒ 00:06:10.939 Awaish Kumar: So it’s okay, like, we want everyone to do this.
57 00:06:11.230 ⇒ 00:06:17.940 Awaish Kumar: But of like, no, I don’t want to
58 00:06:18.260 ⇒ 00:06:21.319 Awaish Kumar: like just push for it. So I think it’s okay.
59 00:06:24.570 ⇒ 00:06:24.970 Annie Yu: Okay.
60 00:06:25.090 ⇒ 00:06:27.750 Awaish Kumar: You know, if you just let me know the timing.
61 00:06:28.040 ⇒ 00:06:28.590 Awaish Kumar: It’s not.
62 00:06:28.590 ⇒ 00:06:35.890 Annie Yu: That reminds me, I will. I will reply to your thread, because I think you.
63 00:06:36.590 ⇒ 00:06:39.309 Awaish Kumar: Yeah, yeah, just reply to me there. And
64 00:06:40.030 ⇒ 00:06:46.950 Awaish Kumar: we have a document. I will just keep there everyone who is interested, and then the.
65 00:06:47.050 ⇒ 00:06:53.229 Awaish Kumar: We will have some check in meetings, and we go from there because that’s something is going to like
66 00:06:53.360 ⇒ 00:06:59.810 Awaish Kumar: you wanted to learn about dbt, so like, that’s going to give you the on, the on, the
67 00:07:00.220 ⇒ 00:07:07.600 Awaish Kumar: learnings you need on data, analytics side, like what happens in the analytic side, what our activity works.
68 00:07:07.770 ⇒ 00:07:10.799 Awaish Kumar: And then the things you use. Right?
69 00:07:12.350 ⇒ 00:07:19.519 Awaish Kumar: So that’s okay. So. And that’s also the carrier plan like, that’s how like you plan your
70 00:07:20.260 ⇒ 00:07:26.250 Awaish Kumar: carrier as well like you work. You want to work on Dbt. Or tableau certification, or any other
71 00:07:26.710 ⇒ 00:07:35.279 Awaish Kumar: certification which you think you you could have done, or your colleagues have done it, or some like something relative data analyst or something.
72 00:07:35.620 ⇒ 00:07:36.060 Annie Yu: Hmm.
73 00:07:36.060 ⇒ 00:07:37.393 Awaish Kumar: What do you think is
74 00:07:38.190 ⇒ 00:07:41.462 Awaish Kumar: is good for your career? You can.
75 00:07:42.040 ⇒ 00:07:54.239 Awaish Kumar: you can send it to us, and we can see if we like. If that’s that’s something we can maybe make it necessary for every data analyst who joins Brainforge.
76 00:07:54.620 ⇒ 00:07:59.169 Awaish Kumar: So you can share what you want to do as well. That’s okay.
77 00:07:59.680 ⇒ 00:08:22.439 Annie Yu: Yeah, got it, I think, so far. Yeah, I’m definitely interested in Dbt and tableau. But I I will still reply to that thread because I’m doing like, as you know, I’m doing like part-time masters. And I’m not taking any courses this summer. So I’m technically on like summer break. So I think it will be good for me to take this time to get the certifications.
78 00:08:23.080 ⇒ 00:08:24.750 Awaish Kumar: Okay, that’s great.
79 00:08:25.180 ⇒ 00:08:26.640 Annie Yu: Master, starts. Yeah.
80 00:08:27.920 ⇒ 00:08:31.729 Awaish Kumar: Okay? So he, we talked about future plans.
81 00:08:32.370 ⇒ 00:08:35.849 Awaish Kumar: And you are okay with workload. You’re happy with it
82 00:08:37.140 ⇒ 00:08:39.860 Awaish Kumar: in terms of projects like, how
83 00:08:41.188 ⇒ 00:08:46.120 Awaish Kumar: on data, port, platform side. Have you started anything? Have you logged into Meta plane?
84 00:08:46.370 ⇒ 00:08:52.309 Annie Yu: I I did lock into it. But I I really don’t. I haven’t
85 00:08:52.800 ⇒ 00:09:04.085 Annie Yu: like for try to figure out where to start, but I also like, haven’t looked into their website or any documentation. So I I just haven’t started there yet. But
86 00:09:04.450 ⇒ 00:09:12.800 Awaish Kumar: You don’t have to start from scratch like you can pair either with me like
87 00:09:14.100 ⇒ 00:09:22.490 Awaish Kumar: anytime you are available. You can ask, like me or Luke, because we both are working on the story.
88 00:09:23.200 ⇒ 00:09:28.469 Awaish Kumar: To give you an an like introduction of the platform like, how can we add new connections?
89 00:09:28.680 ⇒ 00:09:30.900 Awaish Kumar: How to get into this and miss that.
90 00:09:31.000 ⇒ 00:09:34.740 Awaish Kumar: How can that monitors and custom tests
91 00:09:35.030 ⇒ 00:09:50.939 Awaish Kumar: things like that? So you can pair with any one of us like it can be like, maybe in in 30 min you can get overview of everything except like, get going like learning from scratch through the documents, and it will take longer.
92 00:09:51.730 ⇒ 00:09:58.249 Annie Yu: Yeah, that makes sense, and that if that’s the case, will you be able to meet anytime this week
93 00:09:59.410 ⇒ 00:10:02.179 Annie Yu: for for the quick overview?
94 00:10:02.520 ⇒ 00:10:09.199 Annie Yu: But I think I also wanna make sure I do spend some time before having that meeting.
95 00:10:09.630 ⇒ 00:10:12.159 Annie Yu: I mean, spend some time on it before.
96 00:10:12.160 ⇒ 00:10:13.069 Awaish Kumar: Yeah, yeah, you can.
97 00:10:13.300 ⇒ 00:10:14.100 Annie Yu: Yeah.
98 00:10:14.100 ⇒ 00:10:21.220 Awaish Kumar: Just we’ll just get log log in and try and create connection or things like that. That’s completely
99 00:10:22.341 ⇒ 00:10:27.089 Awaish Kumar: okay, so you can try that, and then you can like
100 00:10:27.750 ⇒ 00:10:30.589 Awaish Kumar: ping me on slack like when when you want to meet.
101 00:10:31.450 ⇒ 00:10:33.550 Annie Yu: Okay, okay, that works.
102 00:10:34.410 ⇒ 00:10:36.809 Awaish Kumar: And also, I think, I mean, we have a
103 00:10:36.920 ⇒ 00:10:39.690 Awaish Kumar: do you have time? Okay, yeah, go ahead.
104 00:10:40.687 ⇒ 00:10:49.000 Annie Yu: No one challenge I’m having on data platform documentation is I. If I can share my screen real quick.
105 00:10:49.270 ⇒ 00:10:53.000 Awaish Kumar: Yeah, yeah, sure, we still have 14 min before.
106 00:10:54.155 ⇒ 00:10:55.450 Awaish Kumar: So I’ll do.
107 00:10:55.640 ⇒ 00:10:57.619 Awaish Kumar: He didn’t stand up. Yeah.
108 00:10:57.880 ⇒ 00:11:16.319 Annie Yu: Yeah, yeah. I did finish some of these. But I think, okay, I the the kind of the red font is where I’m not sure I need to verify. But I think I’ll ask probably, Robert, where where I can find these information, because I don’t really know
109 00:11:18.570 ⇒ 00:11:21.500 Awaish Kumar: For the segment. It’s the client.
110 00:11:21.500 ⇒ 00:11:23.750 Annie Yu: The the contract dates.
111 00:11:24.260 ⇒ 00:11:25.220 Awaish Kumar: Yeah.
112 00:11:26.620 ⇒ 00:11:32.499 Awaish Kumar: So dbt, you actually don’t need it because we are just using open source one, it’s.
113 00:11:32.710 ⇒ 00:11:33.320 Annie Yu: That’s credit.
114 00:11:33.320 ⇒ 00:11:43.299 Awaish Kumar: It’s like internal contract with brain food which brings in this polytomic. We are not using it either. We are using it for just for
115 00:11:43.580 ⇒ 00:11:45.470 Awaish Kumar: as a manual.
116 00:11:45.880 ⇒ 00:12:01.349 Awaish Kumar: we don’t have active contact, like we are not paid client, but we are using this tool for one of our source. So it’s like. Just keep it there. But it’s not like, but a 0 cost client like 0 cost tool. For now we are using it
117 00:12:01.980 ⇒ 00:12:05.950 Awaish Kumar: just for one of our.
118 00:12:08.040 ⇒ 00:12:10.070 Annie Yu: I think I saw the thick.
119 00:12:10.070 ⇒ 00:12:12.140 Annie Yu: No, it’s moving something.
120 00:12:12.140 ⇒ 00:12:17.180 Awaish Kumar: Functions and like bigquery and Gcp, things are
121 00:12:18.909 ⇒ 00:12:26.089 Awaish Kumar: coming from client and segment as well. So yeah, it’s okay, like, maybe Robert can tell you more about it.
122 00:12:26.250 ⇒ 00:12:29.499 Awaish Kumar: But that’s okay. Web hooks is has nothing like
123 00:12:29.630 ⇒ 00:12:35.949 Awaish Kumar: it’s a bask, which is the basically the tool which which gets us. The data it’s called bask
124 00:12:38.450 ⇒ 00:12:41.149 Awaish Kumar: webbooks is not a visualization tool, right?
125 00:12:43.490 ⇒ 00:12:47.910 Awaish Kumar: So. But webbooks is nothing. Webbooks is a word used to for the.
126 00:12:49.080 ⇒ 00:12:50.630 Annie Yu: Oh, thank you. Bye.
127 00:12:51.470 ⇒ 00:12:54.500 Awaish Kumar: Yeah, it’s a bask which basically sends the web hooks.
128 00:12:55.160 ⇒ 00:13:00.809 Awaish Kumar: And then we have a it’s a source. I don’t know what it’s we can call.
129 00:13:01.240 ⇒ 00:13:02.309 Annie Yu: Is it data engine?
130 00:13:02.310 ⇒ 00:13:04.570 Annie Yu: It’s not ingestion tourists.
131 00:13:04.740 ⇒ 00:13:05.240 Awaish Kumar: It’s a.
132 00:13:05.240 ⇒ 00:13:05.880 Annie Yu: Good.
133 00:13:05.880 ⇒ 00:13:12.370 Awaish Kumar: Tool. Basically basically, we are not, basically it’s okay. I should not say.
134 00:13:12.370 ⇒ 00:13:12.720 Annie Yu: Out!
135 00:13:12.720 ⇒ 00:13:16.010 Awaish Kumar: We can delete it because this is not a tool we are using.
136 00:13:16.360 ⇒ 00:13:23.450 Awaish Kumar: This is like, we didn’t employ this tool. It’s just they have their like, this
137 00:13:23.740 ⇒ 00:13:28.369 Awaish Kumar: e-commerce platform where they sell these medicines. Basically, it’s a bask.
138 00:13:28.910 ⇒ 00:13:30.509 Annie Yu: Yeah, I think.
139 00:13:30.510 ⇒ 00:13:34.879 Awaish Kumar: It should be in data sources. But we don’t. It’s not. Yeah. Yeah.
140 00:13:34.880 ⇒ 00:13:36.000 Annie Yu: Okay. Okay.
141 00:13:37.970 ⇒ 00:13:44.730 Awaish Kumar: Yeah. Yeah, so okay, yeah, it’s good. Segment was good. Mix panel is, okay.
142 00:13:44.940 ⇒ 00:13:50.389 Awaish Kumar: Poly poly talking is, okay. Yeah, that’s other other user toys. Basically.
143 00:13:51.240 ⇒ 00:13:56.599 Annie Yu: Okay, thank you. But yeah, I I still have to figure out this part. And then.
144 00:13:56.600 ⇒ 00:14:05.090 Awaish Kumar: Asking Robert like for for victory and the segment and the and tableau
145 00:14:05.730 ⇒ 00:14:09.989 Awaish Kumar: and for bigquery and Gcp. Function is kind of same like
146 00:14:10.200 ⇒ 00:14:14.579 Awaish Kumar: it’s ongoing things like we don’t have a I don’t know if they have a contract.
147 00:14:15.450 ⇒ 00:14:18.009 Annie Yu: Oh, you mean for bigquery. And Google Cloud.
148 00:14:18.010 ⇒ 00:14:22.239 Awaish Kumar: Yeah, it’s like Google Cloud Platform, where you basically go in and
149 00:14:23.460 ⇒ 00:14:29.550 Awaish Kumar: run some functions. And when the cost based on usage. But I don’t know if there’s
150 00:14:29.710 ⇒ 00:14:31.280 Awaish Kumar: how they are doing it right.
151 00:14:31.440 ⇒ 00:14:36.840 Awaish Kumar: Bigquery provides both things like you can have a cost based on usage or fixed cost, so.
152 00:14:36.990 ⇒ 00:14:37.810 Annie Yu: Oh!
153 00:14:38.040 ⇒ 00:14:40.530 Awaish Kumar: Okay? So you can. Yeah, it can be confirmed by Robert.
154 00:14:41.250 ⇒ 00:14:47.840 Annie Yu: Okay, yeah. And then moving on to data source. I think this is also where I don’t
155 00:14:48.924 ⇒ 00:14:51.099 Annie Yu: know where to find.
156 00:14:51.710 ⇒ 00:14:57.060 Awaish Kumar: Frequency is basically the time when we do the data right from north beam.
157 00:14:58.386 ⇒ 00:15:07.499 Awaish Kumar: Okay, I can basically typhoon. I don’t know typhoon. Maybe. Can you tell tell more about it?
158 00:15:08.860 ⇒ 00:15:11.843 Awaish Kumar: From Google sheets, which basically
159 00:15:13.220 ⇒ 00:15:19.249 Awaish Kumar: what you say. Basically, it’s just in synced like, it’s it’s
160 00:15:19.700 ⇒ 00:15:29.649 Awaish Kumar: our Gcp tables. Wicker tables are directly linked to the file. So if there’s any change in file, we just get to read it. So it’s not a
161 00:15:30.460 ⇒ 00:15:32.469 Awaish Kumar: oh, so this is like real time.
162 00:15:32.650 ⇒ 00:15:34.389 Awaish Kumar: This is kind of real time. Yeah.
163 00:15:35.950 ⇒ 00:15:41.109 Annie Yu: So maybe I, is it okay? If I type in real time, does that make sense.
164 00:15:42.910 ⇒ 00:15:48.489 Awaish Kumar: This is real time. Bask is a source from where we get the data. It’s also real time. Through segment.
165 00:15:49.420 ⇒ 00:15:51.400 Awaish Kumar: Near real time. You can see
166 00:15:51.530 ⇒ 00:15:56.380 Awaish Kumar: near real time, because it’s not real time. It’s it’s it’s it comes with some delays.
167 00:15:57.890 ⇒ 00:15:58.610 Annie Yu: Hmm.
168 00:16:00.770 ⇒ 00:16:01.939 Awaish Kumar: For the Basque.
169 00:16:03.340 ⇒ 00:16:05.260 Annie Yu: Oh, yeah. So what’s
170 00:16:05.920 ⇒ 00:16:08.739 Annie Yu: You said? It’s near nearly real time.
171 00:16:08.980 ⇒ 00:16:10.590 Awaish Kumar: Yeah, near real time. Like.
172 00:16:10.860 ⇒ 00:16:21.859 Awaish Kumar: so real time is, basically you get instant like we, we get an order, we get the data. But it’s not like that. We get an order. We might get the data after 10 min. So it’s near real time.
173 00:16:22.420 ⇒ 00:16:25.520 Annie Yu: Okay, so what’s the best way to.
174 00:16:25.760 ⇒ 00:16:29.359 Awaish Kumar: Yeah. It’s called near real time. That’s the wording use near any.
175 00:16:29.810 ⇒ 00:16:33.110 Annie Yu: Oh, that’s an like actual term to use in.
176 00:16:33.110 ⇒ 00:16:33.460 Awaish Kumar: Yeah, yeah.
177 00:16:34.280 ⇒ 00:16:35.120 Annie Yu: Okay.
178 00:16:35.640 ⇒ 00:16:36.960 Annie Yu: Near real time.
179 00:16:37.660 ⇒ 00:16:45.389 Awaish Kumar: North meme. I don’t remember right now, so I don’t know. Zendesk is manual Zendesk manual sync.
180 00:16:46.905 ⇒ 00:16:48.209 Annie Yu: What’s this? That’s.
181 00:16:48.210 ⇒ 00:16:52.820 Awaish Kumar: Manual, manual manual, I mean. Well, we do it.
182 00:16:53.360 ⇒ 00:16:54.750 Annie Yu: Oh, interesting!
183 00:16:56.640 ⇒ 00:17:06.750 Awaish Kumar: I don’t remember. Segment is basically a tool which basically ingest the data from multiple sources like bask. So I don’t know.
184 00:17:06.990 ⇒ 00:17:08.780 Annie Yu: Yeah, I should remove it here.
185 00:17:10.050 ⇒ 00:17:14.099 Awaish Kumar: You can delete. Yeah, have Facebook as Google address?
186 00:17:14.250 ⇒ 00:17:15.787 Awaish Kumar: I don’t know.
187 00:17:16.400 ⇒ 00:17:18.240 Awaish Kumar: It comes in through.
188 00:17:18.720 ⇒ 00:17:19.609 Awaish Kumar: Oh, no.
189 00:17:20.410 ⇒ 00:17:27.040 Awaish Kumar: Which sauce, remember, because we are not directly. Haven’t utilized that much
190 00:17:27.853 ⇒ 00:17:34.229 Awaish Kumar: these sources, but it’s maybe some Gcp function or something I don’t remember
191 00:17:34.637 ⇒ 00:17:41.029 Awaish Kumar: like you can check and segment if they are coming through segment or coming through Gcp, because
192 00:17:41.170 ⇒ 00:17:43.950 Awaish Kumar: we do have these sources coming in separately.
193 00:17:44.090 ⇒ 00:17:49.539 Awaish Kumar: But I don’t really haven’t actually worked on. We just looking at north beam and
194 00:17:49.690 ⇒ 00:17:54.849 Awaish Kumar: offline data. We haven’t really work, build any model on top of these.
195 00:17:56.770 ⇒ 00:18:04.610 Annie Yu: Yeah, I did check this figma. That’s kind of how I oh.
196 00:18:05.290 ⇒ 00:18:10.500 Annie Yu: that’s kind of how I wrote down those data sources.
197 00:18:10.500 ⇒ 00:18:12.784 Awaish Kumar: Yeah. So I don’t know who built this.
198 00:18:13.270 ⇒ 00:18:14.216 Awaish Kumar: I don’t.
199 00:18:15.120 ⇒ 00:18:19.420 Annie Yu: Okay. So it’s not up to date, you would say.
200 00:18:19.470 ⇒ 00:18:23.979 Awaish Kumar: I I’m not sure like it might be up to date. But I’m I’m just saying
201 00:18:24.280 ⇒ 00:18:29.589 Awaish Kumar: just maybe log into segment and see if there is a connector for Facebook and Google ads.
202 00:18:29.850 ⇒ 00:18:32.710 Awaish Kumar: If there is, then maybe it’s coming from there. Otherwise
203 00:18:32.850 ⇒ 00:18:36.380 Awaish Kumar: it is coming from Gcp, whatever like it’s, it’s just
204 00:18:36.520 ⇒ 00:18:39.830 Awaish Kumar: we have to reconfirm that, but that’s all.
205 00:18:42.390 ⇒ 00:18:45.389 Annie Yu: Okay, I’m just making some notes.
206 00:18:45.600 ⇒ 00:18:51.310 Annie Yu: Yeah. And and what about destination is, I think I I just.
207 00:18:51.310 ⇒ 00:18:57.899 Awaish Kumar: Let’s more learning, like, where destination is everything. All the data is going to be clearly.
208 00:18:58.280 ⇒ 00:18:59.240 Annie Yu: Right.
209 00:18:59.460 ⇒ 00:19:00.930 Awaish Kumar: So you just have to
210 00:19:01.460 ⇒ 00:19:07.440 Awaish Kumar: figure out what what data sets it’s going into like for north beam and victory.
211 00:19:07.710 ⇒ 00:19:10.750 Awaish Kumar: Where’s wrong or swim data going into
212 00:19:11.331 ⇒ 00:19:17.299 Awaish Kumar: For Google sheets. It’s raw Google sheets, right for Norm. It’s not very good. It’s it’s okay.
213 00:19:17.700 ⇒ 00:19:19.389 Awaish Kumar: Now for Boston.
214 00:19:19.390 ⇒ 00:19:20.010 Annie Yu: Hmm!
215 00:19:20.380 ⇒ 00:19:25.510 Annie Yu: Am I able to see that in Github or not? Because I did
216 00:19:25.630 ⇒ 00:19:28.110 Annie Yu: look through? Kind of get help.
217 00:19:28.110 ⇒ 00:19:33.200 Awaish Kumar: Yeah, basically, you can see it in in our dbt, Github, Dbt, project source.
218 00:19:34.150 ⇒ 00:19:35.800 Awaish Kumar: If you go to source file.
219 00:19:36.230 ⇒ 00:19:38.459 Annie Yu: And the DVD projects. Yeah.
220 00:19:38.610 ⇒ 00:19:43.569 Awaish Kumar: Basically, that’s all the bigquery data sets. So name is bigquery data set.
221 00:19:44.850 ⇒ 00:19:46.520 Annie Yu: Oh! So!
222 00:19:46.750 ⇒ 00:19:48.850 Awaish Kumar: So you can see the data set under the name.
223 00:19:49.880 ⇒ 00:19:56.930 Annie Yu: If I see north beam, but then it’s not called raw, nor Spain.
224 00:19:56.930 ⇒ 00:19:59.570 Awaish Kumar: Oh, is that name of the team
225 00:20:00.770 ⇒ 00:20:05.529 Awaish Kumar: database? But it’s not there. Yeah, you can just go through. It’s not wrong.
226 00:20:05.630 ⇒ 00:20:07.529 Awaish Kumar: because the data set name is not me.
227 00:20:07.990 ⇒ 00:20:11.590 Awaish Kumar: so you can remove the row. It’s just template by created by someone.
228 00:20:12.540 ⇒ 00:20:19.230 Annie Yu: Okay. So as long as I guess I can just double check I can find here. And then just
229 00:20:19.880 ⇒ 00:20:20.560 Annie Yu: type in.
230 00:20:20.560 ⇒ 00:20:22.559 Awaish Kumar: You can just find it from here. You can.
231 00:20:22.560 ⇒ 00:20:26.019 Annie Yu: That’s super helpful. Okay, I. Okay, that’s great.
232 00:20:26.190 ⇒ 00:20:27.915 Annie Yu: I’ll do that.
233 00:20:28.650 ⇒ 00:20:34.399 Annie Yu: yeah, let me just. And I think this is also where I don’t know where to double check.
234 00:20:34.400 ⇒ 00:20:41.709 Awaish Kumar: This is this is from from Google. Github. Right? You go into code. Basically this, you don’t have to actually
235 00:20:41.860 ⇒ 00:20:44.159 Awaish Kumar: do that because I have already done that.
236 00:20:44.410 ⇒ 00:20:48.459 Awaish Kumar: So you can. You can. Yeah, if you find any missing
237 00:20:49.310 ⇒ 00:20:52.649 Awaish Kumar: metrics, because I haven’t filled it for all the
238 00:20:53.431 ⇒ 00:21:01.390 Awaish Kumar: columns. I don’t know, who filled for all the metrics I filled for some of them, but I I left some metrics.
239 00:21:01.390 ⇒ 00:21:01.800 Annie Yu: You know.
240 00:21:01.800 ⇒ 00:21:04.249 Awaish Kumar: I didn’t fill fill out anything.
241 00:21:04.620 ⇒ 00:21:07.437 Awaish Kumar: so I don’t know if somebody did it. But
242 00:21:08.650 ⇒ 00:21:13.990 Awaish Kumar: But that’s up to date, right? So you can just copy paste to the other sheet.
243 00:21:14.800 ⇒ 00:21:18.017 Annie Yu: Yeah. I also added some dashboards here, cause
244 00:21:18.420 ⇒ 00:21:23.060 Awaish Kumar: Yeah, yeah, there, yeah, like, if there are some columns which are missing.
245 00:21:23.758 ⇒ 00:21:26.129 Annie Yu: You can fill them out. But this.
246 00:21:26.400 ⇒ 00:21:30.060 Awaish Kumar: Like data source column data, pipeline column.
247 00:21:30.540 ⇒ 00:21:51.340 Awaish Kumar: They these, both columns are up to date from my side. But yeah, if there’s any missing metric where you don’t have these values for these, you can just verify it from Github, DVD. Projects where it’s how it’s been calculated. And if there are columns which have missing data which you can also find out like, fill it, fill it out. But yeah.
248 00:21:52.210 ⇒ 00:21:58.390 Awaish Kumar: for these both columns, wherever there is some values. Then I have updated them so you can copy paste.
249 00:21:59.380 ⇒ 00:22:02.640 Annie Yu: What about the final table? I I did see there.
250 00:22:02.640 ⇒ 00:22:08.049 Annie Yu: Yeah, it’s final. It’s final table, which basically kind of table of source. Right?
251 00:22:08.400 ⇒ 00:22:09.640 Annie Yu: So you know.
252 00:22:09.880 ⇒ 00:22:23.810 Awaish Kumar: For churn rate. Right? Churn rate is coming from is in retention. Dashboard. Okay? Retention dashboard is built on top of some data source. Right. And what that data source is, some kind of table. And what what table that is. That’s the final table.
253 00:22:24.430 ⇒ 00:22:30.459 Annie Yu: Hmm, so it could be more than one. Right? So here we have. Okay, that makes sense.
254 00:22:31.280 ⇒ 00:22:32.700 Annie Yu: Yeah, I think, yeah.
255 00:22:32.700 ⇒ 00:22:38.219 Awaish Kumar: If you’re using. If you’re using 2 tables to create one metric, you can put 2 final tables.
256 00:22:38.640 ⇒ 00:22:40.380 Annie Yu: Okay, okay.
257 00:22:41.420 ⇒ 00:22:52.789 Annie Yu: that makes sense. Then I think this, this is no problem. And and this part I I do just have to ask kind of Robert again about the I’m just.
258 00:22:53.470 ⇒ 00:22:54.900 Awaish Kumar: Okay, yeah, that’s okay.
259 00:22:54.930 ⇒ 00:23:08.449 Annie Yu: Yeah, whatever I think is. And I think one more question is here. I do see an example of the template file. We have internal folks, and I’m not sure, is it?
260 00:23:09.310 ⇒ 00:23:16.210 Annie Yu: It is the best idea to have both internal and external. Or here we just want to keep the internal.
261 00:23:18.500 ⇒ 00:23:28.502 Awaish Kumar: Yeah, it’s okay. If we have the external people, because we can refer, like, if I don’t know, like who who is looking at marketing data. I can go and see. Okay, mitesh is the one.
262 00:23:29.080 ⇒ 00:23:31.500 Annie Yu: Which basically works for.
263 00:23:33.275 ⇒ 00:23:40.430 Awaish Kumar: Which which we basically interacts with marketing data. And if I have any questions regarding that, I can ask him if there is some things related to
264 00:23:40.780 ⇒ 00:23:45.480 Awaish Kumar: segment, I can ask, but what what’s the name? I don’t.
265 00:23:46.785 ⇒ 00:23:50.529 Awaish Kumar: What’s called like Sebastian, or what how to pronounce.
266 00:23:52.050 ⇒ 00:23:52.630 Annie Yu: Oh!
267 00:23:52.630 ⇒ 00:23:54.340 Awaish Kumar: Called Sebastian, or right.
268 00:23:55.010 ⇒ 00:23:58.689 Annie Yu: Oh, I don’t even know he’s who who he is.
269 00:23:59.040 ⇒ 00:24:07.229 Awaish Kumar: He’s a CTO basically. But yeah, he so like, he’s responsible for Google tag manager and segment work. So
270 00:24:07.380 ⇒ 00:24:09.279 Awaish Kumar: like things like that, if we just
271 00:24:09.510 ⇒ 00:24:15.350 Awaish Kumar: put it here, it’s for everybody’s interest. Right?
272 00:24:15.460 ⇒ 00:24:17.689 Awaish Kumar: So we we want to keep everyone here.
273 00:24:18.190 ⇒ 00:24:19.339 Annie Yu: Okay. Okay.
274 00:24:19.790 ⇒ 00:24:20.960 Annie Yu: All right.
275 00:24:20.960 ⇒ 00:24:24.419 Awaish Kumar: Hey? We are on time, and we have to go into the Indian meeting.
276 00:24:24.820 ⇒ 00:24:25.679 Awaish Kumar: Let’s go there.
277 00:24:26.078 ⇒ 00:24:29.659 Annie Yu: Is there a an office hour today or not?
278 00:24:30.730 ⇒ 00:24:34.321 Awaish Kumar: Yeah, there is I just. I’ll just announce.
279 00:24:35.210 ⇒ 00:24:39.329 Annie Yu: Oh, okay, cool, alright. Thank you so much.
280 00:24:39.540 ⇒ 00:24:40.559 Awaish Kumar: Thank you. Bye.
281 00:24:41.450 ⇒ 00:24:41.910 Annie Yu: Bye.