Meeting Title: Sezim - Demilade - Eden sync Date: 2025-12-11 Meeting participants: Demilade Agboola, Sezim Zhenishbekova
WEBVTT
1 00:01:40.410 ⇒ 00:01:42.370 Sezim Zhenishbekova: Hi there, Melana, how are you?
2 00:01:43.210 ⇒ 00:01:45.180 Demilade Agboola: Hi, Shazam, I’m good, how are you?
3 00:01:45.180 ⇒ 00:01:52.419 Sezim Zhenishbekova: Good, thank you. Thank you so much for taking your time to walk through with me, Establu.
4 00:01:54.340 ⇒ 00:01:59.959 Demilade Agboola: That’s no problem, do you also have access to Tableau?
5 00:02:00.620 ⇒ 00:02:09.860 Sezim Zhenishbekova: So, yes, I got it, I got Robert’s account through his account, and then I have, Google Cloud as well, there’s BigQuery.
6 00:02:10.570 ⇒ 00:02:11.190 Demilade Agboola: Okay.
7 00:02:11.320 ⇒ 00:02:14.369 Sezim Zhenishbekova: For my, for… with Eden’s account.
8 00:02:15.470 ⇒ 00:02:21.269 Demilade Agboola: Okay, so that’s great. Give me one second, I’m trying to sign in.
9 00:02:21.730 ⇒ 00:02:25.070 Sezim Zhenishbekova: Yeah. Where are you based in?
10 00:02:25.600 ⇒ 00:02:40.469 Demilade Agboola: So I’m based in Malta, but I visit the US quite often, because my girlfriend lives here, so I’m actually currently in the US as we speak. But I’m moving… I’m traveling back to Malta, on Saturday. So from next week, I’ll be back in Europe.
11 00:02:40.790 ⇒ 00:02:44.429 Sezim Zhenishbekova: Nice, nice. Yeah, Malker is so beautiful, very true.
12 00:02:44.430 ⇒ 00:02:46.000 Demilade Agboola: Are you fair?
13 00:02:46.000 ⇒ 00:02:49.650 Sezim Zhenishbekova: I haven’t been there, but I lived in the Netherlands for a bit, and then…
14 00:02:49.650 ⇒ 00:02:50.480 Demilade Agboola: Oh, nice.
15 00:02:50.480 ⇒ 00:02:55.170 Sezim Zhenishbekova: My friends went there… went to Malta for the spring break, and they… Oh, nice!
16 00:02:55.700 ⇒ 00:02:57.400 Sezim Zhenishbekova: Yeah, they really love it.
17 00:02:57.950 ⇒ 00:02:59.270 Demilade Agboola: What are you based on?
18 00:02:59.500 ⇒ 00:03:02.139 Sezim Zhenishbekova: I’m based in the US now. Okay.
19 00:03:02.140 ⇒ 00:03:04.480 Demilade Agboola: Makes sense, yeah. What city? New York?
20 00:03:04.480 ⇒ 00:03:08.739 Sezim Zhenishbekova: Yes, yes, I’m in the same seating as Robert, basically.
21 00:03:08.980 ⇒ 00:03:12.730 Demilade Agboola: Okay, that’s cool. I’ve actually been to New York a couple times.
22 00:03:12.730 ⇒ 00:03:13.960 Sezim Zhenishbekova: Do you like it?
23 00:03:15.910 ⇒ 00:03:17.150 Demilade Agboola: Yes, no.
24 00:03:18.720 ⇒ 00:03:29.180 Demilade Agboola: Yes, yes in the sense that, like, it’s definitely a pretty busy city, there’s a lot of stuff to do, it’s… you can obviously go around to restaurants, activities.
25 00:03:30.110 ⇒ 00:03:32.899 Demilade Agboola: maybe shores. Like, there’s just a lot of things going on in New York.
26 00:03:33.160 ⇒ 00:03:36.029 Demilade Agboola: My issue with New York is…
27 00:03:36.240 ⇒ 00:03:52.360 Demilade Agboola: don’t like busy cities. So London, New York, those sort of cities, I don’t really enjoy them. Nigeria has one, because I’m originally from Nigeria. It’s called Lagos. I don’t like Lagos. I, like, again, I grew up in a smaller sort of city with a slower pace.
28 00:03:52.430 ⇒ 00:04:02.549 Demilade Agboola: So living in a city that’s really fast isn’t for me. But I could visit, I could definitely go there for, like, a week or two, have some fun, like, every day, and I don’t think I could do it.
29 00:04:02.900 ⇒ 00:04:09.189 Sezim Zhenishbekova: Yeah, I get that. Yeah, for me, it took some time to get used to it, and I tried to avoid busy places.
30 00:04:09.480 ⇒ 00:04:17.049 Sezim Zhenishbekova: But sometimes it still can get overwhelming, especially now, there’s so many tourists coming in to see the Christmas lights.
31 00:04:17.760 ⇒ 00:04:18.310 Demilade Agboola: Ball.
32 00:04:18.660 ⇒ 00:04:19.740 Sezim Zhenishbekova: So, yeah.
33 00:04:19.899 ⇒ 00:04:20.600 Sezim Zhenishbekova: Sweet.
34 00:04:20.600 ⇒ 00:04:21.300 Demilade Agboola: come up.
35 00:04:21.640 ⇒ 00:04:32.490 Sezim Zhenishbekova: Yeah, but I love nature in general, because I grew up in Kyrgyzstan originally, and I grew up in a small town with mountains everywhere. It’s 3,000 meters above sea level.
36 00:04:32.610 ⇒ 00:04:34.440 Sezim Zhenishbekova: So it’s very…
37 00:04:34.770 ⇒ 00:04:46.800 Sezim Zhenishbekova: Yeah, yeah. I mean, there were… there was, like, too many mountains in the sides, but we could still see snow on the… during… even during the summer on the top of the… some of the mountains.
38 00:04:47.030 ⇒ 00:04:49.840 Sezim Zhenishbekova: So it was very hardcore, so yeah.
39 00:04:50.940 ⇒ 00:04:54.929 Demilade Agboola: Yeah, that definitely is very, very different from New York. Very, very…
40 00:04:54.930 ⇒ 00:04:59.350 Sezim Zhenishbekova: Yeah, yeah, yeah, it’s my first time living in a big city, like, and…
41 00:04:59.350 ⇒ 00:05:00.060 Demilade Agboola: Damn.
42 00:05:00.060 ⇒ 00:05:01.659 Sezim Zhenishbekova: It’s pretty cool.
43 00:05:01.660 ⇒ 00:05:15.120 Demilade Agboola: Yeah, funny enough, I have a friend who… he used to live in the UK. When he went to New York for his master’s, he was like, no, I have to live here. It was just for him, I guess, so… different people, different things.
44 00:05:15.320 ⇒ 00:05:19.219 Sezim Zhenishbekova: I agree. You’d rather hate it or love it, I think, in your case.
45 00:05:19.220 ⇒ 00:05:20.130 Demilade Agboola: Exactly.
46 00:05:20.280 ⇒ 00:05:21.470 Demilade Agboola: Very annoying.
47 00:05:21.470 ⇒ 00:05:23.999 Sezim Zhenishbekova: John should kind of… yeah.
48 00:05:26.510 ⇒ 00:05:28.610 Demilade Agboola: Okay, so…
49 00:05:30.320 ⇒ 00:05:34.900 Demilade Agboola: where do we start? What, what, what, what do you want, like, where do we start? Like, what things…
50 00:05:34.900 ⇒ 00:05:44.320 Sezim Zhenishbekova: So basically, I was tasked to work with Eden, specifically set up financial forecasting models.
51 00:05:44.320 ⇒ 00:05:44.980 Demilade Agboola: Okay.
52 00:05:44.980 ⇒ 00:06:01.060 Sezim Zhenishbekova: And then in the ticket, there’s, like, an Excel sheet with specific forecasting, like, profitability metrics, some of the ads expenses metrics, some retention rate metrics that I need to use to forecast for 48
53 00:06:01.060 ⇒ 00:06:20.819 Sezim Zhenishbekova: Once, ongoing. So, on Tableau, I needed to recreate what he has on Excel to Tableau, and I realized that, like, I didn’t really have access to BigQuery back then, I still needed to get the reissued, and I started working with Tableau, by diving deep into things, but then I was…
54 00:06:20.910 ⇒ 00:06:29.710 Sezim Zhenishbekova: like, I couldn’t understand where I get the data, how I put it in, how I include it, because on my own, I used to upload my own manually.
55 00:06:29.830 ⇒ 00:06:33.529 Sezim Zhenishbekova: But here, we pull it from the BigQuery, right?
56 00:06:34.480 ⇒ 00:06:35.390 Demilade Agboola: Yes.
57 00:06:35.560 ⇒ 00:06:38.719 Demilade Agboola: So let me try to give some context.
58 00:06:39.200 ⇒ 00:06:48.800 Sezim Zhenishbekova: ask you if you could just show me, like, your thinking process behind one dashboard that you have built, how you, like, put certain data, and…
59 00:06:48.960 ⇒ 00:06:51.590 Sezim Zhenishbekova: Aha, like, all that, just like…
60 00:06:51.810 ⇒ 00:06:57.889 Sezim Zhenishbekova: Steps that you did, and what kind of vision you like for dashboards, when you think of it?
61 00:06:58.690 ⇒ 00:07:05.349 Demilade Agboola: Okay, so… I haven’t necessarily built out any dashboards. I’ve worked hand-in-hand with, Annie
62 00:07:05.670 ⇒ 00:07:22.789 Demilade Agboola: who used to be here before, as well as Henry, to build dashboards. I can kind of walk you through what they were giving me, and like, oh, because I just build the models, like, hey, this is not available, you can make it into Tableau, which is kind of… if… if you need my assistance, that’s kind of what I do.
63 00:07:24.570 ⇒ 00:07:27.730 Demilade Agboola: Let me just kind of show you what BigQuery is. So BigQuery is a warehouse.
64 00:07:28.170 ⇒ 00:07:36.610 Demilade Agboola: Oof, that’s a lot going on here. Bigquery’s a warehouse where we keep our, like, data that comes from different spaces, right? So…
65 00:07:37.970 ⇒ 00:07:42.960 Demilade Agboola: the production data… is the,
66 00:07:43.060 ⇒ 00:07:45.379 Demilade Agboola: the RM tool that they use for a lot of stuff.
67 00:07:48.010 ⇒ 00:07:53.730 Demilade Agboola: Belie, AirBytes, like, these are just… there’s a bunch of stuff, like, anything. Google Ads, GTM,
68 00:07:55.280 ⇒ 00:07:56.889 Demilade Agboola: All the data comes in here.
69 00:07:57.270 ⇒ 00:08:01.780 Demilade Agboola: And Shippo is their tracking, shipment tracking software.
70 00:08:01.920 ⇒ 00:08:11.159 Demilade Agboola: So all data comes in here, and so what we start to do is we start to create models, where we start putting different things together.
71 00:08:11.740 ⇒ 00:08:22.870 Demilade Agboola: where we’re like, okay, this is the order, join it to Shippo to see whether it was delivered, or when it was delivered, join it to this to see, like, we just put everything together.
72 00:08:23.070 ⇒ 00:08:26.570 Demilade Agboola: Once we start to put those things together, we now say, hey.
73 00:08:26.710 ⇒ 00:08:41.260 Demilade Agboola: data is now available in BigQuery for you to use for your dashboards. So now someone can start to use it and say, hey, this is when this order was placed, this is when it was shipped, this is when it was delivered, because we’ve been able to see that in BigQuery.
74 00:08:41.260 ⇒ 00:08:41.840 Sezim Zhenishbekova: Hmm.
75 00:08:41.840 ⇒ 00:08:44.659 Demilade Agboola: But that’s kind of what happens here.
76 00:08:44.760 ⇒ 00:08:48.069 Demilade Agboola: And most of the things that we use as in this
77 00:08:48.310 ⇒ 00:08:55.980 Demilade Agboola: folder, it’s called a schema for Prodivity Maths. So, this is where a lot of the models exist.
78 00:08:57.030 ⇒ 00:09:04.420 Demilade Agboola: So, we have… one of the core models that exists is something called broad…
79 00:09:05.440 ⇒ 00:09:09.140 Demilade Agboola: product sales by transaction. So, basically.
80 00:09:09.690 ⇒ 00:09:16.190 Demilade Agboola: What that is, if you want to see it in query, so we can kind of look at it.
81 00:09:17.510 ⇒ 00:09:22.169 Demilade Agboola: Products sold by transactions.
82 00:09:22.310 ⇒ 00:09:30.990 Demilade Agboola: is basically, so for every day, and for every standardized product name, so, or Altima, in Oxidol tablets.
83 00:09:31.120 ⇒ 00:09:34.349 Demilade Agboola: Injectable semi, whatever the product is.
84 00:09:34.570 ⇒ 00:09:38.860 Demilade Agboola: We have an aggregation of how many people bought
85 00:09:39.300 ⇒ 00:09:45.490 Demilade Agboola: how many orders were placed against that product name? So, based off the membership type, the gender.
86 00:09:45.690 ⇒ 00:09:50.510 Demilade Agboola: Whether they were a returning customer or a new customer,
87 00:09:51.800 ⇒ 00:09:56.769 Demilade Agboola: This is… yeah. If they were a new customer, from the offer.
88 00:09:56.930 ⇒ 00:09:58.709 Demilade Agboola: So the offer is there…
89 00:10:00.540 ⇒ 00:10:08.560 Demilade Agboola: or used to be their, you know, still is. Not, you know, used to be, but now it’s Catalyst, one of the affiliate programs to get people in.
90 00:10:08.910 ⇒ 00:10:16.439 Demilade Agboola: So the offer… new customers from Catalyst, new customers…
91 00:10:16.570 ⇒ 00:10:26.150 Demilade Agboola: Including, like, basically, we have all that. So we just, every single day, we’re trying to break down where these new users are coming from, so whether it’s a returning order account.
92 00:10:27.780 ⇒ 00:10:30.650 Demilade Agboola: And all of that. So we start to do that.
93 00:10:32.420 ⇒ 00:10:34.820 Demilade Agboola: Watch what the revenue was.
94 00:10:35.750 ⇒ 00:10:38.150 Demilade Agboola: And what the COGS was.
95 00:10:39.630 ⇒ 00:10:46.699 Demilade Agboola: what, total revenue also, you know, this is for returning customer.
96 00:10:46.840 ⇒ 00:10:51.220 Demilade Agboola: So, instead, we’re looking at, like, total revenue.
97 00:10:52.060 ⇒ 00:10:56.290 Demilade Agboola: Cogs, LTV… Masha’all.
98 00:10:56.910 ⇒ 00:10:58.360 Demilade Agboola: spend…
99 00:11:01.980 ⇒ 00:11:07.130 Demilade Agboola: It’s over, because… Of our things, basically. So we just basically have a bunch of applications.
100 00:11:07.230 ⇒ 00:11:11.810 Demilade Agboola: And then we make that available, and that’s what powers a lot of these dashboards.
101 00:11:12.480 ⇒ 00:11:20.160 Demilade Agboola: And… In… in here. Now, not all, obviously, but lots of them, so, like, if you see this…
102 00:11:20.460 ⇒ 00:11:22.090 Demilade Agboola: dashboard here.
103 00:11:22.870 ⇒ 00:11:27.560 Demilade Agboola: A lot of it is powered by what I just showed you. So every day.
104 00:11:27.850 ⇒ 00:11:36.160 Demilade Agboola: we now start to roll it up for the previously. So this is the CEO’s dashboard, or one of the co-founders’ dashboards for… his name is Josh.
105 00:11:36.370 ⇒ 00:11:42.980 Demilade Agboola: So every day, we roll up by, you know, injectable summer. The revenue yesterday was this.
106 00:11:43.100 ⇒ 00:11:46.909 Demilade Agboola: Out of all the revenue, this is what was the new order revenue.
107 00:11:47.170 ⇒ 00:11:53.129 Demilade Agboola: So they had, you know, a total order of 590… 429 injectable summer.
108 00:11:53.380 ⇒ 00:12:10.590 Demilade Agboola: And then 80 of them were new customers, 4 of them were pending. The total revenue over the last 30 days is 18,000. Like, basically what… so, if you’re doing it for every day, you just roll up for the last 30 days, and you can’t… you will get these numbers.
109 00:12:11.990 ⇒ 00:12:16.949 Demilade Agboola: So, those numbers that you need, like, in terms of, like, customer metrics, they exist.
110 00:12:17.790 ⇒ 00:12:21.430 Demilade Agboola: In… in love times, in that, like, table.
111 00:12:21.660 ⇒ 00:12:32.310 Demilade Agboola: Now, obviously, not everything is there, so we might need to start to make some adjustments, or create new models for you. So if, for instance, you want to see
112 00:12:33.380 ⇒ 00:12:39.329 Demilade Agboola: A certain metric by day, or a certain metric by whatever, you know, by month, or by week.
113 00:12:39.880 ⇒ 00:12:45.250 Demilade Agboola: We don’t have that available. We… that’s when we will model and available for you to utilize.
114 00:12:45.250 ⇒ 00:12:56.919 Sezim Zhenishbekova: I have all the data, and I’m, like, writing it. I can’t, like, directly model it in Tableau, right? By assigning certain formula within Tableau itself? Just like Excel?
115 00:12:57.990 ⇒ 00:13:02.920 Demilade Agboola: It depends. You can actually do certain formulas in Tableau.
116 00:13:03.040 ⇒ 00:13:08.430 Demilade Agboola: But for standardized… How do I put it? For standardized…
117 00:13:10.060 ⇒ 00:13:12.980 Demilade Agboola: standardized form, we try to ensure that, like.
118 00:13:13.140 ⇒ 00:13:15.200 Demilade Agboola: We model as much as possible.
119 00:13:15.500 ⇒ 00:13:31.099 Demilade Agboola: So we can give you the raw numbers, then maybe if you want to apply a formula to, you know, calculate what it would look like in the next 10 days, or over the next 30 days, you could, or 2 months, or 3 months, you can use your formulas yourself to do that. But in terms of, like, having
120 00:13:31.370 ⇒ 00:13:35.010 Demilade Agboola: a track of history, and saying, okay, so this is the…
121 00:13:35.380 ⇒ 00:13:42.479 Demilade Agboola: number of returning customers over the last 2 months for, you know, whatever product. The number of…
122 00:13:42.690 ⇒ 00:13:51.459 Demilade Agboola: this is how much revenue or sale, you know, volume of sale that was done over the last 3 months. We tend to do that, so that we now give you that data.
123 00:13:51.460 ⇒ 00:13:55.449 Sezim Zhenishbekova: Whatever forecasting or whatever analysis you want to do off of that, you can do.
124 00:13:56.130 ⇒ 00:13:57.080 Sezim Zhenishbekova: Okay.
125 00:13:58.870 ⇒ 00:14:09.590 Sezim Zhenishbekova: Okay, now I understand. So, in this function, what kind… what thing did you code it, like, what you… what kind of model you built for this product revenue snapshot?
126 00:14:10.780 ⇒ 00:14:16.659 Demilade Agboola: So for this, so if you click in here for the dashboard, you can actually see the data sources.
127 00:14:16.830 ⇒ 00:14:17.649 Sezim Zhenishbekova: Hmm. Go for it.
128 00:14:18.570 ⇒ 00:14:22.320 Demilade Agboola: it’s basically fact transactions, so fact transactions…
129 00:14:23.180 ⇒ 00:14:26.110 Demilade Agboola: Is… if you check in here…
130 00:14:30.220 ⇒ 00:14:31.879 Demilade Agboola: The sections…
131 00:14:39.350 ⇒ 00:14:45.219 Demilade Agboola: For preview, it’s… Backtransactions is just the details of every single order that happens.
132 00:14:45.690 ⇒ 00:14:47.170 Sezim Zhenishbekova: In Eden.
133 00:14:47.170 ⇒ 00:14:51.440 Demilade Agboola: So… the order date, the ID, the transaction ID,
134 00:14:52.860 ⇒ 00:14:55.549 Demilade Agboola: Bask order ID, the order number.
135 00:14:55.740 ⇒ 00:14:59.530 Demilade Agboola: The treatment’s initial date if they exist, treatment ID.
136 00:15:01.240 ⇒ 00:15:07.349 Demilade Agboola: So you can kind of see almost the last UTM contents before the purchase, the last UTM term.
137 00:15:09.190 ⇒ 00:15:11.099 Demilade Agboola: Yeah, so basically this is…
138 00:15:12.190 ⇒ 00:15:16.859 Sezim Zhenishbekova: You build new documents, you build new CSV files, queries.
139 00:15:17.120 ⇒ 00:15:27.319 Sezim Zhenishbekova: based on the data sources in the format that people want, so they can take your modeled dashboard, or modeled data, and use it in Tableau.
140 00:15:27.900 ⇒ 00:15:47.069 Demilade Agboola: Yes, exactly. Okay. So, if you want to see a certain level of granularity, so if you say, hey, I don’t need to see every single order, because it’s not useful for me, I just need to see the orders by day, like, count of orders by day, instead, cool, we can help you roll it up, the orders by day, by the, the product name.
141 00:15:48.150 ⇒ 00:15:56.939 Demilade Agboola: whatever, like, or by state, or product name by… and states together. So for every day, show me the product name… the products by state.
142 00:15:57.690 ⇒ 00:15:58.350 Demilade Agboola: volume.
143 00:15:58.450 ⇒ 00:16:05.340 Demilade Agboola: So we can do… like, all those kind of things, right? So, if we start having those conversations around, oh, okay, so we might need a new model.
144 00:16:05.710 ⇒ 00:16:15.009 Demilade Agboola: we’ll build out for you. Some of them probably will exist, and we can say, oh, it’s already available here, what you just need to do is aggregate this table, and you’ll get it.
145 00:16:15.240 ⇒ 00:16:17.859 Demilade Agboola: One of our cases, we might have to do something in there for you.
146 00:16:20.000 ⇒ 00:16:20.870 Sezim Zhenishbekova: Okay.
147 00:16:21.050 ⇒ 00:16:29.390 Sezim Zhenishbekova: Perfect. And then, so here, for example, I want to pull, like, specific data, specific columns.
148 00:16:29.860 ⇒ 00:16:32.839 Sezim Zhenishbekova: How can I do that if I want to do that?
149 00:16:34.830 ⇒ 00:16:36.869 Demilade Agboola: specific columns.
150 00:16:37.060 ⇒ 00:16:38.340 Sezim Zhenishbekova: Yeah.
151 00:16:39.480 ⇒ 00:16:43.139 Demilade Agboola: Let’s see… do you have Tableau Desktop?
152 00:16:43.990 ⇒ 00:16:44.690 Sezim Zhenishbekova: No.
153 00:16:44.800 ⇒ 00:16:46.140 Sezim Zhenishbekova: Should I get one?
154 00:16:46.820 ⇒ 00:16:49.689 Demilade Agboola: Yeah, so this is Tableau Desktop, it’s the app.
155 00:16:51.840 ⇒ 00:16:53.330 Demilade Agboola: You will need to sign in.
156 00:16:53.550 ⇒ 00:16:58.639 Demilade Agboola: People say it’s much better to build with desktop, like the cloud, yeah.
157 00:17:03.200 ⇒ 00:17:04.240 Sezim Zhenishbekova: App Store.
158 00:18:02.670 ⇒ 00:18:06.659 Sezim Zhenishbekova: Okay. Other than that,
159 00:18:09.700 ⇒ 00:18:13.279 Sezim Zhenishbekova: So you run such a query, right? Because all the…
160 00:18:15.780 ⇒ 00:18:21.499 Demilade Agboola: I’m just… I just saw that we have order discounts, and I just realized that I sent something to the finance team.
161 00:18:21.710 ⇒ 00:18:22.300 Sezim Zhenishbekova: Mmm.
162 00:18:22.300 ⇒ 00:18:25.439 Demilade Agboola: And I didn’t include this.
163 00:18:26.380 ⇒ 00:18:28.180 Demilade Agboola: So that’s what I want to speak to you.
164 00:18:29.740 ⇒ 00:18:32.250 Demilade Agboola: So, this is,
165 00:18:39.490 ⇒ 00:18:43.059 Demilade Agboola: Sorry, I just… I scripted, so I’d also get,
166 00:18:46.770 ⇒ 00:18:52.240 Demilade Agboola: Right, cool, so this is… Are you at a date?
167 00:18:52.630 ⇒ 00:18:55.560 Demilade Agboola: Number of orders done.
168 00:18:57.500 ⇒ 00:19:07.020 Demilade Agboola: In different states by state, basically. So, number of orders, the gross sales, total refund, total discount, and then the net sale. Okay.
169 00:19:15.540 ⇒ 00:19:17.310 Demilade Agboola: This is what finance offers.
170 00:19:20.930 ⇒ 00:19:22.340 Sezim Zhenishbekova: Good morning.
171 00:19:37.600 ⇒ 00:19:39.569 Demilade Agboola: Would you not try its, SQL?
172 00:19:40.470 ⇒ 00:19:44.730 Sezim Zhenishbekova: I have used it a couple times for Udacity. I ran the.
173 00:19:44.730 ⇒ 00:19:45.380 Demilade Agboola: What’s that?
174 00:19:45.780 ⇒ 00:19:47.839 Sezim Zhenishbekova: But I haven’t, like, worked in…
175 00:19:47.840 ⇒ 00:19:49.290 Demilade Agboola: My… by myself.
176 00:19:50.540 ⇒ 00:19:51.160 Demilade Agboola: Okay.
177 00:19:51.350 ⇒ 00:19:52.469 Demilade Agboola: No problem.
178 00:19:53.880 ⇒ 00:20:02.689 Sezim Zhenishbekova: But I think it shouldn’t be that big of a difference from Excel, right? Like, and Python? It’s very straightforward, but you interact within the…
179 00:20:02.960 ⇒ 00:20:07.220 Sezim Zhenishbekova: Data… different data sheets, kind of.
180 00:20:07.220 ⇒ 00:20:10.240 Demilade Agboola: Yeah, it’s basically tables.
181 00:20:10.500 ⇒ 00:20:12.129 Sezim Zhenishbekova: Yeah, different tables.
182 00:20:12.130 ⇒ 00:20:12.670 Demilade Agboola: Yes.
183 00:20:12.670 ⇒ 00:20:14.780 Sezim Zhenishbekova: Back to one another, yeah.
184 00:20:14.780 ⇒ 00:20:22.540 Demilade Agboola: Exactly, so it’s not… it’s not, necessarily the hardest in the world. But if it does get complicated, or you need some help, just let me know.
185 00:20:22.680 ⇒ 00:20:24.270 Sezim Zhenishbekova: Okay, thank you so much.
186 00:20:25.140 ⇒ 00:20:25.830 Demilade Agboola: Yeah.
187 00:20:26.180 ⇒ 00:20:32.110 Sezim Zhenishbekova: I’m downloading Tableau right now. It’s, like, 3GB. I think it’s gonna take some time.
188 00:20:34.120 ⇒ 00:20:34.760 Demilade Agboola: Yeah.
189 00:20:35.360 ⇒ 00:20:37.120 Sezim Zhenishbekova: But, so you…
190 00:20:37.120 ⇒ 00:20:37.929 Demilade Agboola: So if you…
191 00:20:37.930 ⇒ 00:20:44.030 Sezim Zhenishbekova: You haven’t built Tableau itself, right? Like, it’s Henry’s job to do that, mostly?
192 00:20:44.030 ⇒ 00:20:44.790 Demilade Agboola: Yeah, mostly.
193 00:20:44.980 ⇒ 00:20:46.470 Sezim Zhenishbekova: Prayers for him.
194 00:20:46.790 ⇒ 00:20:49.759 Demilade Agboola: Yeah, yeah, I built the models for him, and then if…
195 00:20:49.870 ⇒ 00:20:57.050 Demilade Agboola: the things I do in Tableau tend to be, like, if something is going bad, I might just fix it. Like, I do know a bit of Tableau.
196 00:20:57.770 ⇒ 00:21:04.970 Demilade Agboola: I don’t do enough to push. Basically, you would want to connect to the server.
197 00:21:07.050 ⇒ 00:21:11.000 Demilade Agboola: You’ll want to sign in using service accounts.
198 00:21:11.150 ⇒ 00:21:13.980 Demilade Agboola: One already, so you could just download it.
199 00:21:14.600 ⇒ 00:21:25.029 Demilade Agboola: So this service.com basically is, like, a file that contains authorization and access keys to the BigQuery.
200 00:21:25.740 ⇒ 00:21:26.740 Sezim Zhenishbekova: Mmm…
201 00:21:26.990 ⇒ 00:21:33.649 Demilade Agboola: Right? So when you… so when you point your… when you point Tableau to that file in your local device, you have to download it.
202 00:21:33.690 ⇒ 00:21:39.139 Sezim Zhenishbekova: It just will take… it’ll take the credentials, and even so, like…
203 00:21:39.820 ⇒ 00:21:41.779 Demilade Agboola: I think… do you have access to 1Pass?
204 00:21:43.130 ⇒ 00:21:45.050 Sezim Zhenishbekova: I have one password, yes.
205 00:21:45.790 ⇒ 00:21:50.529 Demilade Agboola: Yeah, I think it is there.
206 00:21:56.550 ⇒ 00:21:57.999 Demilade Agboola: Yeah, it’s… it’s this guy.
207 00:21:59.820 ⇒ 00:22:00.840 Demilade Agboola: Give me one second.
208 00:22:07.730 ⇒ 00:22:08.509 Demilade Agboola: Do it.
209 00:22:10.050 ⇒ 00:22:14.619 Demilade Agboola: Trying to move… I believe this is it.
210 00:22:17.980 ⇒ 00:22:19.930 Demilade Agboola: If you have access to the Eden.
211 00:22:20.140 ⇒ 00:22:23.369 Demilade Agboola: Involt, you should see it, so you can download it.
212 00:22:26.470 ⇒ 00:22:28.209 Sezim Zhenishbekova: now.
213 00:22:32.700 ⇒ 00:22:35.360 Sezim Zhenishbekova: Tableau service account, JSON key, okay.
214 00:22:53.950 ⇒ 00:22:56.439 Demilade Agboola: Okay, so once you put that in there.
215 00:23:00.070 ⇒ 00:23:04.029 Sezim Zhenishbekova: Should it be in certain designated folder, or it can be…
216 00:23:04.840 ⇒ 00:23:11.240 Demilade Agboola: it’s really advisable to have it in a designated folder, so I have a client’s folder for, like, brain fudge stuff.
217 00:23:11.440 ⇒ 00:23:12.360 Sezim Zhenishbekova: Okay.
218 00:23:12.580 ⇒ 00:23:13.620 Sezim Zhenishbekova: But.
219 00:23:15.130 ⇒ 00:23:19.150 Demilade Agboola: And then we can sign in. So once you sign in now, it will give you
220 00:23:19.720 ⇒ 00:23:23.349 Demilade Agboola: It should give you access to all the tables in BigQuery.
221 00:23:28.670 ⇒ 00:23:35.659 Demilade Agboola: So now… you can… context is within Data Warehouse.
222 00:23:36.010 ⇒ 00:23:41.730 Demilade Agboola: So now you can see every single. That’s why I said, if you have the key, it signs you in.
223 00:23:43.270 ⇒ 00:23:47.830 Demilade Agboola: Now, what you need is productive mods, or dbt amount, sorry.
224 00:23:48.280 ⇒ 00:23:51.870 Demilade Agboola: So now you can start to… so you’ve got the different tables there.
225 00:23:52.320 ⇒ 00:23:55.049 Demilade Agboola: You can start to pick the table that you need.
226 00:23:59.040 ⇒ 00:24:01.150 Demilade Agboola: You can start to do things with it.
227 00:24:05.980 ⇒ 00:24:08.019 Demilade Agboola: Let’s go here, I want to see that next chart.
228 00:24:11.880 ⇒ 00:24:18.779 Sezim Zhenishbekova: Putting me up now. Okay, so you just basically… okay, this, this is what I’m familiar with, yes.
229 00:24:20.370 ⇒ 00:24:21.159 Demilade Agboola: And yes.
230 00:24:23.450 ⇒ 00:24:29.140 Sezim Zhenishbekova: So yeah, I think I was receiving only desktop version, that’s why I was… Web version, that’s why I was like…
231 00:24:30.470 ⇒ 00:24:31.990 Sezim Zhenishbekova: Confused, okay.
232 00:24:41.840 ⇒ 00:24:42.730 Sezim Zhenishbekova: Perfect.
233 00:24:53.570 ⇒ 00:25:04.869 Sezim Zhenishbekova: Yeah, it’s still downloading. Okay, so after I open the sheet, the whole… all the tables with all the data will be appearing. I don’t need to re-edit it, right?
234 00:25:06.220 ⇒ 00:25:07.970 Demilade Agboola: No, no, you don’t need to reiterate that.
235 00:25:08.540 ⇒ 00:25:10.839 Sezim Zhenishbekova: It’s already, already up there. Okay.
236 00:25:10.840 ⇒ 00:25:11.380 Demilade Agboola: Yeah.
237 00:25:13.460 ⇒ 00:25:21.309 Demilade Agboola: So now, if there are things you might need, like, you need things in a certain format, but, you know, put in a certain way, just let us know, we’ll let you…
238 00:25:21.540 ⇒ 00:25:23.399 Sezim Zhenishbekova: Get that, of course, G.
239 00:25:23.910 ⇒ 00:25:25.320 Sezim Zhenishbekova: Okay, perfect.
240 00:25:25.830 ⇒ 00:25:34.599 Sezim Zhenishbekova: Yeah, it’s still downloading right now, and… but so far, I think it’s clear, I just need to set it up, the Tableau thing.
241 00:25:34.800 ⇒ 00:25:37.150 Sezim Zhenishbekova: And it makes a bit more sense now.
242 00:25:37.960 ⇒ 00:25:38.730 Demilade Agboola: Okay.
243 00:25:38.910 ⇒ 00:25:39.650 Sezim Zhenishbekova: Yes.
244 00:25:39.890 ⇒ 00:25:43.679 Sezim Zhenishbekova: And I will be tagging you if anything appears.
245 00:25:45.240 ⇒ 00:25:46.629 Demilade Agboola: No problem, just let me know.
246 00:25:46.790 ⇒ 00:25:49.190 Sezim Zhenishbekova: Thank you, have a good evening.
247 00:25:49.600 ⇒ 00:25:50.600 Demilade Agboola: You too…
248 00:25:50.780 ⇒ 00:25:53.700 Sezim Zhenishbekova: Thank you, bye. Yeah.