Meeting Title: Uttam <> Abigail-Weekly Date: 2024-09-03 Meeting participants: Abigail Zhao, Uttam Kumaran
WEBVTT
1 00:01:45.560 ⇒ 00:01:46.509 Uttam Kumaran: There we go!
2 00:01:51.750 ⇒ 00:01:52.240 Abigail Zhao: Hi.
3 00:02:04.210 ⇒ 00:02:05.510 Uttam Kumaran: Hey? How’s it going.
4 00:02:05.858 ⇒ 00:02:07.599 Abigail Zhao: I’m good. How are you?
5 00:02:07.600 ⇒ 00:02:09.330 Uttam Kumaran: Good. Sorry. What happened.
6 00:02:10.330 ⇒ 00:02:14.040 Abigail Zhao: Oh, nothing. I was just a little confused. But it’s okay.
7 00:02:14.289 ⇒ 00:02:16.529 Uttam Kumaran: No, I was just grabbing some of the drink.
8 00:02:17.150 ⇒ 00:02:31.309 Abigail Zhao: Okay, no problem. Yeah. I’m like, I’m situated like in front of a window right now. And like for the past few days, for some reason, like birds have been like flying into it like they’re they’re fine, like they don’t die or anything, but it’s like
9 00:02:31.720 ⇒ 00:02:35.720 Abigail Zhao: it’s always a little alarming when they just like fly up.
10 00:02:36.720 ⇒ 00:02:44.940 Uttam Kumaran: Sitting in front of a pretty big window, too, and I put I put like a bird feeder right at the top, so I can like watch birds
11 00:02:45.370 ⇒ 00:02:50.579 Uttam Kumaran: come and eat, but they have not. If it’s I think some of them now are like figuring out. They’re like
12 00:02:50.840 ⇒ 00:03:01.020 Uttam Kumaran: we found, like we found treasure like, and they’re starting to come every day. But it’s nice, because it’s nice to see move, and there’s a bunch of like lizards in my yard all the time.
13 00:03:01.440 ⇒ 00:03:02.999 Uttam Kumaran: but then sometimes I’ll be like
14 00:03:03.030 ⇒ 00:03:08.039 Uttam Kumaran: what the fuck was that, and I’m like, oh, it’s a lizard outside. It’s like not inside.
15 00:03:08.110 ⇒ 00:03:11.120 Uttam Kumaran: cause I’ll be here staring, and then I see some movement I’m like.
16 00:03:11.380 ⇒ 00:03:13.740 Uttam Kumaran: or I’ll be here with my headphones on, and I’m like
17 00:03:13.770 ⇒ 00:03:24.680 Uttam Kumaran: is someone in my house right now, like who where the fuck is going on, and I’m like yo calm down like cause I’ll be listening like heavy house music, and then I’m like.
18 00:03:24.960 ⇒ 00:03:25.859 Uttam Kumaran: and then I’m like
19 00:03:26.110 ⇒ 00:03:28.059 Uttam Kumaran: I was like, hear something like.
20 00:03:28.260 ⇒ 00:03:31.895 Uttam Kumaran: is there someone is there like an intruder?
21 00:03:32.750 ⇒ 00:03:35.327 Uttam Kumaran: But no, it’s I’m the only one here.
22 00:03:38.110 ⇒ 00:03:40.169 Uttam Kumaran: Nice. What do you think about today?
23 00:03:41.373 ⇒ 00:03:50.579 Abigail Zhao: It was good. I like honestly, I think it was just weird picking it back up because I think it was like it had been like a week since we last met, or something. So
24 00:03:50.710 ⇒ 00:03:53.349 Abigail Zhao: yeah, besides that. But yeah
25 00:03:53.620 ⇒ 00:03:54.560 Abigail Zhao: seemed
26 00:03:54.970 ⇒ 00:03:56.150 Abigail Zhao: productive.
27 00:03:56.460 ⇒ 00:04:01.950 Uttam Kumaran: Yeah, I think the Apollo stuff is good for me. It’s like, before I can kind of break out everything into.
28 00:04:02.650 ⇒ 00:04:10.040 Uttam Kumaran: I kind of just need to see, like, okay, what are the filters you’re working with? Because then I think ideally, what I’m going to do is basically be like cool.
29 00:04:10.430 ⇒ 00:04:14.839 Uttam Kumaran: We’re gonna run this campaign. I’ll probably have you help me like, create
30 00:04:15.390 ⇒ 00:04:24.569 Uttam Kumaran: the saved searches, and we can probably review that, narrow it down and then be like cool. Send that. Now get all those contacts and send it to Hubspot.
31 00:04:24.840 ⇒ 00:04:26.860 Uttam Kumaran: and then, once they’re in Hubspot.
32 00:04:27.490 ⇒ 00:04:30.889 Uttam Kumaran: I think another thing that I can probably start to work with you on
33 00:04:31.608 ⇒ 00:04:35.460 Uttam Kumaran: is some of the automations on sending that to
34 00:04:35.490 ⇒ 00:04:40.379 Uttam Kumaran: the emails. But I’m kind of just wrapping my head around like the whole thing as well.
35 00:04:40.887 ⇒ 00:04:44.709 Uttam Kumaran: So what so far has been like interesting
36 00:04:44.990 ⇒ 00:04:47.430 Uttam Kumaran: and what so far has been like boring?
37 00:04:49.150 ⇒ 00:04:50.630 Uttam Kumaran: be honest.
38 00:04:50.830 ⇒ 00:04:52.750 Abigail Zhao: Like I don’t like, if
39 00:04:53.380 ⇒ 00:04:58.059 Abigail Zhao: like to be honest. Like all, most of this stuff is like completely new to me, cause like.
40 00:04:58.060 ⇒ 00:04:58.640 Uttam Kumaran: Yeah.
41 00:04:58.800 ⇒ 00:05:05.520 Abigail Zhao: Yeah, like, I don’t know. I I just have never even like known that these like automation tools were a thing like.
42 00:05:05.520 ⇒ 00:05:06.390 Uttam Kumaran: Cool.
43 00:05:06.390 ⇒ 00:05:08.890 Abigail Zhao: Crossed my mind so like that entire like
44 00:05:09.020 ⇒ 00:05:25.159 Abigail Zhao: learning about all of them like in general, was just like kind of crazy like, I mean, I know. Obviously, like companies had to use some like they’re not just like individually sending out emails or whatever. But like, I don’t know, it was kind of crazy. And I was also like, when I was on Apollo and stuff. I was like, Wow, like
45 00:05:25.170 ⇒ 00:05:29.399 Abigail Zhao: it. They just like know all these emails. And like they can just like.
46 00:05:29.400 ⇒ 00:05:37.410 Uttam Kumaran: Oh, yeah, I guess I guess that that makes a lot of sense. I guess I didn’t really think about. I mean, I know you’re coming in brand new. But I’m like.
47 00:05:37.700 ⇒ 00:05:39.900 Uttam Kumaran: I mean, this is like.
48 00:05:40.100 ⇒ 00:05:47.180 Uttam Kumaran: this is my brain, basically. So none of the stuff in data anymore is like super new to me. I’m like, Oh, cool! They have everybody’s emails
49 00:05:47.300 ⇒ 00:05:59.690 Uttam Kumaran: in America great, like, perfect. I’ll pay like I’ll pay $1,000 for that right. But I I think it’s helpful for you to see like this is actually how marketing works, and in particularly
50 00:05:59.710 ⇒ 00:06:01.050 Uttam Kumaran: marketing.
51 00:06:01.180 ⇒ 00:06:06.120 Uttam Kumaran: The part of marketing is like, you know, having, like your pitch like your content, right? Like.
52 00:06:06.200 ⇒ 00:06:18.269 Uttam Kumaran: there’s like a sexy side to marketing. But actually, a lot of marketing is this data side, which is, are you getting to the right people at the right time with the right offer? And so tools like Apollo and Hubspot?
53 00:06:18.490 ⇒ 00:06:33.700 Uttam Kumaran: You know our well known tools in like the marketing world. But you’re right. If you’re like a normal person, you have no idea that this is how it’s working. And in particular. This is how business to business marketing works right? Where, like our clients are businesses, our clients aren’t like
54 00:06:33.960 ⇒ 00:06:37.410 Uttam Kumaran: Sally at home, right where we were. We’re not running TV ads.
55 00:06:37.580 ⇒ 00:06:42.273 Uttam Kumaran: we’re, we’re like emailing people directly with, like some specific stuff.
56 00:06:42.950 ⇒ 00:06:47.489 Uttam Kumaran: so it is different. But you’re right. Like, everything is super data driven.
57 00:06:48.428 ⇒ 00:06:50.060 Uttam Kumaran: And yeah, like, if
58 00:06:50.140 ⇒ 00:06:56.964 Uttam Kumaran: if there, if you are online, there is data about you, and there is a hundred ways to get it.
59 00:06:57.650 ⇒ 00:06:59.179 Uttam Kumaran: So yeah, it’s like.
60 00:06:59.470 ⇒ 00:07:05.189 Uttam Kumaran: yeah, it’s basically like, there’s no data privacy like, not that stuff like is like, completely false.
61 00:07:05.240 ⇒ 00:07:05.750 Abigail Zhao: That’s good.
62 00:07:07.620 ⇒ 00:07:25.099 Abigail Zhao: It’s all like I I mean I I’m getting used to it. But like in the beginning, it was definitely all like very new to me cause like also, like what I’m learning in school, like my studies, like at school, are like, very heavily like math focused. I’m not like even taking like marketing class, or like anything like that.
63 00:07:25.100 ⇒ 00:07:27.070 Uttam Kumaran: It improves. Probably I’m like.
64 00:07:27.070 ⇒ 00:07:29.240 Abigail Zhao: I was like doing like number theory, like.
65 00:07:29.240 ⇒ 00:07:32.341 Uttam Kumaran: Yeah, you’re doing theory. And you’re doing, it’s a lot of like,
66 00:07:33.180 ⇒ 00:07:38.659 Uttam Kumaran: yeah, I mean, it’s completely different. I mean, I studied computer engineering. And I took like, diffie queues and
67 00:07:39.060 ⇒ 00:07:40.929 Uttam Kumaran: stuff like that. It’s
68 00:07:41.650 ⇒ 00:07:43.891 Uttam Kumaran: yeah. It’s just not this.
69 00:07:44.340 ⇒ 00:07:45.799 Abigail Zhao: I figured I figured as well, too.
70 00:07:45.800 ⇒ 00:07:55.659 Uttam Kumaran: But like what this is right is like you could sell. How me and Patrick work is like, we break down problems very, very concretely. And it’s like.
71 00:07:55.720 ⇒ 00:08:06.350 Uttam Kumaran: okay, like, what are the primitives here? Like, what are the roles that you can work with? Like, okay, we have these objects. Right? We have like a contact. We have a company, we have attributes, we have filters.
72 00:08:06.510 ⇒ 00:08:14.149 Uttam Kumaran: We didn’t have the scores, the scores now like a math thing, so you can tell how actually like this stuff is like pretty dumb. It’s like pretty basic
73 00:08:14.390 ⇒ 00:08:17.160 Uttam Kumaran: what what the hard part is here is like.
74 00:08:18.235 ⇒ 00:08:22.729 Uttam Kumaran: Actually knowing how, taking what I know from engineering and breaking down marketing.
75 00:08:22.910 ⇒ 00:08:24.360 Uttam Kumaran: That’s something that like.
76 00:08:24.510 ⇒ 00:08:28.689 Uttam Kumaran: if you’re not from a technical background, marketers will have no idea how to do the data stuff
77 00:08:28.790 ⇒ 00:08:36.529 Uttam Kumaran: right? Like our day to day. Job is data work data, pipelines, data, engineering, really technical work. That’s honestly, probably where I think, like.
78 00:08:36.559 ⇒ 00:08:44.620 Uttam Kumaran: The longer you work with us the more stuff will get heads to there, because this stuff is just the thing I really really need help with right now. So that’s particularly.
79 00:08:44.740 ⇒ 00:08:49.999 Uttam Kumaran: But I think, ideally, I think once you see the technical side of things, you’ll see that like, oh, this is like, actually.
80 00:08:50.240 ⇒ 00:08:53.179 Uttam Kumaran: technically like what we’re selling to folks.
81 00:08:53.350 ⇒ 00:08:57.249 Uttam Kumaran: But all the stuff we’re doing is really technical, you know. But it’s
82 00:08:58.460 ⇒ 00:09:02.010 Uttam Kumaran: marketers don’t think like this, like marketers think
83 00:09:02.220 ⇒ 00:09:19.970 Uttam Kumaran: actually don’t know. Cause like I’m not a marketer, but like they’re they’re like business people. They don’t think in systems, they don’t think in like order of operations they don’t think in like. Do this, then this, then this, and this, or a flow right or in in a, in like a theorem, or like a proof you have like an end goal.
84 00:09:20.070 ⇒ 00:09:25.309 Uttam Kumaran: right? And you have some basic blocks that you want to prove something out. They don’t think like that. They’re like
85 00:09:25.880 ⇒ 00:09:33.700 Uttam Kumaran: they just like, Wake up, do the thing, go to bed, wake up, do the thing go to! There’s no like. Oh, what am I doing every day. And can I automate that.
86 00:09:34.080 ⇒ 00:09:45.949 Uttam Kumaran: Or like, how do we scale this up? There’s none of that in their world. So there is a there is a but there’s also not many engineers that kind of like go both ways. Usually in engineering. It’s a lot of creativity.
87 00:09:46.100 ⇒ 00:09:46.785 Uttam Kumaran: But
88 00:09:48.000 ⇒ 00:09:52.119 Uttam Kumaran: it’s like, not much about it’s creative about solving problems, not really interacting with
89 00:09:52.450 ⇒ 00:09:53.799 Uttam Kumaran: business or sales.
90 00:09:54.720 ⇒ 00:09:55.460 Uttam Kumaran: Hmm.
91 00:09:56.060 ⇒ 00:10:04.530 Abigail Zhao: Yeah, I mean, like, right now, it’s all all seems like pretty intuitive, so like no, no like major obstacles on my end. But.
92 00:10:04.530 ⇒ 00:10:05.300 Uttam Kumaran: Echo.
93 00:10:05.950 ⇒ 00:10:10.030 Abigail Zhao: I guess, like short term, though I’m just kind of wondering
94 00:10:10.398 ⇒ 00:10:15.310 Abigail Zhao: like what immediate next steps are. I know you like. Went over that a little bit already, but like.
95 00:10:16.060 ⇒ 00:10:20.730 Uttam Kumaran: Yeah, I think the so kind of 2 things. One, I wanted to kind of get a sense of like.
96 00:10:21.167 ⇒ 00:10:33.050 Uttam Kumaran: you know, we’ve probably been just doing like a few hours a day. Maybe you know, probably so far, one I was. Gonna ask you like, how do you feel about that going to the school year? Are you like feeling like that’s probably like a good amount of work.
97 00:10:33.730 ⇒ 00:10:40.049 Uttam Kumaran: Or you’re like, actually, I can take on more like time after, or weekends like. How do you feel about that?
98 00:10:42.650 ⇒ 00:10:47.239 Abigail Zhao: I mean, like right now, it’s like, still kind of hard to tell, just cause it hasn’t.
99 00:10:47.240 ⇒ 00:10:47.810 Uttam Kumaran: 12.
100 00:10:47.810 ⇒ 00:10:49.429 Abigail Zhao: Picked up yet.
101 00:10:50.070 ⇒ 00:10:55.250 Abigail Zhao: but I mean, when it does, I’ll definitely like communicate that. But I feel like weekends.
102 00:10:55.270 ⇒ 00:10:59.039 Abigail Zhao: I could definitely dedicate some time as well, if, yeah.
103 00:10:59.040 ⇒ 00:11:02.250 Uttam Kumaran: I mean, it’s still gonna be work where it’s just like it’s not like.
104 00:11:02.420 ⇒ 00:11:08.239 Uttam Kumaran: I don’t want to put you in a position where it’s like this needs to go out. Now. It’s it’s gonna be work where it’s like.
105 00:11:08.650 ⇒ 00:11:12.780 Uttam Kumaran: I just want to give you lead time on it. But also knowing that helps you, because
106 00:11:13.080 ⇒ 00:11:16.369 Uttam Kumaran: we still have some help that we need on this, like
107 00:11:16.430 ⇒ 00:11:25.740 Uttam Kumaran: Apollo, Hubspot side. But now that me and Pat kind of have a good understanding of it. I get it. I I do want to get some of your help on some like actual data stuff, though.
108 00:11:26.910 ⇒ 00:11:28.309 Uttam Kumaran: In particular.
109 00:11:30.350 ⇒ 00:11:33.289 Uttam Kumaran: I’ll even share with you. Actually, this could be great because
110 00:11:34.217 ⇒ 00:11:38.112 Uttam Kumaran: there’s this is like all internal, basically. But
111 00:11:38.660 ⇒ 00:11:42.949 Uttam Kumaran: we’re using this tool called rail
112 00:11:43.510 ⇒ 00:11:44.640 Uttam Kumaran: data.
113 00:11:44.660 ⇒ 00:11:47.649 Uttam Kumaran: basically a real data. Sorry, let me share my screen.
114 00:11:48.110 ⇒ 00:11:51.040 Uttam Kumaran: Real data is just the dashboarding tool. Actually.
115 00:11:52.960 ⇒ 00:11:54.110 Uttam Kumaran: so
116 00:11:59.300 ⇒ 00:12:05.940 Uttam Kumaran: so we’re using this tool called rail real. Basically, you have these like sorts of dashboards. In particular, if I go
117 00:12:06.160 ⇒ 00:12:06.865 Uttam Kumaran: to
118 00:12:08.830 ⇒ 00:12:10.390 Uttam Kumaran: the actual site.
119 00:12:12.250 ⇒ 00:12:13.270 Uttam Kumaran: let’s see.
120 00:12:17.180 ⇒ 00:12:23.539 Uttam Kumaran: So these are like demo dashboards, actually, that like we have. And we’re gonna plan on actually sending this
121 00:12:23.660 ⇒ 00:12:24.690 Uttam Kumaran: to
122 00:12:25.346 ⇒ 00:12:37.209 Uttam Kumaran: to customers as basically like a hey, here’s an example of like, here’s example, like what a dashboard we could work on, for you would look like one of the things that we’re doing. And this is kind of similarly.
123 00:12:37.780 ⇒ 00:12:43.020 Uttam Kumaran: in the marketing lenses. We’re actually creating like dummy dashboards, basically
124 00:12:43.140 ⇒ 00:12:44.419 Uttam Kumaran: where it’s like
125 00:12:44.480 ⇒ 00:12:47.659 Uttam Kumaran: we have like a bunch of fake dashboards with fake data.
126 00:12:47.968 ⇒ 00:13:01.139 Uttam Kumaran: Where it’s like when we go to market, for example, like, for example, when I when we go market to those logistics people or I’m gonna send them like one of these, which is like supplier management. This is. This is one of the things I’m showing is that some of these dashboards
127 00:13:01.470 ⇒ 00:13:04.449 Uttam Kumaran: look, don’t look great at all like there’s no data here.
128 00:13:04.842 ⇒ 00:13:14.520 Uttam Kumaran: But this all orders. Dashboard, for example. This is something that we would send to like an e-commerce customer, as like, Hey, come, check out this dashboard. This is public.
129 00:13:15.060 ⇒ 00:13:21.620 Uttam Kumaran: So we’d send out a public facing link. They can come on here and like, Oh, yeah, it’d be cool if I could see like the last 12 months, and like
130 00:13:21.630 ⇒ 00:13:26.300 Uttam Kumaran: I can click on a filter here, and then I can like
131 00:13:26.910 ⇒ 00:13:29.660 Uttam Kumaran: I could change like this dimension.
132 00:13:29.770 ⇒ 00:13:31.950 Uttam Kumaran: I could compare some stuff
133 00:13:32.000 ⇒ 00:13:37.570 Uttam Kumaran: right like this is actually the look and feel that I want to give some people in an email
134 00:13:39.250 ⇒ 00:13:42.779 Uttam Kumaran: And so actually, I think what would be really interesting is like if you can.
135 00:13:43.190 ⇒ 00:13:50.899 Uttam Kumaran: If, like, basically, I can get your help working on creating some more of these and improving some of the ones that we have.
136 00:13:51.870 ⇒ 00:13:55.470 Uttam Kumaran: This is, gonna get a lot more technical, though, than
137 00:13:55.590 ⇒ 00:13:59.519 Uttam Kumaran: the work you’re doing in Apollo, which is why, I’m kind of like
138 00:14:00.150 ⇒ 00:14:04.039 Uttam Kumaran: I want to give you like a couple of ideas, and then
139 00:14:04.050 ⇒ 00:14:13.259 Uttam Kumaran: but you play around and tell me like, Hey, this is like way too much. Because this is actually work that like takes a little bit of time to like wrap your head around but it’s
140 00:14:14.010 ⇒ 00:14:16.909 Uttam Kumaran: basically like the data work that we’re doing?
141 00:14:17.568 ⇒ 00:14:23.419 Uttam Kumaran: What this involves. And have you used Github? Or you’ve done any like coding locally before on your laptop.
142 00:14:24.300 ⇒ 00:14:27.479 Uttam Kumaran: Okay, so you’re familiar with pushing pull requests and stuff. Yeah.
143 00:14:27.980 ⇒ 00:14:33.130 Uttam Kumaran: Oh, okay. So basically, this all sits as like a repo.
144 00:14:36.000 ⇒ 00:14:37.470 Uttam Kumaran: github
145 00:14:39.570 ⇒ 00:14:40.370 Uttam Kumaran: that
146 00:14:43.760 ⇒ 00:14:45.859 Uttam Kumaran: I go to Github and
147 00:14:46.340 ⇒ 00:14:48.970 Uttam Kumaran: and I go to.
148 00:15:00.270 ⇒ 00:15:02.739 Uttam Kumaran: We have our Brainforge rail here.
149 00:15:04.210 ⇒ 00:15:05.580 Uttam Kumaran: Basically
150 00:15:06.075 ⇒ 00:15:08.329 Uttam Kumaran: the back end code for rail.
151 00:15:08.400 ⇒ 00:15:11.819 Uttam Kumaran: You’ve like, run stuff locally like localhost. And like, run
152 00:15:12.290 ⇒ 00:15:14.980 Uttam Kumaran: packages locally on your computer.
153 00:15:15.340 ⇒ 00:15:16.859 Uttam Kumaran: Have you used Vs code.
154 00:15:17.440 ⇒ 00:15:18.699 Abigail Zhao: I’m not. No.
155 00:15:18.900 ⇒ 00:15:20.900 Uttam Kumaran: Are you just using a terminal for stuff.
156 00:15:20.900 ⇒ 00:15:21.530 Abigail Zhao: Yeah.
157 00:15:22.160 ⇒ 00:15:23.920 Abigail Zhao: And then I also
158 00:15:24.340 ⇒ 00:15:29.040 Abigail Zhao: was, yeah, I mean, I guess it’s not as pickable. But I was also using github for like web dev stuff.
159 00:15:29.330 ⇒ 00:15:35.889 Uttam Kumaran: Oh, nice. Okay, okay, cool. So you’re familiar with like pull requests. That’s actually like a huge thing. Because
160 00:15:36.180 ⇒ 00:15:38.840 Uttam Kumaran: you’d be surprised. Very people like
161 00:15:39.650 ⇒ 00:15:43.820 Uttam Kumaran: who are very smart, have no idea how to do that. And it’s basically like, Okay, you’re really fucked.
162 00:15:45.500 ⇒ 00:15:48.779 Uttam Kumaran: So basically like real sits on like this repo
163 00:15:49.298 ⇒ 00:15:53.001 Uttam Kumaran: and I’ll and I’ll send you this, and we have some more.
164 00:15:53.430 ⇒ 00:16:00.820 Uttam Kumaran: we have a bunch of documentation on how to get started with real but real sits on this repo. There are sources, models and dashboards.
165 00:16:01.410 ⇒ 00:16:05.969 Uttam Kumaran: Sources are basically just definitions of how do you access a table?
166 00:16:06.110 ⇒ 00:16:11.180 Uttam Kumaran: So we we talked a little bit. How most of the stuff we do is all sequel right? And like sequel, is.
167 00:16:11.220 ⇒ 00:16:14.179 Uttam Kumaran: is it just a language? Have you done any sequel before.
168 00:16:14.490 ⇒ 00:16:18.410 Abigail Zhao: Like, I have, like a basic knowledge of it, like foundation.
169 00:16:18.410 ⇒ 00:16:22.189 Uttam Kumaran: See, you could recognize that this is a sequel. Right? Like this is like, select
170 00:16:22.540 ⇒ 00:16:24.360 Uttam Kumaran: from something. Yeah.
171 00:16:24.400 ⇒ 00:16:31.020 Uttam Kumaran: So basically, this is like, select all from a table. This is a table in our snowflake instance.
172 00:16:31.130 ⇒ 00:16:39.910 Uttam Kumaran: snowflake is a data warehouse is like basically what we specialize in. This is selecting the data from a data table data table you can think of like a
173 00:16:40.110 ⇒ 00:16:42.380 Uttam Kumaran: basically like a
174 00:16:43.090 ⇒ 00:16:47.599 Uttam Kumaran: how would you call it like, just like an Excel sheet on steroids. Basically.
175 00:16:48.515 ⇒ 00:16:53.390 Uttam Kumaran: So this is like selecting all the columns from a data table as a source.
176 00:16:53.690 ⇒ 00:16:56.420 Uttam Kumaran: We then have a models layer here.
177 00:16:57.725 ⇒ 00:16:58.730 Uttam Kumaran: Models.
178 00:16:59.020 ⇒ 00:17:00.040 Uttam Kumaran: Again.
179 00:17:00.150 ⇒ 00:17:07.300 Uttam Kumaran: you have the ability to join models together in this situation. We’re just selecting all the columns from this one source.
180 00:17:07.410 ⇒ 00:17:09.299 Uttam Kumaran: And then in the dashboard
181 00:17:09.490 ⇒ 00:17:10.670 Uttam Kumaran: file.
182 00:17:11.849 ⇒ 00:17:13.850 Uttam Kumaran: we’re actually declaring.
183 00:17:14.050 ⇒ 00:17:16.419 Uttam Kumaran: what’s a dimension versus what’s a measure?
184 00:17:17.394 ⇒ 00:17:23.229 Uttam Kumaran: And that’s gonna that’s gonna dictate. For example, on this dashboard what shows up as a time series.
185 00:17:23.800 ⇒ 00:17:29.149 Uttam Kumaran: Right? Like a measure, is it on a time series? For example, let’s say you have a list of transactions.
186 00:17:29.470 ⇒ 00:17:37.080 Uttam Kumaran: You have a transaction date, and like a transaction Id and an amount. Right? So you typically want to see like amount over time.
187 00:17:37.140 ⇒ 00:17:39.039 Uttam Kumaran: But let’s say you want to filter
188 00:17:39.070 ⇒ 00:17:41.990 Uttam Kumaran: by your date. So your date is a dimension.
189 00:17:42.180 ⇒ 00:17:44.359 Uttam Kumaran: and your amount is a measure.
190 00:17:44.590 ⇒ 00:17:52.090 Uttam Kumaran: So an amount is anything that you would aggregate up like a sum or an average, and a dimension is attributes about that amount.
191 00:17:52.180 ⇒ 00:17:54.239 Uttam Kumaran: For example, where did you buy it from?
192 00:17:54.280 ⇒ 00:17:55.970 Uttam Kumaran: What’s the order? Id.
193 00:17:56.080 ⇒ 00:17:58.640 Uttam Kumaran: what’s the customer email things like that?
194 00:17:58.710 ⇒ 00:18:02.210 Uttam Kumaran: Don’t worry about grasping this. All right. Now, I’ll send you a ton of stuff after this.
195 00:18:03.790 ⇒ 00:18:08.120 Uttam Kumaran: but basically, this is kind of how our real code is structured.
196 00:18:08.430 ⇒ 00:18:10.120 Uttam Kumaran: The kind of the ask
197 00:18:10.370 ⇒ 00:18:13.079 Uttam Kumaran: from my side will be one
198 00:18:13.150 ⇒ 00:18:20.590 Uttam Kumaran: to kind of get familiar with rail and get familiar with Snowflake, and I’ll send you a bunch of resources on that. We have a lot of documentation that other interns
199 00:18:20.740 ⇒ 00:18:31.679 Uttam Kumaran: have gone through so far. The second thing that would be great to do is, I have some ideas about some more dashboards that we want to create.
200 00:18:31.840 ⇒ 00:18:33.610 Uttam Kumaran: For example, we want to create
201 00:18:34.480 ⇒ 00:18:43.629 Uttam Kumaran: kind of different dashboard projects, one for, like e-commerce, one for manufacturing, one for logistics. And we have some AI code that actually generates
202 00:18:43.970 ⇒ 00:18:51.100 Uttam Kumaran: the sequel to generate fake data sets that I can give to you as well. So all this data is fake right? But the thing is is like
203 00:18:51.270 ⇒ 00:18:56.393 Uttam Kumaran: it’s fake. But it’s also not that realistic meaning. If you look at like
204 00:18:57.820 ⇒ 00:19:00.149 Uttam Kumaran: if you look like total orders.
205 00:19:02.890 ⇒ 00:19:04.620 Uttam Kumaran: these are all very similar.
206 00:19:05.800 ⇒ 00:19:07.779 Uttam Kumaran: right? There’s like no variation.
207 00:19:08.240 ⇒ 00:19:10.359 Uttam Kumaran: So some stuff like that which is like
208 00:19:10.880 ⇒ 00:19:14.029 Uttam Kumaran: having ups and downs and variation in the data
209 00:19:14.050 ⇒ 00:19:16.669 Uttam Kumaran: is actually going to be something that we work on
210 00:19:16.770 ⇒ 00:19:18.580 Uttam Kumaran: as well as like creating
211 00:19:19.130 ⇒ 00:19:22.429 Uttam Kumaran: better dashboards. So that’s like the overall task.
212 00:19:24.730 ⇒ 00:19:27.199 Uttam Kumaran: if you go into notion
213 00:19:27.320 ⇒ 00:19:29.143 Uttam Kumaran: and you go into
214 00:19:30.150 ⇒ 00:19:31.710 Uttam Kumaran: documentation.
215 00:19:31.780 ⇒ 00:19:35.539 Uttam Kumaran: you’re actually gonna see a some how to guides on
216 00:19:35.920 ⇒ 00:19:37.859 Uttam Kumaran: on the real walkthrough
217 00:19:39.232 ⇒ 00:19:41.499 Uttam Kumaran: which is just like how to set up real
218 00:19:42.275 ⇒ 00:19:44.830 Uttam Kumaran: how to create github pull requests.
219 00:19:45.350 ⇒ 00:19:47.899 Uttam Kumaran: which is just how to do. Pull requests.
220 00:19:48.304 ⇒ 00:19:50.850 Uttam Kumaran: There’s also something here on how to set up rail.
221 00:19:51.945 ⇒ 00:19:55.050 Uttam Kumaran: Which you can follow is very easy to follow.
222 00:19:55.772 ⇒ 00:20:06.020 Uttam Kumaran: So that’s I’m gonna I’ll send a couple of links after this on like just things to go through to like, get real set up on your machine. And then also I’ll I can add you to github
223 00:20:06.602 ⇒ 00:20:11.569 Uttam Kumaran: the other things that I’m gonna ask, even before that is one
224 00:20:11.590 ⇒ 00:20:15.259 Uttam Kumaran: to go through this
225 00:20:15.970 ⇒ 00:20:19.169 Uttam Kumaran: sequel, like learning tutorial called sequel. Zoo.
226 00:20:20.800 ⇒ 00:20:24.039 Uttam Kumaran: You’ll probably just breeze through this. But this will give you like
227 00:20:24.430 ⇒ 00:20:27.470 Uttam Kumaran: the basics of like sequel. Basically.
228 00:20:27.480 ⇒ 00:20:31.760 Uttam Kumaran: Yeah. So I’ll have you go through like exercises, 0 through
229 00:20:32.360 ⇒ 00:20:41.199 Uttam Kumaran: like 8 plus basically, that’ll give you the basics. And then I’ll also have you go through Snowflake.
230 00:20:45.500 ⇒ 00:20:49.901 Uttam Kumaran: go through this snowflake. Intro course, which is
231 00:20:50.940 ⇒ 00:20:54.390 Uttam Kumaran: basically like introduction to snowflake. I’m just gonna find out which one it is.
232 00:21:08.400 ⇒ 00:21:10.179 Uttam Kumaran: Yeah. So
233 00:21:17.850 ⇒ 00:21:19.490 Uttam Kumaran: might be this one.
234 00:22:03.080 ⇒ 00:22:04.000 Uttam Kumaran: See.
235 00:22:26.840 ⇒ 00:22:32.880 Uttam Kumaran: Okay, let me find one that’s good. But basically, I’ll just give you an overview of like how to log into snowflake and how to manage tables and stuff.
236 00:22:35.310 ⇒ 00:22:41.070 Uttam Kumaran: but yeah, I think we’ll probably start there. So the big things are like getting rail set up on your laptop.
237 00:22:41.390 ⇒ 00:22:43.160 Uttam Kumaran: getting familiar with sequel
238 00:22:43.300 ⇒ 00:22:46.020 Uttam Kumaran: and then getting familiar with Snowflake
239 00:22:47.780 ⇒ 00:22:56.119 Uttam Kumaran: and I think hopefully that should kind of like last year, at least like of the week. And you can kind of poke around all that stuff. Then that’ll be actual like data work where
240 00:22:57.060 ⇒ 00:23:01.709 Uttam Kumaran: we’re the client. But it’s basically like creating these demo dashboards with demo data.
241 00:23:03.150 ⇒ 00:23:06.620 Uttam Kumaran: honestly, like a very interesting project like not something I’ve
242 00:23:06.950 ⇒ 00:23:09.310 Uttam Kumaran: something that I wanted to do because
243 00:23:09.650 ⇒ 00:23:13.500 Uttam Kumaran: I want to give out something to clients so they can feel. But before, like
244 00:23:14.210 ⇒ 00:23:27.069 Uttam Kumaran: coming and paying for our services, and in our in our world, like a designer, is a portfolio right like in our world is not really much like that, because all these tools take a long time. But we have a really good relationship with this company.
245 00:23:27.450 ⇒ 00:23:34.360 Uttam Kumaran: And they were like, yeah, you feel free to create, like your your own internal one that you can market. And I’m like
246 00:23:34.520 ⇒ 00:23:35.980 Uttam Kumaran: word, cool.
247 00:23:37.360 ⇒ 00:23:38.640 Uttam Kumaran: so
248 00:23:39.650 ⇒ 00:23:40.515 Uttam Kumaran: yeah,
249 00:23:42.600 ⇒ 00:23:45.552 Uttam Kumaran: cool. I think we’ll probably I think we’ll start there and let’s see.
250 00:23:46.350 ⇒ 00:23:49.600 Uttam Kumaran: Let’s see how we feel after Friday, and kind of like this week.
251 00:23:49.940 ⇒ 00:23:50.320 Abigail Zhao: Okay.
252 00:23:50.743 ⇒ 00:23:53.949 Uttam Kumaran: So I’ll send you some of these links, and then I’ll also send to you
253 00:23:54.180 ⇒ 00:23:56.700 Uttam Kumaran: this recording as well. And then
254 00:23:57.270 ⇒ 00:24:00.610 Uttam Kumaran: we have a bunch of in our in the notion, docs, we have a ton of stuff
255 00:24:00.760 ⇒ 00:24:04.599 Uttam Kumaran: on rail and snowflake. So you should have. And then, of course, like
256 00:24:04.890 ⇒ 00:24:07.559 Uttam Kumaran: the team, everybody. This is all we do. So.
257 00:24:08.740 ⇒ 00:24:09.490 Abigail Zhao: Sounds good.
258 00:24:12.080 ⇒ 00:24:13.110 Uttam Kumaran: Cool.
259 00:24:16.000 ⇒ 00:24:17.420 Uttam Kumaran: yeah. Anything else
260 00:24:17.790 ⇒ 00:24:20.580 Uttam Kumaran: to chat about. What do you think about the high agency stuff?
261 00:24:20.700 ⇒ 00:24:21.420 Uttam Kumaran: Oof!
262 00:24:23.300 ⇒ 00:24:26.435 Abigail Zhao: Oh, yeah.
263 00:24:28.256 ⇒ 00:24:30.584 Uttam Kumaran: It’s all right.
264 00:24:31.500 ⇒ 00:24:37.710 Uttam Kumaran: Well, I have to. I gotta get people to feel motivated, you know, like cause. Sometimes people just wait for stuff, and
265 00:24:37.880 ⇒ 00:24:40.340 Uttam Kumaran: it’s painful, you know, so
266 00:24:41.480 ⇒ 00:24:46.239 Uttam Kumaran: I don’t know. I don’t like those meetings where it’s like mostly me talking anyways, but sometimes
267 00:24:47.170 ⇒ 00:24:52.769 Uttam Kumaran: I I gotta be like yo, we’re a business like we need people to kind of be motivated and stuff so.
268 00:24:53.600 ⇒ 00:24:54.430 Abigail Zhao: yeah.
269 00:24:57.150 ⇒ 00:25:01.142 Abigail Zhao: I’m just, I don’t know. I just feel like, i’m, just like there.
270 00:25:02.730 ⇒ 00:25:10.549 Uttam Kumaran: I mean, you’re part. You’re part of it, like you’re clearly working on stuff. So if you have a comment, you should totally give it, because people will value your opinion, you know, for sure.
271 00:25:11.485 ⇒ 00:25:12.160 Abigail Zhao: Yeah.
272 00:25:12.160 ⇒ 00:25:14.000 Uttam Kumaran: Because you’re seeing it through a new lens.
273 00:25:14.000 ⇒ 00:25:15.370 Abigail Zhao: Yeah.
274 00:25:19.280 ⇒ 00:25:20.630 Abigail Zhao: room 2.
275 00:25:21.750 ⇒ 00:25:26.759 Abigail Zhao: Oh, I also just wanted to ask about, like, if
276 00:25:26.810 ⇒ 00:25:30.510 Abigail Zhao: you still needed, me to like sign any of those forms, because I never got
277 00:25:30.640 ⇒ 00:25:31.250 Abigail Zhao: them.
278 00:25:31.250 ⇒ 00:25:32.840 Uttam Kumaran: I do. Yes.
279 00:25:33.120 ⇒ 00:25:36.390 Uttam Kumaran: Good point. Okay, let me try to take care of that. It’s on my to do list.
280 00:25:37.400 ⇒ 00:25:41.449 Uttam Kumaran: I’ll send you that, and I’ll also send you like a formal offer, letter and stuff.
281 00:25:41.450 ⇒ 00:25:42.390 Abigail Zhao: That’s good.
282 00:25:44.810 ⇒ 00:25:47.910 Abigail Zhao: Oh, and then I also just had like a really bad question.
283 00:25:47.960 ⇒ 00:25:56.069 Abigail Zhao: Oh, yeah, you like you mentioned earlier today that you just like made a tick tock for brain forge like, what type of
284 00:25:56.100 ⇒ 00:25:59.270 Abigail Zhao: stuff do you plan on doing like with that?
285 00:25:59.470 ⇒ 00:26:02.589 Uttam Kumaran: Yeah, I mean, if you go. Well.
286 00:26:02.630 ⇒ 00:26:04.560 Uttam Kumaran: this is like kind of like
287 00:26:04.740 ⇒ 00:26:08.210 Uttam Kumaran: stuff I’m like, kind of thinking about on the edge of marketing for us. But
288 00:26:09.064 ⇒ 00:26:13.799 Uttam Kumaran: if you search data engineering on Tiktok right now, it’s total shit like
289 00:26:13.870 ⇒ 00:26:17.059 Uttam Kumaran: it’s like super boomer people
290 00:26:17.250 ⇒ 00:26:22.469 Uttam Kumaran: like really garbage content. So like, there’s really nobody posting content about
291 00:26:23.110 ⇒ 00:26:31.940 Uttam Kumaran: data analytics. There’s people posting content about excel. There’s like that excel, lady. And like there’s some excel related content. But there’s nothing about like
292 00:26:32.520 ⇒ 00:26:33.990 Uttam Kumaran: our sort of work.
293 00:26:35.065 ⇒ 00:26:39.490 Uttam Kumaran: Mainly because everybody that works in my industry is like really old, like 40 plus
294 00:26:41.140 ⇒ 00:26:52.299 Uttam Kumaran: So I do think there’s some way for us to. For example, if we write a blog post I would love for us to be like. Here’s like the 3 things we learn on a, on a, on a tick tock.
295 00:26:52.310 ⇒ 00:26:53.540 Uttam Kumaran: and then
296 00:26:53.570 ⇒ 00:26:54.700 Uttam Kumaran: publish it out.
297 00:26:55.770 ⇒ 00:27:03.150 Uttam Kumaran: But also again, like I purposely did not get on Tiktok. I’m still pretty tapped in. But like I get sent a lot of stuff
298 00:27:03.950 ⇒ 00:27:07.110 Uttam Kumaran: I’m on Twitter. So I just like didn’t want to get addicted.
299 00:27:07.940 ⇒ 00:27:13.269 Uttam Kumaran: But I do think nobody’s posting like great data content. The thing is, I don’t know whether
300 00:27:13.340 ⇒ 00:27:23.710 Uttam Kumaran: here’s my thing. I think millennials and Gen. Z. Are starting to get into management positions and are gonna start consuming a lot of their business news
301 00:27:24.010 ⇒ 00:27:25.360 Uttam Kumaran: from Tiktok.
302 00:27:25.500 ⇒ 00:27:26.020 Abigail Zhao: Yeah.
303 00:27:26.020 ⇒ 00:27:33.159 Uttam Kumaran: As a part to Linkedin at Linkedin audience is very adult, like 30, plus, I’m like the youngest person.
304 00:27:33.410 ⇒ 00:27:34.620 Uttam Kumaran: And
305 00:27:34.660 ⇒ 00:27:37.190 Uttam Kumaran: so I want to kind of be ahead of the curve on that
306 00:27:38.120 ⇒ 00:27:39.779 Uttam Kumaran: but also
307 00:27:40.350 ⇒ 00:27:43.809 Uttam Kumaran: like I don’t. We don’t have full time like social people. So it’s kind of like
308 00:27:44.690 ⇒ 00:27:47.149 Uttam Kumaran: it’s kind of tough, but I don’t know. What do you think.
309 00:27:48.922 ⇒ 00:28:04.889 Abigail Zhao: I don’t get served a lot of data content on tikka. Being honest. I don’t. What I have seen about it is a lot of like data scientists, or like data analysts, or whatever just kind of like romanticizing their job, being like.
310 00:28:04.890 ⇒ 00:28:12.639 Uttam Kumaran: Yeah, they’re like day in the life. So there’s a lot of day in the life as a data scientist shit. But dude one thing one. All those people
311 00:28:12.940 ⇒ 00:28:17.039 Uttam Kumaran: definitely like. It’s some of that’s true, like, there’s a lot of people.
312 00:28:17.590 ⇒ 00:28:20.620 Uttam Kumaran: including me, including a lot of people that work for this company that like.
313 00:28:20.670 ⇒ 00:28:23.459 Uttam Kumaran: we’re data people that just didn’t do anything sometimes.
314 00:28:23.630 ⇒ 00:28:32.939 Uttam Kumaran: And they’re like, Yeah, I wake up and I have a matcha and I journal. And then I like work for 2 h, and I go to gym. It’s actually somewhat like that, I would say, because.
315 00:28:33.360 ⇒ 00:28:43.089 Uttam Kumaran: like this is a kind of a. It might not seem like it, because I’m starting like a business. But this is like a pretty chill job, like doing data stuff usually. And that’s like. Not that
316 00:28:43.450 ⇒ 00:28:50.259 Uttam Kumaran: that hectic. It’s pretty technical, but it’s not that hectic day to day. But yeah, it’s not. It’s not about like those people are just like
317 00:28:50.590 ⇒ 00:28:53.959 Uttam Kumaran: day in the life about being that. And it’s kind of like
318 00:28:54.550 ⇒ 00:28:56.390 Uttam Kumaran: it’s kind of. They usually just get
319 00:28:56.410 ⇒ 00:29:01.529 Uttam Kumaran: like hate. They just get hate in the comments like this isn’t that I’m not like
320 00:29:01.730 ⇒ 00:29:11.759 Uttam Kumaran: trying to share about like what it’s. I guess we could do like what it’s like to be a data engineer. But I want to show, like, how data could work for your business or like what it is
321 00:29:11.830 ⇒ 00:29:18.969 Uttam Kumaran: content like that, like nothing. I’m not trying to do like real content. Creator type of shit, you know. Yeah.
322 00:29:20.130 ⇒ 00:29:26.019 Uttam Kumaran: I’ve seen a lot of them like daily life as a Meta product manager and stuff like that like that stuff is so goofy
323 00:29:26.941 ⇒ 00:29:29.379 Uttam Kumaran: and you’ll get fired like you’ll totally get fired.
324 00:29:29.560 ⇒ 00:29:30.530 Abigail Zhao: Oh, okay.
325 00:29:30.690 ⇒ 00:29:33.399 Uttam Kumaran: Yeah. Like, if those, if your company finds that out.
326 00:29:33.440 ⇒ 00:29:43.479 Uttam Kumaran: especially if they find that you’re doing absolutely not like if someone posts that a brain forge, and was like day life of Brainforge. I wake up and like do nothing, and I go to bed. I totally, I totally find, like
327 00:29:43.530 ⇒ 00:29:47.187 Uttam Kumaran: I mean no way. What is this like a joke?
328 00:29:47.920 ⇒ 00:29:54.265 Uttam Kumaran: You know I’m not saying this. You don’t have to work 24 h like I take a lot of like, you know. But
329 00:29:55.230 ⇒ 00:29:56.660 Uttam Kumaran: yeah, I just think.
330 00:29:56.960 ⇒ 00:30:01.859 Uttam Kumaran: But if are you? Have, you have your. You have Tiktok right now search up. Search up data engineering
331 00:30:02.000 ⇒ 00:30:04.359 Uttam Kumaran: like the hashtag. I’ll show you what I mean.
332 00:30:12.000 ⇒ 00:30:15.129 Uttam Kumaran: like, if you search up data engineering.
333 00:30:16.230 ⇒ 00:30:17.530 Abigail Zhao: These are some really neat.
334 00:30:18.600 ⇒ 00:30:19.170 Abigail Zhao: interesting.
335 00:30:19.170 ⇒ 00:30:21.660 Uttam Kumaran: See, you see, like a couple of them, which is like
336 00:30:25.460 ⇒ 00:30:30.700 Uttam Kumaran: like this guy, this guy, Zack Wilson. He’s like kind of a big like Linkedin influencer.
337 00:30:33.181 ⇒ 00:30:35.849 Uttam Kumaran: But like there’s not much right, like
338 00:30:35.880 ⇒ 00:30:39.699 Uttam Kumaran: you see a couple of same guys over, you see, like resume stuff.
339 00:30:40.100 ⇒ 00:30:41.719 Uttam Kumaran: And these are all from April.
340 00:30:42.380 ⇒ 00:30:45.790 Abigail Zhao: Yeah, I’m getting a like 2023, like.
341 00:30:45.790 ⇒ 00:30:49.109 Uttam Kumaran: You see what I mean. So there’s nothing getting posted on it.
342 00:30:51.260 ⇒ 00:30:55.119 Uttam Kumaran: And if you search up like snowflake analytics, there’s also like
343 00:30:56.590 ⇒ 00:30:59.699 Uttam Kumaran: there’s like a couple, but they’re so bad they’re like
344 00:31:00.180 ⇒ 00:31:02.280 Uttam Kumaran: ugly dudes. What’s your
345 00:31:03.150 ⇒ 00:31:04.360 Uttam Kumaran: being like?
346 00:31:04.570 ⇒ 00:31:06.189 Uttam Kumaran: I don’t know what they’re doing like.
347 00:31:06.290 ⇒ 00:31:08.432 Uttam Kumaran: It’s just cause the thing is like my
348 00:31:09.470 ⇒ 00:31:12.690 Uttam Kumaran: My industry is filled with like nerds.
349 00:31:13.020 ⇒ 00:31:20.610 Uttam Kumaran: It’s all like nerds, but the thing is, I’m selling to like executives and heads of marketing. So there’s always this gap.
350 00:31:20.840 ⇒ 00:31:24.799 Uttam Kumaran: That’s why a lot of our marketing needs to be is gonna be super super polished
351 00:31:24.880 ⇒ 00:31:30.360 Uttam Kumaran: is I’m not selling to engineers like we’re selling to decision makers. So that’s why I’m thinking about
352 00:31:30.670 ⇒ 00:31:35.770 Uttam Kumaran: doing stuff on reels doing stuff on Tiktok, because
353 00:31:37.810 ⇒ 00:31:42.080 Uttam Kumaran: a lot of the future decision makers like me are are on those platforms like
354 00:31:42.870 ⇒ 00:31:47.650 Uttam Kumaran: my friends that are gonna be like running companies or like running big parts of companies are not on
355 00:31:47.740 ⇒ 00:31:51.190 Uttam Kumaran: like they’re not gonna be on Linkedin for much longer. It’s kind of just boring
356 00:31:52.780 ⇒ 00:32:00.859 Uttam Kumaran: you know. So. But if you want, if you want to help run that that would be amazing. Actually, I mean, that’s actually would be. Iv.
357 00:32:01.020 ⇒ 00:32:05.640 Uttam Kumaran: I will cause that’s something that’s really like. I have. I have not only no clue what I’m doing.
358 00:32:05.750 ⇒ 00:32:10.775 Uttam Kumaran: It would take me so long to figure out how to do the green screen thing, and then, like
359 00:32:11.550 ⇒ 00:32:13.379 Abigail Zhao: Don’t worry. That’s easy.
360 00:32:13.380 ⇒ 00:32:18.180 Uttam Kumaran: Like I would have to call my sister just graduated like I would have to call her and be like.
361 00:32:18.550 ⇒ 00:32:23.789 Uttam Kumaran: Hey, I want to make this green screen tick tock where I’m like in front of a dashboard like, how do I do it?
362 00:32:23.840 ⇒ 00:32:26.850 Uttam Kumaran: I’m also like I don’t. I consume
363 00:32:26.880 ⇒ 00:32:28.540 Uttam Kumaran: some Tiktok. But
364 00:32:29.750 ⇒ 00:32:32.809 Uttam Kumaran: I don’t know. I needed to be someone who’s literally like
365 00:32:34.040 ⇒ 00:32:35.230 Uttam Kumaran: he was just like
366 00:32:35.480 ⇒ 00:32:38.060 Uttam Kumaran: this is how you talk on Tiktok. Basically.
367 00:32:38.220 ⇒ 00:32:41.560 Uttam Kumaran: cause if I do it. I’m gonna ramble on, and it’s gonna be boring.
368 00:32:42.400 ⇒ 00:32:47.250 Uttam Kumaran: I mean, I was thinking about trying to get AI to do as much of this like I should have fake.
369 00:32:47.630 ⇒ 00:32:55.050 Uttam Kumaran: But if you want to run that you could. You have full creative, you can have full creative ownership. I’m telling you.
370 00:32:55.050 ⇒ 00:32:59.310 Abigail Zhao: But I’ll think about it. I’ll do. I’ll do some research.
371 00:32:59.310 ⇒ 00:33:07.690 Uttam Kumaran: Think about it, I mean, do some research actually look up like, if you look up on Instagram, too. There’s not much content. All of the corporate accounts.
372 00:33:08.010 ⇒ 00:33:12.160 Uttam Kumaran: It’s not like Wendy’s in my industry, where it’s like a cool corporate accounts.
373 00:33:12.160 ⇒ 00:33:12.660 Abigail Zhao: Yeah.
374 00:33:12.660 ⇒ 00:33:18.959 Uttam Kumaran: In my industry. It’s so boring. Nobody likes it. It’s like it’s someone like us who’s working on their social media team.
375 00:33:19.270 ⇒ 00:33:28.930 Uttam Kumaran: but is like hates their job because they’re posting like corny corporate shit like. It’s our like 2 year anniversary of our partnership with this other boomer firm like
376 00:33:28.970 ⇒ 00:33:31.699 Uttam Kumaran: celebrating that, and like no likes.
377 00:33:31.710 ⇒ 00:33:32.789 Uttam Kumaran: but not that.
378 00:33:33.530 ⇒ 00:33:34.010 Abigail Zhao: Yeah.
379 00:33:34.010 ⇒ 00:33:34.450 Uttam Kumaran: It’s the.
380 00:33:34.450 ⇒ 00:33:44.589 Abigail Zhao: It’s a hard line to cross, too, because then, like people make fun of like any company who like also tries too hard and like tries to like, really like play into like the truth, or whatever.
381 00:33:44.590 ⇒ 00:33:48.430 Uttam Kumaran: Let’s see, I don’t. I’m not like going after. I’m not selling to consumers right.
382 00:33:48.820 ⇒ 00:33:49.210 Abigail Zhao: So.
383 00:33:49.210 ⇒ 00:33:52.870 Uttam Kumaran: I’m not like. There’s not a risk of us saying like crazy shit.
384 00:33:52.900 ⇒ 00:33:54.030 Uttam Kumaran: or like
385 00:33:54.370 ⇒ 00:33:56.930 Uttam Kumaran: I don’t. I couldn’t see us hopping on trenches.
386 00:33:57.290 ⇒ 00:33:59.859 Uttam Kumaran: The people that look at the trends aren’t buying
387 00:34:00.171 ⇒ 00:34:12.109 Uttam Kumaran: data services. I’m looking. I wanted, like, I want to talk like, how do I get in front of when how do I get in front of the heads of marketing when they’re on tiktok, looking at data stuff, or like interested in like
388 00:34:12.120 ⇒ 00:34:18.629 Uttam Kumaran: making money or like analyzing stuff. But for but a lot of these, the the hashtags that
389 00:34:18.710 ⇒ 00:34:31.259 Uttam Kumaran: we’re in have no saturation, meaning nobody’s posting in those, and if they are, all of them are again very nerdy dudes just sitting in front. No creativity.
390 00:34:31.409 ⇒ 00:34:37.430 Uttam Kumaran: no flare like, and they’re getting a lot of views. So that’s what I’m saying, it’s like pretty ripe
391 00:34:39.080 ⇒ 00:34:42.849 Uttam Kumaran: but I I just don’t know what like what the concept would be. And again, like
392 00:34:43.480 ⇒ 00:34:48.210 Uttam Kumaran: I have. I have like a negative time like. So that’s the thing.
393 00:34:50.040 ⇒ 00:34:55.944 Uttam Kumaran: But think about it. I’ll send. I’ll send you our. I’ll send you our Tiktok account. You can do whatever.
394 00:34:56.659 ⇒ 00:34:59.099 Abigail Zhao: I’ll look into it. I’ll look into some of the
395 00:34:59.799 ⇒ 00:35:03.419 Abigail Zhao: data related Tiktoks or Instagram mails.
396 00:35:04.090 ⇒ 00:35:07.099 Uttam Kumaran: Yeah, I want it. So I also wanted, I don’t. Are you on threads?
397 00:35:07.780 ⇒ 00:35:09.100 Abigail Zhao: No, I’m not.
398 00:35:09.670 ⇒ 00:35:15.139 Uttam Kumaran: Yeah, I don’t know anyone is, but I was kind of like maybe we should post on threads. I’m really big on Twitter.
399 00:35:16.980 ⇒ 00:35:19.369 Uttam Kumaran: I don’t think Instagram is kind of like.
400 00:35:19.960 ⇒ 00:35:24.059 Uttam Kumaran: I don’t know. I guess reels would be okay. But Instagram, like as a corporate account.
401 00:35:25.230 ⇒ 00:35:25.890 Abigail Zhao: Yeah.
402 00:35:25.890 ⇒ 00:35:26.740 Uttam Kumaran: I don’t know.
403 00:35:27.480 ⇒ 00:35:28.135 Uttam Kumaran: Oh,
404 00:35:28.970 ⇒ 00:35:31.089 Uttam Kumaran: But also what I would do is I would totally
405 00:35:31.480 ⇒ 00:35:33.579 Uttam Kumaran: take the reels and put them on. Linkedin.
406 00:35:34.100 ⇒ 00:35:37.670 Uttam Kumaran: I mean. Take the take. Take your Tiktoks and put them on Linkedin.
407 00:35:37.910 ⇒ 00:35:42.339 Uttam Kumaran: because someone on Linkedin the Tiktoks are doing really really well.
408 00:35:42.570 ⇒ 00:35:47.660 Uttam Kumaran: because Linkedin is mostly text. So people are watching a lot of videos. So they’re wasting a lot of time on work
409 00:35:47.900 ⇒ 00:35:49.449 Uttam Kumaran: at work on Linkedin.
410 00:35:50.520 ⇒ 00:35:53.340 Uttam Kumaran: And Linkedin added short form video to their app.
411 00:35:53.340 ⇒ 00:35:55.110 Abigail Zhao: Yeah.
412 00:35:55.310 ⇒ 00:35:56.240 Abigail Zhao: yeah, so.
413 00:35:56.240 ⇒ 00:35:59.083 Uttam Kumaran: This I’m telling you there’s some opportunity.
414 00:36:02.080 ⇒ 00:36:09.590 Uttam Kumaran: but that feels see like. But then that’s not like that’s like social media work. But I guess the social media work for a data company. So there’s
415 00:36:10.240 ⇒ 00:36:11.229 Uttam Kumaran: something there.
416 00:36:14.200 ⇒ 00:36:19.149 Abigail Zhao: Well, I don’t know. I like I I truthfully have never thought too hard about like
417 00:36:19.310 ⇒ 00:36:20.360 Abigail Zhao: the
418 00:36:21.090 ⇒ 00:36:21.950 Abigail Zhao: like.
419 00:36:22.310 ⇒ 00:36:23.519 Uttam Kumaran: The business of it.
420 00:36:24.060 ⇒ 00:36:26.740 Abigail Zhao: Yeah, the corporate of it all, like the tick.
421 00:36:26.740 ⇒ 00:36:34.940 Uttam Kumaran: No, you wouldn’t think about it. But you gotta think about like on Tiktok, I mean, I’m sure you bought something off tick tock like that’s someone sold that right. There’s a business behind that.
422 00:36:35.320 ⇒ 00:36:42.959 Uttam Kumaran: And so I think the biggest difference on us is like we’re selling business to business. But, for example, you think about shopify.
423 00:36:43.080 ⇒ 00:36:43.399 Abigail Zhao: You would.
424 00:36:43.400 ⇒ 00:36:48.290 Uttam Kumaran: Like you’re using shopify for selling something. You’re a business, right? But you know, shopify?
425 00:36:48.400 ⇒ 00:36:57.709 Uttam Kumaran: Right? So that means that’s business to business advertising. Right. They’re on a very lower scale, but that’s what it is like. Banking, shopify.
426 00:36:57.730 ⇒ 00:37:03.619 Uttam Kumaran: business insurance. All of that is all business to business. So we’re not that. Far from it. Also, like
427 00:37:03.880 ⇒ 00:37:15.410 Uttam Kumaran: we’re not going after like millions and millions of we’re going after people who are looking up data engineering on Tiktok. Those are people who want some data engineering content? Right? That’s look so specific.
428 00:37:16.351 ⇒ 00:37:18.599 Uttam Kumaran: So there’s already like a lot of intent
429 00:37:18.770 ⇒ 00:37:19.839 Uttam Kumaran: there, you know.
430 00:37:27.480 ⇒ 00:37:32.070 Uttam Kumaran: But yeah, we need we, we’re gonna try to do some more stuff on social. I think. So.
431 00:37:33.660 ⇒ 00:37:34.320 Uttam Kumaran: yeah.
432 00:37:34.320 ⇒ 00:37:37.110 Abigail Zhao: I mean, it’s all interesting. I personally just
433 00:37:38.252 ⇒ 00:37:41.930 Abigail Zhao: unsure how I could contribute. But.
434 00:37:41.930 ⇒ 00:37:51.629 Uttam Kumaran: I mean, you would just make you could just make the tech make a good good tiktok about about a thing that would be way more cause it would take me. It would take me so much
435 00:37:52.670 ⇒ 00:37:57.689 Uttam Kumaran: courage to do that. I still have not convinced myself to do that.
436 00:38:01.500 ⇒ 00:38:05.470 Uttam Kumaran: yeah, cause. I’m just not like A, I just don’t like doing that sort of stuff.
437 00:38:06.130 ⇒ 00:38:08.592 Uttam Kumaran: I’ve been on podcasts and stuff. But I
438 00:38:09.710 ⇒ 00:38:11.600 Uttam Kumaran: it’s just like not my thing like.
439 00:38:11.600 ⇒ 00:38:14.800 Abigail Zhao: Like it has to be like short, too, because, like I don’t know.
440 00:38:14.800 ⇒ 00:38:17.929 Uttam Kumaran: No, I need to be a tech. But see, I need someone who’s like, really.
441 00:38:18.300 ⇒ 00:38:19.330 Uttam Kumaran: who’s like.
442 00:38:19.350 ⇒ 00:38:20.830 Uttam Kumaran: basically likes
443 00:38:21.160 ⇒ 00:38:24.910 Uttam Kumaran: super plugged in to make up because I can’t have someone like
444 00:38:25.030 ⇒ 00:38:28.110 Uttam Kumaran: a lot of the people making them. Are people like me making them.
445 00:38:28.760 ⇒ 00:38:39.658 Uttam Kumaran: Which I like again, I think I could probably get there. But I need someone who’s I need like people like chronic Internet addiction to like. Come, make Tiktoks for us.
446 00:38:42.330 ⇒ 00:38:47.230 Uttam Kumaran: cause it needs to be so, Cap. It needs to just be like great captivating, you know.
447 00:38:48.585 ⇒ 00:38:52.365 Uttam Kumaran: Every you need to be doing wired headset like stuff like that.
448 00:38:58.580 ⇒ 00:39:04.469 Uttam Kumaran: Okay, cool. So I’ll send a couple of these your way, and then I’ll send you the zoom recording. And then, yeah, let’s challenge.
449 00:39:04.860 ⇒ 00:39:06.229 Abigail Zhao: Okay. Sounds good.
450 00:39:06.230 ⇒ 00:39:08.559 Uttam Kumaran: Okay. Thank you talk soon.
451 00:39:08.560 ⇒ 00:39:09.449 Abigail Zhao: Have a good one.