Meeting Title: Analytics Engineering Daily Sync Date: 2025-03-10 Meeting participants: Aakash Tandel, Luke Daque, Uttam Kumaran, Demilade Agboola, Awaish Kumar, Caio Velasco


WEBVTT

1 00:01:40.930 00:01:42.420 Demilade Agboola: Hello! Good morning.

2 00:01:42.820 00:01:43.969 Demilade Agboola: Morning! How are you?

3 00:01:44.530 00:01:47.869 Aakash Tandel: Not bad. How are you doing? My name’s Akash. We haven’t met yet.

4 00:01:48.872 00:02:02.209 Demilade Agboola: Yeah, we have my one on one so my name is I am the new Ae hire. I just started probably this week, but you know I’ve been. I’ve been in and out of meetings from last week.

5 00:02:02.880 00:02:10.209 Aakash Tandel: Cool. Yeah, I also just just started kind of doing some pn advisory stuff. So yeah.

6 00:02:11.100 00:02:15.549 Aakash Tandel: Luke, nice to meet you. My name’s Akash. We haven’t met one on one yet, but look forward to it in future.

7 00:02:16.400 00:02:20.630 Luke Daque: Yeah, Hi, guys, Hi, Kash, welcome to Brainforge.

8 00:02:21.430 00:02:22.070 Aakash Tandel: Thanks.

9 00:02:23.840 00:02:24.710 Luke Daque: Cool.

10 00:02:26.140 00:02:28.300 Luke Daque: You’re from Willow Tree, right?

11 00:02:28.460 00:02:30.790 Luke Daque: Or are you still working at Willatra? At the moment.

12 00:02:31.640 00:02:38.219 Aakash Tandel: Yeah. Still working at wheelchair at the moment doing this is kind of a contractor consultant style for a bit. Yeah.

13 00:02:39.493 00:02:53.279 Aakash Tandel: Yeah. And I won’t be able to meet with you guys every I just wanted to meet one time for this sync I won’t be able to do this every day, because I’m in the office 3 days a week, so it’s a little hard.

14 00:02:53.280 00:02:54.250 Luke Daque: Yeah.

15 00:02:54.930 00:02:57.459 Luke Daque: Cool. Where are you located at at the moment.

16 00:02:58.489 00:03:02.679 Aakash Tandel: I’m outside of Washington, DC, about 2 h Charlottesville, Virginia.

17 00:03:04.050 00:03:04.910 Luke Daque: Nice.

18 00:03:05.300 00:03:16.710 Demilade Agboola: Interesting. I was in Virginia last year, actually. Well, not the not that side of it. Yeah, but I just I just feel like the Dnb. And it was it was nice to be able to like. Go around.

19 00:03:17.420 00:03:21.490 Demilade Agboola: Went to the White House just like just random terrorist stuff.

20 00:03:22.090 00:03:29.220 Aakash Tandel: Nice. Yeah, I lived in DC for a really long time. It’s yeah. It’s a lot of fun. Living up there. It’s a good area.

21 00:03:29.350 00:03:30.150 Aakash Tandel: Food’s really good.

22 00:03:30.150 00:03:31.740 Demilade Agboola: Traffic. It’s a lot of traffic, though.

23 00:03:32.120 00:03:34.659 Aakash Tandel: Traffic sucks. That is the one thing that traffic doesn’t get.

24 00:03:38.440 00:03:44.669 Luke Daque: Yeah, I might be there this July, and like end of July to early August, I guess.

25 00:03:47.130 00:03:47.490 Aakash Tandel: Yeah.

26 00:03:47.490 00:03:53.918 Luke Daque: Some of the museums in DC. As well, something like the White House and stuff hopefully.

27 00:03:54.530 00:04:01.860 Aakash Tandel: Yeah, all the museums and stuff on the Mall are free, too, which is really nice. You don’t have to pay like an arm and a leg to go to a bazillion of them.

28 00:04:02.860 00:04:04.900 Luke Daque: Yeah. Nice cool.

29 00:04:10.420 00:04:15.220 Luke Daque: I wonder if it comes to joining or somebody? Kyle’s joining.

30 00:04:16.089 00:04:17.559 Caio Velasco: Hello, guys, certainly.

31 00:04:19.420 00:04:22.109 Caio Velasco: Hi, guys. Hi Kyle, no.

32 00:05:40.350 00:05:42.220 Luke Daque: How are you guys this weekend?

33 00:05:44.080 00:05:48.380 Luke Daque: Anything exciting that happened during the weekend.

34 00:05:53.710 00:05:59.420 Caio Velasco: Not on my end. It was like just a chill home weekend stay at home.

35 00:06:03.270 00:06:05.480 Luke Daque: Yeah. Same here. Nothing much. As well.

36 00:06:06.020 00:06:11.060 Aakash Tandel: I went to a 3 year old, they party at a bowling alley. I don’t know how entertaining that sounds, but

37 00:06:11.300 00:06:12.679 Aakash Tandel: it was interesting.

38 00:06:13.270 00:06:14.419 Demilade Agboola: Did the 3 year old win.

39 00:06:14.420 00:06:15.330 Luke Daque: Interesting.

40 00:06:16.140 00:06:30.910 Aakash Tandel: The the 3 year old. Well, there was like mostly a lot of little kids that were doing the bowling, and they they didn’t have fun they had like a slide they put the ball on so it rolls down, and then the bumpers are up, so there’s like no way for them to completely lose, which I guess makes sense.

41 00:06:32.030 00:06:33.009 Luke Daque: Yeah, that’s cool.

42 00:06:33.840 00:06:40.670 Luke Daque: Do they have like, do have? Do they have the the lighter balls, though, like, how they care? Yeah.

43 00:06:41.510 00:06:52.936 Aakash Tandel: And it was different than the bowling alley I grew up with. The bowling alley I grew up with is like kind of dark and dingy and filled with smoke, cause you could smoke back then indoors, and that was not the case nowadays.

44 00:06:54.440 00:06:55.090 Luke Daque: Yeah.

45 00:07:01.390 00:07:02.476 Uttam Kumaran: Hey, guys?

46 00:07:03.150 00:07:04.090 Uttam Kumaran: Sorry for the delay.

47 00:07:04.090 00:07:06.180 Luke Daque: Hey? Tom no worries.

48 00:07:06.180 00:07:08.049 Uttam Kumaran: What’s what’s the topic?

49 00:07:10.340 00:07:13.786 Luke Daque: Nothing much. We were just talking about our weekend.

50 00:07:14.950 00:07:15.520 Uttam Kumaran: Nice.

51 00:07:15.520 00:07:17.480 Demilade Agboola: 3 and 3. I was born in.

52 00:07:20.390 00:07:22.290 Uttam Kumaran: I haven’t been bowling in a long time.

53 00:07:23.640 00:07:25.750 Uttam Kumaran: although they have like. They have like bowling.

54 00:07:26.240 00:07:28.490 Uttam Kumaran: They have like bowling bars now.

55 00:07:28.750 00:07:33.459 Uttam Kumaran: but I like the I’d I’d rather go to ping pong like I’d rather go to spin.

56 00:07:33.620 00:07:35.319 Uttam Kumaran: or one of the ping pong

57 00:07:36.100 00:07:41.459 Uttam Kumaran: like bars. It’s and there’s like a thousand balls everywhere. It’s like super fun.

58 00:07:43.450 00:07:44.839 Luke Daque: Yeah, I’m I’m

59 00:07:45.110 00:07:51.189 Luke Daque: I recently got back, you know, to to table tennis as well, and they got addicted to it. So

60 00:07:52.140 00:07:53.279 Luke Daque: every day, you know.

61 00:07:57.980 00:08:07.369 Uttam Kumaran: Awesome cool. Well, let’s maybe kick off. I think. Devil lotto welcome aish welcome back.

62 00:08:08.960 00:08:15.105 Uttam Kumaran: Yeah, I I guess I I just wanted to talk about. Probably the core theme of

63 00:08:15.670 00:08:23.540 Uttam Kumaran: this week, which is just gonna probably be a lot of knowledge transfer and documentation around

64 00:08:23.710 00:08:33.344 Uttam Kumaran: Eden around joby especially as we look to onboard amber and then soon Stephen onto

65 00:08:34.340 00:08:37.580 Uttam Kumaran: onto clients in terms of project management.

66 00:08:39.539 00:08:41.859 Uttam Kumaran: So we can take this conversation.

67 00:08:42.418 00:08:48.690 Uttam Kumaran: Anywhere we’d like to go. I mean, definitely, we can spend some time talking about Eden.

68 00:08:49.172 00:08:53.760 Uttam Kumaran: I think I can also spend some time talking about the Javi roadmap.

69 00:08:54.334 00:08:58.809 Uttam Kumaran: We have our even. We had a conversation about Eden right after this.

70 00:09:00.560 00:09:07.789 Uttam Kumaran: I think probably where this this conversation will go more over. Time is as a horizontal ae group.

71 00:09:08.330 00:09:10.370 Uttam Kumaran: We’ll start to use this time.

72 00:09:10.863 00:09:23.849 Uttam Kumaran: You know, to do like probably like live Pr reviews or more like conversations about concepts or platform things. But I think while we’re sort of in this knowledge, transfer mode.

73 00:09:25.005 00:09:32.699 Uttam Kumaran: I think that’s probably the number one priority. I don’t know any other. Any thoughts for for anyone else about what we want to cover today.

74 00:09:37.090 00:09:50.849 Demilade Agboola: No, I think that’s pretty fine. Just being able to talk about like what’s going on, as well as maybe the opportunity to talk about anything that could also just be generally helpful. So new tips or tricks could also always be helpful.

75 00:09:51.190 00:09:58.730 Uttam Kumaran: Yeah, I feel I feel the same way. I mean, I think it’s it’s nice to get this crew together outside of like a specific client meeting.

76 00:09:59.286 00:10:11.343 Uttam Kumaran: And I think over time, especially as my time frees up. I’m gonna start looking more horizontally at options about how we can speed up developer workflows and and things like that.

77 00:10:12.950 00:10:17.219 Uttam Kumaran: So maybe let’s talk a little bit about Eden. I think maybe

78 00:10:17.350 00:10:27.772 Uttam Kumaran: them a lot. It may be helpful just to hear what your questions are. And then I want to give you guys a little bit of a sense of

79 00:10:28.860 00:10:34.870 Uttam Kumaran: like what we’re gonna be working on in terms of each client. I think. Akash, the document you sent

80 00:10:35.040 00:10:40.479 Uttam Kumaran: was was really really good. I’ll actually probably just send it here

81 00:10:40.700 00:10:43.220 Uttam Kumaran: for for everyone to take a look at

82 00:10:46.340 00:10:52.259 Uttam Kumaran: And I I think this is helpful actually, for everybody who’s on a client team to have this?

83 00:10:53.600 00:11:03.669 Uttam Kumaran: I guess maybe. Akash, do you want to talk about like, when is this? This? Is this a similar document that’s used to onboard, even like engineers onto new projects. Do you?

84 00:11:03.860 00:11:09.170 Uttam Kumaran: Do you? Do you find that like it’s just necessary to keep one of these up to date for every single client.

85 00:11:09.820 00:11:34.799 Aakash Tandel: Yeah, I think it definitely is helpful, especially at the project. Kickoff. Like the engineering team. And folks on this call might not feel like you need to know all the details of like the industry of the client which makes total sense. But having, like a high level overview of the client. Their business. Kind of how we are scoping our project. The ways that we’re being paid. That type of thing is all helpful. Just so we have

86 00:11:34.800 00:11:47.720 Aakash Tandel: have an idea of how the project is going to go forward. So yeah, I think this is definitely a document. I used to like to have business development or sales. Folks write this type of thing up and give it to project teams before we kicked off.

87 00:11:47.994 00:12:00.609 Aakash Tandel: That usually helps with like just getting general understanding of what was talked about during the sales process and into the people who are actually doing the works hands but that doesn’t always happen so. You know, if this is retrospective, that’s fine, too.

88 00:12:01.540 00:12:14.820 Uttam Kumaran: Okay? Cool. Yeah, I think, for we’re in a funny mode where we are adapting our existing clients the best process. And then, of course, every new client we want to use this. So there’s gonna be a little bit of

89 00:12:14.930 00:12:17.442 Uttam Kumaran: transition here. That’s fine.

90 00:12:18.910 00:12:25.014 Uttam Kumaran: so I think this is probably what we’re going to look to fill out for each client. I know.

91 00:12:26.190 00:12:38.999 Uttam Kumaran: Demolata. You also have some questions about Eden. I guess, to give you a state of also the team there. So right now it’s it’s primarily been me, Robert. Bo

92 00:12:39.060 00:12:59.848 Uttam Kumaran: and Sahana Aish joined and is also taking on some analytics engineering work, especially around building data marts. Of course, my time has been very, very limited. I just sort of play hero when I need to be last week a waste was out, so there was just a lot of stuff

93 00:13:00.420 00:13:02.390 Uttam Kumaran: I had to push through. That was like

94 00:13:02.640 00:13:18.400 Uttam Kumaran: it just was kind of hard to to make all that happen. But I’m hopeful that between you and a waste here there’s enough to sort of push forward on the analytics engineering side that the analyst team there is also struggled on

95 00:13:18.730 00:13:22.910 Uttam Kumaran: like, okay, where is where is this data? And then

96 00:13:23.040 00:13:32.149 Uttam Kumaran: there’s like a couple of pieces of core logic around orders, bundles, subscriptions that they’re often confused on

97 00:13:33.900 00:13:40.679 Uttam Kumaran: And yeah, they just basically our group just needs to really own and be able to explain

98 00:13:42.200 00:13:47.396 Uttam Kumaran: logic. A waste. You want to give any other context to

99 00:13:48.450 00:13:52.419 Uttam Kumaran: to demo Ade about the client, or what’s been done so far.

100 00:13:55.282 00:14:03.399 Awaish Kumar: Like follow Eden, we have built like we are. We are transitioning their old data marks to the

101 00:14:03.990 00:14:05.160 Awaish Kumar: fine.

102 00:14:05.400 00:14:13.720 Awaish Kumar: to the new building. New models for them. So we have built like sales data mark marketing customer support.

103 00:14:14.150 00:14:22.830 Awaish Kumar: And while doing that we are still supporting the old ones for for the day-to-day

104 00:14:23.646 00:14:27.303 Awaish Kumar: like to answer their day to day analytical queries.

105 00:14:28.290 00:14:32.909 Awaish Kumar: and but we want to like move all their old models to the new ones.

106 00:14:37.640 00:14:39.400 Uttam Kumaran: So yeah, go ahead.

107 00:14:39.800 00:14:48.609 Demilade Agboola: I was curious as to what exactly does it seem like? They have like expectations, and I’ve seen that a bunch of dashboards in tableau.

108 00:14:48.730 00:14:52.400 Demilade Agboola: and I think for me it’s a question of like bomb.

109 00:14:52.710 00:14:56.599 Demilade Agboola: the dashboards that we have or we’ve created. Are they not sufficient?

110 00:14:57.015 00:15:04.320 Demilade Agboola: Or are they looking for something in particular? Do they need like particular metrics that they’re trying to, you know. Get on top of. And we’re not able to provide that.

111 00:15:05.850 00:15:06.610 Uttam Kumaran: Yeah. Good question.

112 00:15:06.610 00:15:09.639 Uttam Kumaran: So there’s yeah, go ahead. Go ahead. Go ahead. Aish.

113 00:15:10.550 00:15:20.169 Awaish Kumar: Yeah, they like they have dashboards which are in the looker studio. So number one, we are thing we are handling is like migrating from looker studio to the tableau.

114 00:15:20.530 00:15:28.429 Awaish Kumar: and secondly, like in the local studio, they have. They have some models, and on top of it.

115 00:15:29.061 00:15:36.999 Awaish Kumar: they have built the dashboards, but those how they have built the models they are not like properly.

116 00:15:38.562 00:15:54.079 Awaish Kumar: what you say, a model, you know, like such as as a star schema or something. So they’re just pulling data from different tables and have built something. But we just want to optimize it and standardize it. How we do the modeling and

117 00:15:54.510 00:15:59.599 Awaish Kumar: make it more readable, and how we then build the dashboards.

118 00:16:01.280 00:16:03.999 Demilade Agboola: Okay, alright, and so

119 00:16:04.120 00:16:13.439 Demilade Agboola: I think that gives us an advantage. Because if we have access to look at studio, we can kind of ensure that our numbers are accurate without giving them like bad numbers? Right?

120 00:16:15.380 00:16:18.339 Demilade Agboola: Or do we have access to local city? I think that’s the 1st question.

121 00:16:20.190 00:16:21.509 Uttam Kumaran: We do have access.

122 00:16:21.960 00:16:25.929 Demilade Agboola: I’ll consider find great. So I mean we have kind of.

123 00:16:25.930 00:16:34.869 Uttam Kumaran: Access. Yeah, we have access there. So we we inherited sort of a look at studio. And I don’t know you have. You used looker studio before it. Kind of sucks.

124 00:16:35.510 00:16:40.609 Demilade Agboola: Yeah, yeah, it’s not the best, but I mean it. It can work for some people. So like, for example.

125 00:16:41.060 00:16:41.510 Uttam Kumaran: Yeah.

126 00:16:41.650 00:16:42.210 Demilade Agboola: It’s the.

127 00:16:42.210 00:16:52.150 Uttam Kumaran: They? They had some. They had some needs that were outside of that. We made a decision on tableau

128 00:16:52.596 00:17:04.040 Uttam Kumaran: that I don’t know. I feel like team is kind of regretting but you know, like at at the Enterprise level, like there’s only a couple of options right? There’s tableau, Sigma.

129 00:17:04.390 00:17:07.010 Uttam Kumaran: and like probably look her right.

130 00:17:07.440 00:17:10.609 Uttam Kumaran: It’s not like one is like so much better than the other.

131 00:17:10.810 00:17:13.760 Uttam Kumaran: I mean, I’m kind of like I

132 00:17:14.180 00:17:18.609 Uttam Kumaran: my career. I did a lot of looker, so I’m a little bit biased, but

133 00:17:19.400 00:17:21.630 Uttam Kumaran: I don’t know. It’s I feel like it’s not.

134 00:17:22.030 00:17:29.870 Uttam Kumaran: You can get away with with any of them. But the one thing is yes, there’s a big question about data accuracy.

135 00:17:29.980 00:17:32.760 Uttam Kumaran: In fact, every week we find out

136 00:17:32.870 00:17:37.610 Uttam Kumaran: like, Oh, there’s some other nuance to a metric.

137 00:17:37.770 00:17:40.940 Uttam Kumaran: or the way we calculate a shipping thing.

138 00:17:42.340 00:17:49.049 Uttam Kumaran: so there’s 1 a lot of complications there where each week, as we dig further, we find more information.

139 00:17:49.230 00:17:57.638 Uttam Kumaran: The problem is, there’s nobody on the business side that is like, I would say, the

140 00:17:58.440 00:18:01.520 Uttam Kumaran: the product owner who should have that context,

141 00:18:03.220 00:18:05.470 Uttam Kumaran: which makes it really tough, because

142 00:18:05.680 00:18:09.969 Uttam Kumaran: we just have a stakeholder. We don’t have someone who’s like helping us.

143 00:18:10.520 00:18:21.750 Uttam Kumaran: which you know, it’s it’s it’s like hard mode. So that’s 1 thing. The second thing is that we’re getting. There is some complicated logic

144 00:18:21.890 00:18:28.609 Uttam Kumaran: around revenue recognition, around subscriptions and a few other items that we have modeled.

145 00:18:29.390 00:18:37.610 Uttam Kumaran: However, I don’t think the analyst team, still like, has a very concrete like

146 00:18:38.000 00:18:40.829 Uttam Kumaran: set of definitions that they feel

147 00:18:41.310 00:18:48.300 Uttam Kumaran: they can like explain to the business anytime they get a question. It’s almost like anytime. We get a question about one of those concepts.

148 00:18:48.460 00:18:50.689 Uttam Kumaran: We rehash the whole system.

149 00:18:51.570 00:18:53.879 Uttam Kumaran: I’m sure you know how that goes, you know.

150 00:18:55.870 00:19:01.030 Uttam Kumaran: So there’s 1 piece there. The second piece is like we do have.

151 00:19:01.170 00:19:05.729 Uttam Kumaran: It took us like 2, 3 weeks to get tableau to a good state.

152 00:19:07.035 00:19:10.740 Uttam Kumaran: And I think the team struggled on the building side a little bit.

153 00:19:10.920 00:19:12.760 Uttam Kumaran: It’s getting better now.

154 00:19:13.226 00:19:17.680 Uttam Kumaran: But of course, in that process there was a lot of Where is this data?

155 00:19:17.830 00:19:19.760 Uttam Kumaran: Where can I get this column?

156 00:19:19.870 00:19:21.150 Uttam Kumaran: Things like that?

157 00:19:21.380 00:19:24.959 Uttam Kumaran: So there’s 1 piece there that I think

158 00:19:25.510 00:19:33.515 Uttam Kumaran: the analyst team, especially now it’s it’s just gonna be Sahana and Robert. They need some support

159 00:19:34.670 00:19:42.133 Uttam Kumaran: with like, where to get this data and things like that. The la I see on in your document to you have what needs to be done.

160 00:19:42.850 00:19:50.090 Uttam Kumaran: I think I’ll start. I’m not. I think I’ll let the mixed panel data. I’ll let Robert explain today. I don’t have context on that

161 00:19:50.320 00:19:51.830 Uttam Kumaran: offboard, Rob.

162 00:19:52.110 00:19:54.720 Uttam Kumaran: Yes, he’s not

163 00:19:54.910 00:20:02.220 Uttam Kumaran: scheduled to leave right now. However, he is just like a loan data engineer they hired before us.

164 00:20:02.370 00:20:07.879 Uttam Kumaran: and they’re sort of like we can’t fire this guy. He has a lot of knowledge about stuff.

165 00:20:08.870 00:20:18.659 Uttam Kumaran: So this is where Rob isn’t isn’t like so helpful, and just tell and like giving us very clear answers about where stuff is so. But we basically need to

166 00:20:18.790 00:20:23.689 Uttam Kumaran: have a plan towards knowledge transfer of everything he knows into our team.

167 00:20:25.027 00:20:26.340 Uttam Kumaran: Do you have.

168 00:20:26.340 00:20:26.890 Aakash Tandel: Have.

169 00:20:27.290 00:20:27.610 Uttam Kumaran: Yeah.

170 00:20:27.950 00:20:31.490 Aakash Tandel: Do we have any like architectural diagrams that would

171 00:20:31.670 00:20:35.485 Aakash Tandel: visualize how like the different systems are talking together.

172 00:20:35.910 00:20:36.330 Uttam Kumaran: Yes.

173 00:20:36.330 00:20:38.060 Aakash Tandel: It, says he. I mean, okay.

174 00:20:38.810 00:20:41.260 Uttam Kumaran: We do? It’s in.

175 00:20:41.580 00:20:43.139 Uttam Kumaran: I’ll just share.

176 00:20:48.440 00:20:55.210 Uttam Kumaran: yeah, we have this fig jam. So far we can continue to evolve it. And then we also have

177 00:20:58.020 00:20:59.040 Uttam Kumaran: you do.

178 00:21:06.430 00:21:12.229 Uttam Kumaran: Yeah, we we started up the platform documentation, but we haven’t made a ton of progress there.

179 00:21:13.780 00:21:15.870 Uttam Kumaran: But I will share this as well.

180 00:21:24.620 00:21:31.770 Uttam Kumaran: So I think, between me. So really, the state of this has been awaii

181 00:21:32.300 00:21:36.650 Uttam Kumaran: sort of hustling on, just getting the marks out, and then

182 00:21:36.930 00:21:43.470 Uttam Kumaran: me picking up slack wherever I need. But no one has been. It’s it’s been hard to dedicate any time towards

183 00:21:44.470 00:21:49.833 Uttam Kumaran: slowing down, which it has been extremely frustrating here.

184 00:21:50.540 00:21:59.960 Uttam Kumaran: and so there’s 1 piece there. So I think Dev, with your support, will have a little bit of that I have. I have some context on.

185 00:22:00.350 00:22:19.700 Uttam Kumaran: like the data modeling. But really a wish, I think, will be the number one primary on that. I’m happy to fill in or play tie break if we need to. And then Robert is the product owner on this client meeting. He has a really really good understanding of where they want to go what they want to see who they are.

186 00:22:20.480 00:22:28.759 Uttam Kumaran: The only other overarching challenge with this client, is there? Yeah, there.

187 00:22:29.320 00:22:37.620 Uttam Kumaran: I don’t know. I wouldn’t say I think they’re in a state of like disappointment which causes every interaction to be tougher than it needs to be.

188 00:22:38.220 00:22:41.300 Uttam Kumaran: I don’t wanna blame them, although they

189 00:22:41.440 00:22:44.220 Uttam Kumaran: I had. I’ve had one conversation with them, and they

190 00:22:44.770 00:22:54.289 Uttam Kumaran: like as a human being. They were very like not that nice to me, but like that aside, and I don’t talk to them anymore because of that. Robert talks to them, but

191 00:22:54.610 00:22:59.620 Uttam Kumaran: I feel like they probably have attendance. They have high. Also do this like, they have high expectations.

192 00:23:00.020 00:23:07.140 Uttam Kumaran: Okay? So that being said like, we have not reached them, because I think

193 00:23:07.320 00:23:22.020 Uttam Kumaran: there’s there’s kind of 2 problems here. One, I think they on we underestimated in our initial proposal to them how much work this would be, and so we set their expectations really high, and a timeline is really high. That is something that

194 00:23:22.430 00:23:27.660 Uttam Kumaran: you know we learn from. We won’t be doing. But there’s an issue with that. We have to climb there.

195 00:23:27.770 00:23:29.780 Uttam Kumaran: The second piece is

196 00:23:30.100 00:23:51.300 Uttam Kumaran: we’ve we’re having like multiple work streams. We’re moving from no document, no modeling documentation at all to Dbt, so we migrated everything to Dbt. Then we set up all these Dbt marts. We’re also doing looker studio to tableau. We’re also having to deal with migrating logic to new logic in comparison. So we took on like

197 00:23:51.480 00:23:55.650 Uttam Kumaran: 5 really complicated things all at the same time.

198 00:23:57.720 00:24:03.720 Uttam Kumaran: And yeah, it’s hard. So that’s that’s sort of where we are. Week. We finished

199 00:24:03.830 00:24:09.900 Uttam Kumaran: the core logic. We finished the core parts. We finished the Dbt migration. We are literally at the tableau.

200 00:24:10.040 00:24:18.500 Uttam Kumaran: just need to be right? And the data accuracy piece is the biggest thing. So I think them a lot in your document. You have data accuracy. I think we do need to implement some level of

201 00:24:18.620 00:24:21.719 Uttam Kumaran: of testing. And we need to understand what the actual.

202 00:24:21.840 00:24:26.549 Uttam Kumaran: what the correct values are. To do that. But

203 00:24:27.780 00:24:30.190 Uttam Kumaran: that’s sort of where? Where we’re at right now.

204 00:24:32.520 00:24:36.219 Demilade Agboola: Okay. But I mean, personally, I can. I can always

205 00:24:38.274 00:25:02.470 Demilade Agboola: again. I’m looking around since last night. So I’ll definitely look more into it this today. I’ll definitely see how far we can push things. And I would like to like have a plan of action from today. Towards the end of the week till we know what we need to turn around and what we need to be able to let them know that we’re working on so that they have some level of comfort in the fact that we’re doing something towards, you know, bridging the gap between the expectations and reality.

206 00:25:02.970 00:25:04.570 Demilade Agboola: So yeah.

207 00:25:05.910 00:25:11.610 Uttam Kumaran: Okay, great. So I think we can spend time in the next stand up meeting going through that. And Robert will be

208 00:25:11.840 00:25:15.709 Uttam Kumaran: very excited to do that. If you can take that today.

209 00:25:16.590 00:25:21.220 Uttam Kumaran: I think between me, you and a wish we should be able to answer any question.

210 00:25:21.570 00:25:26.150 Uttam Kumaran: I would like to not be considered the primary, because I’m going to

211 00:25:26.490 00:25:39.809 Uttam Kumaran: let you down in terms of getting you that information quickly, although it would be helpful for me to go through and do this. Pm. Handoff today, spend an hour and do that, and so I need a little bit of time to do some deep work there.

212 00:25:39.950 00:25:44.690 Uttam Kumaran: But any questions, I think, for this next meeting I’m gonna let you and a waste

213 00:25:44.910 00:25:49.090 Uttam Kumaran: sort of litigate directly with Robert. I will be there to play backup.

214 00:25:49.190 00:25:54.039 Uttam Kumaran: But I think it’s helpful for you guys to own that conversation and start to own the project.

215 00:25:56.050 00:26:08.409 Uttam Kumaran: I’m I’m gonna I’ll I’ll keep answering questions in here. For what I know. But overall, I think this document seems great, and we could probably run the next meeting off of this document. To be quite honest.

216 00:26:11.250 00:26:12.480 Aakash Tandel: Do we have

217 00:26:12.890 00:26:13.220 Uttam Kumaran: Go ahead!

218 00:26:13.555 00:26:23.284 Aakash Tandel: Prioritizing those multiple paths. Maybe it’s like, Hey, we want to do this first, st this second, this 3, rd or do they want everything kind of done in parallel.

219 00:26:24.500 00:26:29.869 Uttam Kumaran: Yeah. So the I think the primary work here is data accuracy.

220 00:26:30.050 00:26:36.700 Uttam Kumaran: And the second work is basically allowing them to finish the tableau dashboards.

221 00:26:36.860 00:26:44.710 Uttam Kumaran: The second piece is where I’m it’s a i’m because I’m not in tableau doing it. It’s hard for me to understand like what the

222 00:26:45.010 00:26:50.970 Uttam Kumaran: what’s the core blockers there. I think a lot of the back and forth

223 00:26:51.210 00:27:00.689 Uttam Kumaran: stress has come from that data accuracy not being there meaning every time we push a model update. Again, it’s like, there’s a huge data validation exercise.

224 00:27:01.230 00:27:08.660 Uttam Kumaran: And then, of course, last week I was the only ae. So I’m getting questions about like, is this accurate? I’m like I spent like an

225 00:27:09.080 00:27:20.659 Uttam Kumaran: I spend like an hour in like while I was in like another meeting doing the model like I just picked up what I could. So I’m like not the best to answer a lot. I figured it out like you can look in the channel.

226 00:27:20.960 00:27:26.140 Uttam Kumaran: It’s like it was like ridiculous, you know the extent we went to to figure out some of these answers, but

227 00:27:26.760 00:27:40.159 Uttam Kumaran: it like took me way too long to do like to do that because I did it like at like 11 o’clock. So I think part of this is also just having some having aes active in that channel while the analysts are working

228 00:27:41.930 00:27:47.009 Uttam Kumaran: and just being a partner to that team, because that was it was me in a waysh

229 00:27:47.170 00:28:08.130 Uttam Kumaran: I know. Wish time zone wise after around 1112 is sort of off. So I was picking it up. I think that’s where I think it’ll be helpful, but I mean we got a lot of the marks done so like it. We got it. There’s a ton of stuff there, so that’s why I would hate for this project to slip further because the data is like, actually all there modeled.

230 00:28:08.340 00:28:12.080 Uttam Kumaran: They’re just stuck in this tableau data accuracy problem. So.

231 00:28:15.650 00:28:18.629 Aakash Tandel: That sounds sounds like the 1st thing to tackle makes sense.

232 00:28:19.120 00:28:20.080 Uttam Kumaran: Yeah. Okay.

233 00:28:24.510 00:28:27.340 Uttam Kumaran: Great demoda. Any questions there?

234 00:28:29.230 00:28:43.799 Demilade Agboola: I think the questions cause you, said Robert, is the product on? I think the next question I have it to Robert in terms of like how exactly we want to. You know what exactly will satisfy the customer today, or like this week. Generally speaking.

235 00:28:44.530 00:28:47.990 Uttam Kumaran: Okay, okay, perfect.

236 00:28:50.270 00:28:52.302 Uttam Kumaran: So let’s talk about

237 00:28:53.660 00:29:02.790 Uttam Kumaran: I think I’m gonna I’ll plan on running another meeting around Javi later. And I’ll probably I’ll probably just call

238 00:29:03.675 00:29:16.020 Uttam Kumaran: Kyle. Maybe I might call you directly they’re meeting with. There’s a meeting with the Javi CEO Cmo. Today to do a walkthrough of all of our work that we pushed out.

239 00:29:16.730 00:29:21.869 Uttam Kumaran: They’re gonna be building out the roadmap today a little bit and getting a sense of that.

240 00:29:22.110 00:29:27.410 Uttam Kumaran: So I’m not. I don’t. I don’t have a clear answer about what’s next for that client, probably until tomorrow.

241 00:29:27.630 00:29:30.580 Uttam Kumaran: But let’s see

242 00:29:31.022 00:29:36.007 Uttam Kumaran: I think the only other thing. Maybe we can spend 20 min just talking about.

243 00:29:36.520 00:29:44.140 Uttam Kumaran: the messages, Kyle, that that you sent and like, if we want to just have a brief discussion on any of those like concepts while we’re on the phone.

244 00:29:46.010 00:29:46.730 Uttam Kumaran: Okay?

245 00:29:47.037 00:29:53.240 Uttam Kumaran: The the yeah. I think I think you sent a message, and data team last

246 00:29:54.066 00:29:56.919 Uttam Kumaran: last week. Right? And I’ll send this here.

247 00:30:27.500 00:30:28.479 Caio Velasco: Can I see it?

248 00:30:29.450 00:30:34.349 Uttam Kumaran: Yeah. So I think, just give it a quick read, I think maybe let’s let’s.

249 00:30:34.470 00:30:40.883 Uttam Kumaran: I think we’ll boil this down into a couple of core question. So one is, one is

250 00:30:43.270 00:30:52.620 Uttam Kumaran: like, how how do we do this documentation for our core dashboards? Right now, we have this platform documentation. I don’t think that’s ultimately

251 00:30:52.730 00:30:56.460 Uttam Kumaran: where we’re going to end up.

252 00:30:56.988 00:31:03.219 Uttam Kumaran: I don’t. I also don’t know whether there is a tool that’s gonna solve this. That’s like really effective.

253 00:31:03.829 00:31:13.699 Uttam Kumaran: I do think that there is a probably a really clear process for us to fill that documentation up using AI like, I feel like that’s

254 00:31:14.280 00:31:20.384 Uttam Kumaran: that’s a pretty feasible milestone that we can get to eventually. But it’s probably not going to be there soon.

255 00:31:22.070 00:31:34.930 Uttam Kumaran: So I don’t know. I still think we have the the problem one we could. I don’t know. I I kind of don’t want to prescribe an issue until, like everybody is sort of on board with this being a problem, and like what’s the best way to solve it?

256 00:31:36.940 00:31:41.710 Uttam Kumaran: So I think we’re still kind of in the same place, which is

257 00:31:42.920 00:31:45.690 Uttam Kumaran: the Aes are just gonna get asked for the logic.

258 00:31:46.440 00:31:51.859 Uttam Kumaran: and that logic should be somewhere in in a spreadsheet or documentation. But it’s not there today.

259 00:31:55.640 00:32:05.009 Caio Velasco: Yeah, the yeah. That’s the that’s my feeling as well as this at the end of the day, is it’s let’s say that I’m a new way coming in.

260 00:32:05.170 00:32:08.050 Caio Velasco: And then there is some, you know.

261 00:32:08.300 00:32:12.969 Caio Velasco: like a request, let’s say, when I was looking into the Amazon

262 00:32:13.080 00:32:17.560 Caio Velasco: source and trying to understand the Dbt models associated with that source.

263 00:32:18.006 00:32:26.539 Caio Velasco: And then, yeah, at some point, I just get lost even in trying to understand, like anything like how the columns were built or anything. And then.

264 00:32:26.980 00:32:28.620 Caio Velasco: for some reason, I feel that

265 00:32:28.890 00:32:32.900 Caio Velasco: the documentation should be answering this to

266 00:32:33.060 00:32:42.290 Caio Velasco: so that anyone can understand what has happened and move forward because the data model will get always larger and more complicated, but something to

267 00:32:43.455 00:32:46.029 Caio Velasco: to solve the situation right?

268 00:32:46.512 00:32:53.940 Caio Velasco: Otherwise, just to, you know, make a change like, why, shipping cost is not there or something? Yeah, I have no idea what

269 00:32:54.450 00:33:05.120 Caio Velasco: you know 100 columns are talking about. So it’s something that could try to solve this situation, which I think will make the process between aes Da’s and client

270 00:33:05.480 00:33:10.699 Caio Velasco: smoother. At least that’s an expectation. I really don’t know if it’s even possible.

271 00:33:12.088 00:33:16.360 Demilade Agboola: I I noticed in the products that I have looked through.

272 00:33:16.893 00:33:21.610 Demilade Agboola: But we don’t seem to be using yaml files for documentation.

273 00:33:21.970 00:33:22.396 Uttam Kumaran: Yeah,

274 00:33:23.580 00:33:48.259 Demilade Agboola: And I was wondering like, why? Because I know, like to be fair. It slows down the process. It’s a very frustrating part of the process, however, like being able to set up our Dbt test to ensure data quality to ensure, for instance, the count of, you know, whatever this table is matches to that table. Things like that. Primary keys remain primary. So if there’s if there are duplicates, we know we catch that like on a Dbt run

275 00:33:48.580 00:33:57.150 Demilade Agboola: long. Dbt build or just being able to put like the logic of what? Exactly setting

276 00:33:57.810 00:34:03.070 Demilade Agboola: So it’s in columns mean. So being able to say, Hey, this column

277 00:34:03.480 00:34:22.459 Demilade Agboola: gross margin is calculated by doing this minus this plus this, or you know that sort of clearly defined way, such that anybody who hops in can have an idea of what’s going on. And we can figure out ways to be able to visualize it like in terms of, I know, like there’s a

278 00:34:22.600 00:34:42.049 Demilade Agboola: you can use Dbt docs to generate a local HTML file. So it kind of creates like a local web page on your system, and you can kind of go through and figure out what exactly is going on. I do know. They also tools that help like auto generate. I guess if you’ve done, you’ve created the

279 00:34:42.159 00:34:43.469 Demilade Agboola: the.

280 00:34:44.760 00:34:54.729 Demilade Agboola: the actual models in the data warehouse, the tools that can allow you to, you know, automatically create the framework of a

281 00:34:55.161 00:35:01.869 Demilade Agboola: of the yaml file. So all you just need to do just fill in the information with what you need to do on whatever test you want to do.

282 00:35:02.080 00:35:06.070 Demilade Agboola: which I believe that’s code, Jen. I’ll send out. Look for it. I’ll send it.

283 00:35:06.260 00:35:13.249 Demilade Agboola: But things like that would allow us to be able to to some of these things.

284 00:35:16.290 00:35:20.179 Uttam Kumaran: Yeah, I guess here’s my, I guess. Yeah, I guess here’s my concern about

285 00:35:20.470 00:35:31.380 Uttam Kumaran: documentation, as we all know. Is it like it’s typically like immediately tech debt. So meaning it’s immediately stale.

286 00:35:31.710 00:35:38.630 Uttam Kumaran: And so I want to try to find a process that not only works like, if I spend 2 days I will write

287 00:35:38.870 00:35:43.829 Uttam Kumaran: all the documentation. But what’s to say that that isn’t immediately sort of like

288 00:35:44.180 00:35:47.139 Uttam Kumaran: out of sync right? The next day?

289 00:35:48.106 00:35:51.670 Uttam Kumaran: So there’s 1 question for me. Second question is

290 00:35:51.980 00:35:54.329 Uttam Kumaran: like, ultimately, we have these like.

291 00:35:54.490 00:35:58.490 Uttam Kumaran: okay, here. All the sports is blah blah. But maybe we should just

292 00:35:58.730 00:36:02.570 Uttam Kumaran: go one degree simpler and just do like Faqs.

293 00:36:02.970 00:36:14.490 Uttam Kumaran: I mean, this is something that we commonly do like when you’re a product manager and you’re building new product. You realize the customer always has, like 1015 questions.

294 00:36:14.620 00:36:18.140 Uttam Kumaran: Maybe we should just do an FAQ, and we sort of can

295 00:36:19.030 00:36:23.769 Uttam Kumaran: have different business domains. And we have, like the top 15 common questions.

296 00:36:23.990 00:36:32.830 Uttam Kumaran: And we start there. I think that feel like that’s 1 layer higher than the data platform documentation list. I don’t know. What do you guys think.

297 00:36:35.620 00:36:38.240 Demilade Agboola: I’m listening less intensive.

298 00:36:40.920 00:36:53.420 Demilade Agboola: but how often would that be updated like, is that going to be like an idea of how often we update that because the answers to faqs also change based on like whatever changes we’re making into models.

299 00:36:54.930 00:36:57.349 Demilade Agboola: And also the list of questions could also grow.

300 00:36:58.100 00:37:01.259 Uttam Kumaran: Yeah, I I think it’s a short term like.

301 00:37:01.970 00:37:07.859 Uttam Kumaran: just to sort of give what I think this is gonna evolve into. And I’m pretty confident we will end up doing. This is

302 00:37:08.100 00:37:23.999 Uttam Kumaran: like, I think, the Dbt docs hire that you sent is also a good solution. However, again, it’s built on the notion that someone is in there writing definitions for every single thing in that. When a Pr comes and gets changed.

303 00:37:24.110 00:37:26.770 Uttam Kumaran: you’re doing that like at an enterprise level.

304 00:37:27.100 00:37:34.760 Uttam Kumaran: I didn’t adhere to that. I worked in a lot of enterprises where this process is about taking up

305 00:37:35.100 00:37:40.040 Uttam Kumaran: exactly the same amount of time as it does to actually write the logic, and

306 00:37:40.390 00:38:05.229 Uttam Kumaran: it’s going to put a huge dent into our speed. But of course there’s benefit. I just think a good middle ground is something like an Faqs. I think eventually we will. I’m pretty sure we’ll just generate most of this with AI, and that’s going to be the main process by which we maintain this sort of documentation, because for one of us, as an individual, to maintain column, level lineage

307 00:38:05.390 00:38:08.280 Uttam Kumaran: in a system even with 10 models.

308 00:38:08.730 00:38:13.930 Uttam Kumaran: is part, and many of our clients have 50 to 100 models.

309 00:38:16.270 00:38:20.100 Uttam Kumaran: So it’s difficult for me to accept that. That’s gonna be the process.

310 00:38:22.890 00:38:34.960 Uttam Kumaran: I think. Also, like I’ve I’ve used Dbt docs for years now, and it’s never gotten adopted like the analysts, never log in. It’s also a requirement of Dbt. Cloud, and we don’t pay for Dbt. Cloud for many clients.

311 00:38:35.220 00:38:41.560 Uttam Kumaran: so I don’t know. I think I think Faqs sound like somewhere in the middle.

312 00:38:41.860 00:38:46.139 Uttam Kumaran: and then we can use the Faqs to sort of dictate what happens next.

313 00:38:48.940 00:38:49.719 Caio Velasco: With. I don’t know.

314 00:38:50.238 00:38:56.459 Caio Velasco: You mean at the customer level or at the data modeling level.

315 00:38:57.470 00:39:00.299 Uttam Kumaran: At at yeah, at any, at anything like sort of.

316 00:39:00.300 00:39:00.890 Caio Velasco: Okay.

317 00:39:01.450 00:39:09.810 Uttam Kumaran: Yeah, I mean, for for example, in New York, it would say, a question be like, How does gross margin calculated? And the FAQ has the answer there.

318 00:39:09.970 00:39:10.790 Uttam Kumaran: right?

319 00:39:10.920 00:39:12.409 Uttam Kumaran: That’s what we would do.

320 00:39:12.520 00:39:15.220 Uttam Kumaran: And yeah, the other. The other. I could look

321 00:39:15.400 00:39:24.789 Uttam Kumaran: once we have that, I think that’s a lot easier to maintain. It’s it is ultimately still have to maintain it. But that is easier to maintain than

322 00:39:25.870 00:39:29.139 Uttam Kumaran: right. Let’s say you push a Pr. It affects 50 columns.

323 00:39:29.590 00:39:34.030 Uttam Kumaran: It’s gonna take you 5 h to go. Do the documentation for that.

324 00:39:34.030 00:39:34.430 Caio Velasco: For sure.

325 00:39:35.890 00:39:36.300 Demilade Agboola: Problems.

326 00:39:36.300 00:39:39.900 Uttam Kumaran: So it’s like it’s dead on. It’s dead on arrival, you know.

327 00:39:39.900 00:39:44.120 Demilade Agboola: But but but there was the ways, it could also be done faster to be fair.

328 00:39:44.556 00:39:53.110 Demilade Agboola: So dbt, now has this thing where you can put the you can define a certain metric in one place, a certain column in one place.

329 00:39:53.270 00:39:55.489 Demilade Agboola: and then you put the macro.

330 00:39:56.100 00:40:02.010 Demilade Agboola: but the the macro of that definition in every single place it’s used.

331 00:40:02.200 00:40:16.510 Demilade Agboola: So now, if you change it in that one place, it would perpetrate throughout the entire system across all the definitions. So that’s 1 way. But even beyond that, I think also another thing and a huge advantage. At least, I think with documentation.

332 00:40:17.110 00:40:19.320 Demilade Agboola: is the benefits of testing

333 00:40:19.440 00:40:45.150 Demilade Agboola: being able to keep our primary keys primary being able to ensure that if we have a fact table that we know is fine, and we’re not making any changes to. If I’m doing any joins on a higher level or the math level, we can ensure that the count of the table in the mark level ensure exact things like that. So I feel like that. I’m not saying we need to go all deep in on documentation. I know that’s a very

334 00:40:45.470 00:40:49.670 Demilade Agboola: consuming process, but I feel like we definitely could do some of the advantages.

335 00:40:50.310 00:40:54.930 Uttam Kumaran: Okay. So then, maybe it’s a conversation about what pieces we want, what pieces we don’t.

336 00:40:58.070 00:41:03.809 Uttam Kumaran: I think. Maybe let’s let’s plan on that I mean, I think I think short term

337 00:41:04.070 00:41:06.360 Uttam Kumaran: like short term, as in this week.

338 00:41:06.950 00:41:12.349 Uttam Kumaran: Faqs make some sense for this sort of handoff process.

339 00:41:12.480 00:41:15.940 Uttam Kumaran: I think totally for data quality. I’m gonna lean on.

340 00:41:16.510 00:41:41.190 Uttam Kumaran: I’m gonna lean on you, demo it because I haven’t worked at a place where they’ve got it super right? And if yes, if we can start to basically have systems by which we say, Okay, if this is a primary key, then automatically, these like 6 tests run. And we sort of build up that library. That is like an amazing quality of life quality of developer life sort of addition that we can implement.

341 00:41:43.860 00:41:51.080 Uttam Kumaran: I think, like, I’m just looking at the data platform documentation that we sort of proposed. And it’s gonna be really hard to

342 00:41:51.320 00:41:52.800 Uttam Kumaran: maintain

343 00:41:52.920 00:42:09.450 Uttam Kumaran: the march documentation for every single thing. However, I think, Kyle, for some of the questions, I think we should just maybe try to do like, okay, here’s all. The here’s 10 faqs on shipping. Here’s 10 faqs on gross margin. Here’s 10 faqs on this. And

344 00:42:09.920 00:42:14.549 Uttam Kumaran: I think, like I mean, honestly, what I’m gonna do is I’ll probably just try and

345 00:42:14.880 00:42:19.369 Uttam Kumaran: export all the messages out of the Channel and have AI. Just write the 1st version.

346 00:42:20.460 00:42:44.799 Caio Velasco: Yeah, yeah, no. I totally agree. And one thing that I think that we should also see here is that there are intangible things doing this work, at least for me, because I’m gonna learn during the process how things are usually defined in in different business domain. So it’s not only about whatever we are. Gonna serve the client at the end of the day. But as an engineer learning those things makes everything efficient,

347 00:42:45.540 00:42:50.309 Caio Velasco: you know, with time, at least, for me. I will learn a lot by doing that.

348 00:42:51.060 00:42:52.000 Uttam Kumaran: Yeah, okay.

349 00:42:54.680 00:43:02.190 Uttam Kumaran: I think that’s an easier place to start. Cause cause here’s the other thing I’m wrangling with is, I can go write all the documentation.

350 00:43:02.500 00:43:03.780 Uttam Kumaran: But then

351 00:43:03.900 00:43:09.770 Uttam Kumaran: it’s good. I’m gonna start to get the questions from you guys. So I’m sort of trying to balance a process that we can all collaborate on it.

352 00:43:10.498 00:43:15.169 Uttam Kumaran: You know, while we’re while we’re figuring this out. So

353 00:43:16.280 00:43:24.250 Uttam Kumaran: yeah, let’s keep talking this week about how we do this. I think today, maybe if I have 30 min, I’m gonna try to put something together for Javi. That’s like faqs.

354 00:43:26.020 00:43:28.520 Uttam Kumaran: but I think this is a good conversation. Yeah.

355 00:43:34.720 00:43:39.420 Uttam Kumaran: Cool anything else?

356 00:43:47.610 00:43:57.419 Uttam Kumaran: Cool? Okay. So probably. I think we’ll we have the Eden call next. I’ll probably follow up directly. In the Javi channel. So Kyle, on any

357 00:43:58.010 00:44:03.552 Uttam Kumaran: on any next steps there, and kind of we can work together on on an Faqs. And then

358 00:44:04.660 00:44:18.030 Uttam Kumaran: yeah, I mean hopefully, I’m I’m looking forward to being able to rely on you guys, demo A, in a way, some of the even stuff this weekend that’ll free my time up to go up Luke on sack blitz, and sort of keep working on a few things.

359 00:44:20.180 00:44:24.060 Uttam Kumaran: Any questions for today?

360 00:44:28.540 00:44:29.400 Uttam Kumaran: Okay.

361 00:44:29.400 00:44:37.069 Demilade Agboola: I don’t know. I don’t know if you saw, but just dropped the link to something he has started so you might not always have. You might not need to start from scratch.

362 00:44:37.840 00:44:38.990 Uttam Kumaran: Oh, nice!

363 00:44:39.700 00:44:45.089 Caio Velasco: Yeah, we can rename it for saps or anything else, just just to get it to get it started.

364 00:44:45.950 00:44:49.540 Uttam Kumaran: Okay, okay, this is helpful. Thank you.

365 00:44:55.430 00:44:57.690 Uttam Kumaran: Okay, thanks. Guys. Talk soon.

366 00:44:57.690 00:44:59.030 Caio Velasco: Thank you. Appreciate. One day.

367 00:44:59.500 00:45:00.569 Aakash Tandel: So like.

368 00:45:00.570 00:45:01.780 Demilade Agboola: Thanks, bye.