Meeting Title: Brainforge x Element Team Onboarding Date: 2026-03-24 Meeting participants: Advait Nandakumar Menon, Awaish Kumar
WEBVTT
1 00:02:56.680 ⇒ 00:02:57.450 Awaish Kumar: Hi.
2 00:03:00.370 ⇒ 00:03:01.250 Advait Nandakumar Menon: Hey!
3 00:03:01.710 ⇒ 00:03:02.370 Advait Nandakumar Menon: Hi.
4 00:03:02.370 ⇒ 00:03:03.399 Awaish Kumar: I agree.
5 00:03:04.380 ⇒ 00:03:05.909 Advait Nandakumar Menon: I’m doing good, how are you?
6 00:03:06.450 ⇒ 00:03:07.550 Awaish Kumar: I’m good as well.
7 00:03:09.580 ⇒ 00:03:12.689 Awaish Kumar: So, yeah, where are you located?
8 00:03:13.680 ⇒ 00:03:17.979 Advait Nandakumar Menon: I am in Cincinnati, Ohio. I am based in the US.
9 00:03:18.570 ⇒ 00:03:24.330 Advait Nandakumar Menon: But, yeah, I came from India to US for my Master’s, so that’s my situation right now.
10 00:03:25.840 ⇒ 00:03:30.630 Awaish Kumar: Okay, so you have… like,
11 00:03:31.240 ⇒ 00:03:34.240 Awaish Kumar: So how did you like being at Brangeford so far?
12 00:03:35.440 ⇒ 00:03:55.410 Advait Nandakumar Menon: So far, it’s good. It’s pretty fast moving, I would say, so I… I’ve managed to onboard pretty quickly. So, I love the environment, what you guys have built here, especially with the Brainforge platform, and, like, everything is automated, and you’re using AI extensively, so… so far, it’s going good.
13 00:03:57.290 ⇒ 00:04:03.729 Awaish Kumar: Okay, great that you liked it. So it has changed a lot since I joined.
14 00:04:05.410 ⇒ 00:04:14.599 Awaish Kumar: There’s been a really… Fast-paced, and yeah, great, great that you liked it. So…
15 00:04:14.940 ⇒ 00:04:20.890 Awaish Kumar: Yeah, how did you find being… working with the Element team?
16 00:04:21.940 ⇒ 00:04:32.420 Advait Nandakumar Menon: Yeah, I’m slowly trying to understand the data environment. I have… I’m specifically focusing on Omni, like, the topic development with Amber, so I’m pairing with her on that.
17 00:04:32.810 ⇒ 00:04:44.060 Advait Nandakumar Menon: I’m trying to understand what you guys have built here, and I think the dashboard design and the topic deliverables are high priority right now, so…
18 00:04:44.180 ⇒ 00:04:49.539 Advait Nandakumar Menon: I’m focusing on that right now, and trying to just wrap my head around things.
19 00:04:51.790 ⇒ 00:05:03.270 Awaish Kumar: Okay, yeah, so there’s a lot of, context for the element, so I think it will be… it will be okay, onboarding on it, because,
20 00:05:03.430 ⇒ 00:05:06.790 Awaish Kumar: We have all the transcripts from the beginning of this class.
21 00:05:06.790 ⇒ 00:05:07.809 Advait Nandakumar Menon: I am sorry.
22 00:05:08.000 ⇒ 00:05:08.600 Advait Nandakumar Menon: Everything.
23 00:05:08.600 ⇒ 00:05:12.199 Awaish Kumar: We talked about, we have really extensive documentation.
24 00:05:12.630 ⇒ 00:05:19.529 Awaish Kumar: Before this time, so… Oh, hopefully it’s going to get, better, with time.
25 00:05:19.530 ⇒ 00:05:20.160 Advait Nandakumar Menon: Yeah.
26 00:05:20.780 ⇒ 00:05:24.419 Awaish Kumar: But, yeah. So, apart from that, like,
27 00:05:24.720 ⇒ 00:05:27.829 Awaish Kumar: So you just graduated? .
28 00:05:28.250 ⇒ 00:05:35.959 Advait Nandakumar Menon: I graduated in December 2024, so after that, I was working with another company, a similar startup, like, consulting.
29 00:05:36.080 ⇒ 00:05:42.909 Advait Nandakumar Menon: I was delivering dashboards and analysis for small to medium businesses here in Ohio.
30 00:05:43.060 ⇒ 00:05:46.909 Advait Nandakumar Menon: So, from there, now I’m moving on to Brainforge.
31 00:05:47.910 ⇒ 00:05:52.080 Awaish Kumar: So what do you think are the diff… is the difference between that company and Brainforge?
32 00:05:52.520 ⇒ 00:06:06.049 Advait Nandakumar Menon: One, I would say the AI adoption is… it’s, like, the norm here in Brainforge, and I think that’s where all the companies are going, like, streamlining your workflow using AI, so I think that’s pretty important in the…
33 00:06:06.130 ⇒ 00:06:18.009 Advait Nandakumar Menon: upcoming days, so that’s something I have loved so far. Like, you mentioned the transcripts, everything is readily accessible, and AI can quickly… if I’m a news…
34 00:06:18.010 ⇒ 00:06:25.390 Advait Nandakumar Menon: coming into the project, and that’s what I used as well with Purther, like, you can have the GitHub repositories with the transcripts, and…
35 00:06:25.390 ⇒ 00:06:38.960 Advait Nandakumar Menon: the summaries, and, like, someone can catch up pretty quickly on what you guys have been doing, and what the next steps are, and that’s something I’ve not experienced in the… any of my previous companies in my career so far, so… that’s the biggest…
36 00:06:39.010 ⇒ 00:06:42.820 Advait Nandakumar Menon: a standard difference I’m seeing with Brainford, so that’s really nice.
37 00:06:43.690 ⇒ 00:06:48.660 Awaish Kumar: Okay, and how… Like, do you… so…
38 00:06:49.430 ⇒ 00:06:54.370 Awaish Kumar: Before, like, your master’s, did you have any experience, like, working?
39 00:06:54.370 ⇒ 00:07:02.430 Advait Nandakumar Menon: Yeah, yeah, I had close to 3 years in India, so I was working for TCS, for this airline and loyalty client.
40 00:07:02.490 ⇒ 00:07:19.079 Advait Nandakumar Menon: I started as a BI analyst over there, building dashboards, doing analysis, and then did a little of data engineering work as well, doing some migrations and some automations and things. So, towards the end of that, I decided to do my master’s.
41 00:07:19.080 ⇒ 00:07:25.550 Advait Nandakumar Menon: So that’s when I came over here and graduated in December 2024, and yeah, have been working since then.
42 00:07:27.430 ⇒ 00:07:30.969 Awaish Kumar: Okay, great. So where are you in India?
43 00:07:31.810 ⇒ 00:07:38.660 Advait Nandakumar Menon: I’m from, southern India, like, Tamil Nadu, Chennai, I’m from the…
44 00:07:39.870 ⇒ 00:07:45.110 Awaish Kumar: Okay, yeah, I had the names, obviously, in movies.
45 00:07:45.510 ⇒ 00:07:47.500 Advait Nandakumar Menon: Sorry, what’s that?
46 00:07:48.270 ⇒ 00:07:51.440 Awaish Kumar: I said I have heard the names in movies.
47 00:07:51.440 ⇒ 00:07:53.499 Advait Nandakumar Menon: Hahahaha, where are you from?
48 00:07:54.630 ⇒ 00:07:56.159 Awaish Kumar: I’m from Pakistan.
49 00:07:56.620 ⇒ 00:07:57.820 Advait Nandakumar Menon: Okay, okay.
50 00:07:57.870 ⇒ 00:08:00.009 Awaish Kumar: Okay, currently in UAE.
51 00:08:00.410 ⇒ 00:08:01.609 Awaish Kumar: I’m from Pakistan.
52 00:08:02.370 ⇒ 00:08:07.630 Advait Nandakumar Menon: so, how long have you been, like, what’s your background?
53 00:08:08.210 ⇒ 00:08:13.989 Advait Nandakumar Menon: I saw that you’re a data engineer, but how long have you been working here, and what were you doing before that?
54 00:08:15.570 ⇒ 00:08:21.149 Awaish Kumar: So, yeah, I’m a data engineer, have been working as a data engineer for the last, maybe, 9, 10 years.
55 00:08:21.430 ⇒ 00:08:21.810 Advait Nandakumar Menon: Oh.
56 00:08:22.790 ⇒ 00:08:28.769 Awaish Kumar: I graduated… I just did my bachelor’s from Pakistan.
57 00:08:29.840 ⇒ 00:08:34.590 Awaish Kumar: Then I’ve worked for, like, 2 years,
58 00:08:34.799 ⇒ 00:08:37.370 Awaish Kumar: two and a half years in Pakistan.
59 00:08:38.360 ⇒ 00:08:43.889 Awaish Kumar: When I moved to Denmark, I’ve worked there for 3 years.
60 00:08:45.160 ⇒ 00:08:50.900 Awaish Kumar: Then I moved, like, back to Pakistan for… Hmm, maybe…
61 00:08:51.060 ⇒ 00:08:55.519 Awaish Kumar: One, two years, then I also went to Canada, I worked there for one year.
62 00:08:55.800 ⇒ 00:08:56.520 Advait Nandakumar Menon: Okay.
63 00:08:56.520 ⇒ 00:08:58.720 Awaish Kumar: come back, to UAES.
64 00:08:59.420 ⇒ 00:09:04.199 Awaish Kumar: So, yeah, and I’ve been, like, at Brainforge for a year now.
65 00:09:05.040 ⇒ 00:09:12.580 Advait Nandakumar Menon: Okay, okay, you’ve got an interesting, like, the place you have worked in, it’s really interesting to hear. So…
66 00:09:12.970 ⇒ 00:09:19.600 Advait Nandakumar Menon: how come you came across those opportunities and even Brainforge? Like, how did… how was that working out for you?
67 00:09:21.060 ⇒ 00:09:27.480 Awaish Kumar: It was great, like, I had great experiences, Working at a…
68 00:09:27.950 ⇒ 00:09:33.700 Awaish Kumar: con… like, this consulting company, like, Tata, but it was, like, a…
69 00:09:34.710 ⇒ 00:09:35.590 Advait Nandakumar Menon: Oh, okay.
70 00:09:36.650 ⇒ 00:09:43.070 Awaish Kumar: I started off with a company, services company like Tata, but it was, like, not that big like Tata.
71 00:09:43.070 ⇒ 00:09:43.840 Advait Nandakumar Menon: Bro.
72 00:09:43.950 ⇒ 00:09:47.030 Awaish Kumar: Maybe, like, maybe 500, 600 employees.
73 00:09:47.410 ⇒ 00:09:47.760 Advait Nandakumar Menon: Company.
74 00:09:47.760 ⇒ 00:09:50.050 Awaish Kumar: name upsan that I started with.
75 00:09:50.480 ⇒ 00:10:04.730 Awaish Kumar: And then I got amazing opportunities, to work for Danish startups, and then, then I… yeah, my Den… actually, the Danish startup I was working with also acquired by a company, Indian company, called…
76 00:10:05.010 ⇒ 00:10:06.050 Awaish Kumar: Oyo?
77 00:10:06.310 ⇒ 00:10:07.110 Awaish Kumar: oil rooms?
78 00:10:07.110 ⇒ 00:10:09.610 Advait Nandakumar Menon: Oh yeah, yeah, I’ve heard of it, yeah, yeah, yeah, yeah.
79 00:10:09.900 ⇒ 00:10:14.579 Awaish Kumar: Yeah, so in, in Europe, they, they came in, and,
80 00:10:14.710 ⇒ 00:10:19.060 Awaish Kumar: actually entered into hospitality industry. They were…
81 00:10:19.060 ⇒ 00:10:19.410 Advait Nandakumar Menon: Okay.
82 00:10:19.410 ⇒ 00:10:23.619 Awaish Kumar: in Europe, they are called, like, oil vacation homes.
83 00:10:24.570 ⇒ 00:10:26.639 Awaish Kumar: subsidiary of Oyo Rooms.
84 00:10:27.660 ⇒ 00:10:30.419 Awaish Kumar: So, yeah, I’ve worked with them for 2 years.
85 00:10:30.630 ⇒ 00:10:32.340 Awaish Kumar: And then…
86 00:10:33.120 ⇒ 00:10:39.789 Awaish Kumar: It was a great experience. I had an exposure working in a European market, then in a Canadian market, which is…
87 00:10:40.650 ⇒ 00:10:46.040 Awaish Kumar: like, really opposite, and then a U.S. company, like, Wind Forge.
88 00:10:46.510 ⇒ 00:10:49.950 Awaish Kumar: So all different cultures, environments.
89 00:10:51.690 ⇒ 00:10:52.920 Advait Nandakumar Menon: That’s nice.
90 00:10:53.650 ⇒ 00:10:57.790 Awaish Kumar: But most of my career, I work with startups and growth stage companies.
91 00:10:57.930 ⇒ 00:11:00.560 Awaish Kumar: So I like being there, for enterprise…
92 00:11:00.560 ⇒ 00:11:01.420 Advait Nandakumar Menon: I need to talk.
93 00:11:02.210 ⇒ 00:11:05.219 Awaish Kumar: enterprise environment don’t really suit me. I…
94 00:11:05.220 ⇒ 00:11:05.900 Advait Nandakumar Menon: Yeah.
95 00:11:05.900 ⇒ 00:11:10.470 Awaish Kumar: Work a little bit. I’ve worked for an year or something.
96 00:11:10.790 ⇒ 00:11:12.440 Awaish Kumar: Yeah, I didn’t like that.
97 00:11:14.660 ⇒ 00:11:33.210 Advait Nandakumar Menon: That’s what I have also felt working with something like Data, and then the previous consulting company was also a small startup, like, we were a lean team, so yeah, that’s something I’ve seen, the nuances as well, like, the benefits of working in a, like, a startup environment. So you mentioned, like.
98 00:11:33.210 ⇒ 00:11:42.840 Advait Nandakumar Menon: the influence has changed a lot, like, how it was before, and you’ve been a year, so how would you say it has changed? How has your experience been? I’m just curious, it’s your mission, but…
99 00:11:43.080 ⇒ 00:11:54.069 Awaish Kumar: Jane, like, we started… when I joined, maybe we were in total 8 people, 10 people… 8 people, maybe? And then now it has grown to become, like, 30 people right now in Printforge.
100 00:11:55.440 ⇒ 00:11:58.910 Awaish Kumar: It has grown in terms of number of people, number of clients…
101 00:11:59.640 ⇒ 00:12:09.909 Awaish Kumar: We have a fully, running AI team right now. We didn’t have that before, so we had a small AI team.
102 00:12:10.100 ⇒ 00:12:12.120 Awaish Kumar: I was also going in.
103 00:12:12.330 ⇒ 00:12:18.290 Awaish Kumar: And now we have the defined roles and responsibilities for each person.
104 00:12:19.440 ⇒ 00:12:26.339 Awaish Kumar: It’s a dedicated AI team, And that is also growing, like, a lot faster.
105 00:12:28.470 ⇒ 00:12:32.040 Awaish Kumar: We just had one, two people there, and now it’s like…
106 00:12:32.600 ⇒ 00:12:37.180 Awaish Kumar: 4 or 5 already with their own lead and all of that.
107 00:12:39.120 ⇒ 00:12:40.110 Awaish Kumar: Lord.
108 00:12:40.690 ⇒ 00:12:51.430 Awaish Kumar: And the processes have changed, so it was, like, one person doing a lot of things. Now that we are standardizing the roles, responsibilities, we have PMs, we have…
109 00:12:52.940 ⇒ 00:13:02.310 Awaish Kumar: use of AI, like, obviously it’s growing. When we started, maybe we were using AI, but not at the scale at which we are using it right now.
110 00:13:03.110 ⇒ 00:13:20.449 Awaish Kumar: the platform is matured. We started off with, like, nothing, on platform. Like, I was also participated in, like, building some basics, but now it has gone to become a product itself, right?
111 00:13:21.810 ⇒ 00:13:23.950 Awaish Kumar: AI team is maintaining it right now.
112 00:13:24.180 ⇒ 00:13:30.240 Awaish Kumar: So, a lot has changed, At every…
113 00:13:30.360 ⇒ 00:13:34.809 Awaish Kumar: what’d you say? At every domain, like, in delivery.
114 00:13:35.280 ⇒ 00:13:38.440 Advait Nandakumar Menon: Sales, GTM, yeah.
115 00:13:38.440 ⇒ 00:13:39.400 Awaish Kumar: fail, right?
116 00:13:39.500 ⇒ 00:13:44.519 Awaish Kumar: So at every, work stream, it has grown so rapidly, so…
117 00:13:46.140 ⇒ 00:13:47.290 Advait Nandakumar Menon: Pretty nice.
118 00:13:49.620 ⇒ 00:13:54.340 Awaish Kumar: Okay, okay, yeah, I’m basically…
119 00:13:54.580 ⇒ 00:14:06.279 Awaish Kumar: Here, I have Element as a client, but I’m basically leading all the data engineering things, slash data, so…
120 00:14:07.200 ⇒ 00:14:12.119 Awaish Kumar: If you need anything, for Element, or for any other client, you can just…
121 00:14:12.670 ⇒ 00:14:17.290 Awaish Kumar: Right? Yeah. Especially, yeah. For the element, like, especially I’m the only one…
122 00:14:17.800 ⇒ 00:14:23.930 Awaish Kumar: as a data engineer, data analytics engineer, you can just ask me.
123 00:14:24.170 ⇒ 00:14:33.790 Awaish Kumar: Yeah. Do you have any questions regarding… Infrastructure, setups, texting… data, and,
124 00:14:34.070 ⇒ 00:14:39.600 Awaish Kumar: And apart from that, you can also ping me if you are working on any other client.
125 00:14:40.250 ⇒ 00:14:47.710 Advait Nandakumar Menon: Yeah, for sure. I just pinged one question, like, a couple of minutes back before this meeting about Element. So, I was…
126 00:14:47.710 ⇒ 00:15:01.429 Advait Nandakumar Menon: as someone new who’s coming into this, the data platform doc and the dbt models you have set up, it’s pretty self-explanatory, like, it’s pretty useful to understand the context of what each table or field represents.
127 00:15:01.430 ⇒ 00:15:14.220 Advait Nandakumar Menon: But I was wondering if there is something like a standardized data dictionary document, like, someone new is coming, it’s easier for them to understand what this particular field represents. So, do we have any document like that? Because
128 00:15:14.270 ⇒ 00:15:21.599 Advait Nandakumar Menon: I was thinking of feeding that to the cursor, and so it could get a better sense of which table to…
129 00:15:22.150 ⇒ 00:15:25.439 Advait Nandakumar Menon: Or field to refer to when developing the topic, so…
130 00:15:26.010 ⇒ 00:15:28.490 Advait Nandakumar Menon: Is that something that’s available in a data dictionary?
131 00:15:28.490 ⇒ 00:15:33.089 Awaish Kumar: Yeah, we don’t have anything like that, but that is, like, really,
132 00:15:33.310 ⇒ 00:15:37.889 Awaish Kumar: Like, really, really extensive work, you need to maintain that.
133 00:15:38.430 ⇒ 00:15:38.760 Advait Nandakumar Menon: Yeah.
134 00:15:38.760 ⇒ 00:15:51.060 Awaish Kumar: each field. So we… we also try to use… use AI to build that. We have this data platform documentation, which has metrics, which has,
135 00:15:53.060 ⇒ 00:16:02.899 Awaish Kumar: if you go into the specifics of, like, wholesale, there’s a file called Wholesale Reporting. If you go into that, you will find exact table names, exact
136 00:16:03.060 ⇒ 00:16:04.360 Awaish Kumar: Feels, everything.
137 00:16:04.360 ⇒ 00:16:05.190 Advait Nandakumar Menon: Okay.
138 00:16:05.190 ⇒ 00:16:06.400 Awaish Kumar: Yeah, but… Okay.
139 00:16:06.750 ⇒ 00:16:11.590 Awaish Kumar: But it might not have all the descriptions, because…
140 00:16:12.150 ⇒ 00:16:21.189 Awaish Kumar: They are AI-generated, like, it’s impossible to write a description for each column, since we are already in a fast-paced environment, we are delivering.
141 00:16:22.320 ⇒ 00:16:28.920 Awaish Kumar: Deliver that, and deliver that, and it’s a lot of things, and so it’s really hard to maintain any descriptions.
142 00:16:28.920 ⇒ 00:16:30.030 Advait Nandakumar Menon: Yeah, yeah, yeah, yeah.
143 00:16:30.030 ⇒ 00:16:37.769 Awaish Kumar: So, I don’t think we maintain that, but yeah, we have generated these documents, and there is a Google Doc.
144 00:16:37.910 ⇒ 00:16:53.520 Awaish Kumar: For wholesale reporting, oh, sorry, a spreadsheet for wholesale reporting, which one of the tabs might have the data you are looking for, like table names, and the fields, and maybe some of them have description, but yeah, that’s how.
145 00:16:53.980 ⇒ 00:16:55.060 Awaish Kumar: Oh, yeah.
146 00:16:55.350 ⇒ 00:16:59.930 Awaish Kumar: And then apart from that, in Snowflake, you also have a catalog.
147 00:17:00.140 ⇒ 00:17:00.910 Awaish Kumar: But…
148 00:17:00.910 ⇒ 00:17:01.480 Advait Nandakumar Menon: Okay.
149 00:17:02.920 ⇒ 00:17:06.180 Awaish Kumar: But even before that, like, you can…
150 00:17:06.369 ⇒ 00:17:09.529 Awaish Kumar: Use the eye to query some of, like, schemas.
151 00:17:10.750 ⇒ 00:17:11.730 Advait Nandakumar Menon: Okay.
152 00:17:12.119 ⇒ 00:17:13.149 Awaish Kumar: newscasting.
153 00:17:13.349 ⇒ 00:17:16.409 Awaish Kumar: You can… I don’t know if you have already utilized that.
154 00:17:17.630 ⇒ 00:17:19.560 Advait Nandakumar Menon: Catalog, I haven’t.
155 00:17:19.990 ⇒ 00:17:22.960 Awaish Kumar: No, no, I mean, have you used cursor with the…
156 00:17:23.130 ⇒ 00:17:30.570 Awaish Kumar: Like, have you asked Khasar in the chat that, okay, connect… to this element snowflake instance.
157 00:17:30.710 ⇒ 00:17:38.550 Awaish Kumar: And then carry information schema for this database, and… and read all the metadata for all the… all the…
158 00:17:38.810 ⇒ 00:17:46.649 Advait Nandakumar Menon: No, I don’t think I did that. I asked it to use the… because I only have access to the Omni aspect.
159 00:17:46.650 ⇒ 00:18:01.949 Advait Nandakumar Menon: So, I have, synced the Omni, files to my local, and I asked Cursor, like, so these are the topics, these are the dbt models available, these are the views and tables available. So, based on this, help me understand, and then
160 00:18:02.030 ⇒ 00:18:05.420 Advait Nandakumar Menon: like, refine or create the topic as well, so I’ve used it, like, that…
161 00:18:05.420 ⇒ 00:18:11.139 Awaish Kumar: This is… there are different things, like, there’s one thing that usually use, like.
162 00:18:11.460 ⇒ 00:18:11.940 Advait Nandakumar Menon: Hmm.
163 00:18:11.940 ⇒ 00:18:18.009 Awaish Kumar: Omni, on your local machine, you use the dbt models which are already there, you use the…
164 00:18:18.140 ⇒ 00:18:21.340 Awaish Kumar: Maybe, all the contacts we might have.
165 00:18:21.780 ⇒ 00:18:32.310 Awaish Kumar: the transcripts, but you can also use Google Data Platform documentation. As I mentioned, it might have those names for the fields, but also ask…
166 00:18:32.470 ⇒ 00:18:39.890 Awaish Kumar: cursor to basically query if… I don’t know, why haven’t you got the access to Snowflake? Is it because,
167 00:18:40.230 ⇒ 00:18:43.500 Awaish Kumar: You haven’t started full-time yet, or, like, is there any…
168 00:18:43.500 ⇒ 00:18:50.479 Advait Nandakumar Menon: I have access to Snowflake, but I’m not sure if I can connect it to Cursor. Is there a way to connect it?
169 00:18:50.480 ⇒ 00:18:54.719 Awaish Kumar: Yes, use, like, for example.
170 00:18:55.840 ⇒ 00:19:03.900 Awaish Kumar: you can ask, I think, Amber, she’s already doing it, but it’s like, you can use your own…
171 00:19:04.380 ⇒ 00:19:12.480 Awaish Kumar: Credentials, or to ask Kherson to use these and connect to Snowflake.
172 00:19:12.480 ⇒ 00:19:14.470 Advait Nandakumar Menon: Okay, that’s a good point, yeah.
173 00:19:14.470 ⇒ 00:19:19.080 Awaish Kumar: Second thing is that you can also ask, like, you can use service user.
174 00:19:19.210 ⇒ 00:19:27.559 Awaish Kumar: The keys, if you have access to 1Pass, it also has the, public and private key for service users.
175 00:19:28.910 ⇒ 00:19:42.459 Awaish Kumar: It’s for Element. So you can basically use that account, and you can download the public key, and the private key, basically, and then service user name is also there in the OnePass.
176 00:19:42.460 ⇒ 00:19:42.990 Advait Nandakumar Menon: Hmm.
177 00:19:42.990 ⇒ 00:19:47.290 Awaish Kumar: And then you can ask Kherson, basically, use this service user
178 00:19:48.040 ⇒ 00:19:55.420 Awaish Kumar: service account user, and this key, and then using Snow CLI, Okay.
179 00:19:55.680 ⇒ 00:20:02.659 Awaish Kumar: connect to the Snowflake account, and then once it’s connected, you can carry it, you can ask, okay, connect…
180 00:20:03.240 ⇒ 00:20:13.869 Awaish Kumar: read all the schema for the raw database, all the… all its schemas and the tables, and it will read it for you, you can ask them to create a CSV for you, you can ask
181 00:20:13.980 ⇒ 00:20:16.420 Awaish Kumar: Okay, read it, and then…
182 00:20:17.070 ⇒ 00:20:17.850 Advait Nandakumar Menon: Okay.
183 00:20:17.850 ⇒ 00:20:26.580 Awaish Kumar: Give me, Also maybe, can help you create topics and things like that.
184 00:20:27.100 ⇒ 00:20:43.000 Advait Nandakumar Menon: Yeah, that’s a useful information. I didn’t connect it to Snowflake directly, so maybe I’ll try that. But yeah, I understand, like, since it’s a fast-moving project and team, I assume that’s the reason why something like that may not exist, so I just thought it’s…
185 00:20:43.000 ⇒ 00:20:46.699 Awaish Kumar: Even if we go for that, we use AI, like…
186 00:20:46.700 ⇒ 00:20:47.290 Advait Nandakumar Menon: Yeah, yeah.
187 00:20:47.290 ⇒ 00:20:58.959 Awaish Kumar: Let me try that for wholesale reporting. Utam already created something like that in a wholesale reporting spreadsheet, but that’s all losing whiser, right? We are not writing it down.
188 00:21:00.880 ⇒ 00:21:01.800 Advait Nandakumar Menon: Yeah.
189 00:21:02.990 ⇒ 00:21:10.250 Advait Nandakumar Menon: Yeah, that makes sense, I just wanted to get that out, but yeah, I don’t think I have anything else to ask you, but…
190 00:21:10.570 ⇒ 00:21:21.939 Advait Nandakumar Menon: really looking forward to collaborate with you with any, other work in future, and if possible, also, like, DE or AE work as well, since I have some experience with that. So, yeah, it’s pretty…
191 00:21:22.260 ⇒ 00:21:25.599 Advait Nandakumar Menon: Great to know that you guys have set up this platform.
192 00:21:26.070 ⇒ 00:21:33.090 Awaish Kumar: Yeah, you can also look in Benfudge platform. It might have the playbook for how to connect to Snowflake.
193 00:21:33.250 ⇒ 00:21:38.870 Awaish Kumar: Okay. And then, yeah, but I have already explained that you can use one of the service accounts.
194 00:21:39.440 ⇒ 00:21:40.430 Advait Nandakumar Menon: Just…
195 00:21:40.930 ⇒ 00:21:46.560 Awaish Kumar: Scarser, and it will use it to connect… ask… ask Cursor to connect using Snow CLI, and that’s all.
196 00:21:47.350 ⇒ 00:21:51.049 Advait Nandakumar Menon: Okay, okay, yeah. I’ve noted it down, I’ll try it.
197 00:21:52.220 ⇒ 00:21:57.200 Awaish Kumar: Okay, great, then… CEO in Slack, and yeah.
198 00:21:57.200 ⇒ 00:21:57.990 Advait Nandakumar Menon: Yep.
199 00:21:58.340 ⇒ 00:22:00.190 Awaish Kumar: Thank you for… for your time.
200 00:22:00.690 ⇒ 00:22:03.010 Advait Nandakumar Menon: Yeah, thanks for your time, looking forward to working with you.
201 00:22:03.720 ⇒ 00:22:04.860 Awaish Kumar: Okay, bye.
202 00:22:04.860 ⇒ 00:22:06.050 Advait Nandakumar Menon: Take care. Bye-bye.