Meeting Title: Bi-weekly Data infra／tech debt call Date: 2025-02-18 Meeting participants: Luke Daque, Nicolas Sucari, Uttam Kumaran, Awaish Kumar, Caio
WEBVTT
1 00:04:08.800 ⇒ 00:04:09.879 Uttam Kumaran: Hey! Good morning!
2 00:04:10.190 ⇒ 00:04:12.100 Luke Daque: Good morning. How’s it going.
3 00:04:12.990 ⇒ 00:04:13.590 Uttam Kumaran: Okay.
4 00:04:24.630 ⇒ 00:04:29.860 Uttam Kumaran: that we have more people like excited to meet every day and like
5 00:04:32.320 ⇒ 00:04:37.624 Uttam Kumaran: basic get back into like what I was doing when we 1st when you 1st join, which is like actually
6 00:04:38.030 ⇒ 00:04:40.020 Uttam Kumaran: doing like data stuff. So.
7 00:04:42.170 ⇒ 00:04:43.000 Luke Daque: Yeah.
8 00:04:52.890 ⇒ 00:04:55.780 Luke Daque: do you enjoy doing data stuff? Is it like.
9 00:04:55.780 ⇒ 00:05:02.440 Uttam Kumaran: Yeah, dude, this is all I want to do. It’s like a reason I started a business is because I
10 00:05:03.830 ⇒ 00:05:11.009 Uttam Kumaran: I felt like the stuff we’re doing is actually not complicated. It’s just we have clients who really need our work. Right?
11 00:05:11.553 ⇒ 00:05:16.400 Uttam Kumaran: It’s the problem with a business is that it takes like
12 00:05:17.480 ⇒ 00:05:20.020 Uttam Kumaran: it takes like starter energy, right like.
13 00:05:20.510 ⇒ 00:05:22.732 Uttam Kumaran: And they took 2 years of like
14 00:05:23.200 ⇒ 00:05:26.950 Uttam Kumaran: before I can go back to finally trying to do some data work.
15 00:05:27.527 ⇒ 00:05:34.379 Uttam Kumaran: Right? But like, that’s it. Like, you have to kind of get more clients. Be kind of all over the place. And now
16 00:05:34.560 ⇒ 00:05:38.749 Uttam Kumaran: I’m really really focused on building great data team.
17 00:05:38.950 ⇒ 00:05:51.530 Uttam Kumaran: You know. I think one of the things that we sort of struggled with early on is we just had like we have. We have a lot of folks on part time, right? So now, more focused on building a core like full time, ae, and analyst group.
18 00:05:51.860 ⇒ 00:05:57.749 Uttam Kumaran: Luckily, like we don’t do a lot of data engineering. So I can continue to be the only data engineer. But
19 00:05:58.462 ⇒ 00:06:01.990 Uttam Kumaran: over time, like we’ll start to get more technical. There.
20 00:06:03.140 ⇒ 00:06:21.049 Uttam Kumaran: but yeah, like i, i, 1 of the things that I did over the weekend was, and over the last 2 weeks is basically just like, clear out a lot of my schedule. So we started removing a lot of things that I was involved in, and sort of. Most of my time now will go towards data work, and Robert is taking on a lot of the stuff on the sales side.
21 00:06:21.210 ⇒ 00:06:23.765 Uttam Kumaran: We both sort of split everything else.
22 00:06:24.860 ⇒ 00:06:37.060 Uttam Kumaran: But that means kind of a couple of things. And I think one we’re gonna try to meet like as this group hopefully every day. I know I’m in a lot more meetings than everybody here, but I do think it’s good for our crew to build
23 00:06:37.270 ⇒ 00:06:52.570 Uttam Kumaran: like camaraderie and also understand, like, okay, what are the tough like data modeling challenges that we’re trying to get through? Similarly with the analysts, I will try to meet with them every day. And then, I think, as a for each client, we’ll try to, for every client that each of us is on. We’ll have one of those group meetings.
24 00:06:53.950 ⇒ 00:06:56.170 Uttam Kumaran: You know, and ideally, that sort of
25 00:06:57.000 ⇒ 00:07:02.910 Uttam Kumaran: gives us the context of the client and sort of interacting with that team. It lets us sort of like
26 00:07:03.090 ⇒ 00:07:07.489 Uttam Kumaran: bring that back to this conversation and be like, Okay, how do we like actually execute this?
27 00:07:08.378 ⇒ 00:07:18.119 Uttam Kumaran: And I think, having those, that sort of mentality is good, because there’s gonna be things that we can’t talk about as Aes in that call. Right? It’s gonna be like too esoteric or like.
28 00:07:18.120 ⇒ 00:07:18.709 Luke Daque: Thank you.
29 00:07:18.870 ⇒ 00:07:22.865 Luke Daque: People may not get what we’re talking about, but the our group can definitely talk and say.
30 00:07:23.380 ⇒ 00:07:42.340 Uttam Kumaran: Hey? Like I’m dealing with this. Another example is a lot of the stuff we’re dealing with. Javi. We did for pool parts already, you know, and that’s the thing I’m like. I’m trying to be like, hey, we already did this, but then I forget that nobody knows that we already did that work? Right? So one of the things that I’m balancing is, how do we learn from every additional client?
31 00:07:42.880 ⇒ 00:07:46.340 Uttam Kumaran: Right? So yeah, welcome.
32 00:07:46.340 ⇒ 00:07:46.880 Luke Daque: There!
33 00:07:47.030 ⇒ 00:07:48.330 Uttam Kumaran: Yeah, welcome. Guys.
34 00:07:49.400 ⇒ 00:07:50.280 Nicolas Sucari: Hi guys.
35 00:07:50.620 ⇒ 00:07:51.050 Uttam Kumaran: Hey!
36 00:07:52.770 ⇒ 00:07:56.750 Nicolas Sucari: I’ll turn on my camera in like 2 min. I’m preparing the mate.
37 00:07:57.190 ⇒ 00:07:58.590 Uttam Kumaran: No problem.
38 00:07:58.740 ⇒ 00:08:02.019 Uttam Kumaran: I had mate like the rest of the afternoon yesterday.
39 00:08:02.250 ⇒ 00:08:05.445 Uttam Kumaran: It helped me not fall asleep. It was good.
40 00:08:05.800 ⇒ 00:08:08.390 Nicolas Sucari: Yeah, it gives you a lot of energy.
41 00:08:08.750 ⇒ 00:08:13.300 Uttam Kumaran: But it was smooth like, but then I woke up today, and I had to have espresso. So.
42 00:08:13.960 ⇒ 00:08:14.980 Nicolas Sucari: It’s like that.
43 00:08:17.600 ⇒ 00:08:24.269 Uttam Kumaran: What about you, Kyle? How’s your what? What’s your? How’s your family? And like, what do you guys do during the day? Usually these days.
44 00:08:25.580 ⇒ 00:08:43.710 Caio: Oh, well, here in Portugal the weather is still a bit like in a transitioning from winter to something else, because depending on the the year, or or or like literally the city here it can get it can get warmer quickly here. I don’t know. It’s been
45 00:08:44.260 ⇒ 00:08:54.890 Caio: not the best this this winter, but we usually I really like to take walks when I wake up. I usually gets me going, and then a coffee as well, and then
46 00:08:55.552 ⇒ 00:09:07.500 Caio: when I am with my family during the week it’s mo mostly like house stuff. But then, when it’s good, get to the weekend, we always try to do something, visit, visit a new place.
47 00:09:07.660 ⇒ 00:09:11.593 Caio: Everything’s really close here, I mean Portugal. It’s it’s
48 00:09:13.289 ⇒ 00:09:21.280 Caio: it’s smaller than maybe the city of Rio Janeiro. Like no, the State of Rio, we have 26 states.
49 00:09:21.680 ⇒ 00:09:24.269 Uttam Kumaran: That’s crazy. No, it’s so small like, even when I was in
50 00:09:24.560 ⇒ 00:09:29.340 Uttam Kumaran: I was just in Argentina. And and but then I’m in the us. I’m like, Oh, this is like one state.
51 00:09:29.490 ⇒ 00:09:38.630 Uttam Kumaran: you know. It’s but it’s it’s interesting to kind of see. But then there’s still so much culture, just one area, there’s different language, different culture. Yeah, it’s interesting.
52 00:09:41.430 ⇒ 00:09:42.220 Uttam Kumaran: Awesome.
53 00:09:42.960 ⇒ 00:09:50.629 Uttam Kumaran: Okay, cool. So I think, you know, we originally had this meeting as sort of a talking about tech debt and our process.
54 00:09:50.890 ⇒ 00:10:09.890 Uttam Kumaran: I think I may commandeer the meeting to talk a little bit about our actual work given like I/O, is just hopping on and we talk about a lot of things yesterday with clients. I think what we’re gonna do. Moving forward is just have one of these per day where this group sort of talks about
55 00:10:10.110 ⇒ 00:10:15.189 Uttam Kumaran: what we’re doing on the analytics engineering side. The nice thing about our work is.
56 00:10:15.300 ⇒ 00:10:17.779 Uttam Kumaran: it’s a lot. It’s a little bit slower.
57 00:10:17.980 ⇒ 00:10:22.989 Uttam Kumaran: and it’s much different than the analysts work. You’re always going to hear. The analysts are going to be the ones on fire.
58 00:10:23.730 ⇒ 00:10:29.629 Uttam Kumaran: Our goal as a crew is to balance that out right? So couple of things. And I and I, hopefully, this is
59 00:10:29.980 ⇒ 00:10:45.229 Uttam Kumaran: this is all sort of in your brain somewhere, but maybe not clearly articulated. I think one thing is, we’re always gonna have a difference between the speed at which the analytics engineering crew works versus the analyst crew. The analysts are sort of in a whatever it takes.
60 00:10:45.600 ⇒ 00:10:58.980 Uttam Kumaran: Let’s get the analysis out. Let’s get the dashboard out right. Our group is more in like, okay, what can we pick off to make their iteration process faster? Right? Ultimately, our customers are the analysts.
61 00:10:59.080 ⇒ 00:11:08.559 Uttam Kumaran: Right? I think we were. We’ve been in a mode where maybe our customers are the business, but we actually our crew benefits from them as the shield in front of us.
62 00:11:08.660 ⇒ 00:11:21.399 Uttam Kumaran: because we don’t want to be taking direct things from the business, because then our work gets sloppy and our work starts to have issues with get anything out as fast as possible right at the moment. We are sort of in that zone
63 00:11:21.830 ⇒ 00:11:38.349 Uttam Kumaran: for a couple of reasons, one across every single client. Our goal is to build a great data mart right? And that starts with getting data in. Really, each of us who are on a client having a great understanding of not only their business, but then how the data shows up.
64 00:11:38.570 ⇒ 00:11:40.730 Uttam Kumaran: Commonly, as you guys know.
65 00:11:40.850 ⇒ 00:12:01.560 Uttam Kumaran: we will end up knowing more than they know about how their business works, not not, maybe in one area, but in terms of having a full understanding of how everything works. We become the experts right? That comes with a lot of benefits, but also takes a lot of time. So I think one thing is, we are playing a little bit of catch up on both Javi and Eden with that sort of work.
66 00:12:01.850 ⇒ 00:12:27.159 Uttam Kumaran: You’re gonna see that friction in our data team calls where you’re gonna see? Like the analyst. Seems like, I don’t know if this is the right measure. I keep making mistake with a column, and you’re gonna be like, Damn yeah, we could totally build you that model. And so that’s the friction that we’re still seeing. So we still have a long ways to go to build a really, really core data mart. But I want to set the stage as like our clients for this team is gonna be the analysts.
67 00:12:27.589 ⇒ 00:12:44.219 Uttam Kumaran: Typically, there’s at least one, if not 2 analysts per client. Our goal is is a couple of things, one for them to speed up the rate at which they can produce analytical assets, dashboards, reports, and analysis. The 3rd thing is, we want them to trust our data.
68 00:12:45.050 ⇒ 00:13:12.349 Uttam Kumaran: Trust is like, of course, it’s like transitory meaning, like if they don’t trust it. And the client ultimately, when asked them if they trust it, they’re going to be like, I don’t know. And then it’s gonna be, you know, so ultimately we want, and but also we are probably the best people that know whether the data is trustworthy, and it increasingly gets harder and harder for the next stakeholder to validate is that, like the client, they may not know even where to start to be like? Is this data trustworthy? The analyst? Maybe they they can do some sums, but like
69 00:13:12.510 ⇒ 00:13:17.760 Uttam Kumaran: they go into Github, they’re gonna be like, I have no idea like what the fuck is going on. And so for us.
70 00:13:18.050 ⇒ 00:13:26.709 Uttam Kumaran: We need to convey that trust to them. And we do that, of course, one being there in slack and answering their questions.
71 00:13:26.840 ⇒ 00:13:35.290 Uttam Kumaran: But then we want to think about, how do we build process around it? So one of the things that we’ll look at over the next, you know, month or 2, as we get the data mart set up is adding testing.
72 00:13:35.390 ⇒ 00:13:44.852 Uttam Kumaran: We have the Ci CD process. We will start to be able to take requests in more formally and sort of building out this analytics engineering crew.
73 00:13:45.660 ⇒ 00:14:15.280 Uttam Kumaran: the last thing I’ll mention is, if if you’ve worked in a company, you probably know that the data team is sort of the service group for the rest of the company. Right? So typically, there’s 2 models, there’s like embedded analysts, right? So people are assigned to one business unit. There’s also decentralized, which is sort of our centralized, which means there’s 1 data team and sort of we take requests from everything. We’re almost like centralized squared, right? Because we’re taking requests from multiple clients
74 00:14:15.620 ⇒ 00:14:22.719 Uttam Kumaran: on multiple business domains. So it’s almost like multiple stakeholders within multiple clients all attack us.
75 00:14:22.820 ⇒ 00:14:26.680 Uttam Kumaran: So this is the hardest. This is the hardest of all those scenarios.
76 00:14:26.810 ⇒ 00:14:31.869 Uttam Kumaran: The nice thing is this is also where the money is made right.
77 00:14:32.130 ⇒ 00:14:54.900 Uttam Kumaran: like we have the opportunity to do what we were doing for one person, for 5, 1015 people, and, in fact, for us, we should expect it to get easier over time, right? The challenge of a course, I think, is the amount of volume, and that we will solve by making sure that we have enough people on the team. And we’re we’re sort of having even workloads. But what we learn from one client
78 00:14:54.920 ⇒ 00:15:03.230 Uttam Kumaran: we should be able to apply to the next client faster, cheaper, you know, and with more confidence. Right? And so that’s the that’s the thing that this team is. Gonna get good at
79 00:15:03.741 ⇒ 00:15:09.939 Uttam Kumaran: I think there’s very similar learnings for the analyst team that I’ll be talking to them about. But that’s the goal.
80 00:15:10.250 ⇒ 00:15:16.299 Uttam Kumaran: And that’s something that I think you can leave up to me to sort of identify and sort of fill the gaps where, if one
81 00:15:16.390 ⇒ 00:15:36.830 Uttam Kumaran: person is working on something for a client and I. And we recognize that we just did that for someone else. There’s a perfect way for us to speed that up like, hey? We also just join Amazon and shopify, and we we found some nuances. Here’s go check out the sequel we already, but we already had, like a whole discussion about how to do that. That probably saves like a week of like nonsense back and forth, right? And
82 00:15:36.890 ⇒ 00:15:51.339 Uttam Kumaran: that happens every day. And so the lovely thing is that now that we have a crew, and now that every client is sort of at least has one person that’s dedicated on the analytics engineering side, I think we’re gonna start to see some of those benefits before we were sort of just running.
83 00:15:51.420 ⇒ 00:15:58.960 Uttam Kumaran: We’re waking up every day and doing as much as we can and sort of calling it. Now we want to start to build our machine within the larger machine.
84 00:15:59.531 ⇒ 00:16:03.630 Uttam Kumaran: So I’ll kind of stop there. I don’t know Kyle awaish.
85 00:16:04.259 ⇒ 00:16:11.800 Uttam Kumaran: I know, Luke, you’ve sort of seen us kind of evolve here. But curious about how you guys feel about that. If you have any comments or
86 00:16:12.280 ⇒ 00:16:14.039 Uttam Kumaran: any thoughts on on that.
87 00:16:16.158 ⇒ 00:16:21.871 Awaish Kumar: Yeah, I I feel good, like we. We are now standardizing the process. And
88 00:16:22.990 ⇒ 00:16:30.919 Awaish Kumar: and the kind of standardizing how we are doing the modeling work. It’s it’s like.
89 00:16:32.099 ⇒ 00:16:35.859 Awaish Kumar: It feels really good. Yeah, we are going in the right right direction.
90 00:16:38.120 ⇒ 00:16:38.490 Uttam Kumaran: Cool.
91 00:16:38.490 ⇒ 00:16:55.710 Caio: Yeah, on my side, I’m still learning about everything. What is happening. I saw the tickets, and then I’ll I’ll try to see if I understand from end to end what was happening. And and at the end of the day I totally understand what is said. Like, you have multiple stakeholders, multiple people, multiple things happening at the same time and building trust. It’s really difficult.
92 00:16:56.241 ⇒ 00:17:00.468 Caio: So yeah, I think I’m I’m on board with that. Yeah. But I’m still.
93 00:17:00.990 ⇒ 00:17:03.700 Caio: I still have to learn a lot of things. And yeah.
94 00:17:04.099 ⇒ 00:17:11.189 Uttam Kumaran: Yeah, I think. You know, we all worked in data. And we all know, like what the sort of fire pressure looks like where it’s like.
95 00:17:11.919 ⇒ 00:17:26.549 Uttam Kumaran: I need this now, right? And you’re sort of like, okay, should I write the sequel? Should I try to push a model? Should I like break something. Should I send something? Not really be sure. So that all is gonna that’s not gonna stop right? So I’m not gonna say this process is, gonna stop any of that
96 00:17:26.979 ⇒ 00:17:28.449 Uttam Kumaran: so I don’t want to create a
97 00:17:28.739 ⇒ 00:17:47.109 Uttam Kumaran: feeling that like we’re not going to still be running. And it’s things are not going to see feel like fire. However, this crew is here to build sort of a little bit of a bubble around. How we process that it’s always gonna be on fire. If anybody’s worked in data for a number of years, it’s always like this. It doesn’t change
98 00:17:47.547 ⇒ 00:17:50.719 Uttam Kumaran: right? And we always have opportunity to get better. But
99 00:17:50.819 ⇒ 00:18:20.419 Uttam Kumaran: it takes a little bit of like stepping outside of the like atmosphere, and that why, that’s why I wanted to have this meeting every day, where anything where we talk to the analysts or talk to the client is always gonna be on fire, isn’t I? I? We’re always gonna feel, or that crew is always gonna feel like we’re behind for this crew. We are focused on solving their short term problem, but also laying the foundation. Of course, our goal is set, that everything is sort of self service in the, in, the, in the warehouse
100 00:18:20.869 ⇒ 00:18:25.785 Uttam Kumaran: new analysts are clear where to go for things, but they’re always gonna have questions that we’re gonna start to answer.
101 00:18:26.509 ⇒ 00:18:32.109 Uttam Kumaran: However, that sort of fire should not translate into like every one of us freaking out
102 00:18:32.269 ⇒ 00:18:39.009 Uttam Kumaran: right? And that’s the thing that if you guys know from working with me and hopefully, Kyle, you’ll sort of notice from working with the company is that
103 00:18:39.199 ⇒ 00:18:45.009 Uttam Kumaran: we don’t. I don’t want to foster an environment that’s probably like environments you’ve seen where it’s like
104 00:18:45.439 ⇒ 00:19:05.499 Uttam Kumaran: so crazy and chaotic and rude, and people get disrespectful because and and honestly, it’s no fault to them, because they’re under fire by someone else. So we’re trying to cut all of that right. And we do that through trust. We also do that through very candid conversations about like what’s going wrong. You’ll see a lot of that in slack right where it’s like.
105 00:19:05.629 ⇒ 00:19:08.289 Uttam Kumaran: and you’ll see that in the Javi Channel from yesterday, where I was like
106 00:19:08.469 ⇒ 00:19:11.029 Uttam Kumaran: someone. Just tell me like what is going on.
107 00:19:11.719 ⇒ 00:19:41.559 Uttam Kumaran: and when you ask a question like that. I’m not. I don’t want to hear jargon, because I guess so. I know all the jargon, so you can’t really hit me with jargon you can’t hit me with like, oh, well, this thing like I’m like in English. Just tell me like, is it like a dashboard? That’s wrong, is it a calculation that’s wrong, right? And so those are the types of things we want to do. That cuts a lot of the sort of like your brain having resentment and being like this person’s lazy blah blah, like, we’re kind of get rid of all that right. And so
108 00:19:41.799 ⇒ 00:19:48.879 Uttam Kumaran: we’re always gonna have candid conversations it for people that are new to that you’re gonna feel like, Oh, my God! Am I like
109 00:19:49.149 ⇒ 00:19:52.739 Uttam Kumaran: getting on this per people? Am I stepping on this person’s toes or blah blah. But
110 00:19:52.899 ⇒ 00:20:16.479 Uttam Kumaran: the company is here to protect you from any of those thoughts, so that Will won’t be a problem. In fact, the only goal for our crew is to solve our clients problems. And so as long as we are doing that in a principled manner, and ultimately all of our questions are geared towards solving their problem. There’s no issue, right? And so that’s something that I really want to make clear is this, we’re in the communication business, you know, and and
111 00:20:16.549 ⇒ 00:20:24.799 Uttam Kumaran: we’re always going to be interacting with analysts, clients ourselves. And so it’s important that we have very, very candid conversations, in fact.
112 00:20:25.079 ⇒ 00:20:33.979 Uttam Kumaran: asking a question like, Tell me exactly like what’s wrong. And then, being like that didn’t help tell me again very clearly. That’s like what a client would say.
113 00:20:34.389 ⇒ 00:20:42.529 Uttam Kumaran: right cause I don’t want to read a paragraph like, just tell me, is this a chart is wrong, right? And that helps us really get to like the the core of what’s going on.
114 00:20:42.936 ⇒ 00:21:01.189 Uttam Kumaran: And so I want us to really build that. And I’ll keep reminding you guys even in slack, and it’s hard. It’s hard to build that because that’s not really common in in companies to speak that way, because people feel afraid or they feel like worried. That’s the only way we’re gonna be able to do this. Right? That’s the difference. When we have
115 00:21:01.739 ⇒ 00:21:06.466 Uttam Kumaran: 1015, 2030 stakeholders and one core group of analytics, engineers.
116 00:21:07.289 ⇒ 00:21:17.139 Uttam Kumaran: communication to do prioritization communication, to really understand what the problem is, and then for us as a crew to decide what the best next step is is really really important.
117 00:21:18.279 ⇒ 00:21:24.359 Uttam Kumaran: So I guess I’ll stop there with sort of the pep talk. But like I’ll keep reminding everybody about those things.
118 00:21:24.938 ⇒ 00:21:32.989 Uttam Kumaran: Because naturally, our instinct is to kind of go back to those. So I’ll keep reminding everybody of those I think today I wanted to just maybe go run through
119 00:21:33.189 ⇒ 00:21:42.859 Uttam Kumaran: each client, but particularly talk about Javi and talk about Eden. I think maybe we can talk about Javi. And I know Kyle. There’s probably a lot of messages you’re catching up on.
120 00:21:42.979 ⇒ 00:21:44.499 Uttam Kumaran: I’ll sort of give you the.
121 00:21:44.749 ⇒ 00:21:46.839 Uttam Kumaran: I’ll sort of give you the high level on
122 00:21:47.339 ⇒ 00:22:02.459 Uttam Kumaran: what the state of the client is, and then I think you’re perfect to kind of go in and sort of come with fresh eyes and see everything. There’s gonna be short term asks. But there’s also gonna be a goal of setting like a foundational data model.
123 00:22:04.399 ⇒ 00:22:09.139 Uttam Kumaran: I’ll just pull up the repo and maybe we can walk. We can just walk through that if that’s
124 00:22:10.039 ⇒ 00:22:11.199 Uttam Kumaran: fine. I mean, I
125 00:22:11.579 ⇒ 00:22:24.477 Uttam Kumaran: I don’t know. I think that’s probably the best way to look at this stuff. I’ll also kind of walk through a little bit about how we actually execute the project and things like that, but probably do another brown bag session on how we run dbt, core and things. But
126 00:22:25.399 ⇒ 00:22:33.869 Uttam Kumaran: So, as I mentioned yesterday, Davi, coffee is
127 00:22:35.569 ⇒ 00:22:43.069 Uttam Kumaran: this company. They sell coffee, concentrates, bunch of different coffee related products. They just launched protein coffee, collagen, coffee
128 00:22:43.299 ⇒ 00:22:44.449 Uttam Kumaran: they make.
129 00:22:45.469 ⇒ 00:22:55.466 Uttam Kumaran: I don’t know. Like 50 to 100 million a year. They saw on Amazon and shopify. They use a ton of different softwares to power their business, as we saw in
130 00:22:56.329 ⇒ 00:22:58.659 Uttam Kumaran: as we saw on the chart yesterday.
131 00:22:59.230 ⇒ 00:23:17.809 Uttam Kumaran: Yeah, they’re using shopify Amazon or kendo for reviews, gorgeous for customers, for recharge, for subscriptions, because you can subscribe and get something weekly. You can also buy one time. They have a bunch of Google sheets for cost of goods. Right? So the operations team puts in like, Okay, right now, this this thing costs this much.
132 00:23:18.039 ⇒ 00:23:23.449 Uttam Kumaran: This is a pretty thing. This is the thing we’re doing for almost like 4 clients right now, is this cost of goods through spreadsheets? Problem
133 00:23:23.569 ⇒ 00:23:26.998 Uttam Kumaran: we ingest via portable portable is like 5 tran
134 00:23:27.859 ⇒ 00:23:44.369 Uttam Kumaran: just syncs the data into Snowflake. Everything goes into raw. And then we’re sort of building out these sorts of marts right for each of these areas. And then we have sort of like a reporting layer in Meta base and in real. I’ll sort of leave this
135 00:23:44.569 ⇒ 00:23:56.189 Uttam Kumaran: out of this conversation, because this is a lot of what’s going on the analyst side. But ultimately we want our goal. For this crew is just to build a great data mart across these 4 areas.
136 00:23:58.029 ⇒ 00:24:00.989 Uttam Kumaran: If we look at the actual repo.
137 00:24:02.619 ⇒ 00:24:14.739 Uttam Kumaran: what you’ll see in in Dbt project. And again, everything is sort of happening in models is that we have marts intermediate and raw. Right now, primarily, we’re doing some processing on the raw shopify models.
138 00:24:14.899 ⇒ 00:24:16.459 Uttam Kumaran: But we’re not doing any
139 00:24:16.689 ⇒ 00:24:23.089 Uttam Kumaran: pre-processing on any other raw models. I don’t know whether that’s right or wrong right now. But basically.
140 00:24:23.239 ⇒ 00:24:28.659 Uttam Kumaran: I think the number one things to really understand are how shopify and Amazon works.
141 00:24:29.159 ⇒ 00:24:32.129 Uttam Kumaran: I know you have those 2 tickets on Okendo, and gorgeous.
142 00:24:32.439 ⇒ 00:24:34.639 Uttam Kumaran: Those are sort of a way for you to
143 00:24:35.119 ⇒ 00:24:38.409 Uttam Kumaran: push something end to end and sort of see it go.
144 00:24:38.589 ⇒ 00:24:43.779 Uttam Kumaran: But frankly, I’m considering you as the owner for everything on Javi ae
145 00:24:45.009 ⇒ 00:24:51.909 Uttam Kumaran: so take some time to kind of walk through. Some of you could look through slack, and see some of the questions and sort of walk through where people are getting things from
146 00:24:52.099 ⇒ 00:24:57.559 Uttam Kumaran: in in the intermediate schema. We sort of organize things by source.
147 00:24:57.789 ⇒ 00:25:01.229 Uttam Kumaran: So on Amazon, you’ll see all the Amazon intermediate models
148 00:25:01.717 ⇒ 00:25:14.579 Uttam Kumaran: in shopify. You’ll see all the shopify intermediate models. Both of those are modeled. Very similarly, however, the data we get from both platforms are different from shopify. We don’t get a lot of customer identification information.
149 00:25:16.069 ⇒ 00:25:20.319 Uttam Kumaran: Right? We don’t even get addresses for a lot of customers. We basically just get the orders.
150 00:25:20.429 ⇒ 00:25:26.659 Uttam Kumaran: Amazon is tough because they keep a lot of that data for themselves. They don’t want you to steal the customers from there and sort of retarget.
151 00:25:28.059 ⇒ 00:25:34.289 Uttam Kumaran: the second thing we go through is for shopify. We get a lot of information, but we’re also using recharge.
152 00:25:34.539 ⇒ 00:25:44.459 Uttam Kumaran: Recharge is another sort of complication here, because some customers purchase subscriptions. Some customers purchase one time, so we have sort of 2. We have now a 3rd source
153 00:25:44.649 ⇒ 00:25:50.829 Uttam Kumaran: for order related information that, of course, subscriptions needs to be modeled a little bit of a different way.
154 00:25:50.989 ⇒ 00:26:00.909 Uttam Kumaran: And then finally, in in our marts model, we sort of bring everything together. So if you go to sales. You’ll see that we bring all the customers together. We bring orders together, order lines together.
155 00:26:01.019 ⇒ 00:26:02.689 Uttam Kumaran: of course. Order lines.
156 00:26:02.949 ⇒ 00:26:04.999 Uttam Kumaran: For example, if you go to.
157 00:26:05.379 ⇒ 00:26:07.579 Uttam Kumaran: If you go to Javi, you can order.
158 00:26:08.229 ⇒ 00:26:10.679 Uttam Kumaran: Let’s let’s just go through an example. So
159 00:26:11.969 ⇒ 00:26:14.999 Uttam Kumaran: I want to go here and say one time purchase.
160 00:26:15.659 ⇒ 00:26:21.129 Uttam Kumaran: And I want to add this
161 00:26:22.619 ⇒ 00:26:25.019 Uttam Kumaran: right. And then let’s say, I also want to add this
162 00:26:25.879 ⇒ 00:26:28.449 Uttam Kumaran: in my order. There’s going to be 2 order lines
163 00:26:29.309 ⇒ 00:26:32.659 Uttam Kumaran: right? And so the orders ultimately.
164 00:26:32.979 ⇒ 00:26:41.069 Uttam Kumaran: can we? We sum up the the totals, we sum up the discounts, but we can’t do us. We can’t say what prod we can’t do like.
165 00:26:41.209 ⇒ 00:26:48.489 Uttam Kumaran: what product was this order? Because the order has multiple products? So that was one problem that just happened this week where they were like, hey, how do we? How do I
166 00:26:48.609 ⇒ 00:26:53.529 Uttam Kumaran: see the amount of orders like, see? See the amount of cookies and creams orders? And I’m like.
167 00:26:53.699 ⇒ 00:26:59.829 Uttam Kumaran: Well, you really can’t like you could see how many orders contain one of those, and so I can build you an array.
168 00:26:59.929 ⇒ 00:27:00.729 Uttam Kumaran: But
169 00:27:01.029 ⇒ 00:27:17.879 Uttam Kumaran: otherwise like that’s that’s not a valid like you have to go to order lines to get that information. The second thing, there are both order line related discounts, and order related discounts. For example, if I continue with one time, and let’s say they offer me like a code like 20% off.
170 00:27:18.349 ⇒ 00:27:25.589 Uttam Kumaran: That is an order discount on my entire basket. But then each individual product may have
171 00:27:25.849 ⇒ 00:27:28.179 Uttam Kumaran: minus 12, minus whatever right?
172 00:27:28.349 ⇒ 00:27:31.596 Uttam Kumaran: So there’s these nuances to the aggregation.
173 00:27:32.419 ⇒ 00:28:00.209 Uttam Kumaran: But again, like I, nothing different than we’ve seen with some other customers. And that’s really, I think, between that is really still the core problem. With Javi. We still have not nailed the modeling between shopify, which, of course, is their site and Amazon, and making sure that the components of what we call gross margin are all accurate, gross margin, which is the revenue minus discounts minus refunds.
174 00:28:00.499 ⇒ 00:28:04.759 Uttam Kumaran: and then they charge for shipping. So it’s actually a revenue source for them.
175 00:28:04.919 ⇒ 00:28:10.229 Uttam Kumaran: So plus shipments revenue. And you’ll see that that’s what Pius was telling me about yesterday, and I was like.
176 00:28:10.399 ⇒ 00:28:14.790 Uttam Kumaran: I don’t really get like, please make it clear. And then
177 00:28:15.579 ⇒ 00:28:34.699 Uttam Kumaran: subtracting the cost of goods sold right? So that’s the core profit calculation. And then to tie this all in the goal for the analyst team, they’re building this gross margin dashboard, which is basically showing profitability across skews, across different product lines and across the the entire.
178 00:28:34.929 ⇒ 00:28:37.049 Uttam Kumaran: You know, company as a whole.
179 00:28:37.539 ⇒ 00:28:42.479 Uttam Kumaran: So our goal is really to enable that dashboard short term, but
180 00:28:42.589 ⇒ 00:28:48.909 Uttam Kumaran: like, I think, the shortest term goal is just to nail everything around, shopify Amazon and recharge
181 00:28:49.289 ⇒ 00:28:55.419 Uttam Kumaran: the okendo and gorgeous stuff, I think is, keep it in your top of mind may be helpful to.
182 00:28:55.539 ⇒ 00:29:08.019 Uttam Kumaran: Those are like fresh slates. It’s it’s great like we can start with those Amazon. There’s already some work, but it’s definitely more complicated, like the reviews. Those things are just tickets, and there’s reviews and things like that.
183 00:29:08.179 ⇒ 00:29:09.529 Uttam Kumaran: So I’ll stop there.
184 00:29:11.369 ⇒ 00:29:17.519 Uttam Kumaran: I guess. Let me know what you think. What are your thoughts on seeing sort of this stuff? Any questions I can. I can answer.
185 00:29:19.040 ⇒ 00:29:21.300 Caio: So far, so good, was really good. And
186 00:29:21.640 ⇒ 00:29:24.520 Caio: it’s it’s great that is recording. Because I’m gonna definitely check.
187 00:29:24.520 ⇒ 00:29:24.940 Uttam Kumaran: Yes.
188 00:29:25.537 ⇒ 00:29:41.669 Caio: But yeah, it gave me a good overview, and I think at the end of the day, but I’m not sure like I’ll have to check everything to see what has been done so far. But I remember that one thing that I was doing in in the last phone
189 00:29:42.171 ⇒ 00:30:08.329 Caio: that helped, even though some people might think, you don’t really need it is I understood that they wanted to build a dashboard which is for similar here. And then we have a lot of metrics, a lot of stuff that goes in there. But then, when problems come like the slack and whatever or email it’s everywhere it’s captured, you know. And then one thing that I did was just a a Google sheet or something
190 00:30:08.855 ⇒ 00:30:23.969 Caio: with a tab where I was following up literally everything related to each metric. And if you have this little metric here, this is all the problems I can have within that one or something else, and then kind of like it is manual work
191 00:30:24.284 ⇒ 00:30:29.190 Caio: it takes time. But usually at the end of the day I felt that people were like, Okay, I can see what is happening.
192 00:30:29.190 ⇒ 00:30:29.850 Uttam Kumaran: I’m a fan.
193 00:30:29.850 ⇒ 00:30:31.490 Caio: Them. It’s nice. Yeah.
194 00:30:31.490 ⇒ 00:30:36.189 Uttam Kumaran: So you’re saying for for, like gross margin dashboard, you would say, Okay, what are the 15 or 20 metrics?
195 00:30:36.470 ⇒ 00:30:38.490 Uttam Kumaran: Let’s just put that in a Google sheet.
196 00:30:38.710 ⇒ 00:30:42.990 Uttam Kumaran: And then you can just comment on like, what’s the issue? What’s the issue? What’s the issue? Okay? We should do that.
197 00:30:42.990 ⇒ 00:30:52.070 Caio: Yeah. Yeah. And then we can add also, like a sore like, what are all the sources we have this or and then the thing it’s like it goes live by itself.
198 00:30:52.210 ⇒ 00:30:52.580 Uttam Kumaran: Yeah.
199 00:30:52.580 ⇒ 00:30:53.470 Caio: You know.
200 00:30:53.640 ⇒ 00:31:06.169 Nicolas Sucari: We should do that with them, like, yeah. Have like a sheet for like a page for each dashboard, and like list, all the metrics that that dashboard contains right, and try to have, like everything there.
201 00:31:06.440 ⇒ 00:31:11.789 Nicolas Sucari: a place in motion where we can just list out all of the metrics that we’re using per dashboard right.
202 00:31:11.790 ⇒ 00:31:18.890 Uttam Kumaran: Nico, can you get both of those for the gross margin dash and for Amazon and Kyle? What fields you want? You just want, like
203 00:31:19.300 ⇒ 00:31:22.969 Uttam Kumaran: the metric, the definition, and then we’ll fill out like
204 00:31:23.220 ⇒ 00:31:25.370 Uttam Kumaran: what table it’s coming from the source.
205 00:31:25.370 ⇒ 00:31:40.440 Caio: Yeah, it started. It’s starting with the metrics and as granular as you can be. I think it’s a good way to start. And then at the end of the day would be just, you know. Let’s say another column with comments really like, okay, person next said this on the select. So we added, Here we take a look.
206 00:31:40.440 ⇒ 00:31:41.090 Uttam Kumaran: Perfect, and then.
207 00:31:41.090 ⇒ 00:31:50.639 Nicolas Sucari: Okay, it will be name metric. Name, right, metric name, metric definition and comments. That’s fine.
208 00:31:50.640 ⇒ 00:31:52.230 Caio: Perfect, perfect, yes.
209 00:31:52.230 ⇒ 00:31:58.339 Uttam Kumaran: Yeah, let’s start. Let’s just start with that. And we can keep that in notion. That’s a great idea. Actually, like that way.
210 00:31:58.340 ⇒ 00:31:59.360 Caio: I think that was.
211 00:31:59.360 ⇒ 00:32:00.050 Uttam Kumaran: Yeah. Go. Yeah.
212 00:32:00.050 ⇒ 00:32:00.570 Caio: Sure.
213 00:32:00.570 ⇒ 00:32:01.060 Uttam Kumaran: Bye.
214 00:32:01.375 ⇒ 00:32:06.429 Caio: No one thing that I remember that was also nice from from that is that that
215 00:32:07.020 ⇒ 00:32:11.639 Caio: you kind of put the the the light on Duchen.
216 00:32:11.640 ⇒ 00:32:12.080 Uttam Kumaran: Totally.
217 00:32:12.080 ⇒ 00:32:13.310 Caio: Them, because like.
218 00:32:13.310 ⇒ 00:32:13.889 Uttam Kumaran: 100%.
219 00:32:13.890 ⇒ 00:32:17.830 Caio: Didn’t see the comment. You didn’t. I’m waiting for your answer. It’s there.
220 00:32:18.270 ⇒ 00:32:33.419 Uttam Kumaran: 100. That’s actually why I want to separate us. Because most of our problems are because of like the requirements, you know, are tough, and they don’t. They need to go figure out. However, the analysts don’t think like us like they don’t think like engineering.
221 00:32:33.560 ⇒ 00:32:38.660 Uttam Kumaran: right? So they don’t have. They don’t look at every problem like the same. I look at every problem like
222 00:32:38.830 ⇒ 00:32:48.399 Uttam Kumaran: the same problem. They’re like, this is a new day. Oh, my God, you know, and I don’t like. So we fix their we put like the blinders on the horse that’s like.
223 00:32:48.400 ⇒ 00:32:49.410 Caio: Exactly, perfect.
224 00:32:49.410 ⇒ 00:32:53.580 Uttam Kumaran: Dash, for if we do a dashboard, it’s this is the process. So I think that’s perfect.
225 00:32:53.720 ⇒ 00:32:54.250 Uttam Kumaran: So.
226 00:32:54.250 ⇒ 00:32:54.840 Caio: Okay.
227 00:32:55.010 ⇒ 00:33:08.999 Uttam Kumaran: Let’s move forward with those. I think, Kyle, if you and Nico want to just work together, maybe today, on getting a version of that I also think you can. I mean, I don’t know whether we need Jacob or whatever. But
228 00:33:09.000 ⇒ 00:33:27.759 Uttam Kumaran: those Jacob and pies are, gonna be hard to get on the phone. So just do what you can with what’s there now, and let’s keep that as the as a document goal for each dashboard. I am gonna have time later tonight to work on the dashboard as well, and then the rest of the week. But let’s like try today to see if we can get
229 00:33:27.880 ⇒ 00:33:30.969 Uttam Kumaran: 2 of those documents for the 2 core dashboards.
230 00:33:30.970 ⇒ 00:33:31.290 Nicolas Sucari: Yeah.
231 00:33:31.290 ⇒ 00:33:32.330 Uttam Kumaran: On an Amazon dashboard.
232 00:33:32.330 ⇒ 00:33:32.840 Nicolas Sucari: Anyone.
233 00:33:32.840 ⇒ 00:33:33.930 Uttam Kumaran: Working on a gross margin.
234 00:33:33.930 ⇒ 00:33:34.780 Nicolas Sucari: Gross margin.
235 00:33:35.010 ⇒ 00:33:40.730 Nicolas Sucari: Yeah, I’d say, we start with the metabase dashboards, real dashboards. We can leave aside because.
236 00:33:40.730 ⇒ 00:33:55.580 Uttam Kumaran: Yeah, leave all that. I just want to continue just to focus on getting these 2 out. So let’s just do that. We’ll test this process, and ideally, I think we should. If we can nail that requirement stock, then it’s sort of again, as you mentioned, just like trying to check everything off as we go.
237 00:33:57.950 ⇒ 00:34:05.379 Nicolas Sucari: Also, I wanted to say, maybe so that I gave a little bit of context on Kayo, on those 2 tickets that we have for gorgeous, and the Kendall
238 00:34:05.380 ⇒ 00:34:29.390 Nicolas Sucari: for gorgeous. We have, like some questions that the client gave us, that we should be able to answer in that dashboard that we need to create. So that’s kind of the guidelines that we have. So when you go back and look at the data, if you want, you can have those questions in mind so that you can see and look through the data having those questions and trying to answer those ones. And for Okendo we have a dashboard in amplitude that we need to replicate.
239 00:34:29.469 ⇒ 00:34:30.400 Nicolas Sucari: So
240 00:34:30.480 ⇒ 00:34:50.229 Nicolas Sucari: it should be easier to understand what is the data on akendo, having the other dashboard and looking at that at the same time. Where, while you’re looking at the data, so I can share everything with you, Caius, if you need. And and yeah, and just show you a little bit what we need there. So that when you go see deeper into the data, you can yeah, start thinking about that, too.
241 00:34:50.900 ⇒ 00:35:06.290 Caio: No perfect. Thank you. Thank you. And just a last question, if we have time, is, I would like to take also a look, I can take a look on my own. But if you can also help me. What is the state of snow of our snowflake so far?
242 00:35:06.640 ⇒ 00:35:08.859 Caio: Just so that I have a
243 00:35:09.441 ⇒ 00:35:13.490 Caio: a quick learning curve for that part. But we can also do differently.
244 00:35:13.710 ⇒ 00:35:16.209 Uttam Kumaran: Yeah, maybe I have to jump to an interview. But let’.
245 00:35:16.210 ⇒ 00:35:17.660 Nicolas Sucari: I can. I can stay.
246 00:35:17.990 ⇒ 00:35:19.050 Uttam Kumaran: Yeah, maybe I’ll yeah
247 00:35:19.050 ⇒ 00:35:26.919 Uttam Kumaran: make you host. And if you want to stay and walk, walk through that, I think, Luke, also, you may have some good context on how the Javi Snowflake is set up.
248 00:35:27.080 ⇒ 00:35:33.470 Uttam Kumaran: But maybe I’ll leave you guys here. I know the last thing I didn’t. We didn’t get to cover today is a wish I wanted to talk a little bit about
249 00:35:33.790 ⇒ 00:35:36.460 Uttam Kumaran: the data mark for Eden.
250 00:35:36.961 ⇒ 00:35:48.560 Uttam Kumaran: I guess I don’t know where. Like, how do you feel about that? Do we want to meet like later today to chat about that? I sort of want to make sure that you have enough support there and and sort of want to get a sense of like
251 00:35:49.590 ⇒ 00:35:57.030 Awaish Kumar: So, yeah, so in the in the project architecture diagram, I have built a a kind of star schema.
252 00:35:57.180 ⇒ 00:35:59.019 Uttam Kumaran: Okay for the sales mode.
253 00:35:59.150 ⇒ 00:36:11.659 Awaish Kumar: So it doesn’t have like all the fields there. But I just like a logical view of what could be there, and how we will approach that. So it’s a basic model
254 00:36:11.770 ⇒ 00:36:18.270 Awaish Kumar: where there is a transaction in the like effect transaction table and the
255 00:36:18.480 ⇒ 00:36:47.180 Awaish Kumar: customer order products, shipments, and all of these are dimension models. They are connected through the middle transaction table. This is the basic model where we separate out everything and connect through a transaction. And then the tables which we see like product, one of the tables you created like product sales summary, or the there is one other called order. Summary like these will be like a kind of summary tables that will be built on top of that model.
256 00:36:47.430 ⇒ 00:36:51.109 Uttam Kumaran: Okay. Can you send me the link? I’ll review it, and I can send you some notes.
257 00:36:51.555 ⇒ 00:36:57.340 Awaish Kumar: Yeah, it’s the same like, project Figma Link for the project architecture.
258 00:36:57.340 ⇒ 00:37:03.520 Uttam Kumaran: Okay. Okay. Alright, Nico. I may need this. I may need my zoom link.
259 00:37:04.080 ⇒ 00:37:04.870 Nicolas Sucari: We want it.
260 00:37:04.870 ⇒ 00:37:05.189 Uttam Kumaran: Do you want.
261 00:37:05.190 ⇒ 00:37:05.540 Nicolas Sucari: Understood.
262 00:37:05.540 ⇒ 00:37:07.259 Uttam Kumaran: You want to start a new one and send it in. Okay? Cool.
263 00:37:08.030 ⇒ 00:37:08.670 Uttam Kumaran: Okay. Chest.
264 00:37:08.670 ⇒ 00:37:14.050 Nicolas Sucari: One question before you leave Utam. We’re not using analytics anymore. Right? We’re using only pro march.
265 00:37:15.040 ⇒ 00:37:17.529 Uttam Kumaran: Metabase is still pulling from analytics.
266 00:37:17.800 ⇒ 00:37:23.150 Uttam Kumaran: Okay, I tried to migrate this yesterday night and like it’s it’s.
267 00:37:23.150 ⇒ 00:37:24.020 Awaish Kumar: It’s like.
268 00:37:24.020 ⇒ 00:37:27.270 Uttam Kumaran: I don’t. I didn’t want to create. I don’t want to create a scene about.
269 00:37:28.190 ⇒ 00:37:30.879 Nicolas Sucari: Don’t worry. Don’t worry. Okay, just so that I know.
270 00:37:30.880 ⇒ 00:37:34.120 Uttam Kumaran: Totally don’t agree about what Pie said, because he was like
271 00:37:34.550 ⇒ 00:37:37.860 Uttam Kumaran: something something like, Oh, we need to make sure that.
272 00:37:39.140 ⇒ 00:37:44.836 Uttam Kumaran: we need to have sequel here. And I’m like, I don’t think you get it like we, it’s impossible to maintain the sequel. That’s in the bi tool.
273 00:37:45.180 ⇒ 00:37:49.501 Uttam Kumaran: So either way. Okay? So if yeah. Nico, do you mind just sending another
274 00:37:50.030 ⇒ 00:37:52.710 Nicolas Sucari: Yeah, of course, I’ll send a new, a new link in the.
275 00:37:52.710 ⇒ 00:37:53.269 Awaish Kumar: That’s internal.
276 00:37:53.270 ⇒ 00:37:57.329 Uttam Kumaran: Or actually, I don’t know. I think this maybe this may be a wastes meeting. So I think maybe I’m okay
277 00:37:59.010 ⇒ 00:38:02.819 Uttam Kumaran: or Ryan. This is, I think this is your meeting. So you guys can stay on. I’ll I’ll
278 00:38:03.780 ⇒ 00:38:05.239 Uttam Kumaran: yeah. I think it’s mine. Yeah, okay.
279 00:38:05.240 ⇒ 00:38:05.720 Awaish Kumar: Yeah.
280 00:38:05.720 ⇒ 00:38:06.270 Uttam Kumaran: All right.
281 00:38:07.270 ⇒ 00:38:08.250 Nicolas Sucari: Thanks. Atom, bye-bye.
282 00:38:08.250 ⇒ 00:38:09.299 Uttam Kumaran: Okay, thanks guys.
283 00:38:09.550 ⇒ 00:38:10.200 Awaish Kumar: Okay.
284 00:38:11.960 ⇒ 00:38:15.180 Nicolas Sucari: I wish you don’t need to say if you need to. Yeah, okay.
285 00:38:16.770 ⇒ 00:38:17.310 Caio: Okay.
286 00:38:17.745 ⇒ 00:38:26.020 Nicolas Sucari: Great! Let me share back again the snowflake. I think I already have access to Javi Snowflake right.
287 00:38:27.160 ⇒ 00:38:31.190 Caio: I think so. I just had the access to Snowflake today, and.
288 00:38:31.190 ⇒ 00:38:34.889 Nicolas Sucari: I think it’s yeah. You’re here. So yeah.
289 00:38:34.890 ⇒ 00:38:35.340 Caio: Yeah, sure.
290 00:38:35.340 ⇒ 00:38:36.800 Nicolas Sucari: Be able rolex.
291 00:38:36.950 ⇒ 00:38:39.759 Caio: Yeah role transform, I believe.
292 00:38:39.760 ⇒ 00:38:43.430 Nicolas Sucari: Role transform. That’s okay. Yeah, let me check. But
293 00:38:45.110 ⇒ 00:38:48.680 Nicolas Sucari: I think virtual transform is enough for what you need. Yeah, okay.
294 00:38:49.137 ⇒ 00:38:51.880 Caio: Have analytics that that marks. Yeah.
295 00:38:52.950 ⇒ 00:39:15.420 Nicolas Sucari: Exactly. You can see everything that I’m looking here. Also, as was saying, like, Yeah, we have analytics. And we’re trying to move everything to broadmarts, every every raw database, every raw data source that we are getting. We’re getting it here through portable. All of the portable connectors that we are using. We get them here in these tables
296 00:39:15.580 ⇒ 00:39:35.909 Nicolas Sucari: in the schemas. So if we check, for example, shopify, we can go here and we can see all the tables that we are getting through portable. This is all of the raw data. Then look, maybe can help. But after we receive everything here in this raw database we move everything into pro intermediate, where we do
297 00:39:36.100 ⇒ 00:39:40.150 Nicolas Sucari: into dev intermediate right first, st and then into broad intermediate right, Luke.
298 00:39:40.780 ⇒ 00:39:42.270 Nicolas Sucari: I’m not sure about this one.
299 00:39:44.020 ⇒ 00:39:44.819 Luke Daque: Yeah.
300 00:39:47.520 ⇒ 00:39:55.710 Luke Daque: yeah, I’m not from. I think me, I’m not sure if the this was changed already, because I don’t think we’re using analytics anymore. Right?
301 00:39:56.230 ⇒ 00:40:07.519 Nicolas Sucari: Yeah, yeah, we we are trying. Yeah, we’re trying to move away from analytics. But in terms of like, how we’re structuring. This is everything. We get it 1st in raw in the broader base, then we do the.
302 00:40:07.520 ⇒ 00:40:08.960 Luke Daque: These are the sources.
303 00:40:09.190 ⇒ 00:40:11.220 Nicolas Sucari: Yeah, here we have all the sources.
304 00:40:11.646 ⇒ 00:40:21.469 Nicolas Sucari: Coming through portable. We have some sources that were not coming yet. These ones that don’t don’t say portable were the ones that we were syncing through 5 tron before.
305 00:40:21.812 ⇒ 00:40:42.109 Nicolas Sucari: We should get rid of these at some point. But right now we’re like we were in the in the middle of the transition between 5 turn and portable. We’re moving into portable because portable has a fixed costs and 5 turn was the costs were depending on the amount of rows that we have in each
306 00:40:42.180 ⇒ 00:41:06.950 Nicolas Sucari: in each of the data sources. That’s why, it was cheaper to use portable. And the client wanted to reduce that cost. That’s why we’re using portable. And then, yeah, we do all of the transformations that was showing in the dpt folder in the Github we get everything in intermediate, and when that is done we move everything to prodmart, so the ones. The prod tables that we should be using are these ones.
307 00:41:07.320 ⇒ 00:41:18.679 Nicolas Sucari: We still have some issues with database, as he was saying, and that we we are still using analytics. But we should be able to move everything from away from analytics into per bars. Okay.
308 00:41:18.680 ⇒ 00:41:20.060 Luke Daque: Yeah. So the the.
309 00:41:20.060 ⇒ 00:41:20.390 Nicolas Sucari: Year.
310 00:41:20.390 ⇒ 00:41:31.669 Luke Daque: The environments basically are the staging, the the dev, the staging, and the march the prod so dev staging and prod so the dev would be the
311 00:41:32.227 ⇒ 00:41:38.132 Luke Daque: anything like dev intermediate and dev march that are, those were. Those would be the databases that
312 00:41:39.090 ⇒ 00:41:47.789 Luke Daque: Well, when you’re developing like in Dbt, and we’re like targeting the development environment. Then all the models will be materialized there.
313 00:41:48.020 ⇒ 00:41:57.030 Luke Daque: And once you push anything in the Pr like, yeah, you create you. You push any commit to the Pr. The
314 00:41:57.380 ⇒ 00:42:01.780 Luke Daque: Github action will run for that specific Br, and
315 00:42:02.460 ⇒ 00:42:08.949 Luke Daque: all the models will be materialized during by that github action in the staging
316 00:42:09.320 ⇒ 00:42:14.229 Luke Daque: environment. So stage underscore intermediate and stage underscore marks.
317 00:42:14.690 ⇒ 00:42:21.539 Luke Daque: And then once you yeah, once you commit the I mean the once you merge the Pr.
318 00:42:21.670 ⇒ 00:42:28.990 Luke Daque: anything that gets run after that would be in the in the prod intermediate invites basically.
319 00:42:30.220 ⇒ 00:42:32.230 Caio: Okay, okay, go ahead.
320 00:42:32.230 ⇒ 00:42:37.509 Nicolas Sucari: Yeah, and we’re getting all of these tables. These are, yeah. So orders is coming from
321 00:42:37.640 ⇒ 00:43:03.080 Nicolas Sucari: shopify and Amazon. And then, yeah, we have the recharge stuff. We have the okay and gorgeous stuff. Here’s yeah. So we have like these tables. Probably we’ll need to understand like reviews. It’s coming from, I think, from gorgeous or Canda. I’m not sure about that one. But yeah, maybe we should change the name and try to see where we are getting from which data source we’re creating each of the table
322 00:43:03.624 ⇒ 00:43:10.620 Nicolas Sucari: orders. It’s okay, because we know what orders are. But reviews, tickets, all of those ones. Maybe we should, we should do that.
323 00:43:11.111 ⇒ 00:43:18.549 Nicolas Sucari: But yeah, everything that we are using should be here. And every time. Any of the analysts need to
324 00:43:19.060 ⇒ 00:43:27.710 Nicolas Sucari: to use one of these tables, they should be using the protomarts table. Yeah, to create the dashboards. Okay.
325 00:43:28.360 ⇒ 00:43:33.770 Nicolas Sucari: okay, that’s we are structuring these, the snowflake. Any idea or any
326 00:43:33.880 ⇒ 00:43:48.119 Nicolas Sucari: thing that you can see here, that we are not seeing, or that you should, that you think that we should do differently. Please say it. And we can, yeah, talk about that. We do, too. How to structure this better? Or what do you think? Okay.
327 00:43:48.840 ⇒ 00:43:59.780 Caio: Okay, perfect. Yeah. The only thing that came up to mind is the idea of using or or portable, just so that I understand? We have the the ingestion.
328 00:44:00.780 ⇒ 00:44:03.270 Caio: Do we have anything historical
329 00:44:04.340 ⇒ 00:44:12.499 Caio: or our data just being updated daily and like with a full refresh? And then whatever is lost is lost.
330 00:44:12.630 ⇒ 00:44:14.250 Caio: What is the state of this part.
331 00:44:14.250 ⇒ 00:44:28.409 Nicolas Sucari: No, no. So we did an initial sync let me see if I can log in. No, okay, I need to. Use a VPN, because it’s only us based. So I can do it later. But yeah.
332 00:44:28.660 ⇒ 00:44:39.680 Nicolas Sucari: we are using portable to ingest. We did an initial sync with all of the historical stuff that they they have there in each of the different sources. And right now we
333 00:44:40.114 ⇒ 00:44:57.950 Nicolas Sucari: we set up the connector so that it syncs incrementally every day. I think it’s set like I I don’t know if it is every hour or every 24 h I need to go into portable and check out that. But yeah, well, we we are getting like, incrementally all the new stuff.
334 00:44:58.511 ⇒ 00:45:01.319 Nicolas Sucari: For each data source into here. Okay.
335 00:45:01.930 ⇒ 00:45:02.770 Caio: Perfect.
336 00:45:03.960 ⇒ 00:45:19.500 Nicolas Sucari: We’re just. We’re just using Fivetran for Amazon right now. Amazon connector is not available at port in portable right now. The team is still working on that. So we’re still using Fivetran only for Amazon
337 00:45:19.820 ⇒ 00:45:24.280 Nicolas Sucari: order from and everything else should be portable. I think. Yeah.
338 00:45:25.420 ⇒ 00:45:31.549 Caio: Okay. And we are doing so an Elt, everything. There’s no transformation in in the 1st step.
339 00:45:31.910 ⇒ 00:45:32.480 Caio: Just.
340 00:45:32.480 ⇒ 00:45:32.930 Nicolas Sucari: Know what’s.
341 00:45:32.930 ⇒ 00:45:34.950 Caio: That’s Roara Roy. Okay.
342 00:45:35.410 ⇒ 00:45:40.199 Nicolas Sucari: Everything gets raw. Yeah, the only one that I think we’re getting.
343 00:45:40.850 ⇒ 00:45:47.649 Nicolas Sucari: Yeah. So Tom said, Yeah, the shopify one. I I don’t know if you’re doing something there after we get it. Robot. Yeah.
344 00:45:47.760 ⇒ 00:45:49.650 Nicolas Sucari: everything. We’re getting raw here.
345 00:45:50.100 ⇒ 00:45:50.780 Nicolas Sucari: Yeah.
346 00:45:52.150 ⇒ 00:46:05.761 Caio: Perfect. Okay, then I’ll then I’ll check the. I’ll check the tickets, and I’ll try to familiarize myself with the with the issues, so that I can well have a better understanding what is happening. But okay, it’s
347 00:46:06.970 ⇒ 00:46:11.980 Nicolas Sucari: Yeah. Also, I wanted to show you a little bit of notion. I don’t know if would. I’m show you notion
348 00:46:12.430 ⇒ 00:46:14.620 Nicolas Sucari: a bit a bit. Yes, a bit.
349 00:46:14.620 ⇒ 00:46:16.099 Caio: Oh, a little bit a little bit. Yeah.
350 00:46:17.010 ⇒ 00:46:20.361 Nicolas Sucari: So notion we have the home page
351 00:46:21.420 ⇒ 00:46:30.639 Nicolas Sucari: and if you go to personal dashboard, I don’t know if we can show you this. But personal dashboards. You’re gonna be able to see here a page for you that, says Kayo. Okay.
352 00:46:30.850 ⇒ 00:46:32.010 Caio: Yes. Okay.
353 00:46:33.120 ⇒ 00:46:42.879 Nicolas Sucari: This is like the best access for you to see all of your the tickets that you are assigned to. Across all clients. What we created like. We have like one
354 00:46:42.900 ⇒ 00:47:00.890 Nicolas Sucari: same database with all of the tickets for every client that we are using. And then these personal dashboards are filtered down by person. And you’ll you’ll be able to see, like all of the tasks assigned to you right now. I I only assign, like these 2 to you, the one that we were discussing about gorgeous dashboard
355 00:47:01.254 ⇒ 00:47:13.630 Nicolas Sucari: version one and the okay and the dashboard that we need to migrate from monthly to to metase so ideally here. Like, I try to add as much as description, context, specification as possible.
356 00:47:13.660 ⇒ 00:47:35.020 Nicolas Sucari: When we got the requirements, you’ll see that there are some properties here, but we don’t need to to do anything with this. I’m trying to. Yeah, just add as much as context possible. And for, for example, for gorgeous? These were the questions that the client gave us. And that we need to try to answer in like the 1st version of this dashboard. Right?
357 00:47:35.490 ⇒ 00:48:02.569 Nicolas Sucari: So if you if you wanna go then and just like go deeper into the data and Snowflake, try to. Yeah, just mess around a little bit on what we have there. You can also access these and have like these questions in in mind, for example, which Macros are being used the most. And if we go to Snowflake you’re gonna see that in gorgeous. We have these macros table right and you will be able to see. I don’t know the data preview here.
358 00:48:02.820 ⇒ 00:48:13.420 Nicolas Sucari: Yeah. And what are the macros like? What? What are all the information that we have? And what? Yeah, the usage of each macros, for example. So we can easily.
359 00:48:13.790 ⇒ 00:48:31.080 Nicolas Sucari: I think we can. We can just go around and see like, which are the macros being used the most right. That’s what we need to do. And that’s what we’ll need to finally create like a table or chart or something in a dashboard in metabase and try to show the answer to all of these questions.
360 00:48:31.300 ⇒ 00:48:32.000 Nicolas Sucari: Right?
361 00:48:32.610 ⇒ 00:48:39.705 Caio: Okay, okay? And for example, 1st question, if I wanna add something in this page, I can. I can.
362 00:48:40.060 ⇒ 00:48:47.950 Nicolas Sucari: Of course, of course you can. You can come down here. You can add, Yeah, I don’t know. You can just align and add.
363 00:48:48.060 ⇒ 00:48:54.830 Nicolas Sucari: and H, 2. This is in like Markdown format. So you can. Yeah, do it as well. And you can do updates
364 00:48:55.120 ⇒ 00:48:57.630 Nicolas Sucari: or, yeah, task updates, if you want. I don’t know.
365 00:48:57.630 ⇒ 00:48:58.340 Caio: Okay?
366 00:48:58.780 ⇒ 00:49:00.320 Caio: And another question is like.
367 00:49:01.440 ⇒ 00:49:24.869 Nicolas Sucari: Yeah, you can write here. You can tag people if you want, for example. Yeah, you see, I and you’ll get like a notification. Or if you don’t want to add it here, you can add, here comments, and yeah, you can tag also. For example, Kyle, let me tag you, and you will, you know, in notion you’ll receive like in the inbox you’ll receive like, Hey, you’ve been mentioned in a comment or something, so that you can easily find this.
368 00:49:26.050 ⇒ 00:49:32.939 Caio: Okay. And for example, let’s say that I’m I’m looking at the macro part, just for example, and then
369 00:49:33.070 ⇒ 00:49:53.420 Caio: I feel that I need some. Let’s say that I feel that I need a specification or something for a specific metric or column, or something. Is there a documentation for that, or or can I present already start building something? For example, what is the definition of I don’t know what is an agent? Let’s say. Well to understand more about.
370 00:49:53.420 ⇒ 00:49:53.750 Nicolas Sucari: Yeah.
371 00:49:53.750 ⇒ 00:49:54.460 Caio: Buying.
372 00:49:54.880 ⇒ 00:50:03.249 Nicolas Sucari: No, we don’t have. I don’t think we have these definitions, but we can go in the like gorgeous docs. Maybe.
373 00:50:03.930 ⇒ 00:50:04.600 Caio: Okay.
374 00:50:05.140 ⇒ 00:50:11.419 Nicolas Sucari: We can look into this gorgeous, and we can try to understand like, I don’t know, Macros.
375 00:50:11.970 ⇒ 00:50:12.720 Caio: Perfect.
376 00:50:12.720 ⇒ 00:50:14.220 Nicolas Sucari: Let me see.
377 00:50:15.530 ⇒ 00:50:36.739 Nicolas Sucari: And yeah, we can. Yeah, try to understand. I don’t know. Manage Macros. What are the macros? What? Yeah. And all of these we we should go to the each tool docs and try to see like what these are used to. Or also you can ask, you can. If if you know the questions, or if you know what you need to know, let me know, and we can. Yeah. Should those questions to the client to yep.
378 00:50:36.890 ⇒ 00:50:40.159 Caio: Perfect. Perfect. Yeah, cool. Cool.
379 00:50:40.723 ⇒ 00:50:45.209 Caio: Yeah, I think, for now those are those are my questions. Yeah, I have already some, some.
380 00:50:45.210 ⇒ 00:50:45.980 Nicolas Sucari: Excellent.
381 00:50:46.090 ⇒ 00:50:52.810 Nicolas Sucari: Also, if you want, like more context on the client, you can go. You can go back to the home page.
382 00:50:53.362 ⇒ 00:51:11.110 Nicolas Sucari: There is a lot of information here, like, internally, we have, like this data homepage where we have, like all tasks or all clients with due dates like, yeah, that a team overview who has, like the most task assigned, and all of that we have here all of the tasks per per client
383 00:51:11.397 ⇒ 00:51:25.459 Nicolas Sucari: by engine by engineer. Here, we need to create a view for you. I can do that. On the same by status, like all of the tasks, for every client is here. But then, if you want to go to clients, you can go to the homepage back.
384 00:51:25.970 ⇒ 00:51:29.160 Nicolas Sucari: and you can go to clients.
385 00:51:30.700 ⇒ 00:51:38.180 Nicolas Sucari: and here you’ll see, like all the clients that we have, you can click on one of the pages of the clients, and we have, like these
386 00:51:38.420 ⇒ 00:51:40.920 Nicolas Sucari: internal page for for us
387 00:51:41.050 ⇒ 00:51:53.390 Nicolas Sucari: with a lot of information. We have, like, yeah, company information here, the website, some scope that we initially had company profile, which are the members. Yeah, we we have, like the 3 contacts.
388 00:51:53.840 ⇒ 00:51:59.940 Nicolas Sucari: the like, high end objectives for the client. And yeah, we have, like here all of the tasks
389 00:52:00.320 ⇒ 00:52:02.909 Nicolas Sucari: that we have assigned for this client.
390 00:52:03.509 ⇒ 00:52:10.769 Nicolas Sucari: With all of the history, the done ones and all of the pipeline. We have, like these objectives with this roadmap kind of what we are working on.
391 00:52:11.342 ⇒ 00:52:16.350 Nicolas Sucari: And what we are working right now with the client is like having, like the estimations
392 00:52:16.380 ⇒ 00:52:36.810 Nicolas Sucari: for for each of the tasks that we have. So before you start working like we have this client view in in here. Yesterday I was working with the client on this, trying to understand. Like, yeah, what are the estimations of how? How we can estimate the Max estimated hours that we are gonna spend working on each of these clients on on each of these tasks. Sorry
393 00:52:37.080 ⇒ 00:52:54.919 Nicolas Sucari: the client wants us to like. Try to estimate, and then try to actually say how many hours we spend on this because we have, like, yeah, an a fixed amount of hours per month that we need to work with them. And yeah. And they are trying to have, like all of that detail. So this is what I’m working on right now.
394 00:52:55.040 ⇒ 00:53:13.779 Nicolas Sucari: And if you need to create like that, Doc, that we were talking about for the metrics definition, and to have, like all of the comments we can go back here. We have Javi resources here. And in here we have like more information. Yeah, we have, like, yeah, so the data pipeline.
395 00:53:13.920 ⇒ 00:53:32.809 Nicolas Sucari: Yeah, this is something old that we have for the pipeline. We need to change this. These were how the each of these tables were created, or lineage for all of them. We have like this diagram that we created to here all of the links to the real dashboards that we have.
396 00:53:33.258 ⇒ 00:53:38.339 Nicolas Sucari: Yeah, I don’t know. We have like a lot of stuff here in resources, too.
397 00:53:39.110 ⇒ 00:53:40.070 Nicolas Sucari: Some don’t
398 00:53:40.390 ⇒ 00:53:51.879 Nicolas Sucari: documents on how to set up real. This is all stuff that we created, but we have it here just in case you need, or anyone needs to go and look at at something. See.
399 00:53:52.630 ⇒ 00:54:01.310 Caio: Okay, no, perfect. I think that’s the place I was talking about like something like that, so that I can if I’m starting, you know my ticket and everything. I feel that I miss.
400 00:54:01.440 ⇒ 00:54:15.299 Caio: You know, some definition or something that I can create the page for definitions over there, so that we have more documentation, so that anyone that comes on board in Javi would understand exactly what each table is, what column is or whatever is needed.
401 00:54:15.300 ⇒ 00:54:18.480 Luke Daque: Yeah, that’s a, that’s a great point. We
402 00:54:19.250 ⇒ 00:54:24.754 Luke Daque: yeah, we we currently don’t have something like that yet. So that would be great to have. Yeah.
403 00:54:25.420 ⇒ 00:54:45.480 Nicolas Sucari: Yeah. So if you want yeah, I can. I can give you some time to look at the data to. Just yeah, get to know all the bit. All of this information that we have some some information is sold, so don’t rely on that as much, but it will give you context about what we’ve been doing for the client and then looking at the snowflake, will.
404 00:54:45.910 ⇒ 00:55:07.080 Nicolas Sucari: Yeah. Let you understand a little bit more what we are doing right now, looking at the notion board with the tasks will also let you know what we are like actively working on right now, and if then, if we need to create like that metric definition document for each dashboard, we can create it here like we can come here at a page.
405 00:55:07.320 ⇒ 00:55:10.539 Nicolas Sucari: maybe right with the I don’t know.
406 00:55:10.830 ⇒ 00:55:21.070 Nicolas Sucari: Dashboards, metric matrix notification right? And we can start. I don’t know. We can say gross margin dash.
407 00:55:22.380 ⇒ 00:55:24.270 Nicolas Sucari: We can create a table here
408 00:55:25.100 ⇒ 00:55:30.400 Nicolas Sucari: with, you say, metric name right? Metric definition.
409 00:55:32.940 ⇒ 00:55:35.850 Nicolas Sucari: And we can add column with comments
410 00:55:36.940 ⇒ 00:55:45.489 Nicolas Sucari: right? And we can use this to just start adding, like all of the metrics, what are the comments that you have for the gross margin? Dash! We can duplicate these.
411 00:55:46.420 ⇒ 00:55:47.580 Nicolas Sucari: maybe right?
412 00:55:49.350 ⇒ 00:55:54.730 Nicolas Sucari: Here and we can do for the Amazon dash.
413 00:55:55.930 ⇒ 00:55:58.679 Nicolas Sucari: Okay, you can add the link here
414 00:56:01.520 ⇒ 00:56:03.420 Nicolas Sucari: and like another link for this one.
415 00:56:03.770 ⇒ 00:56:07.810 Nicolas Sucari: so that we can have yeah, all of these, and we can start working at this.
416 00:56:09.060 ⇒ 00:56:09.460 Caio: Amazing.
417 00:56:09.460 ⇒ 00:56:13.420 Nicolas Sucari: This was, this was what you were like trying to picture.
418 00:56:13.920 ⇒ 00:56:14.400 Nicolas Sucari: Yeah.
419 00:56:14.400 ⇒ 00:56:16.379 Caio: Yeah, yeah, something like that is.
420 00:56:17.460 ⇒ 00:56:18.160 Nicolas Sucari: Great.
421 00:56:18.460 ⇒ 00:56:18.780 Caio: Okay.
422 00:56:19.980 ⇒ 00:56:26.580 Nicolas Sucari: Yeah, I can add the links for metabase. Let me see if I can easily find those ones.
423 00:56:29.380 ⇒ 00:56:36.140 Nicolas Sucari: But yeah, I’ll I’ll try to give you access. I think we have. Did you have access to one password? Did do them give you access.
424 00:56:36.140 ⇒ 00:56:37.630 Caio: Yes, yes.
425 00:56:38.670 ⇒ 00:56:44.960 Nicolas Sucari: Okay, perfect. This is the gross margin dashboard. It’s now called net margin. But this is the one
426 00:56:45.890 ⇒ 00:56:47.129 Nicolas Sucari: that we are using
427 00:56:51.200 ⇒ 00:56:57.769 Nicolas Sucari: there and back to the Amazon dashboard.
428 00:57:00.170 ⇒ 00:57:00.840 Nicolas Sucari: Yeah.
429 00:57:02.660 ⇒ 00:57:03.490 Caio: Okay.
430 00:57:06.950 ⇒ 00:57:08.470 Nicolas Sucari: Yep, perfect.
431 00:57:09.230 ⇒ 00:57:12.460 Nicolas Sucari: Okay, but that’s a little bit of
432 00:57:12.690 ⇒ 00:57:19.999 Nicolas Sucari: contacts. And Javi, let me know I’m free today. In the afternoon, if you want to go back to those metrics
433 00:57:20.544 ⇒ 00:57:33.219 Nicolas Sucari: to those dashboards. Try to figure out the metrics and try to start adding like information. If you wanna work with me on that. That’d be great. I can. Yeah, we can go together and try to look at at those ones. Okay.
434 00:57:33.660 ⇒ 00:57:37.149 Caio: Okay, cool. I’ll take a look into everything, and then I’ll open you.
435 00:57:39.240 ⇒ 00:57:51.719 Nicolas Sucari: Excellent anything else. You need any any explanation on notion stuff, Snowflake anything? Let me know. We can. Obviously, with Luke, we can help on anything. Yeah.
436 00:57:52.270 ⇒ 00:57:53.700 Caio: Okay, cool. Yeah, perfect.
437 00:57:53.700 ⇒ 00:57:57.440 Luke Daque: You can. You can slack us any anytime as well like. Don’t be.
438 00:57:57.440 ⇒ 00:57:57.760 Caio: Okay.
439 00:57:59.450 ⇒ 00:58:01.710 Luke Daque: Okay, like, yeah, cool.
440 00:58:01.710 ⇒ 00:58:05.200 Caio: Perfect perfect will do. Thank you. Guys really appreciate it.
441 00:58:05.980 ⇒ 00:58:13.030 Nicolas Sucari: Are you? Gonna stay? You’re gonna stay at Portugal, or you’re gonna or you’re living in Portugal, right? Or you’re.
442 00:58:13.357 ⇒ 00:58:15.320 Caio: Living in Spain officially in Spain.
443 00:58:15.320 ⇒ 00:58:15.670 Nicolas Sucari: Oh!
444 00:58:15.670 ⇒ 00:58:20.419 Caio: But I came here to spend a bit of the winter here because I have family here, so it’s a bit easier.
445 00:58:20.420 ⇒ 00:58:20.740 Nicolas Sucari: Okay.
446 00:58:20.740 ⇒ 00:58:27.090 Caio: But I’m moving. I’m moving back still. Don’t know yet. Maybe next week or 2 weeks I’m still doing the logistics.
447 00:58:27.200 ⇒ 00:58:27.760 Caio: Yeah.
448 00:58:27.760 ⇒ 00:58:29.030 Nicolas Sucari: In Madrid, or where.
449 00:58:29.560 ⇒ 00:58:52.173 Caio: So I was in Barcelona which is really nice, but it’s getting a bit complicated over there. There’s a huge rent situation. It’s really complicated. And yeah, and then also, there’s some problems happening in the city all the time. Then I’m thinking, maybe moving to Madrid is nice, but it’s either too cold or too hot.
450 00:58:52.550 ⇒ 00:58:53.020 Nicolas Sucari: Yeah.
451 00:58:53.460 ⇒ 00:58:58.160 Nicolas Sucari: Yeah, summer in Madrid is awful because you don’t have the water. So. But yeah, I know.
452 00:58:58.160 ⇒ 00:59:10.965 Caio: Exactly. And then I’m still trying to see. Maybe people say a lot of good things about Valencia. There’s some other cities as well, so I’m still checking. But yeah, at some point I know that I will, captain. And I say, Hey, I’m gone.
453 00:59:11.430 ⇒ 00:59:11.960 Caio: Yeah.
454 00:59:11.960 ⇒ 00:59:14.559 Nicolas Sucari: And no, no! Coming back to Brazil right.
455 00:59:15.140 ⇒ 00:59:19.920 Caio: For now, for now. No I’m still thinking about like next year.
456 00:59:20.150 ⇒ 00:59:26.230 Caio: but I think this year I’ll be. I’ll be around here. But then, yeah, depending, let’s see what happens.
457 00:59:26.960 ⇒ 00:59:30.739 Nicolas Sucari: Yeah, if you go back to Brazil we can meet. I’m in Buenos Aires. So.
458 00:59:31.072 ⇒ 00:59:32.069 Caio: You’ve been nice.
459 00:59:32.070 ⇒ 00:59:34.799 Nicolas Sucari: Yeah, I’m from Argentina. I live in Buenos Aires.
460 00:59:35.440 ⇒ 00:59:35.970 Caio: Okay.
461 00:59:36.140 ⇒ 00:59:44.399 Nicolas Sucari: Yeah, I haven’t visited Buenos Aires yet. It’s a place that I really like to visit. And when I came to Spain I met a lot of Argentina. So it’s like, Yeah, now, I have to go there.
462 00:59:45.060 ⇒ 00:59:46.110 Nicolas Sucari: Yeah, I have a lot of.
463 00:59:47.610 ⇒ 00:59:51.320 Luke Daque: I am in the other side of the world. I’m in the Philippines.
464 00:59:51.894 ⇒ 01:00:01.900 Caio: Philippines, cool, cool. You know that Philippines also can come to Spain, and and and even I mean, I’m not sure about your situation. But I’m
465 01:00:02.040 ⇒ 01:00:06.680 Caio: this year I will apply to the Spanish citizenship one of the reason why Spain
466 01:00:06.900 ⇒ 01:00:09.900 Caio: and I think Philippines also big. Yeah, you guys.
467 01:00:10.510 ⇒ 01:00:15.510 Nicolas Sucari: Can do the same and live in Spain, you know, if you, if you want that one day.
468 01:00:15.510 ⇒ 01:00:17.300 Luke Daque: The citizenship right.
469 01:00:17.300 ⇒ 01:00:20.320 Nicolas Sucari: Yeah, there is a lot of people who speak Spanish right.
470 01:00:20.560 ⇒ 01:00:30.190 Luke Daque: Yeah, yeah, yeah. Even even the language is very similar to Spanish, like, some words are the same like, and like.
471 01:00:30.190 ⇒ 01:00:30.560 Nicolas Sucari: Yeah.
472 01:00:30.560 ⇒ 01:00:43.802 Luke Daque: Because, yeah, yeah, like Philippines was colonized by Spain a long time ago. So like, there’s a lot of Spanish influence, even like the old houses. The designs are very Spanish, and like stuff like that
473 01:00:44.390 ⇒ 01:00:45.090 Luke Daque: very cool.
474 01:00:45.090 ⇒ 01:00:46.110 Caio: Nice.
475 01:00:46.980 ⇒ 01:00:51.059 Caio: So you are in. How? What is your time? Zone?
476 01:00:52.330 ⇒ 01:00:57.749 Luke Daque: It’s like 1011 pm, right now. So yeah.
477 01:00:58.372 ⇒ 01:01:02.559 Caio: Okay, okay. So let I will let you go. Then it’s already.
478 01:01:03.070 ⇒ 01:01:04.070 Luke Daque: Yeah, that’s fine. No.
479 01:01:04.070 ⇒ 01:01:09.919 Nicolas Sucari: But yeah, he’s working yeah, different time zone, like he’s trying to match the Us.
480 01:01:09.920 ⇒ 01:01:10.300 Luke Daque: Hang on!
481 01:01:10.300 ⇒ 01:01:11.050 Nicolas Sucari: Right, Luke.
482 01:01:11.360 ⇒ 01:01:20.629 Luke Daque: Yeah, something like that. But sometimes like, like 3 in the morning, my brain’s already dead or something. So yeah, I I try.
483 01:01:20.630 ⇒ 01:01:21.220 Caio: I didn’t know.
484 01:01:21.220 ⇒ 01:01:22.940 Luke Daque: Help as much as I can.
485 01:01:23.210 ⇒ 01:01:23.790 Caio: I can.
486 01:01:23.790 ⇒ 01:01:26.510 Nicolas Sucari: For you, Cayo. It’s like 4 pm. Right.
487 01:01:27.100 ⇒ 01:01:29.280 Caio: Now it’s 3 Pm. In Portugal for.
488 01:01:29.280 ⇒ 01:01:29.620 Nicolas Sucari: Privian.
489 01:01:29.620 ⇒ 01:01:32.680 Caio: In the space. Yes, nice. Nice. Yeah.
490 01:01:32.680 ⇒ 01:01:33.200 Nicolas Sucari: Okay.
491 01:01:33.310 ⇒ 01:01:34.240 Caio: So, yeah.
492 01:01:34.240 ⇒ 01:01:34.690 Nicolas Sucari: Cool.
493 01:01:34.690 ⇒ 01:01:37.879 Caio: Okay, perfect guys. Thank you very much. Yeah. I’ll ping you for sure.
494 01:01:38.240 ⇒ 01:01:38.680 Luke Daque: Yeah, sure.
495 01:01:38.680 ⇒ 01:01:41.270 Nicolas Sucari: Course, ping us in slack if you need anything. Okay.
496 01:01:41.570 ⇒ 01:01:43.260 Caio: Perfect. Thank you.
497 01:01:43.450 ⇒ 01:01:43.950 Luke Daque: Weeks.
498 01:01:43.950 ⇒ 01:01:45.759 Nicolas Sucari: See you see you later. Bye, bye, guys.