Meeting Title: Brainforge x Eden Project Data Sync Date: 2025-12-11 Meeting participants: Awaish Kumar, Sezim Zhenishbekova
WEBVTT
1 00:00:24.550 ⇒ 00:00:25.510 Awaish Kumar: Hello?
2 00:00:29.830 ⇒ 00:00:31.949 Sezim Zhenishbekova: Hopping on a call.
3 00:00:32.490 ⇒ 00:00:33.659 Sezim Zhenishbekova: Can you hear me?
4 00:00:35.180 ⇒ 00:00:37.069 Awaish Kumar: Yeah, I can.
5 00:00:37.070 ⇒ 00:00:44.370 Sezim Zhenishbekova: Okay, awesome. So basically, I was assigned to work with Edin Project.
6 00:00:44.850 ⇒ 00:00:46.969 Sezim Zhenishbekova: I can share my screen.
7 00:00:47.960 ⇒ 00:00:48.460 Awaish Kumar: Funny here.
8 00:00:51.070 ⇒ 00:00:51.760 Sezim Zhenishbekova: Sure.
9 00:00:55.010 ⇒ 00:01:06.170 Sezim Zhenishbekova: So, this was my project where I was supposed to take the project cell fund and put it in a Tableau file. So, the…
10 00:01:06.320 ⇒ 00:01:07.850 Sezim Zhenishbekova: VP, the…
11 00:01:08.440 ⇒ 00:01:27.439 Sezim Zhenishbekova: VP of Finance, basically, he runs his… all his queries here for forecasting what are the sales gonna be, what’s gonna be the retention rate, but he said that all this data, months is by 48 months, if he just… it’s not accurate, he just, like, takes from some places and puts it in this sheet.
12 00:01:27.440 ⇒ 00:01:31.320 Sezim Zhenishbekova: So, he asked us to make it more…
13 00:01:31.670 ⇒ 00:01:35.050 Sezim Zhenishbekova: Functionable and more user-friendly and accurate.
14 00:01:35.240 ⇒ 00:01:49.879 Sezim Zhenishbekova: And here’s a lot of information about the customers, how much money they spend on ads, COGS, and some financial metrics, together with other retention cycle information. So, based on this, I realized that
15 00:01:49.900 ⇒ 00:01:55.379 Sezim Zhenishbekova: I will need these metrics specifically, like customer metrics, revenue metrics, COGS,
16 00:01:55.420 ⇒ 00:02:05.799 Sezim Zhenishbekova: like, ad spends, overhead expenses, like, say, salaries and benefits, and at some point in time, I found all this in Tableau, like, some of them.
17 00:02:05.920 ⇒ 00:02:07.869 Sezim Zhenishbekova: I saw a chart.
18 00:02:08.710 ⇒ 00:02:17.639 Awaish Kumar: Like, what you have to do is basically, so I can tell you a few names as… as they are already mentioned in the ticket, in the message.
19 00:02:17.970 ⇒ 00:02:18.610 Awaish Kumar: Sweet.
20 00:02:18.960 ⇒ 00:02:22.869 Awaish Kumar: Order summary, and the product sales summary by transaction.
21 00:02:23.210 ⇒ 00:02:27.980 Awaish Kumar: Yes. So, these are the most important tables.
22 00:02:29.370 ⇒ 00:02:33.630 Awaish Kumar: If you go into the BigQuery, if you have… I don’t know if you have access to BigQuery.
23 00:02:33.860 ⇒ 00:02:34.620 Awaish Kumar: He didn’t project.
24 00:02:34.900 ⇒ 00:02:39.900 Sezim Zhenishbekova: I don’t have, I think… I don’t… I’m not sure. I should check it, yes.
25 00:02:41.110 ⇒ 00:02:46.989 Awaish Kumar: Okay, so, like… Yeah, if you have access to Tableau, Tableau also have a data source.
26 00:02:49.050 ⇒ 00:02:51.199 Awaish Kumar: For product sales summary by transaction.
27 00:02:51.650 ⇒ 00:02:52.130 Sezim Zhenishbekova: Yeah.
28 00:02:52.130 ⇒ 00:02:57.270 Awaish Kumar: So, that is the table which can give you, for example,
29 00:02:57.400 ⇒ 00:03:01.459 Awaish Kumar: New customer count, returning customer count by product.
30 00:03:01.720 ⇒ 00:03:02.460 Awaish Kumar: Like.
31 00:03:02.460 ⇒ 00:03:02.850 Sezim Zhenishbekova: Hmm.
32 00:03:02.850 ⇒ 00:03:08.329 Awaish Kumar: or it’s separated by SEMA, by Triz, or by… if you want to just get a total.
33 00:03:08.580 ⇒ 00:03:12.560 Awaish Kumar: That is the table, basically, from where you can get this data.
34 00:03:13.480 ⇒ 00:03:21.079 Sezim Zhenishbekova: Yeah, can we walk through right now? Because I haven’t really used Tableau, and I don’t want to touch and break anything.
35 00:03:21.670 ⇒ 00:03:22.120 Awaish Kumar: Aww.
36 00:03:22.120 ⇒ 00:03:22.700 Sezim Zhenishbekova: time?
37 00:03:22.700 ⇒ 00:03:35.329 Awaish Kumar: Sorry, if you need any help for Tableau itself, you might have to then schedule a call with Demilade, or Henry himself, because he… Henry is the data analyst.
38 00:03:35.330 ⇒ 00:03:35.890 Sezim Zhenishbekova: Huh.
39 00:03:35.890 ⇒ 00:03:39.420 Awaish Kumar: works on Tableau, and then Demilade…
40 00:03:39.580 ⇒ 00:03:46.859 Awaish Kumar: has worked on the tableau itself also, like, but he’s not a data analyst, so he might not be able to
41 00:03:47.000 ⇒ 00:03:51.469 Awaish Kumar: answer everything regarding Tableau, but he can… Give you an overview.
42 00:03:51.470 ⇒ 00:03:56.159 Sezim Zhenishbekova: How about you? What, what do you specialize in?
43 00:03:56.160 ⇒ 00:04:00.409 Awaish Kumar: Yeah, I’m a data engineer and an analytics engineer. I can tell you
44 00:04:00.790 ⇒ 00:04:06.680 Awaish Kumar: Where this data is coming from, what data is powering these data sources?
45 00:04:07.040 ⇒ 00:04:07.660 Sezim Zhenishbekova: Beautiful.
46 00:04:07.900 ⇒ 00:04:17.579 Awaish Kumar: from the which tables, what is the context of that data, and then what are these data sources which are listed in the table?
47 00:04:17.589 ⇒ 00:04:18.149 Sezim Zhenishbekova: Hmm…
48 00:04:18.149 ⇒ 00:04:22.969 Awaish Kumar: maybe I can give you some overview of different dashboards, but the thing is that
49 00:04:23.129 ⇒ 00:04:27.579 Awaish Kumar: I’m not, I’m not, like, building any of these.
50 00:04:27.680 ⇒ 00:04:32.770 Sezim Zhenishbekova: Okay, so, then my question’s not about, like, my specific requests.
51 00:04:32.900 ⇒ 00:04:36.810 Sezim Zhenishbekova: was to know where each data is located, so I can…
52 00:04:37.380 ⇒ 00:04:38.600 Awaish Kumar: Yeah, if you live…
53 00:04:38.600 ⇒ 00:04:47.179 Sezim Zhenishbekova: work on it and create a new sheet with all this information, and then I can get in touch with them a lot, because Henry, I think, is on vacation. I mean, traveling, so…
54 00:04:47.180 ⇒ 00:04:50.000 Awaish Kumar: But can you click on Publish Data Sources?
55 00:04:51.120 ⇒ 00:04:53.330 Sezim Zhenishbekova: Yeah, here’s the… .
56 00:04:53.330 ⇒ 00:04:53.810 Awaish Kumar: a source.
57 00:04:53.810 ⇒ 00:04:56.639 Sezim Zhenishbekova: Publish the data sources, yes.
58 00:04:57.370 ⇒ 00:05:02.460 Awaish Kumar: So, these are the data sources from where data is coming from, and.
59 00:05:03.050 ⇒ 00:05:09.559 Awaish Kumar: sources are 101 mapped with tables in BigQuery. So, our right.
60 00:05:10.260 ⇒ 00:05:13.589 Awaish Kumar: but BigQuery is a data warehouse which stores the data.
61 00:05:14.750 ⇒ 00:05:17.700 Awaish Kumar: Or… for Aiden, regarding all the…
62 00:05:17.960 ⇒ 00:05:21.579 Awaish Kumar: marketing spend, all the sales orders, everything, right?
63 00:05:23.080 ⇒ 00:05:31.620 Awaish Kumar: the data is stored in BigQuery, and we create some models and create, like, some tables, which are then used by data analysts in Tableau.
64 00:05:31.640 ⇒ 00:05:33.100 Sezim Zhenishbekova: So, in the…
65 00:05:33.310 ⇒ 00:05:43.790 Awaish Kumar: the names you are seeing, you can… you can access it afterwards, that’s, like, using Ethernet binforce.ai Gmail account.
66 00:05:45.650 ⇒ 00:05:49.319 Sezim Zhenishbekova: So this is the one, right? Triadin, BigQuery?
67 00:05:49.840 ⇒ 00:05:55.810 Awaish Kumar: you can search for, like, a Google account, which is, like, Eden at brainforge.ai.
68 00:05:56.730 ⇒ 00:05:58.770 Sezim Zhenishbekova: Oh, I don’t know if I.
69 00:05:58.770 ⇒ 00:06:02.459 Awaish Kumar: In the OnePass, you can search Eden 8.
70 00:06:03.130 ⇒ 00:06:04.850 Awaish Kumar: Brainforce.ai.
71 00:06:05.130 ⇒ 00:06:06.919 Sezim Zhenishbekova: It’s Gmail, right? Or…
72 00:06:07.110 ⇒ 00:06:12.370 Awaish Kumar: It’s a Gmail account, so if you just maybe scroll down, you can see…
73 00:06:13.110 ⇒ 00:06:21.929 Awaish Kumar: why I don’t have you access to that? Like, I don’t know, ask Rico that, if you see that.
74 00:06:22.920 ⇒ 00:06:26.650 Sezim Zhenishbekova: This one, right? This one. This is this one? Okay.
75 00:06:26.650 ⇒ 00:06:31.690 Awaish Kumar: Yeah, this is a Google account. You can log into this account, and then you open console.com.
76 00:06:31.910 ⇒ 00:06:40.620 Awaish Kumar: com. Like, console, like, cloud.console or something for BigQuery, and then you can… you can see the… the data.
77 00:06:42.640 ⇒ 00:06:43.720 Sezim Zhenishbekova: Okay?
78 00:06:43.720 ⇒ 00:06:48.199 Awaish Kumar: But for now, we can go to the tableau, basically. The names are same.
79 00:06:48.620 ⇒ 00:06:52.610 Awaish Kumar: We can find the same table names in, in BigQuery.
80 00:06:52.750 ⇒ 00:06:53.750 Awaish Kumar: So…
81 00:06:53.750 ⇒ 00:06:55.680 Sezim Zhenishbekova: cohort.
82 00:06:55.830 ⇒ 00:07:05.289 Awaish Kumar: So I can tell you where you should look for. This is not something, like, this is for a venue retention, and I don’t think you are looking for that right now. Are you?
83 00:07:05.290 ⇒ 00:07:09.180 Sezim Zhenishbekova: So basically, I need some retention data, because.
84 00:07:09.650 ⇒ 00:07:10.700 Awaish Kumar: Okay, yeah, yeah.
85 00:07:10.700 ⇒ 00:07:11.400 Sezim Zhenishbekova: Hold on.
86 00:07:11.900 ⇒ 00:07:16.050 Sezim Zhenishbekova: I need retention rate by month, since acquisition by product.
87 00:07:17.340 ⇒ 00:07:18.170 Sezim Zhenishbekova: Councillor.
88 00:07:18.620 ⇒ 00:07:23.319 Awaish Kumar: Okay, so… Retention rate by months.
89 00:07:24.280 ⇒ 00:07:32.139 Awaish Kumar: Okay, months since acquisition. Okay, yeah, this is the table, like, cohort, tables.
90 00:07:34.130 ⇒ 00:07:34.860 Awaish Kumar: Yeah.
91 00:07:35.080 ⇒ 00:07:35.700 Awaish Kumar: So these…
92 00:07:35.700 ⇒ 00:07:36.360 Sezim Zhenishbekova: Just…
93 00:07:36.360 ⇒ 00:07:41.630 Awaish Kumar: maybe if you can export the dashboards, there’s a lot of already built. I don’t…
94 00:07:42.060 ⇒ 00:07:47.089 Awaish Kumar: I don’t think, like, like, like, the person before who built all these dashboards.
95 00:07:47.290 ⇒ 00:07:51.550 Awaish Kumar: has really built, well, like, quite a lot of them, so you…
96 00:07:51.550 ⇒ 00:07:52.040 Sezim Zhenishbekova: Hmm…
97 00:07:52.040 ⇒ 00:07:58.319 Awaish Kumar: just explore these dashboards. These are sources, but explore published in dashboards.
98 00:07:58.320 ⇒ 00:08:14.429 Awaish Kumar: And you will see and review them. You will see, like, there will be some… some charts related to retention, some charts related to new customer count, the returning customer count, new and returning by product, and all of this is there. You can see cash.
99 00:08:14.850 ⇒ 00:08:28.439 Awaish Kumar: And the customer acquisition cost, or ROAS metrics, all of… all of them are there. And I can just name, like, product sales summary by transaction. That is one of the data sources, right? If you click.
100 00:08:28.440 ⇒ 00:08:28.885 Sezim Zhenishbekova: impact.
101 00:08:29.330 ⇒ 00:08:35.260 Awaish Kumar: you can see, yeah, if you follow, like, what I’m saying, you… Yeah.
102 00:08:35.520 ⇒ 00:08:36.429 Sezim Zhenishbekova: Yes.
103 00:08:36.480 ⇒ 00:08:40.839 Awaish Kumar: Yeah, if you search for product sales summary by transaction.
104 00:08:41.659 ⇒ 00:08:43.439 Sezim Zhenishbekova: Products there…
105 00:08:45.440 ⇒ 00:08:47.960 Awaish Kumar: Summary, sales summary.
106 00:08:50.130 ⇒ 00:08:51.520 Sezim Zhenishbekova: Summary by…
107 00:08:51.910 ⇒ 00:08:53.250 Awaish Kumar: transaction, I…
108 00:08:53.770 ⇒ 00:08:54.959 Sezim Zhenishbekova: for a transaction.
109 00:08:55.170 ⇒ 00:08:57.549 Awaish Kumar: It should be in the sources, I’m not sure.
110 00:08:58.120 ⇒ 00:08:59.410 Awaish Kumar: Like, if you just…
111 00:09:01.720 ⇒ 00:09:04.389 Sezim Zhenishbekova: This is the one per transaction.
112 00:09:05.760 ⇒ 00:09:06.600 Sezim Zhenishbekova: Yeah.
113 00:09:06.600 ⇒ 00:09:08.839 Awaish Kumar: This one? Up-to-date one.
114 00:09:09.010 ⇒ 00:09:12.789 Sezim Zhenishbekova: So, what does ProdPtart mean?
115 00:09:12.790 ⇒ 00:09:19.969 Awaish Kumar: Yeah, it is… basically means that it is coming from this data set in BigQuery.
116 00:09:20.840 ⇒ 00:09:22.200 Sezim Zhenishbekova: Okay.
117 00:09:22.440 ⇒ 00:09:23.900 Awaish Kumar: Right? So…
118 00:09:24.320 ⇒ 00:09:30.340 Awaish Kumar: I would love you to… yeah, maybe I can give you an overview, I can show you a picture.
119 00:09:30.340 ⇒ 00:09:31.730 Sezim Zhenishbekova: Yes, please.
120 00:09:32.560 ⇒ 00:09:35.120 Awaish Kumar: That will be easy, otherwise…
121 00:09:35.120 ⇒ 00:09:35.900 Sezim Zhenishbekova: Yeah.
122 00:09:38.810 ⇒ 00:09:42.240 Awaish Kumar: Okay, let me… let me share my screen.
123 00:09:46.150 ⇒ 00:09:47.660 Awaish Kumar: So, we are here.
124 00:09:51.580 ⇒ 00:09:54.250 Sezim Zhenishbekova: Yeah, this is much better. Okay.
125 00:09:54.980 ⇒ 00:09:56.300 Awaish Kumar: Can you see my screen?
126 00:09:56.490 ⇒ 00:09:57.160 Sezim Zhenishbekova: Yes.
127 00:09:57.480 ⇒ 00:09:58.120 Awaish Kumar: Okay.
128 00:09:58.480 ⇒ 00:10:03.119 Awaish Kumar: So this is… I have logged into this account, Eden at brainforge.ai.
129 00:10:03.260 ⇒ 00:10:10.009 Awaish Kumar: So, when you log in, and if you can… you have to go to this URL, console.cloud.google.com.
130 00:10:10.010 ⇒ 00:10:11.770 Sezim Zhenishbekova: query.
131 00:10:11.770 ⇒ 00:10:13.300 Awaish Kumar: I can maybe send it.
132 00:10:13.860 ⇒ 00:10:15.060 Sezim Zhenishbekova: Yes, please.
133 00:10:17.290 ⇒ 00:10:18.879 Awaish Kumar: I can send you on Slack.
134 00:10:22.520 ⇒ 00:10:31.740 Awaish Kumar: Sorry, we… we went straight into the work. I didn’t have a chance to, like, get your introduction. Sorry for that.
135 00:10:31.740 ⇒ 00:10:39.930 Sezim Zhenishbekova: No, no, it’s me also, like, I was slow asking questions, I guess. Okay,
136 00:10:40.610 ⇒ 00:10:46.679 Awaish Kumar: So basically, if you log in and you click on that URL, which I’ve sent, you…
137 00:10:47.050 ⇒ 00:10:52.700 Awaish Kumar: project, Aiden Data Warehouse. So this is a data warehouse. It’s a BigQuery.
138 00:10:52.880 ⇒ 00:10:53.840 Awaish Kumar: And it is.
139 00:10:54.300 ⇒ 00:10:59.809 Awaish Kumar: data warehouse for Eden. If you open this project, there are quite a lot of data sets here.
140 00:11:00.120 ⇒ 00:11:00.770 Awaish Kumar: There are multiple
141 00:11:01.580 ⇒ 00:11:08.259 Awaish Kumar: Right? So what you have to focus on, and what you know will be… what you all…
142 00:11:08.770 ⇒ 00:11:17.129 Awaish Kumar: how can I frame it? Like, what all you will be touching in Tableau will be coming from, this, dataset?
143 00:11:17.750 ⇒ 00:11:19.570 Awaish Kumar: broad DBT marts.
144 00:11:21.950 ⇒ 00:11:31.430 Awaish Kumar: whatever is there now is coming from this dataset. Whatever you will be referencing in future will also be coming from this dataset.
145 00:11:32.190 ⇒ 00:11:32.940 Sezim Zhenishbekova: Okay.
146 00:11:33.810 ⇒ 00:11:37.839 Awaish Kumar: And we don’t want you to touch any of… any other dataset.
147 00:11:40.210 ⇒ 00:11:41.710 Awaish Kumar: Unless, like, unless…
148 00:11:42.060 ⇒ 00:11:59.090 Awaish Kumar: really necessary, and we discussed that, and things like that, but otherwise, you only… what you have to do is just look at the… what is in there, and work with that. If something is missing, if you’re thinking, I need XYZ, and it is not… I can’t find it in this dataset.
149 00:11:59.090 ⇒ 00:12:04.270 Awaish Kumar: you can ping us that, like, you need help, or you can ping me or Demilade.
150 00:12:04.350 ⇒ 00:12:06.739 Awaish Kumar: Or in Slack, right?
151 00:12:07.780 ⇒ 00:12:09.499 Awaish Kumar: It’s only, yeah, three of us.
152 00:12:09.500 ⇒ 00:12:11.610 Sezim Zhenishbekova: Yep, okay.
153 00:12:12.930 ⇒ 00:12:16.540 Awaish Kumar: Oh… So, this is all the…
154 00:12:17.440 ⇒ 00:12:28.940 Awaish Kumar: tables, basically, in that data set. These are all the tables, which can give you some useful information.
155 00:12:28.940 ⇒ 00:12:32.299 Sezim Zhenishbekova: Click on any of them, I want to see the data structure.
156 00:12:33.310 ⇒ 00:12:33.780 Awaish Kumar: I’m, I’m…
157 00:12:33.780 ⇒ 00:12:35.720 Sezim Zhenishbekova: Just regular.
158 00:12:36.080 ⇒ 00:12:40.489 Awaish Kumar: Let me just go to the table which we… which is useful for us, not any…
159 00:12:42.010 ⇒ 00:12:43.919 Awaish Kumar: So, let… let me just…
160 00:12:48.310 ⇒ 00:12:52.969 Awaish Kumar: When? Go to the product summary by transaction.
161 00:12:54.120 ⇒ 00:12:56.110 Awaish Kumar: Yeah, this is the table.
162 00:12:57.670 ⇒ 00:13:03.979 Awaish Kumar: Basically, which I’m referencing to, and which you haven’t seen in the Tableau data source.
163 00:13:05.460 ⇒ 00:13:13.030 Awaish Kumar: So, these are all the fields, and why… when I’m saying that we have new customer count, then you can see this. We have returning customer count.
164 00:13:13.240 ⇒ 00:13:16.179 Awaish Kumar: Similarly, we have fields like new customer.
165 00:13:21.050 ⇒ 00:13:24.479 Awaish Kumar: And… Yeah, this…
166 00:13:27.710 ⇒ 00:13:29.859 Awaish Kumar: Okay, so these are a few fields which…
167 00:13:30.380 ⇒ 00:13:39.170 Awaish Kumar: you can… you have to sum, like… So, new customer count, including offer, this may be… this will give you the… all new customers.
168 00:13:41.460 ⇒ 00:13:54.270 Awaish Kumar: And these are basically offer and the catalyst. These are just basically marketing platforms. So you’re saying the new customers came from this platform, or came from this platform.
169 00:13:54.810 ⇒ 00:14:06.679 Awaish Kumar: Otherwise, if you need just how many new customers I got in a month of December 2025 for 18, if you have to answer that question, what you have to do is, if you have to just select this.
170 00:14:06.930 ⇒ 00:14:09.959 Awaish Kumar: New customer counting clinic offer, summit.
171 00:14:10.400 ⇒ 00:14:25.059 Awaish Kumar: And, some, and then you have filtered from the dates, like, we have a date field. So you have to, basically use the date field to filter the date range in the month of December 2025.
172 00:14:26.170 ⇒ 00:14:28.310 Awaish Kumar: A new custom account, literally custom account.
173 00:14:28.760 ⇒ 00:14:38.669 Awaish Kumar: We have gender, like, we have membership plan, quite a few things here. We have standardized product name. That is what we will be using for, like, if you want customer count, buy product.
174 00:14:39.690 ⇒ 00:14:40.190 Sezim Zhenishbekova: Mmm.
175 00:14:40.190 ⇒ 00:14:51.789 Awaish Kumar: customer count by Semagle at YouTube, or by Tris, or whatever, then you have to use this field, the standardized product name. And all of these will… you will find in Tableau.
176 00:14:52.270 ⇒ 00:14:53.250 Awaish Kumar: The data source side.
177 00:14:55.600 ⇒ 00:14:57.290 Awaish Kumar: Basically, it’s not this.
178 00:14:57.650 ⇒ 00:15:05.439 Awaish Kumar: few things. You can see new customer count, new customer cogs, and then you find a total order count, right?
179 00:15:05.440 ⇒ 00:15:05.910 Sezim Zhenishbekova: Beautiful.
180 00:15:05.910 ⇒ 00:15:15.099 Awaish Kumar: customer revenue, new customer. From new customers, you can find revenue from returning customers, And, LTV, edgesband.
181 00:15:15.370 ⇒ 00:15:20.589 Awaish Kumar: On this date for this product, what was the ad spend, and then…
182 00:15:21.440 ⇒ 00:15:29.380 Awaish Kumar: You… what you have to do is, if you have to calculate some things, like KCAT, like customer acquisition cost, that’s basically…
183 00:15:29.380 ⇒ 00:15:29.900 Sezim Zhenishbekova: Hmm.
184 00:15:29.900 ⇒ 00:15:32.670 Awaish Kumar: All the revenue divided by spend, right?
185 00:15:34.080 ⇒ 00:15:40.069 Awaish Kumar: So… In a month, if… for a product, you spend, like, 500.
186 00:15:40.470 ⇒ 00:15:46.409 Awaish Kumar: Well, sorry, you spent, like, $50,000, and you just acquired 10 new customers, so…
187 00:15:47.220 ⇒ 00:15:50.240 Awaish Kumar: If you divide 550,000 by 10, it’s like…
188 00:15:50.240 ⇒ 00:15:50.910 Sezim Zhenishbekova: 10.
189 00:15:51.270 ⇒ 00:15:51.880 Awaish Kumar: useful.
190 00:15:52.580 ⇒ 00:15:53.559 Awaish Kumar: is your CAC.
191 00:15:53.800 ⇒ 00:15:54.500 Awaish Kumar: So…
192 00:15:54.500 ⇒ 00:15:55.110 Sezim Zhenishbekova: Yes.
193 00:15:55.110 ⇒ 00:16:01.190 Awaish Kumar: And all of this is already available in the dashboard. I’m not saying anything which is net new, right?
194 00:16:01.450 ⇒ 00:16:16.850 Awaish Kumar: Whatever caveats were used, whatever filters were used, whatever fields were used, if you go into one of the dashboards, just, like, kind of reverse engineer back to, okay, this is the dashboard, this is the metric, how it is calculated.
195 00:16:17.050 ⇒ 00:16:20.460 Awaish Kumar: you can all get it, and get this info from Tableau.
196 00:16:21.220 ⇒ 00:16:28.020 Sezim Zhenishbekova: So basically, since in my linear ticket, I have, like, a…
197 00:16:28.400 ⇒ 00:16:43.979 Sezim Zhenishbekova: doc, right, Excel sheet, and he wants me to build the same forecast, but in Tableau, with similar formulas, so I will be creating a new dashboard that’s entirely just called forecasting, based on his analysis, like, VP Finance Analysis.
198 00:16:43.980 ⇒ 00:16:44.840 Awaish Kumar: Okay. So…
199 00:16:44.840 ⇒ 00:16:51.910 Sezim Zhenishbekova: In that scenario, I, for example, will select specific datasets that I specifically need from this list.
200 00:16:52.020 ⇒ 00:16:59.290 Sezim Zhenishbekova: And then… after that, I download it all, and then build myself, or…
201 00:16:59.770 ⇒ 00:17:00.299 Awaish Kumar: No, no, this is.
202 00:17:00.300 ⇒ 00:17:02.109 Sezim Zhenishbekova: How does it… yeah.
203 00:17:02.110 ⇒ 00:17:08.660 Awaish Kumar: I don’t know what he wants, like, what is the requirement? Like, for him, if you have to build a dashboard in Tableau.
204 00:17:08.900 ⇒ 00:17:09.630 Awaish Kumar: Then you don’t.
205 00:17:09.630 ⇒ 00:17:10.040 Sezim Zhenishbekova: Hmm.
206 00:17:10.040 ⇒ 00:17:15.080 Awaish Kumar: it anywhere. You just link the Tableau, with the source.
207 00:17:15.359 ⇒ 00:17:15.900 Awaish Kumar: Right.
208 00:17:16.579 ⇒ 00:17:18.559 Awaish Kumar: And you can already use some of, like…
209 00:17:18.769 ⇒ 00:17:25.109 Awaish Kumar: the list I showed for the data sources, that they’re already connected directly to BigQuery.
210 00:17:25.139 ⇒ 00:17:28.419 Sezim Zhenishbekova: Data is from BigQuery into the Tableau.
211 00:17:28.419 ⇒ 00:17:32.099 Awaish Kumar: And what you have to do is just select that source.
212 00:17:32.299 ⇒ 00:17:36.229 Awaish Kumar: for your dashboard, in the tableau itself, and start using it.
213 00:17:36.789 ⇒ 00:17:43.629 Awaish Kumar: But then there will be a second exercise. If you need a table, which you can see here.
214 00:17:43.899 ⇒ 00:18:01.129 Awaish Kumar: But you can’t see it in Tableau. For example, if that is the case, for example, you need a product channel summary, performance summary. You see this table is here, but you can’t see this in Tableau. Then you have to establish a connection between this table and
215 00:18:01.289 ⇒ 00:18:04.019 Awaish Kumar: And then create a data source in Tableau.
216 00:18:04.119 ⇒ 00:18:05.009 Awaish Kumar: So, that
217 00:18:05.460 ⇒ 00:18:10.659 Awaish Kumar: We’ll just… what will it do? It will just generate a connection directly to this table, so that.
218 00:18:11.380 ⇒ 00:18:13.040 Awaish Kumar: this data in Tableau.
219 00:18:14.200 ⇒ 00:18:15.240 Sezim Zhenishbekova: True. Yes.
220 00:18:15.240 ⇒ 00:18:31.200 Awaish Kumar: That you have to do for new tables, but for existing tables, which are already in Tableau, you don’t have to do this exercise. You can just go on, create a new worksheet, select a data source, start building charts and things.
221 00:18:31.870 ⇒ 00:18:33.050 Sezim Zhenishbekova: Okay.
222 00:18:33.050 ⇒ 00:18:43.689 Awaish Kumar: But you have to figure out how there will be, like, in a dashboard, there will be multiple charts, so how would you go in a worksheet, create multiple charts, then we’ll create a.
223 00:18:43.690 ⇒ 00:18:44.589 Sezim Zhenishbekova: this one.
224 00:18:44.590 ⇒ 00:18:46.610 Awaish Kumar: Add all those charts into the dashboard.
225 00:18:46.850 ⇒ 00:18:50.020 Awaish Kumar: If you need a more, to say.
226 00:18:50.520 ⇒ 00:18:53.970 Awaish Kumar: You have to call Demilati or Henry.
227 00:18:54.100 ⇒ 00:19:13.480 Sezim Zhenishbekova: Okay, okay, I got it. So, basically, so I don’t… so, based on my understanding, I will just read to read what you said, and just confirm if that’s what I understood correctly. So, BigQuery is automatically connected to our Tableau sheet, and whenever I just pull the specific datas.
228 00:19:13.840 ⇒ 00:19:19.999 Sezim Zhenishbekova: like, like, specific data sources, it can automatically start building for me whatever I want.
229 00:19:21.370 ⇒ 00:19:30.799 Sezim Zhenishbekova: Right? So I don’t need to upload anything new to the Tableau, because BigQuery already has all the data set that we have on the project right now in hand.
230 00:19:31.060 ⇒ 00:19:31.910 Awaish Kumar: Egg.
231 00:19:32.340 ⇒ 00:19:36.480 Awaish Kumar: So, all the data which is available to us.
232 00:19:36.800 ⇒ 00:19:38.850 Sezim Zhenishbekova: Right? Yes.
233 00:19:38.880 ⇒ 00:19:40.660 Awaish Kumar: And that is on BigQuery.
234 00:19:40.990 ⇒ 00:19:41.900 Sezim Zhenishbekova: Okay.
235 00:19:41.900 ⇒ 00:19:43.110 Awaish Kumar: That is one thing.
236 00:19:43.260 ⇒ 00:19:45.609 Awaish Kumar: Second thing I will add is that
237 00:19:45.720 ⇒ 00:19:53.410 Awaish Kumar: if you can’t see that the data you are looking for in this dataset does not say that it is not in the BigQuery.
238 00:19:55.550 ⇒ 00:20:04.749 Awaish Kumar: it might be possible to bring it in, it might be possible it is… it lives somewhere else in the query not in this dataset. What you have to do is ask
239 00:20:05.160 ⇒ 00:20:05.730 Awaish Kumar: Awesome.
240 00:20:06.100 ⇒ 00:20:13.870 Awaish Kumar: the channel, ask me, ask Damonade, ask Ashwani. Ashwani’s new, you can ask me and Demonadeh, basically.
241 00:20:14.490 ⇒ 00:20:30.809 Awaish Kumar: Find… if you think your use case is not being met by existing data sources or existing tables, what you have to do is, you can ask us that this is your requirement, you need this data, and you can’t find it here.
242 00:20:32.550 ⇒ 00:20:36.680 Awaish Kumar: that it’s my job to figure out how can I bring it in.
243 00:20:37.470 ⇒ 00:20:44.550 Sezim Zhenishbekova: Let’s then completely, just on one example. For example, I need a cost of goods sold.
244 00:20:45.380 ⇒ 00:20:46.480 Sezim Zhenishbekova: By product.
245 00:20:46.700 ⇒ 00:20:55.519 Sezim Zhenishbekova: For example, let’s assume that right now I still need it, but I just want to see the syncing practices behind it. So, I will, for example, say I need cost of goods
246 00:20:56.050 ⇒ 00:20:58.699 Sezim Zhenishbekova: by product. Cox byproduct.
247 00:20:58.700 ⇒ 00:21:02.860 Awaish Kumar: Yeah, you can see it here. I… Oh, this is gone.
248 00:21:04.730 ⇒ 00:21:07.099 Awaish Kumar: And I can say… date.
249 00:21:08.380 ⇒ 00:21:13.170 Awaish Kumar: Yeah, it’s… it’s not selecting all the fields, I can just show you, like.
250 00:21:14.850 ⇒ 00:21:18.050 Awaish Kumar: So we have date, right? We have product name.
251 00:21:18.690 ⇒ 00:21:19.480 Sezim Zhenishbekova: Yes.
252 00:21:20.460 ⇒ 00:21:23.729 Sezim Zhenishbekova: So… So I need that… Now go to the cogs, right?
253 00:21:24.580 ⇒ 00:21:26.549 Awaish Kumar: Let’s go here. You have the cogs.
254 00:21:26.940 ⇒ 00:21:29.860 Awaish Kumar: So, for a date For our product.
255 00:21:30.510 ⇒ 00:21:31.660 Awaish Kumar: And you’ve got the corpse.
256 00:21:31.660 ⇒ 00:21:32.440 Sezim Zhenishbekova: Hmm.
257 00:21:32.440 ⇒ 00:21:36.070 Awaish Kumar: We have to… Join it for a month, you can say, okay.
258 00:21:36.270 ⇒ 00:21:38.539 Awaish Kumar: From the date, figure out the month.
259 00:21:40.270 ⇒ 00:21:41.969 Awaish Kumar: Product, and then some…
260 00:21:41.970 ⇒ 00:21:50.549 Sezim Zhenishbekova: And then how do you download it, export it, in case I want to work on Excel sheet? Is there a possibility to export it?
261 00:21:51.840 ⇒ 00:21:52.950 Awaish Kumar: Thank you.
262 00:21:52.950 ⇒ 00:21:57.280 Sezim Zhenishbekova: And preview will be good, yes. I want to see if you have all information on Cox.
263 00:21:57.280 ⇒ 00:22:06.600 Awaish Kumar: You can just see it here, number one. Secondly, you can download, like, you can do it in Tableau if you want to do any exploration, but you.
264 00:22:07.350 ⇒ 00:22:11.799 Awaish Kumar: Twin Sheets, what you have to do is maybe run a query and export, like.
265 00:22:13.400 ⇒ 00:22:21.150 Awaish Kumar: For example, I don’t know, I can’t see anything directly. What you have to do is, for example, I can say, select everything.
266 00:22:21.360 ⇒ 00:22:33.900 Awaish Kumar: from this table, for month of December, right? I can say maybe date between December…
267 00:22:33.900 ⇒ 00:22:34.720 Sezim Zhenishbekova: Hmm.
268 00:22:37.110 ⇒ 00:22:38.720 Awaish Kumar: Oh, sorry, and .
269 00:22:40.650 ⇒ 00:22:42.960 Awaish Kumar: 2095, 20, 31.
270 00:22:42.960 ⇒ 00:22:43.760 Sezim Zhenishbekova: True.
271 00:22:43.960 ⇒ 00:22:45.719 Awaish Kumar: data on it.
272 00:22:46.940 ⇒ 00:22:48.879 Awaish Kumar: Right, I can just close.
273 00:22:49.930 ⇒ 00:22:53.660 Awaish Kumar: And once the result is there, you can just go and save results.
274 00:22:54.570 ⇒ 00:22:58.700 Sezim Zhenishbekova: Oh, save results, okay, this is how I export by CSV, okay.
275 00:23:00.500 ⇒ 00:23:01.400 Sezim Zhenishbekova: Okay.
276 00:23:01.450 ⇒ 00:23:02.790 Awaish Kumar: Okay.
277 00:23:02.820 ⇒ 00:23:15.180 Sezim Zhenishbekova: Okay, I got how BigQuery works. How often do you renew the data? Is it automatically tracked, here? Like, I just want to know how data is collected.
278 00:23:16.300 ⇒ 00:23:20.159 Awaish Kumar: Like, if you mean, like, how frequently the data is… is.
279 00:23:20.160 ⇒ 00:23:21.970 Sezim Zhenishbekova: Updated, yes.
280 00:23:21.970 ⇒ 00:23:22.989 Awaish Kumar: It’s every hour.
281 00:23:23.630 ⇒ 00:23:25.260 Sezim Zhenishbekova: It’s every hour. Okay.
282 00:23:26.320 ⇒ 00:23:29.510 Sezim Zhenishbekova: We have it on the BigQuery. Okay.
283 00:23:29.510 ⇒ 00:23:30.020 Awaish Kumar: Budget.
284 00:23:30.020 ⇒ 00:23:30.670 Sezim Zhenishbekova: Sid?
285 00:23:31.040 ⇒ 00:23:50.419 Awaish Kumar: But that said, I… I don’t know, like, for the Tableau stuff, you have to go in Tableau. The data in BigQuery is being refreshed every hour, but it depends on how Tableau is pulling from BigQuery. Either it is pulling by hour, or a day, or whatever.
286 00:23:51.950 ⇒ 00:24:01.420 Awaish Kumar: It completely depends. Tableau might… like, we are… I refresh a table every hour, but it’s possible that Tableau might not get that refreshed data if it is not.
287 00:24:01.420 ⇒ 00:24:02.010 Sezim Zhenishbekova: different.
288 00:24:02.010 ⇒ 00:24:08.310 Awaish Kumar: live the hour. It refreshes every day. You will get new data in Tableau next day, but if it refreshes.
289 00:24:09.000 ⇒ 00:24:11.299 Awaish Kumar: FDR and Tableau as well, then you also
290 00:24:11.690 ⇒ 00:24:20.400 Awaish Kumar: get the data refreshed every hour. So, it depends how these sources are set up. You have to explore in Tableau. You have to go in Tableau.
291 00:24:20.400 ⇒ 00:24:20.770 Sezim Zhenishbekova: Hmm.
292 00:24:20.770 ⇒ 00:24:24.780 Awaish Kumar: Source, and see the schedule for refresh.
293 00:24:25.980 ⇒ 00:24:37.220 Sezim Zhenishbekova: Okay, okay. Thank you, then I will, try to find everything that I need on my own, and then… Guys, if I have any issues, yes?
294 00:24:37.420 ⇒ 00:24:38.500 Awaish Kumar: Suggest?
295 00:24:39.750 ⇒ 00:24:46.649 Awaish Kumar: exercise is that, like, in the ticket, there’s a lot of information, and also there’s a lot of information
296 00:24:46.970 ⇒ 00:24:49.830 Awaish Kumar: spread across the sheet, right? What do you need.
297 00:24:49.830 ⇒ 00:24:50.390 Sezim Zhenishbekova: Yeah.
298 00:24:50.390 ⇒ 00:24:53.130 Awaish Kumar: What you can do is create a single column.
299 00:24:54.480 ⇒ 00:24:57.869 Awaish Kumar: List… start listing out all the metrics you need.
300 00:24:58.230 ⇒ 00:24:59.249 Awaish Kumar: To build your…
301 00:24:59.250 ⇒ 00:24:59.610 Sezim Zhenishbekova: Pardon.
302 00:24:59.610 ⇒ 00:25:00.950 Awaish Kumar: dashboard. For example.
303 00:25:00.950 ⇒ 00:25:01.540 Sezim Zhenishbekova: Yes.
304 00:25:01.540 ⇒ 00:25:21.099 Awaish Kumar: A few from sheets, some from text, and the ticket, or something. If you list down these 10, 20 metrics that you need to build all your charts, then you can share it with me or Tamilade. How can I get this metrics formula, like, calculated? Where can I find this information?
305 00:25:21.370 ⇒ 00:25:25.669 Awaish Kumar: we might… Help you figure out where this data will be coming from.
306 00:25:26.790 ⇒ 00:25:41.250 Sezim Zhenishbekova: So, so you’re saying, like, analyze my Excel sheet, find what kind of metrics I need, build a list, try to find by myself, make a list by finding it on my own, and if I have anything missing, I will check here.
307 00:25:41.390 ⇒ 00:25:45.830 Awaish Kumar: make a list, try to find out on your own, but in parallel. You don’t have to wait.
308 00:25:45.830 ⇒ 00:25:46.380 Sezim Zhenishbekova: Yes.
309 00:25:46.380 ⇒ 00:25:50.109 Awaish Kumar: In parallel, you can just send a message in Slack, right?
310 00:25:50.530 ⇒ 00:25:51.240 Sezim Zhenishbekova: Yes.
311 00:25:51.240 ⇒ 00:26:02.299 Awaish Kumar: asking me and Damilade, that if we know anything, like, Rick will fill it out, like, okay, I… if I know, like, I remember, this data is there. I will just fill it out. We don’t have to then go through a…
312 00:26:02.300 ⇒ 00:26:02.730 Sezim Zhenishbekova: Yeah.
313 00:26:02.730 ⇒ 00:26:09.360 Awaish Kumar: hard exercise. But if I got it demolotic unanswered, then maybe you have to explore yourself.
314 00:26:09.360 ⇒ 00:26:19.060 Sezim Zhenishbekova: Yeah, yeah, yeah, that’s what I was doing all this time, trying to pinpoint, but clearly I… I didn’t know BigQuery good enough.
315 00:26:19.060 ⇒ 00:26:25.309 Awaish Kumar: Yeah, what I’m trying to say is don’t waste your time trying to get everything by your own. Ask the team.
316 00:26:26.580 ⇒ 00:26:33.050 Awaish Kumar: Henry, me, Damilade, Ashwani, we might be able to answer, like, 80% of your questions.
317 00:26:33.560 ⇒ 00:26:43.589 Sezim Zhenishbekova: Okay, okay, sounds good then. I’ll get the data, and then maybe I will schedule a call with the Milada just to know how… is it he or she?
318 00:26:44.330 ⇒ 00:26:44.850 Sezim Zhenishbekova: It’s true.
319 00:26:46.020 ⇒ 00:26:58.029 Sezim Zhenishbekova: he… so basically he… I’ll ask him to do the walkthrough on Tableau. I just want to see how he thinks about Tableau and, how he builds charts and stuff like that. Can we give you a walkthrough?
320 00:26:58.030 ⇒ 00:27:03.569 Awaish Kumar: But again, he can give you a walkthrough of how to start working in Tableau, but then you…
321 00:27:03.670 ⇒ 00:27:07.620 Awaish Kumar: Again, to build those charts, you have to go through this mental exercise that.
322 00:27:07.620 ⇒ 00:27:08.120 Sezim Zhenishbekova: Yes.
323 00:27:08.120 ⇒ 00:27:13.529 Awaish Kumar: In this dashboard, I… okay, I need this chart. For this chart, I need this data.
324 00:27:13.530 ⇒ 00:27:14.720 Sezim Zhenishbekova: Makes sense.
325 00:27:14.720 ⇒ 00:27:15.839 Awaish Kumar: That’s my rigged.
326 00:27:16.050 ⇒ 00:27:24.580 Awaish Kumar: Where is that data? I don’t know, let’s ask someone, if… if anybody knows in the team, right? So maybe you will find answers to those questions.
327 00:27:24.730 ⇒ 00:27:25.440 Sezim Zhenishbekova: Andrew.
328 00:27:25.660 ⇒ 00:27:28.130 Sezim Zhenishbekova: Yes, and also,
329 00:27:28.790 ⇒ 00:27:35.629 Sezim Zhenishbekova: It’s probably… okay, it’s… I think it’s a question to Demilada. I just was wondering, for example, in Excel, it’s so easy to tweak numbers.
330 00:27:35.780 ⇒ 00:27:48.730 Sezim Zhenishbekova: But Tableau doesn’t really allow you to alter the datasets. For example, if I want to experiment, right, the forecasting is all about experimenting, changing numbers, seeing the best, worst, and mid scenarios.
331 00:27:48.790 ⇒ 00:27:56.450 Sezim Zhenishbekova: And then when I want to see those scenarios, I want them to be able to tweak the dashboard, and…
332 00:27:56.680 ⇒ 00:28:04.330 Sezim Zhenishbekova: does, like, can user alter BigQuery database directly, or how does it work in that way?
333 00:28:04.560 ⇒ 00:28:06.980 Awaish Kumar: I don’t know, like, I don’t think.
334 00:28:06.980 ⇒ 00:28:07.640 Sezim Zhenishbekova: Yeah.
335 00:28:07.860 ⇒ 00:28:11.610 Awaish Kumar: I don’t think you can alter data in either BigQuery. In BigQuery.
336 00:28:11.610 ⇒ 00:28:12.030 Sezim Zhenishbekova: Yeah.
337 00:28:12.030 ⇒ 00:28:13.819 Awaish Kumar: Create a table, like, once.
338 00:28:14.450 ⇒ 00:28:17.720 Awaish Kumar: If you have some data, you can create it from Excel sheet and put it.
339 00:28:17.720 ⇒ 00:28:18.300 Sezim Zhenishbekova: Yeah.
340 00:28:18.300 ⇒ 00:28:22.040 Awaish Kumar: If you want, then connect it with Tableau and show it. But for.
341 00:28:22.040 ⇒ 00:28:22.640 Sezim Zhenishbekova: Yeah.
342 00:28:22.640 ⇒ 00:28:27.569 Awaish Kumar: or, like, if you want to modify things, I think the best place is Excel.
343 00:28:29.360 ⇒ 00:28:30.230 Sezim Zhenishbekova: Yeah.
344 00:28:30.760 ⇒ 00:28:36.780 Sezim Zhenishbekova: So Tableau is visually pleasant, but we are giving up that flexibility then. Okay.
345 00:28:36.950 ⇒ 00:28:50.409 Awaish Kumar: But yeah, like, for Tableau is basically, we are building… it’s not for, like, running these kind of experiments. Tableau is basically, once you run through all the experiments, you build something.
346 00:28:50.710 ⇒ 00:28:53.970 Awaish Kumar: It’s to showcase to the executives, like.
347 00:28:54.360 ⇒ 00:28:57.650 Awaish Kumar: That’s how the forecast looks like.
348 00:28:58.540 ⇒ 00:28:59.750 Sezim Zhenishbekova: Yeah, yeah.
349 00:29:00.140 ⇒ 00:29:17.380 Sezim Zhenishbekova: Okay, sounds good then. I think I also need to build myself Excel sheet, like, just a backup to structure everything, and then once I have the backbone transfer to Tableau, because Tableau is… has, like, much bigger range of data. So yeah, okay.
350 00:29:17.380 ⇒ 00:29:29.630 Awaish Kumar: Yeah, for forecasting, I don’t know if you really need… like, do you really… like, for experiments, I understand that you need to tweak numbers, but real thing, like, you might not… you might not need to, like.
351 00:29:29.750 ⇒ 00:29:32.930 Awaish Kumar: manually update numbers. You might have to come up with a logic.
352 00:29:33.620 ⇒ 00:29:37.459 Sezim Zhenishbekova: That will do the Tableau thing, yeah. Just, like, input numbers.
353 00:29:37.460 ⇒ 00:29:40.020 Awaish Kumar: And then it automatically affects the…
354 00:29:40.270 ⇒ 00:29:45.430 Sezim Zhenishbekova: analysis based on the inputs that manager makes, right? Yeah, okay.
355 00:29:45.550 ⇒ 00:29:49.059 Sezim Zhenishbekova: Okay, sounds good. Thank you so much for your time. Yes.
356 00:29:49.610 ⇒ 00:30:02.990 Awaish Kumar: Sorry, with that… with that thing, I… I think what you can do is build in, like, in the Tableau, you have a possibility to build custom fields. For example… Yes. Or custom query. You can write your own query, if you don’t think.
357 00:30:03.250 ⇒ 00:30:05.400 Awaish Kumar: Table is doing whatever you want.
358 00:30:05.980 ⇒ 00:30:19.030 Awaish Kumar: Because you can build calculated feeds. So, for example, my revenue is something that’s real values, but you need some… for projections, you maybe need to… okay, let’s multiply it by 1.5, and some do something gets
359 00:30:19.270 ⇒ 00:30:23.790 Awaish Kumar: get some different numbers, or things like that. That you can do internally.
360 00:30:24.340 ⇒ 00:30:28.030 Awaish Kumar: But not exactly can’t modify the individual values.
361 00:30:28.750 ⇒ 00:30:41.779 Sezim Zhenishbekova: Yeah, yeah. Okay, sounds good. Yeah, I think it’s good for accuracy, just to not alter anything, but yeah, makes sense. Okay, I will be pinning you throughout the day today, and think.
362 00:30:41.780 ⇒ 00:30:45.399 Awaish Kumar: Okay, yeah, maybe we can take 2 minutes to introduce each other now.
363 00:30:45.400 ⇒ 00:31:01.570 Sezim Zhenishbekova: Yeah, yes. So, my name is Sazim, and I’m originally from Kyrgyzstan, but I moved to the U.S. three years ago to get my master’s degree, in New York at Fordham University, where Robert is getting his another law degree.
364 00:31:01.570 ⇒ 00:31:10.460 Sezim Zhenishbekova: We met through close connections, and he just proposed, like, if I want to give it a try to, work with him, basically, in the firm.
365 00:31:10.470 ⇒ 00:31:13.220 Sezim Zhenishbekova: And that’s how I appear. How about you?
366 00:31:13.530 ⇒ 00:31:17.150 Awaish Kumar: So, my name is Avesh Kumar. I…
367 00:31:17.260 ⇒ 00:31:21.999 Awaish Kumar: been… I was working as a data engineer for more than 9 years. I…
368 00:31:23.470 ⇒ 00:31:27.459 Awaish Kumar: Right now, I’m working with Brainforge for a year, almost.
369 00:31:29.240 ⇒ 00:31:32.700 Awaish Kumar: Yeah, I do data engineering, analytics engineering.
370 00:31:32.880 ⇒ 00:31:37.540 Awaish Kumar: I have worked in Europe, I have worked in Canada, now.
371 00:31:39.490 ⇒ 00:31:42.570 Sezim Zhenishbekova: Where? Where are you based in?
372 00:31:42.570 ⇒ 00:31:43.400 Awaish Kumar: Pakistan.
373 00:31:43.890 ⇒ 00:31:45.700 Sezim Zhenishbekova: Pakistan. Vanish.
374 00:31:45.700 ⇒ 00:31:50.700 Awaish Kumar: There’s my home country, now I’m in Pakistan, yeah, that’s all.
375 00:31:51.030 ⇒ 00:31:53.380 Awaish Kumar: So, I have… yeah, I don’t know.
376 00:31:53.820 ⇒ 00:31:58.489 Awaish Kumar: Kyrgyzstan, like, there are many people who go to Kyrgyzstan, basically, from Pakistan to study.
377 00:31:58.970 ⇒ 00:32:03.830 Sezim Zhenishbekova: Yeah, they have medical school, and a lot of Pakistan people come to do that, yeah.
378 00:32:04.280 ⇒ 00:32:09.590 Awaish Kumar: Yeah, my few… like, one of my friends was there also for education.
379 00:32:10.850 ⇒ 00:32:15.069 Awaish Kumar: I’ve visited Azerbaijan, but I never had a chance to visit Kyrgyzstan yet.
380 00:32:15.070 ⇒ 00:32:23.450 Sezim Zhenishbekova: Azerbaijan, Bali is there, right? Yeah, it’s really nice. Baku, oh yeah, Baku. That’s really good. I have never been there, too.
381 00:32:23.630 ⇒ 00:32:31.169 Sezim Zhenishbekova: That’s cool. How do you like working remote? Like, have you been working remote for 8 straight years throughout your experience?
382 00:32:31.170 ⇒ 00:32:35.769 Awaish Kumar: I have been working remote, like, for 7 years at least, so…
383 00:32:35.770 ⇒ 00:32:38.759 Sezim Zhenishbekova: Wow. You’re, like, pro. Yeah.
384 00:32:38.930 ⇒ 00:32:44.949 Awaish Kumar: First turn off, first and half… first and half year, like, I basically work in a…
385 00:32:45.120 ⇒ 00:32:53.299 Awaish Kumar: on-site company. After that, I started remote. And then, since then, I’ve been, like, remote, or hybrid, or something like that.
386 00:32:54.510 ⇒ 00:32:55.720 Awaish Kumar: structures, yeah.
387 00:32:55.980 ⇒ 00:32:57.679 Sezim Zhenishbekova: Nice, that’s awesome.
388 00:32:57.860 ⇒ 00:33:08.660 Sezim Zhenishbekova: Okay, that’s cool. Yeah, I’m still getting used to working remotely. Mostly I was on in person, or, like, maximum 6 to 5 years of small remote jobs.
389 00:33:08.720 ⇒ 00:33:20.680 Sezim Zhenishbekova: But other than that, yeah, this is my first real experience. It’s cool how teams, like, set everything up to make sure that everyone is aligned, is the goal. Yeah.
390 00:33:20.680 ⇒ 00:33:26.750 Awaish Kumar: Yeah, like, it’s, like, basically, in the remote, you’ll just, like, write down more.
391 00:33:27.150 ⇒ 00:33:30.510 Awaish Kumar: ask on Slack, like, you don’t have to wait, like…
392 00:33:30.970 ⇒ 00:33:33.900 Awaish Kumar: For things to lighten, because you’re not sitting in for…
393 00:33:34.180 ⇒ 00:33:36.990 Awaish Kumar: Onside of everyone, so you can’t just.
394 00:33:36.990 ⇒ 00:33:37.500 Sezim Zhenishbekova: Yeah.
395 00:33:37.500 ⇒ 00:33:41.700 Awaish Kumar: Like, something will flow in, or some things like that, but you have
396 00:33:42.120 ⇒ 00:33:45.100 Awaish Kumar: You have to specifically send a message.
397 00:33:45.420 ⇒ 00:33:48.049 Awaish Kumar: I’m stuck, I need this, or something like that.
398 00:33:48.430 ⇒ 00:33:48.990 Sezim Zhenishbekova: Yeah.
399 00:33:48.990 ⇒ 00:33:55.440 Awaish Kumar: somebody has experience with this thing or that, so it’s just, like, you just send messages in Slack, and…
400 00:33:56.360 ⇒ 00:33:57.000 Awaish Kumar: And, like.
401 00:33:57.000 ⇒ 00:33:57.730 Sezim Zhenishbekova: Yes.
402 00:33:58.710 ⇒ 00:34:07.140 Sezim Zhenishbekova: Yeah, I get that. Thank you. Okay, that’s awesome, nice to meet you, and looking forward to work from Eden together.
403 00:34:07.970 ⇒ 00:34:09.350 Awaish Kumar: Yeah, nice to meet you too.
404 00:34:09.510 ⇒ 00:34:10.800 Awaish Kumar: Thank you.
405 00:34:10.929 ⇒ 00:34:12.339 Sezim Zhenishbekova: Thank you, bye.