Meeting Title: Uttam <> Ryan—Onboarding-12-8-23 Date: 2023-12-08 Meeting participants: Ryan Luke Daque, Uttam Kumaran
WEBVTT
1 00:00:53.000 ⇒ 00:00:54.220 Uttam Kumaran: Hey? Hello!
2 00:00:54.250 ⇒ 00:00:59.920 Ryan Luke Daque: Hi, Otham! Good good morning. Good evening.
3 00:01:00.000 ⇒ 00:01:04.779 Uttam Kumaran: How does it? How do you like? How long have you been working like shifted hours.
4 00:01:05.150 ⇒ 00:01:07.220 Ryan Luke Daque: It’s been a like
5 00:01:07.510 ⇒ 00:01:29.500 Ryan Luke Daque: ever since the pandemic began. Essentially, yeah, I was like working from home stuff like that. So yeah. And then we have you been doing data even before cause you mentioned, you were doing like, look for stuff for like 2, 3 years like, were you doing data stuff before then a little bit, but it’s not like my main scroll before. I was like a process analyst before, but I had to do also
6 00:01:29.700 ⇒ 00:01:57.490 Uttam Kumaran: creating reports and dashboards in power. Bi. So yeah, I had to do a little bit of data stuff as well. Kind of data. Were they all local companies, or like local in in Asia or, yeah, no. Actually, I’ll I worked at a Lexmark international. I’m not sure
7 00:01:57.490 ⇒ 00:02:05.779 Ryan Luke Daque: I’ve worked there for like 11 years. I think, yeah. So that’s where I like work for the longest time. And yeah, and then
8 00:02:06.170 ⇒ 00:02:08.330 Ryan Luke Daque: during the pandemic, I had basically
9 00:02:08.470 ⇒ 00:02:11.740 Ryan Luke Daque: had to stop because my wife, but it was like a
10 00:02:11.940 ⇒ 00:02:18.750 Ryan Luke Daque: I mean bad timing, really, and my wife gave birth during the pandemic and started pandemic. So I had to stop working.
11 00:02:18.920 ⇒ 00:02:24.160 Ryan Luke Daque: I have to look for like work online stuff like that. That’s where I like decided to
12 00:02:24.340 ⇒ 00:02:25.820 Ryan Luke Daque: go deeply into
13 00:02:26.560 ⇒ 00:02:37.890 Ryan Luke Daque: the world of data, engineering and stuff like that having fun so far with it. Yeah, how is it been? I mean, I think that’s like, recently, kind of like what I’ve
14 00:02:38.030 ⇒ 00:02:51.110 Uttam Kumaran: I was working, you know, for like 6 years in startups doing like data engineering. And only recently, I kind of started going directly and working with clients. But I work for like we work, I work for a bunch of different.
15 00:02:51.250 ⇒ 00:03:00.119 Uttam Kumaran: you know, companies. But like, I guess I was, gonna ask, like, How how does it feel like going like directly, instead of working like
16 00:03:01.000 ⇒ 00:03:08.350 Uttam Kumaran: and oh, individual company, like, you’re working directly. Clients like, what are the what do you think is like the pros and cons.
17 00:03:08.710 ⇒ 00:03:13.700 Ryan Luke Daque: Yeah, there’s definitely a lot like there’s well, so far I’ve been. It’s it’s
18 00:03:13.850 ⇒ 00:03:18.920 Ryan Luke Daque: it’s a mix of pros and cons right like that. The post basically is like
19 00:03:18.970 ⇒ 00:03:21.120 Ryan Luke Daque: working from home is really
20 00:03:21.420 ⇒ 00:03:25.499 big difference for me, especially like when I was working before I had to like
21 00:03:25.520 ⇒ 00:03:39.119 Ryan Luke Daque: commute to to the office, and it’s the traffic is bad, and like I had to stay there for like 8 to 10 HA day, depending on like like the workload and stuff like that. So like working from home really gives me like
22 00:03:39.260 ⇒ 00:03:44.660 Ryan Luke Daque: more energy, basically in in the day to work and focus. And I can like
23 00:03:44.750 ⇒ 00:03:57.350 Ryan Luke Daque: spend time with the family as well. One of the pros, and like working especially I realize, like working with startup companies directly with clients. I get like better
24 00:03:57.570 ⇒ 00:04:02.010 Ryan Luke Daque: freedom, basically like working in an organization.
25 00:04:02.360 ⇒ 00:04:22.180 Ryan Luke Daque: You get siloed. Mostly like you get to just focus on what you’re doing, and you don’t have access and like, get getting access is gonna take a startup. So you just
26 00:04:22.210 ⇒ 00:04:31.679 Ryan Luke Daque: is the client usually just gives you like, II need this and find a way how to do it, whatever you come up with and stuff like that. Yeah.
27 00:04:32.030 ⇒ 00:04:32.870 Ryan Luke Daque: it’s cool.
28 00:04:33.060 ⇒ 00:04:43.540 Uttam Kumaran: Yeah. I think you’ll find. You know I’m excited to kind of walk you through a couple of things today. But I think you’ll find that like, yeah, my career. And like the way I operate
29 00:04:43.670 ⇒ 00:04:47.490 Uttam Kumaran: is like that on like steroids. Like I
30 00:04:47.600 ⇒ 00:04:48.620 Uttam Kumaran: I
31 00:04:48.660 ⇒ 00:04:58.249 Uttam Kumaran: like, I’m an engineer. And so my goal is like anybody that comes and works as an engineer spends their time doing engineering work, which is like
32 00:04:58.320 ⇒ 00:05:06.920 Uttam Kumaran: core development work. You don’t sit in like daily stand ups. You don’t sit in like company update. There’s no like
33 00:05:07.640 ⇒ 00:05:15.969 Uttam Kumaran: I don’t know. There’s like as light operational complexity as possible, though, for my company. If we’re not spending time on
34 00:05:15.990 ⇒ 00:05:26.040 Uttam Kumaran: selling or executing. Then it’s not like we’re losing. We’re losing time right? And so for me, a lot of the operations and things like that
35 00:05:26.270 ⇒ 00:05:29.030 Uttam Kumaran: that way, I can free up everybody on my team to go
36 00:05:29.090 ⇒ 00:05:39.569 Uttam Kumaran: work on just engineering work, cause I again like, I wish I could do that. And that’s what I actually spend, like maybe 4 or 5 HA day doing sequel and doing development work.
37 00:05:39.850 ⇒ 00:05:48.639 Uttam Kumaran: But we have a ton of ideas we’re working on on the Snowflake side on. you know, building different like data applications. And so
38 00:05:48.970 ⇒ 00:05:58.599 Uttam Kumaran: IA lot of agencies. When you work with them, they’ll have like a sales force, and they’ll have product managers. Then they’ll have clients, and then they have to do Demos and like it’s too slow.
39 00:05:58.690 ⇒ 00:06:10.299 Uttam Kumaran: you know, and if you look in like the announcements channel in slack. I don’t know if you’re if you’re able to see that like one of the things that I put in there was like
40 00:06:10.340 ⇒ 00:06:19.190 Ryan Luke Daque: the principles of the company today. Let me add you there.
41 00:06:29.180 ⇒ 00:06:33.920 Ryan Luke Daque: Oh, yeah, I see it now. So II wrote this down when I started the company.
42 00:06:34.220 ⇒ 00:06:37.010 Uttam Kumaran: and I think it’ll evolve over time. But.
43 00:06:37.590 ⇒ 00:06:55.650 Uttam Kumaran: like I think, a lot of companies they don’t have like they have mission statements that like, make the world better. And, like, you know, they’ll be like, Ha! Be happy. It’s like I it’s like, I’m not. That doesn’t mean anything. This is like as a company when you make decisions. This is how I think about things. For example, like.
44 00:06:56.320 ⇒ 00:07:07.020 Uttam Kumaran: what if a company, if a company says we want a demo on something, and they say, let’s schedule a follow up for 2 weeks. If I if I think about the principles that I’m like, actually, let’s schedule a call for Friday.
45 00:07:07.070 ⇒ 00:07:13.999 Uttam Kumaran: and like we’ll get something done right? So a lot of these, the the things I wrote down is like trying to think about how to run the company.
46 00:07:14.180 ⇒ 00:07:15.420 Uttam Kumaran: And
47 00:07:15.600 ⇒ 00:07:29.959 Uttam Kumaran: you know things like work and deliver and communicate like a larger agency. Trying to automate more things develop on world class technology like, I think those are all things that are really like accomplishable
48 00:07:30.170 ⇒ 00:07:55.830 Uttam Kumaran: in addition to, like, you know, find a ways to give back like find ways to either donate time to charity or like, find ways to help people in the whole process learn more, get familiar with data, and, like one of the things I do is like IA lot of people that work with me like I just brought in. And they do other things. I’m like, I have work like, feel free to come, help me. And so that’s one of the things like.
49 00:07:55.940 ⇒ 00:08:05.029 Uttam Kumaran: can everybody? Can everybody ease? Can everybody come and like try, you know, work. And then, yeah, so these are a couple of things that I think I’ve tried the company on. But
50 00:08:05.550 ⇒ 00:08:13.860 Ryan Luke Daque: we’ll see how they evolve over time. So this definitely looks like it aligns with what I also
51 00:08:14.090 ⇒ 00:08:29.250 Ryan Luke Daque: like would have ambition if ever I created my own company. But yeah, that’s like, I’m that’s not like part of my goal. But yeah, I think this is like very engineering mindset. I yeah, I don’t know. I just like
52 00:08:29.590 ⇒ 00:08:42.810 Uttam Kumaran: I think it could be. It is just as simple as this. I think a lot of people over complicate because they’re hiding, or they’re don’t want to be transparent. And for me, I want to be very transparent and like what what we’re trying to do and the projects we take on.
53 00:08:42.990 ⇒ 00:08:50.160 Uttam Kumaran: And I wanna work with people who agree with this. Right? It’s like. I don’t wanna work with unethical people. I don’t wanna work with people who are like
54 00:08:50.460 ⇒ 00:09:03.560 Uttam Kumaran: to business. See? They like complicate everything. I want to work with engineers primarily. And I want people that want to work quick and work on like the best technology. So and that’s the clients I’m kind of trying to get go after, too. So
55 00:09:04.660 ⇒ 00:09:05.570 Ryan Luke Daque: nice.
56 00:09:07.020 ⇒ 00:09:26.220 Uttam Kumaran: Cool. So I’ll give you a little bit of overview of the company, you know, beyond that. And then I think what we can do is just verify that you have access to all of the different like access, like github things like that. And then I can actually walk you through
57 00:09:26.240 ⇒ 00:09:28.840 Uttam Kumaran: snowflake, dbt.
58 00:09:28.920 ⇒ 00:09:32.610 Uttam Kumaran: and like dash.
59 00:09:32.660 ⇒ 00:09:44.519 Uttam Kumaran: and then we can kind of, and I’ll also also just show you kind of like where I’m doing some live task tracking. And then we can think about like a good first thing, and then we can kind of go from there. So basically
60 00:09:44.530 ⇒ 00:09:54.110 Uttam Kumaran: basically brain forage. I started in April. And currently, we have 2 clients primarily that we’re working on. In addition.
61 00:09:54.210 ⇒ 00:10:06.410 Uttam Kumaran: in addition, I’m working with a few other people that I’m currently selling to as well as a few internal development projects. And so a client relationship
62 00:10:06.430 ⇒ 00:10:08.049 Uttam Kumaran: primarily looks like
63 00:10:08.130 ⇒ 00:10:27.210 Uttam Kumaran: there’s a client they need either snowflake Dbt data modeling. They either have a data team or they don’t. And I go in. And I say, Hey, we can actually accomplish your goals. Maybe they have trouble with data accuracy. Maybe they’re having trouble with data visualization. Maybe they have some legacy code that they need migrated.
64 00:10:27.360 ⇒ 00:10:29.900 Uttam Kumaran: So those are the kind of projects that we’re going after.
65 00:10:30.360 ⇒ 00:10:36.829 Uttam Kumaran: And typically the stack that I stand up is 5 tran dbt. Snowflake and light dash.
66 00:10:37.050 ⇒ 00:10:45.140 Uttam Kumaran: I would say. Light dash, and dvt both work very well together, as you’ll kind of see, and so
67 00:10:45.510 ⇒ 00:11:00.160 Uttam Kumaran: and light dash is quite a bit cheaper than Looker 5 tray and Snowflake is just like to take over Etl and data warehousing. And those are pretty common as well. And then, really, most of the work goes into writing data models and then doing data visualization.
68 00:11:00.250 ⇒ 00:11:04.719 That’s all in Dbt, and then everything we have is in Dbt. Cloud.
69 00:11:05.090 ⇒ 00:11:08.619 Uttam Kumaran: I use Vs code, and like Cli to kind of
70 00:11:08.660 ⇒ 00:11:12.390 Uttam Kumaran: run a lot of that. But other folks do everything. Cloud.
71 00:11:12.510 ⇒ 00:11:22.590 Ryan Luke Daque: Don’t have a preference like whatever is best for your like development environment. Alright. So so you use Dbt cloud for you orchestrating the proder runs as well.
72 00:11:22.730 ⇒ 00:11:23.590 Uttam Kumaran: yeah.
73 00:11:24.820 ⇒ 00:11:30.900 Ryan Luke Daque: Are you? Are you doing, are you like? Do we have the paid version for the Liberty cloud, or just the free version?
74 00:11:31.180 ⇒ 00:11:39.950 Uttam Kumaran: So for one of my clients. They’re just you know how they have the 35 35,000 model run
75 00:11:40.240 ⇒ 00:11:51.210 Uttam Kumaran: thing right now, or whatever. So at the moment for the one client. I’m lower than I’m like, right lower than that. So I didn’t want to like charge them
76 00:11:51.280 ⇒ 00:12:09.269 Uttam Kumaran: because we are lower. So I’m kind of just trying to keep it lower through like using like ephemeral models and just like making sure the schedule isn’t like every hour. It’s like currently optimized for their needs. If it ends up increasing. I’m happy to get them to pay or just
77 00:12:09.490 ⇒ 00:12:24.049 Uttam Kumaran: money and not to pay. Dvt. Like, I think the Dvt pricing they’ve changed so many times. So it’s also kind of like I just if I could not pay them. I don’t want to so free money for them. So
78 00:12:26.550 ⇒ 00:12:33.430 Uttam Kumaran: so those are the that’s a typical stack and then really
79 00:12:34.070 ⇒ 00:12:52.930 Uttam Kumaran: for the way kind of worked for, and the client primarily that we’ll be working on. And I think that’s just really, initially, I think, as we’ll see, like as we work together, there’s opportunities to work across other clients or other projects, we’ll definitely be able to loop you in. But primarily we’ll be working on this client called pool parts to go.
80 00:12:52.950 ⇒ 00:13:01.460 Uttam Kumaran: I don’t know if you if you’re able to go onto safari or your your chrome and just go to pool parts to go.com.
81 00:13:05.600 ⇒ 00:13:07.969 Ryan Luke Daque: Do you have any specific
82 00:13:09.000 ⇒ 00:13:12.109 Ryan Luke Daque: like clients and the like, or
83 00:13:12.430 ⇒ 00:13:14.199 Ryan Luke Daque: type of clients, basically.
84 00:13:14.310 ⇒ 00:13:34.180 Uttam Kumaran: or do just do any any kind of client honestly, not really. For me, I really look at people, for I have a lot of background, e-commerce and software. And so if there’s any clients doing that, I’m able to really work really well. However, again, most companies these days are selling online.
85 00:13:34.220 ⇒ 00:13:37.879 Ryan Luke Daque: They’re running advertisement. They have a local dB,
86 00:13:38.140 ⇒ 00:13:40.319 and they have a product database
87 00:13:40.430 ⇒ 00:13:47.519 Uttam Kumaran: like that it’s hard for me to find a company that doesn’t have that. So instead, I look for as like data maturity.
88 00:13:47.620 ⇒ 00:13:54.920 Uttam Kumaran: So I look at how mature is the company in terms of data? Do they have a data warehouse? Do they have a dedicated analyst?
89 00:13:55.100 ⇒ 00:13:58.139 Uttam Kumaran: Some people don’t have anything, and their company is doing really well.
90 00:13:58.630 ⇒ 00:14:04.329 Uttam Kumaran: so the one thing I try to find is companies that
91 00:14:04.870 ⇒ 00:14:25.620 Uttam Kumaran: you know, like need need the data work to get to the next level, and that could be anything. And so I’m having conversations with, with, like all sorts of people. Because now, data is really prevalent in every type of business no longer. Just online businesses. It’s like anybody that runs business. So that’s that’s a good question. And I’m trying to find more of like a niche.
92 00:14:27.350 ⇒ 00:14:31.999 Uttam Kumaran: but it’s kind of like over time. I think I’ll kind of narrow down a little bit more. So.
93 00:14:33.130 ⇒ 00:14:45.290 Ryan Luke Daque: you know. Like is, Yeah, that’s that’s a good point. It’s like it’s you just started like this April. So you also just want to see like which clients like fit, or any client would be
94 00:14:45.330 ⇒ 00:14:51.629 Uttam Kumaran: exactly. And I wanna make sure I could deliver for them. And then I can, what I learned from them.
95 00:14:51.720 ⇒ 00:14:57.470 Uttam Kumaran: make a case study and then kind of go advertise that to other people. So that’s my, that’s my goal.
96 00:14:58.270 ⇒ 00:15:01.790 So this is the client. Right now, it’s called pool parts to go.
97 00:15:01.800 ⇒ 00:15:09.110 Uttam Kumaran: They are online retailer for pool parts. So things like pumps, covers keep pumps.
98 00:15:09.220 ⇒ 00:15:15.899 Uttam Kumaran: Maybe you know, II don’t have a pool, but you know, I know people with pools, and these are all things that they use.
99 00:15:15.980 ⇒ 00:15:21.639 Ryan Luke Daque: So they make. They make quite a bit of revenue, and they’re they, and you’d be surprised in
100 00:15:21.810 ⇒ 00:15:34.700 Uttam Kumaran: the Us. There’s not many online retailers for pool parts, typically the way it works is. And there are primary us, based. If you own a pool. You actually need to get like a pool service technician to come.
101 00:15:35.020 ⇒ 00:15:40.599 Uttam Kumaran: buy the materials from him, and then he installs it instead. A lot of this stuff you can actually install on your own
102 00:15:41.180 ⇒ 00:15:49.290 Uttam Kumaran: advantage of that, and said, we’re gonna sell directly to the customer and then help them implement and install those things.
103 00:15:49.430 ⇒ 00:15:54.600 Uttam Kumaran: And so, this is like, this is primarily the client I work for.
104 00:15:54.770 ⇒ 00:16:05.390 Uttam Kumaran: And couple of things that I have done for them is the big initiatives are one. They want a daily dashboard that’s like
105 00:16:05.740 ⇒ 00:16:10.109 Uttam Kumaran: everything based on the previous day across their whole business. So it’s sales
106 00:16:10.190 ⇒ 00:16:14.530 Uttam Kumaran: profit marketing, shipping.
107 00:16:14.710 ⇒ 00:16:17.579 Uttam Kumaran: and then website traffic.
108 00:16:17.690 ⇒ 00:16:27.919 Uttam Kumaran: And so one of that’s one of the big products I’ve worked for for them second big project that we just closed out is around shipping. So they have contracts with shipping providers.
109 00:16:28.060 ⇒ 00:16:37.159 Uttam Kumaran: And don’t worry about like this is, I’m just gonna kind of tell you everything that’s going on. It’ll take you some time on and see everything, so they’ll don’t worry too much.
110 00:16:37.530 ⇒ 00:16:43.359 Uttam Kumaran: Second project I’ve worked on is understanding, shipping and shipping for these big
111 00:16:43.470 ⇒ 00:17:03.160 Uttam Kumaran: pumps is really expensive for them. If you think they sell pump for like 400 and they pay. Don’t put that on the customer. And so one of the things I did was help them understand how to negotiate with the shipping provider. Given data, get more discounts. And so that project is kind of just wrapping up.
112 00:17:03.250 ⇒ 00:17:09.980 Uttam Kumaran: The next second project I’m kind of working on is marketing and understanding like how they’re spreading, marketing across Facebook and Google
113 00:17:10.020 ⇒ 00:17:15.229 Uttam Kumaran: and understanding working with their marketing analysts to
114 00:17:15.660 ⇒ 00:17:27.480 Uttam Kumaran: working with their marketing analysts to figure out where there’s optimizations to lower spend and target spend better? And so those are a couple of the products that I’ve worked on. There’s still some updates to do
115 00:17:27.550 ⇒ 00:17:55.329 Uttam Kumaran: one on the daily dashboard side. And then also I want to be able to provide them with, you know, some dashboards for each of their shipping, each of their ecommerce partners. So they primarily sell on Amazon and shopify and Amazon shopify, have a couple of differences, and so I want to be able to put together some analysis for them on Amazon and shopify, and a dashboard specific to them, so that may be some of the
116 00:17:55.480 ⇒ 00:17:59.300 Uttam Kumaran: that might be some of the work we do initially together.
117 00:17:59.690 ⇒ 00:18:04.149 And then kind of like I wanna I’m working with them, particularly on the marketing side.
118 00:18:04.800 ⇒ 00:18:20.820 Uttam Kumaran: To kind of understand, hey, they spend a ton of money every month on Facebook and Google ads, where their optimizations for spend. How do I work with their analyst to help her speed up. How much reporting she can do things like that. So
119 00:18:24.110 ⇒ 00:18:28.830 Ryan Luke Daque: yeah, I was like looking into the models
120 00:18:28.910 ⇒ 00:18:40.409 Uttam Kumaran: cool in in in Github. So I did. I did see like adwords. Amazon, Google, analytics is Facebook. So yeah, II figured you were like working on marketing stuff as well for this
121 00:18:40.900 ⇒ 00:18:42.429 Ryan Luke Daque: client.
122 00:18:42.870 ⇒ 00:18:45.349 Uttam Kumaran: Yes, yes, of course.
123 00:18:46.540 ⇒ 00:18:58.990 Uttam Kumaran: Okay, amazing. So that’s like, primarily kind of like the work we’re doing. And then maybe if if I’ll just share or maybe you can share, if you want to share your
124 00:19:01.090 ⇒ 00:19:06.290 Uttam Kumaran: I guess what would be helpful. Maybe you can share your Github.
125 00:19:06.950 ⇒ 00:19:19.829 Uttam Kumaran: and we can walk through the repo structure, and then, additionally, I will go ahead, and I don’t think I ended up sharing with you. Dbt, so I will just add you to that instance
126 00:19:20.820 ⇒ 00:19:22.020 Uttam Kumaran: right now.
127 00:19:22.930 ⇒ 00:19:25.879 Ryan Luke Daque: I guess. Also.
128 00:19:26.100 ⇒ 00:19:29.270 Ryan Luke Daque: I’m not sure if I need access to like dash.
129 00:19:29.410 ⇒ 00:19:36.529 Ryan Luke Daque: Yeah, I will give you access to light dashes. Yeah, I’ve been reading the documentation because I haven’t worked with the light dash
130 00:19:36.620 ⇒ 00:19:41.250 Ryan Luke Daque: yet. But yeah, I’ll be like reading into the documentation how to use it?
131 00:19:42.810 ⇒ 00:19:45.550 Ryan Luke Daque: Yeah, let me share my screen, then.
132 00:19:51.840 ⇒ 00:19:53.340 Ryan Luke Daque: yep. Can you see my screen?
133 00:19:53.380 ⇒ 00:19:59.179 Uttam Kumaran: Yes. So if you want to. Yeah. So if you wanna just go into github
134 00:19:59.240 ⇒ 00:20:13.599 Uttam Kumaran: if you just want to go straight into the Repository for pool parts to go. Yeah. So you’re familiar with, like the general structure of like a Dbt project where we have like models, we have, like the project, file things like that. So I would say, like.
135 00:20:13.860 ⇒ 00:20:20.059 Uttam Kumaran: I know there’s a lot of best practices me and some of the folks on my team. We’ve done Dbt implementations like
136 00:20:20.270 ⇒ 00:20:24.849 Uttam Kumaran: 6 or 7 times. So generally, this is kind of like how we organize things.
137 00:20:25.050 ⇒ 00:20:38.460 Uttam Kumaran: Basically under models. We’ll kind of create different sections for each of the sources. And then there’s a reporting folder that includes everything that actually gets sent to
138 00:20:38.650 ⇒ 00:20:44.490 Uttam Kumaran: yeah, that gets shared. That actually gets reported on everything else is a source or an intermediary model.
139 00:20:44.720 ⇒ 00:20:46.320 And so basically.
140 00:20:46.780 ⇒ 00:20:54.530 Uttam Kumaran: as I mentioned, they use a couple of different sources. So if we start on the sales side primarily, they sell via Amazon.
141 00:20:55.050 ⇒ 00:20:59.240 Uttam Kumaran: And they sell via Amazon and shopify.
142 00:20:59.760 ⇒ 00:21:02.470 Uttam Kumaran: Okay? So if you click on Amazon.
143 00:21:02.830 ⇒ 00:21:08.339 Uttam Kumaran: and you just go into any of the like, if you go to order items.
144 00:21:09.010 ⇒ 00:21:12.100 Everything is currently coming from 5 tran
145 00:21:12.330 ⇒ 00:21:18.569 Uttam Kumaran: and if you don’t mind zooming in a little bit sorry. I think maybe
146 00:21:18.900 ⇒ 00:21:24.909 Uttam Kumaran: So everything currently comes in from 5 train, and I will also give you access to that.
147 00:21:24.920 ⇒ 00:21:32.670 Ryan Luke Daque: It’s III don’t know if you’ve used fiveframe before it’s just a Etl tool.
148 00:21:32.730 ⇒ 00:21:55.729 Uttam Kumaran: Okay, cool. So everything for Amazon, for the Mo. For the most part across the entire instance, everything is coming from 5 train. Amazon is one of the new 5 train connectors. So I’m still working with their team and understanding everything. But pretty much everything comes, and from a source table. All the sources, if you scroll down on the left are under sources, you’ll see all the sources
149 00:21:56.210 ⇒ 00:22:04.599 Uttam Kumaran: we don’t have. We don’t currently have any source, freshness, or anything like that. but generally just to keep like a
150 00:22:04.750 ⇒ 00:22:14.630 Uttam Kumaran: clean instance, everything is referenced and everything is in sources. And so we we’re building like an Amazon order item. So if you think about
151 00:22:14.730 ⇒ 00:22:35.339 Uttam Kumaran: when you order, I don’t know. Have you worked with like sales data before like orders. Okay, okay, great. So you kinda get like order items versus orders, and how there’s like a hierarchy there. So that’s something we’re doing on Amazon and shopify. So we have order items. We have an orders table
152 00:22:35.580 ⇒ 00:22:41.659 Uttam Kumaran: there. Amazon has keys at the order level, so we need to aggregate that at the order level.
153 00:22:41.930 ⇒ 00:22:48.229 Ryan Luke Daque: And you’ll see that that’s happening. I’m still working with them on how to isolate returns.
154 00:22:49.050 ⇒ 00:22:59.800 Uttam Kumaran: So at the moment I’m doing a manual pull to bring in returns. But I’m emailing with our team right now to figure out. So this may be a really good.
155 00:22:59.900 ⇒ 00:23:01.690 Uttam Kumaran: you know. Section room.
156 00:23:01.740 ⇒ 00:23:06.500 Ryan Luke Daque: Have you? Have you tried DVD packages since this is
157 00:23:07.060 ⇒ 00:23:08.550 Ryan Luke Daque: Flagpag?
158 00:23:08.780 ⇒ 00:23:16.960 Uttam Kumaran: I haven’t tried I don’t know whether they have a Dvt package for Amazon.
159 00:23:17.140 ⇒ 00:23:19.299 Uttam Kumaran: They have the Amazon ads. But
160 00:23:20.120 ⇒ 00:23:24.390 Ryan Luke Daque: I don’t think they have one for Amazon seller. It’s just ads for some.
161 00:23:24.430 ⇒ 00:23:33.309 Uttam Kumaran: yeah. And for the for the most part, like I’m we’re not doing any sequel. That’s like crazy complicated. The main thing we’ve been working on the last few months is just getting everything
162 00:23:33.430 ⇒ 00:23:35.060 Uttam Kumaran: organizing. Dbt.
163 00:23:36.140 ⇒ 00:23:42.159 Uttam Kumaran: you know, across sales, across marketing and across shipping and inventory.
164 00:23:42.420 ⇒ 00:23:44.860 Ryan Luke Daque: Okay. yeah. Sounds.
165 00:23:45.740 ⇒ 00:24:00.779 Uttam Kumaran: So I would definitely take a look at this and then just message me in slack. There, there’ll be some, because it’s primarily been me working in the repository with one other person. There’s gonna be a lot of stuff that’s messy. So if you find stuff that’s like.
166 00:24:01.060 ⇒ 00:24:17.229 Uttam Kumaran: what is this or like? Not clear, let me know. We’ve tried to keep it clean, but it’s like when you’re working this with yourself. It’s like you can buy your best, but I think there’s an opportunity for me to clean stuff up. I’ll add a lot of comments, and so just let me know when.
167 00:24:17.840 ⇒ 00:24:21.570 Uttam Kumaran: if you have any questions while we go through that
168 00:24:22.700 ⇒ 00:24:27.069 Uttam Kumaran: And then the second thing is on shopify. Very similarly, we have order items.
169 00:24:28.600 ⇒ 00:24:40.390 Uttam Kumaran: and then I’m not actually, I mean, we have a shopify order still, but I don’t actually use that for anything. But we may cause there’s really nothing at the orders levels that’s like different
170 00:24:40.420 ⇒ 00:24:42.710 Uttam Kumaran: then, except for the shipping.
171 00:24:42.780 ⇒ 00:24:50.240 Uttam Kumaran: then order items. But for order items is the primary shopify table. And so you’ll see in here a ton of logic.
172 00:24:50.290 ⇒ 00:24:51.710 Uttam Kumaran: Regarding
173 00:24:52.180 ⇒ 00:24:53.620 shopify frankly.
174 00:24:56.070 ⇒ 00:24:56.760 The
175 00:24:57.410 ⇒ 00:25:02.040 Uttam Kumaran: the other thing is on if you click on unleashed on the left.
176 00:25:03.180 ⇒ 00:25:12.139 Uttam Kumaran: so unleashed, is actually how we get how much it costs for the product to get made so cost of goods.
177 00:25:13.070 ⇒ 00:25:29.310 Uttam Kumaran: So for for them to measure profit, they have to understand. It took us, you know, 2,000 right? They can now understand that difference. So this is all coming from this unleashed, which is like an inventory processing software.
178 00:25:29.440 ⇒ 00:25:36.800 Uttam Kumaran: Unleashed, however, is not coming from 5 Tran. It’s coming from like a legacy company because 5 Tran doesn’t support them.
179 00:25:37.140 ⇒ 00:25:40.489 Uttam Kumaran: I wouldn’t. I don’t think you need to worry about that. But,
180 00:25:40.780 ⇒ 00:25:54.850 Uttam Kumaran: I am working with 5 train. I’m trying to see whether they can build me a connector for for unleash but at the moment we’re we’re pretty much using it. Just understand. How much does the product itself weigh? And then what was the cost to manufacture it?
181 00:25:54.950 ⇒ 00:25:55.970 Ryan Luke Daque: Gotcha
182 00:25:58.370 ⇒ 00:26:04.239 Uttam Kumaran: if you look at line 7, yeah.
183 00:26:04.440 ⇒ 00:26:05.780 Ryan Luke Daque: Last, constant.
184 00:26:07.260 ⇒ 00:26:08.070 Ryan Luke Daque: Okay?
185 00:26:09.850 ⇒ 00:26:28.029 Uttam Kumaran: The other thing folders. Yeah. So II just have been cleaning up in the last 2 weeks where I there’s like legacy code like we haven’t been using orders. And then some people got confused on like which mob tables are like important or not.
186 00:26:28.070 ⇒ 00:26:30.030 Ryan Luke Daque: gotcha. So I
187 00:26:30.170 ⇒ 00:26:41.139 Uttam Kumaran: just moved it to archive. I’m gonna I think now that you’re we’ll be working with me. We can kind of decide on which models are necessary. I didn’t want to fully delete it in case I needed it.
188 00:26:41.570 ⇒ 00:26:44.169 Uttam Kumaran: So I didn’t have to revert or anything. So
189 00:26:46.270 ⇒ 00:26:52.440 Walmart. They they should. They set. They sell like maybe a couple of items on Walmart every week.
190 00:26:52.660 ⇒ 00:26:56.129 Uttam Kumaran: It’s not really a huge priority for them, but I do have it modeled.
191 00:26:58.840 ⇒ 00:27:04.709 Uttam Kumaran: So I mean, I would say, like Amazon supplies where they do 98 of their volume.
192 00:27:07.700 ⇒ 00:27:14.539 Uttam Kumaran: The other thing is, if you click on shipstation. So ship station is a little bit of a complicated model.
193 00:27:14.880 ⇒ 00:27:28.569 Uttam Kumaran: not complicated in like, I think, to understand this, but mainly it’s that every order from Amazon or shopify gets shipped using ship station. So station is a shipping label
194 00:27:28.640 ⇒ 00:27:39.679 Uttam Kumaran: and a tracking provider. And so, though for the project I had mentioned, where we were negotiating with ups on how to get cheaper prices. I needed to understand
195 00:27:39.720 ⇒ 00:27:51.770 Uttam Kumaran: all of our different pricing for all of our orders, and then able to say like, if we change prices here, if we got a new pricing scheme, how would that impact? So this is all the logic for
196 00:27:52.700 ⇒ 00:27:54.040 Uttam Kumaran: ship station.
197 00:27:54.810 ⇒ 00:27:55.780 Ryan Luke Daque: Gotcha?
198 00:27:56.960 ⇒ 00:28:04.339 Uttam Kumaran: So yeah, nothing crazy. I just think it’s like a good amount of join. But
199 00:28:04.480 ⇒ 00:28:06.699 Ryan Luke Daque: looks like these aren’t coming from
200 00:28:07.030 ⇒ 00:28:19.069 Uttam Kumaran: just directly from the tables. Yeah, you’re right. So that’s as you see, online 54. I probably wrote, need to ask, So
201 00:28:19.490 ⇒ 00:28:21.039 Uttam Kumaran: yeah, that’s it to do
202 00:28:22.190 ⇒ 00:28:41.440 Uttam Kumaran: So I think, like, let’s let’s just like, maybe primarily focus on there. I’ll let you kind of like, look into Amazon. I’ll look at. Let you look into Facebook and Google adwords you want to. But maybe if we can go to reporting, we can kind of just round out how this all gets shared to the client in a in a table. So
203 00:28:41.720 ⇒ 00:28:58.889 Uttam Kumaran: these are all the reporting related tables that actually, I would say we do a lot of reporting on which I consolidate and clear up and union a lot of things. So all order items is the primary table where I bring in all of the order items from all different sources together.
204 00:28:59.280 ⇒ 00:29:14.620 Uttam Kumaran: You can see it’s like a decently big file where I’m just unioning in everything, cleaning some stuff up. changing some column names. now. you know. So here’s where there’s a lot of logic being done.
205 00:29:16.250 ⇒ 00:29:23.360 Uttam Kumaran: and it’s actually helpful even for me to right now to talk to this, because there’s some things I see that I think we can definitely change.
206 00:29:23.560 ⇒ 00:29:33.750 Uttam Kumaran: But yeah, I would say, this is a really good table to really become familiarize with, because this is what’s powering a lot of reporting right now for sales.
207 00:29:33.980 ⇒ 00:29:36.350 Ryan Luke Daque: And so if you call the way to the bottom.
208 00:29:37.000 ⇒ 00:29:39.070 Uttam Kumaran: you’ll see. Then one table.
209 00:29:39.390 ⇒ 00:29:42.390 We have everything related to
210 00:29:42.560 ⇒ 00:30:01.070 Uttam Kumaran: the platform. It was sold on the items that were sold, how much it’s sold for the discounts we have refunds. We also have a shipping information all in one table. And so for so for the client, I’m able to actually say, like everything from the product getting manufactured to the sale.
211 00:30:01.090 ⇒ 00:30:02.520 Uttam Kumaran: to the return.
212 00:30:02.600 ⇒ 00:30:24.360 Uttam Kumaran: and everything in between, I’m able to understand. So the one thing you’ll see here is some stuff actually need to remove, which is, I was doing some analysis for them, for a press, for a projected ups contract, or we were going to sign. So I was joining in some new tables, lines 424, until, like.
213 00:30:24.850 ⇒ 00:30:35.109 Uttam Kumaran: probably like 480 or so, I’m going to or like until 4, like 4 40. I’m going to end up removing just some legacy code.
214 00:30:35.520 ⇒ 00:30:43.279 Uttam Kumaran: But you’ll see in light dash kind of how it’s structured, so you won’t need to query. I think a light dash will help you explore all these things
215 00:30:44.000 ⇒ 00:30:44.730 Ryan Luke Daque: right.
216 00:30:46.190 ⇒ 00:30:52.479 Uttam Kumaran: And then the other thing is, if you look at dim inventory on the left.
217 00:30:56.990 ⇒ 00:30:58.900 Uttam Kumaran: So this is just
218 00:30:59.290 ⇒ 00:31:01.540 Ryan Luke Daque: Aggregate table with
219 00:31:01.630 ⇒ 00:31:08.080 Uttam Kumaran: looking at how inventory has been moving by products. Queue. So if you scroll all the way to the bottom.
220 00:31:09.620 ⇒ 00:31:20.530 Uttam Kumaran: Primarily, this table is meant to be like a helpful guide for taking a specific skew, and then understanding how much was sold
221 00:31:20.750 ⇒ 00:31:28.580 Uttam Kumaran: this month last month, 3 months ago, 6 months ago. So we pre calculate a lot of these like helpful metrics
222 00:31:28.610 ⇒ 00:31:32.079 Uttam Kumaran: for some analysis that A client needed
223 00:31:32.950 ⇒ 00:31:38.380 Uttam Kumaran: additionally on the. If you look at the left, add monthly sales, add
224 00:31:40.210 ⇒ 00:31:41.590 Uttam Kumaran: it’s at the bottom.
225 00:31:43.310 ⇒ 00:31:54.629 Uttam Kumaran: This is actually another helpful thing. So the nice. The thing about pool parts the business is that they sell primarily during the summer months. This is very concentrated, not only during
226 00:31:54.660 ⇒ 00:32:08.619 Uttam Kumaran: April to October. They’re actually very concentrated only in the warm States in the Us. So like Texas, New York, Florida, you know, where there’s a lot of pools and and pool parts, and people like owning pools.
227 00:32:08.650 ⇒ 00:32:31.380 Uttam Kumaran: And so one thing they really care about is not how they don’t really. They care a little bit about how they did before between the previous month and this month, but they actually care more about the same month. How do they do between last August, right? And so so you can see, we’re we’re pre calculating a lot of these things because these all require like lag functions
228 00:32:31.570 ⇒ 00:32:32.630 Uttam Kumaran: to run.
229 00:32:34.180 ⇒ 00:32:38.180 Uttam Kumaran: and so able to use these where possible.
230 00:32:38.700 ⇒ 00:32:44.250 Uttam Kumaran: the last thing I’ll kind of share was, if you go under the Kpi folder on the left.
231 00:32:46.110 ⇒ 00:32:57.450 Uttam Kumaran: you’ll see here if you click on daily. Kpi. Ag, so this is actually a way for me, and if you scroll all the way to the bottom again, I’ll I’ll but what I from what I’m what I’ve done in the past for many clients is
232 00:32:57.450 ⇒ 00:33:21.440 Uttam Kumaran: they? They want a level of a kpi reporting on a daily or a monthly basis, but they want all of their kpis, right? So it’s tough for me to join all orders to marketing, and then to pre calculate like same day last week. Growth those all were would all be on the fly calculations instead. What you’ll see here is like for for a kpi like sales.
233 00:33:21.510 ⇒ 00:33:28.609 Uttam Kumaran: I’m able to do same day versus the same day of week, which is like, How did today do versus an average Sunday?
234 00:33:28.710 ⇒ 00:33:33.300 Uttam Kumaran: How did today do versus like
235 00:33:33.330 ⇒ 00:33:40.369 Uttam Kumaran: this month, this this like last last month last week. So I pre calculate a lot of these metrics.
236 00:33:40.640 ⇒ 00:33:41.699 Ryan Luke Daque: You got you
237 00:33:42.920 ⇒ 00:33:54.069 Uttam Kumaran: and that way. When you see the dashboard you’ll see like I’ll comment. Ask from the this client because you’re they’re like we want to see the growth versus Sunday last week, and an average Sunday
238 00:33:55.720 ⇒ 00:34:00.369 Ryan Luke Daque: gotcha. They wanna they don’t wanna look like Saturday versus Sunday. They wanna look like
239 00:34:00.490 ⇒ 00:34:10.469 Uttam Kumaran: for every Sunday this year. How are we doing? And then also versus last Sunday, same thing on the month they wanna look at versus last month, but also versus same month last year.
240 00:34:11.880 ⇒ 00:34:20.280 Uttam Kumaran: So I pre calculate a lot of these, because it’s really helpful for reporting the downside to a table like this is, you don’t have a lot of dimensionality.
241 00:34:20.440 ⇒ 00:34:22.060 Ryan Luke Daque: So if this is the.
242 00:34:22.320 ⇒ 00:34:28.219 Uttam Kumaran: it’s just the it’s just the metrics calculated by the day. Yeah.
243 00:34:28.530 ⇒ 00:34:31.090 Uttam Kumaran: So it helps for this high level reporting.
244 00:34:31.650 ⇒ 00:34:39.319 Uttam Kumaran: Because these are common questions you’re gonna ask me like, what is it? What’s it between? Last? Say, last week? But if you want to dissect, you have to go to the
245 00:34:39.840 ⇒ 00:34:42.619 Uttam Kumaran: individual dimension tables right
246 00:34:44.820 ⇒ 00:34:45.929 Uttam Kumaran: and
247 00:34:46.389 ⇒ 00:34:56.720 Uttam Kumaran: cool. And so I know this is kind of I’ll probably kind of stop there in the modeling side. There’s a lot of other stuff we can go to. There’s some stuff around forecast, some stuff around marketing.
248 00:34:57.020 ⇒ 00:34:57.930 But
249 00:34:58.300 ⇒ 00:35:06.530 Uttam Kumaran: you know, I think once you get familiarized with these like base models. I’m happy to go into a lot of those there. There’s some interesting stuff we’re trying to do in the forecasting side as well.
250 00:35:07.390 ⇒ 00:35:10.620 maybe we can start then by
251 00:35:11.250 ⇒ 00:35:17.239 Uttam Kumaran: let me just give you access to Dbt cloud.
252 00:35:17.470 ⇒ 00:35:21.930 Ryan Luke Daque: and have you just log into that and take a look.
253 00:35:25.280 ⇒ 00:35:28.109 Ryan Luke Daque: Just go to the restroom real quick.
254 00:36:57.580 ⇒ 00:36:58.600 Ryan Luke Daque: yeah, I’m back.
255 00:36:58.960 ⇒ 00:36:59.780 Uttam Kumaran: Hey?
256 00:37:01.260 ⇒ 00:37:03.050 Ryan Luke Daque: You just send the
257 00:37:03.190 ⇒ 00:37:25.109 Ryan Luke Daque: yeah. So so what we’re gonna I think, for this client in particular. We’re just kind of sharing my Dvt credentials. I don’t lose Cloud for anything. But I’m actually just gonna share with you. So we’re doing everything through one password. Sorry. There’s like a lot of software. I actually just forgot to give you access to.
258 00:37:25.510 ⇒ 00:37:28.810 Uttam Kumaran: Yeah, let me just get that to you as well. And then
259 00:37:29.090 ⇒ 00:37:34.619 Uttam Kumaran: yeah, actually, if you don’t mind putting that in here. Yeah, you’re the best. Thank you so much.
260 00:37:39.310 ⇒ 00:37:40.330 Ryan Luke Daque: There it is.
261 00:37:41.270 ⇒ 00:37:44.150 Uttam Kumaran: Well, I knew I like was forgetting something so.
262 00:37:52.290 ⇒ 00:38:00.640 Uttam Kumaran: And then have you used like one password, or like a password manager like that? I’m not sure if I used one password. But yeah, II was, I did. I was.
263 00:38:00.860 ⇒ 00:38:03.790 Ryan Luke Daque: okay.
264 00:38:03.850 ⇒ 00:38:16.759 Uttam Kumaran: So I’m just gonna share your email to that. And then it’s gonna have you log in, and then I just have everything for this client. And one thing that way we need to share whatever it’s all there. So let me just
265 00:38:18.700 ⇒ 00:38:20.209 Uttam Kumaran: Let me just add you there.
266 00:39:01.870 ⇒ 00:39:06.289 Ryan Luke Daque: Guess like, maybe in the future as well, we can like add tests to the models.
267 00:39:06.610 ⇒ 00:39:14.189 Uttam Kumaran: Yes, I would love to. I would not only love to add test. I have everything kind of coming to slack right now.
268 00:39:14.800 ⇒ 00:39:20.620 Ryan Luke Daque: yeah, for for run failures.
269 00:39:21.220 ⇒ 00:39:26.219 Uttam Kumaran: But yeah, I would prefer to have everything also coming through
270 00:39:26.380 ⇒ 00:39:27.510 Uttam Kumaran: for tests.
271 00:39:27.700 ⇒ 00:39:40.540 Uttam Kumaran: Yeah, but you’ll see like I think it’ll be helpful now, because I was the only I was primarily like the main developer. So now that you’re kind of there, I think it’ll be it’ll go a little bit smoother, because our addicts with that stuff.
272 00:39:48.910 ⇒ 00:39:52.610 Uttam Kumaran: Yeah, I just invited you to one password. So tell me if you see that.
273 00:39:52.720 ⇒ 00:39:53.980 Ryan Luke Daque: Yep.
274 00:39:57.570 ⇒ 00:40:00.590 Uttam Kumaran: So yeah, it’ll have you go through. So
275 00:40:00.610 ⇒ 00:40:02.820 Uttam Kumaran: just go through whatever, and then
276 00:40:02.900 ⇒ 00:40:06.669 Uttam Kumaran: make sure you just save your secret password and stuff
277 00:40:06.970 ⇒ 00:40:10.329 Ryan Luke Daque: and then let me know I’m gonna keep adding you to other smaller stuff.
278 00:40:13.370 ⇒ 00:40:15.700 Ryan Luke Daque: Do I need access to Snowflake?
279 00:40:15.730 ⇒ 00:40:18.420 Uttam Kumaran: Yes, that’s what I’m working on right now, too.
280 00:40:20.840 ⇒ 00:40:30.820 Uttam Kumaran: And also I’m recording this call. So you’ll have it. I’ll send you this.
281 00:40:30.900 ⇒ 00:40:39.909 Uttam Kumaran: I I’m on so many calls. And I don’t have enough time to take notes these days. So I decided, I’m gonna record every call. And Zoom has some really amazing AI features
282 00:40:40.010 ⇒ 00:40:49.699 Uttam Kumaran: where it transcribed and stuff like that. So I’ll send this to you, and you’ll have you can. Yeah. And then hopefully, also, we can. We can keep this. And then next person can reference it to. So
283 00:41:10.230 ⇒ 00:41:18.260 Uttam Kumaran: I’ll be on. Yeah, I mean, I’ll be online usually till like, I probably stop working like 6 or 7 pm. Central time.
284 00:41:18.280 ⇒ 00:41:19.859 Ryan Luke Daque: So that’s like 8 here.
285 00:41:20.100 ⇒ 00:41:23.729 Ryan Luke Daque: 8 or 9. Morning, 8 or 9 am. Yeah.
286 00:41:23.780 ⇒ 00:41:31.699 Uttam Kumaran: I mean, it depends. Like, if you’re if you’re sending, if you’re if you’re like, hey, I’m gonna send update like in the morning I’ll be up. I’ll check before I go to bed?
287 00:41:32.050 ⇒ 00:41:40.920 Uttam Kumaran: so. But again, like II think we’ll figure it out like I have. I’ve I haven’t worked
288 00:41:43.570 ⇒ 00:41:44.830 Uttam Kumaran: I haven’t worked
289 00:41:45.100 ⇒ 00:41:56.359 Uttam Kumaran: too much like with developers outside the country, except for like Ukraine. Maybe so, I think this is this will be helpful to kind of figure out how we can do things more, Async.
290 00:41:57.640 ⇒ 00:41:58.660 Uttam Kumaran: You know
291 00:41:58.730 ⇒ 00:42:02.220 Ryan Luke Daque: we have. An overlap, though, like morning. Your time.
292 00:42:02.420 ⇒ 00:42:05.240 Uttam Kumaran: Yeah.
293 00:42:05.530 ⇒ 00:42:07.110 Ryan Luke Daque: Useless overlap.
294 00:42:11.960 ⇒ 00:42:15.640 Uttam Kumaran: Okay? I you should have gone the light dash link.
295 00:42:26.950 ⇒ 00:42:28.099 Uttam Kumaran: If not.
296 00:42:29.290 ⇒ 00:42:37.740 Uttam Kumaran: I will send you the yeah. URL,
297 00:42:42.210 ⇒ 00:42:45.770 Ryan Luke Daque: what’s happening? You have me, please.
298 00:42:50.370 ⇒ 00:42:53.210 Ryan Luke Daque: 9.
299 00:43:00.130 ⇒ 00:43:02.379 Uttam Kumaran: Okay, let me send you.
300 00:43:07.470 ⇒ 00:43:09.839 Uttam Kumaran: I’m just gonna DM it to slack.
301 00:43:23.460 ⇒ 00:43:24.250 It’s
302 00:43:25.330 ⇒ 00:43:28.419 Ryan Luke Daque: like it’s asking to create it for our chat.
303 00:43:30.590 ⇒ 00:43:32.389 Uttam Kumaran: Interesting?
304 00:43:33.830 ⇒ 00:43:34.850 try it again.
305 00:43:40.330 ⇒ 00:43:41.370 Ryan Luke Daque: Yeah. Excellent.
306 00:43:41.760 ⇒ 00:43:45.239 Uttam Kumaran: See if you can. Oh, did you already create an account with your email?
307 00:43:45.670 ⇒ 00:43:48.050 Ryan Luke Daque: I tried, yeah. But
308 00:43:50.330 ⇒ 00:43:55.169 Ryan Luke Daque: try. Yeah, try logging out and then going to the invite link.
309 00:43:57.600 ⇒ 00:44:00.280 Uttam Kumaran: So now I’ll go directly to the invite link. Yeah.
310 00:44:00.430 ⇒ 00:44:01.510 Ryan Luke Daque: there you go.
311 00:44:15.160 ⇒ 00:44:17.419 Uttam Kumaran: Just click on snowflakes. What happens?
312 00:44:24.520 ⇒ 00:44:25.949 Uttam Kumaran: Oh, yeah. So
313 00:44:27.390 ⇒ 00:44:29.239 Uttam Kumaran: yeah, hit that hit manual.
314 00:44:33.170 ⇒ 00:44:35.389 Uttam Kumaran: Oh, I’m having you go through this
315 00:44:37.410 ⇒ 00:44:38.290 Ryan Luke Daque: right again.
316 00:44:41.410 ⇒ 00:44:43.540 Uttam Kumaran: So I already have a process.
317 00:44:43.880 ⇒ 00:44:45.460 Ryan Luke Daque: John, let’s
318 00:44:47.300 ⇒ 00:44:48.060 Ryan Luke Daque: right in.
319 00:45:12.770 ⇒ 00:45:15.080 Uttam Kumaran: Okay. something’s working
320 00:45:29.170 ⇒ 00:45:30.090 Ryan Luke Daque: excellent.
321 00:46:00.770 ⇒ 00:46:03.679 Ryan Luke Daque: Looks like it’s recording
322 00:46:05.530 ⇒ 00:46:06.460 Ryan Luke Daque: earnings.
323 00:46:10.670 ⇒ 00:46:13.550 Uttam Kumaran: is it not coming through
324 00:46:13.910 ⇒ 00:46:15.569 nice we send.
325 00:46:19.020 ⇒ 00:46:20.910 Uttam Kumaran: Oh, I messed up the email.
326 00:46:21.310 ⇒ 00:46:26.159 Ryan Luke Daque: Brian forage. Hold on.
327 00:46:26.680 ⇒ 00:46:27.530 Ryan Luke Daque: that’s it.
328 00:46:27.700 ⇒ 00:46:30.310 Uttam Kumaran: I’ve had my bad.
329 00:46:38.690 ⇒ 00:46:43.710 Ryan Luke Daque: Yeah. I guess the the biggest thing I need to learn is, I guess light dash is.
330 00:46:44.970 ⇒ 00:46:54.239 Uttam Kumaran: I don’t think it’s that big of. I think you’ll actually like it. And I don’t know if you saw in the in the
331 00:46:54.760 ⇒ 00:46:57.730 Uttam Kumaran: onboarding, Doc, there’s a link to the light. Dash slack.
332 00:46:58.250 ⇒ 00:47:02.610 Ryan Luke Daque: Yeah, I did try. II it needed need to be like part of us?
333 00:47:02.820 ⇒ 00:47:07.040 Ryan Luke Daque: Or do you have a like dash account or something
334 00:47:07.570 ⇒ 00:47:10.520 Ryan Luke Daque: wasn’t able to log in? I cried.
335 00:47:11.160 ⇒ 00:47:14.170 Uttam Kumaran: Check! Now, I just invited your email.
336 00:47:15.610 ⇒ 00:47:20.350 Uttam Kumaran: Oh, it said, email is already used by user and another organization.
337 00:47:20.980 ⇒ 00:47:22.000 Uttam Kumaran: One thing.
338 00:47:25.080 ⇒ 00:47:27.530 Ryan Luke Daque: can I unlink my
339 00:47:28.240 ⇒ 00:47:29.410 Ryan Luke Daque: like this?
340 00:47:33.670 ⇒ 00:47:37.979 Ryan Luke Daque: I guess, cause I tried to sign up with Google earlier.
341 00:47:39.360 ⇒ 00:47:42.150 Uttam Kumaran: I don’t even know how you can delete your thing
342 00:47:44.130 ⇒ 00:47:45.080 Ryan Luke Daque: spring.
343 00:47:47.440 ⇒ 00:47:50.390 Ryan Luke Daque: That’s it. Anyway, I can like join.
344 00:47:52.060 ⇒ 00:47:53.819 Ryan Luke Daque: Can you just hit other
345 00:47:54.310 ⇒ 00:47:55.260 Uttam Kumaran: on here
346 00:47:56.440 ⇒ 00:47:58.410 Uttam Kumaran: and just try our demo project.
347 00:48:01.470 ⇒ 00:48:02.450 Uttam Kumaran: Oh.
348 00:48:03.480 ⇒ 00:48:05.120 Ryan Luke Daque: it’s like creating a view.
349 00:48:05.430 ⇒ 00:48:10.079 Ryan Luke Daque: Oh, again using so now see?
350 00:48:10.390 ⇒ 00:48:14.039 Uttam Kumaran: Oh, no, this is not. This is like a demo. It’s complete, demo. So
351 00:48:18.170 ⇒ 00:48:20.249 Ryan Luke Daque: oh, this is a Denmark kind of
352 00:48:22.130 ⇒ 00:48:24.280 Uttam Kumaran: I don’t know how you can.
353 00:48:26.640 ⇒ 00:48:28.919 Uttam Kumaran: If you go back to the other tab
354 00:48:34.390 ⇒ 00:48:35.710 Ryan Luke Daque: to save.
355 00:48:36.410 ⇒ 00:48:38.650 Uttam Kumaran: Can you like go to the left?
356 00:48:40.420 ⇒ 00:48:42.779 Uttam Kumaran: See? Is there a way you can delete your
357 00:48:44.800 ⇒ 00:48:50.040 Uttam Kumaran: actually, can you update your profile email to something else?
358 00:48:50.610 ⇒ 00:48:53.849 Ryan Luke Daque: And then let let me see whether that
359 00:48:53.900 ⇒ 00:48:55.530 Uttam Kumaran: changes your like
360 00:49:03.740 ⇒ 00:49:05.180 personal email.
361 00:49:06.600 ⇒ 00:49:10.740 Ryan Luke Daque: Okay, I got, I actually was able to invite you.
362 00:49:10.950 ⇒ 00:49:11.670 Ryan Luke Daque: Yes.
363 00:49:14.080 ⇒ 00:49:15.899 Ryan Luke Daque: so I’ll log out.
364 00:49:16.410 ⇒ 00:49:19.400 Uttam Kumaran: you know, log in again.
365 00:49:20.820 ⇒ 00:49:24.670 Ryan Luke Daque: Yeah, that’s I shouldn’t have signed up.
366 00:49:25.070 ⇒ 00:49:29.820 Uttam Kumaran: No, that’s okay. I I’m I’m actually in a channel with like light dash
367 00:49:29.890 ⇒ 00:49:33.279 Uttam Kumaran: folks. I’m gonna add you there in case there’s other bugs.
368 00:49:34.640 ⇒ 00:49:37.809 Uttam Kumaran: So I signed a partnership with them. So
369 00:49:39.900 ⇒ 00:49:43.430 Ryan Luke Daque: maybe you can also try being a d bitty partner.
370 00:49:43.970 ⇒ 00:49:47.510 Uttam Kumaran: Yeah. So I signed a partnership with 5 train and Snowflake.
371 00:49:48.970 ⇒ 00:49:54.480 Uttam Kumaran: And then, yeah, we should. But the promise, dB, I’m not even paying for, so I don’t even need it.
372 00:49:54.660 ⇒ 00:49:56.890 Uttam Kumaran: I feel like they’re not gonna yeah.
373 00:49:57.410 ⇒ 00:50:03.019 Uttam Kumaran: And I don’t. I don’t know. I feel like that’s the one part I’m like, I don’t wanna pay for this open source, you know.
374 00:50:09.960 ⇒ 00:50:11.899 Ryan Luke Daque: Yep, looks like I’m in.
375 00:50:20.320 ⇒ 00:50:21.430 Ryan Luke Daque: Okay. Great
376 00:50:22.550 ⇒ 00:50:33.599 Uttam Kumaran: So if you go to browse on the top left
377 00:50:36.050 ⇒ 00:50:37.460 Uttam Kumaran: all spaces.
378 00:50:38.630 ⇒ 00:50:42.879 Uttam Kumaran: and if you go to if you just go to share
379 00:50:46.750 ⇒ 00:50:48.080 Uttam Kumaran: and dashboards.
380 00:50:50.920 ⇒ 00:50:58.640 Uttam Kumaran: if you click on search, can you see this thing called Vital? Oh, actually here, maybe I have to share, you know. Stuff along. Don’t have it.
381 00:51:02.990 ⇒ 00:51:04.569 How do I
382 00:51:08.610 ⇒ 00:51:09.520 Uttam Kumaran: hold on?
383 00:51:51.280 ⇒ 00:51:56.769 Uttam Kumaran: Okay, you actually. Now, if you go back, you should see a a space called vital signs.
384 00:52:00.100 ⇒ 00:52:03.720 Uttam Kumaran: Yeah, that dashboard. the first one.
385 00:52:04.940 ⇒ 00:52:15.560 Uttam Kumaran: So this is like their daily dashboard that I’m working on. There’s some stuff that’s messed up. II made some changes to
386 00:52:16.200 ⇒ 00:52:22.199 Uttam Kumaran: I made some changes to one of the models, and so there’s some stuff that’s I think
387 00:52:23.190 ⇒ 00:52:33.519 Uttam Kumaran: like that’s like got messed up this week. So I’m like making some changes. But basically on here, you’ll see all metrics that they’re they’re supposed to be looking at daily.
388 00:52:33.770 ⇒ 00:52:34.550 Ryan Luke Daque: Okay.
389 00:52:37.080 ⇒ 00:52:39.260 Ryan Luke Daque: so daily sales daily profit
390 00:52:40.330 ⇒ 00:52:41.630 Ryan Luke Daque: month today.
391 00:52:45.470 ⇒ 00:52:47.870 Ryan Luke Daque: So I guess if I click on one
392 00:52:49.750 ⇒ 00:52:54.270 Ryan Luke Daque: like this 10, it’s just it’s kind of like Looker Gotcha.
393 00:52:56.620 ⇒ 00:53:07.330 Uttam Kumaran: So the only difficult part, I think, is going to be this. But then, also on the development side. you actually need to. So the one thing I’ll show you is if you go back to Github.
394 00:53:13.440 ⇒ 00:53:20.270 Uttam Kumaran: If you look at the actual. Yeah. So if this is, this is actually, if you go to like all orders, all order items. Yaml.
395 00:53:20.440 ⇒ 00:53:25.409 Uttam Kumaran: So light dash actually requires you to put all of your
396 00:53:25.780 ⇒ 00:53:29.870 Uttam Kumaran: a and dimension and metric information here
397 00:53:30.490 ⇒ 00:53:36.199 Uttam Kumaran: in a yaml file so you can see I’m creating dimension type group all that stuff.
398 00:53:37.510 ⇒ 00:53:52.770 Uttam Kumaran: So if you look at the light dash docs, there will be a setting up. The cli that suggests is probably the first thing you go through is going through this and going through setting up the cli and like being able to run models locally.
399 00:53:53.620 ⇒ 00:53:54.470 Ryan Luke Daque: Yeah.
400 00:53:54.650 ⇒ 00:53:59.239 Ryan Luke Daque: I think I saw that in the in the initial video she was discussing here late.
401 00:53:59.500 ⇒ 00:54:08.049 Uttam Kumaran: Exactly. Yeah. So that’ll be like how you add dimension metrics somewhere in Looker, where you you modify the view.
402 00:54:08.230 ⇒ 00:54:15.030 Uttam Kumaran: This is like the exact same things. Everything happens in code. Everything happens in Dbt and
403 00:54:15.520 ⇒ 00:54:16.260 Ryan Luke Daque: cool
404 00:54:20.470 ⇒ 00:54:23.830 Ryan Luke Daque: like this one symmetric. This is like that.
405 00:54:24.610 ⇒ 00:54:29.730 Uttam Kumaran: Exactly. So you’re doing like a yeah, you’re doing a calculation.
406 00:54:33.500 ⇒ 00:54:40.180 Ryan Luke Daque: So this basically, this field doesn’t exist in the order order.
407 00:54:40.300 ⇒ 00:54:46.270 Uttam Kumaran: So and so and so it does. So region exists.
408 00:54:46.900 ⇒ 00:54:54.210 Uttam Kumaran: But I want to create a calculated metric leveraging other metrics. So I need to create this new metric
409 00:54:54.960 ⇒ 00:54:55.670 Ryan Luke Daque: check
410 00:54:55.800 ⇒ 00:54:59.090 Uttam Kumaran: and net profit. So net profit doesn’t exist in there.
411 00:55:02.470 ⇒ 00:55:11.080 Uttam Kumaran: So this is where, like, I’m this is like I’m using light dash to for like the for this is my first like 6 months using it. It’s not clear to me
412 00:55:11.330 ⇒ 00:55:21.949 Uttam Kumaran: when to put things into the table versus keeping things in the animal. And so I’m I’m game to do either one
413 00:55:21.960 ⇒ 00:55:26.260 Uttam Kumaran: like if we’re like, Hey, actually, let’s just make this a metric and have no logic here.
414 00:55:26.500 ⇒ 00:55:28.750 Have all the logic live in
415 00:55:28.810 ⇒ 00:55:36.760 Uttam Kumaran: sequel. I’m totally fine with that. I just haven’t this kind of it playing around with both strategies and try to think about what’s best
416 00:55:37.340 ⇒ 00:55:38.330 Ryan Luke Daque: make sense.
417 00:55:41.090 ⇒ 00:55:50.009 Ryan Luke Daque: Okay. yeah, I’ll definitely try it reading the documentation and just try to understand like, how let’s get it’s working.
418 00:55:50.400 ⇒ 00:55:51.190 Uttam Kumaran: Okay?
419 00:55:52.550 ⇒ 00:55:59.880 Uttam Kumaran: And then the last thing last thing, maybe before we go is, if we could look at the Github projects
420 00:56:01.310 ⇒ 00:56:04.109 Ryan Luke Daque: repo seattle weekend.
421 00:56:04.890 ⇒ 00:56:09.550 Uttam Kumaran: Yeah, if you go back to pool, if you go back to to yeah, yeah, here. So
422 00:56:09.570 ⇒ 00:56:16.180 Uttam Kumaran: this is where, like, I’ll be beginning to track everything. I think before I log off today.
423 00:56:16.430 ⇒ 00:56:20.830 Uttam Kumaran: I will kind of send you a little bit of an update with like, Hey, maybe
424 00:56:20.960 ⇒ 00:56:25.310 Uttam Kumaran: for this next week. Here’s like a small task we could try.
425 00:56:25.470 ⇒ 00:56:37.019 Uttam Kumaran: I will create a ticket and kind of have all the details there and then. I think it’ll primarily be one. I’ll try to think of something that’s related to modifying the light. Dash
426 00:56:37.100 ⇒ 00:56:47.249 Uttam Kumaran: that way. You can have a sense of like that entire developer experience, making the modification, running it, testing it, and then pushing the Pr.
427 00:56:47.330 ⇒ 00:56:52.750 Ryan Luke Daque: And then, second, will be like a database thing. And that way you can have a little bit of familiarity with
428 00:56:53.500 ⇒ 00:56:56.550 Uttam Kumaran: using creating a light dash data visualization.
429 00:56:57.360 ⇒ 00:57:02.670 Ryan Luke Daque: Okay, does that make sense? Yeah, sounds like a a good plan.
430 00:57:03.010 ⇒ 00:57:03.790 Uttam Kumaran: Okay.
431 00:57:05.740 ⇒ 00:57:11.529 Uttam Kumaran: and then the last thing, I guess, is just on like time tracking like, what? What do you think works best
432 00:57:12.740 ⇒ 00:57:13.960 Uttam Kumaran: for you?
433 00:57:14.620 ⇒ 00:57:18.150 Ryan Luke Daque: I don’t know. What do you usually use, or do you use any?
434 00:57:18.360 ⇒ 00:57:28.309 Uttam Kumaran: I don’t use anything right now. I’m trying to think of like, what’s the most lightweight way to do it.
435 00:57:29.550 ⇒ 00:57:38.179 Uttam Kumaran: And again, I’m not really trying to like. Be super strict on like, oh, you work like 30 min here, 30 min there! It’s more like.
436 00:57:38.280 ⇒ 00:57:52.210 Uttam Kumaran: Hey, you know, we kind of agreed on, like you know about like couple of hours a day, like making sure I have an understanding of like, hey? Today, I work like 4 or 5 h. That’s really all I need to know. And then
437 00:57:52.230 ⇒ 00:57:57.339 Uttam Kumaran: that way. I also can keep track and kind of like pay on schedule.
438 00:57:57.480 ⇒ 00:58:02.219 Uttam Kumaran: If you don’t have an opinion, maybe I can think about it and come up with a
439 00:58:02.310 ⇒ 00:58:04.490 Ryan Luke Daque: way of doing it.
440 00:58:05.390 ⇒ 00:58:09.179 Ryan Luke Daque: Yeah, I’ll I’ll also try to think about it, because I don’t
441 00:58:09.610 ⇒ 00:58:17.440 Ryan Luke Daque: like in a one of the we usually just use clickup in the previous in the other company that I work with.
442 00:58:17.540 ⇒ 00:58:25.259 Ryan Luke Daque: Okay? And then there’s like a time tracking like for each task. There’s like, you can track your time for each task.
443 00:58:25.810 ⇒ 00:58:28.469 Ryan Luke Daque: But yeah. and I don’t know.
444 00:58:28.680 ⇒ 00:58:37.030 Uttam Kumaran: Yeah, II would say, I don’t care too much about like your 10 min here. 10 min there, it’s more like. Hey, II work today.
445 00:58:37.430 ⇒ 00:58:49.320 Uttam Kumaran: Great. I can pay you. Mainly. I mainly. It’s like I don’t wanna mistake and like pay you less anything else. Like as long as I see tasks moving and we’re communicating, I don’t.
446 00:58:49.470 ⇒ 00:58:51.369 Uttam Kumaran: I don’t not really to worry.
447 00:58:51.670 ⇒ 00:58:54.240 Uttam Kumaran: So
448 00:58:54.420 ⇒ 00:58:59.259 Uttam Kumaran: yeah, I guess next steps I will send you this recording. I’ll also
449 00:59:00.540 ⇒ 00:59:12.140 Uttam Kumaran: I will think about 2 tasks. I know you’re it’s you’re probably logging off today, so I’ll think about 2 tasks, one on the light that side, one on the data this side, and then we’ll have them here assigned to you.
450 00:59:12.240 ⇒ 00:59:14.179 Ryan Luke Daque: And then
451 00:59:15.200 ⇒ 00:59:33.060 Uttam Kumaran: just slack me, if anything else. Again. I know it’s a lot of stuff on the light dash side to get accustomed to, but the one thing they have really great documentation. And then their slack is really, really helpful. Just I should be able to log in there. Yeah.
452 00:59:41.440 ⇒ 00:59:44.370 Ryan Luke Daque: I think it’s on the next one.
453 00:59:44.420 ⇒ 00:59:46.700 Uttam Kumaran: Yeah, like, community slack. Yeah.
454 00:59:56.970 ⇒ 00:59:58.230 Ryan Luke Daque: Oh, yeah, this is
455 00:59:58.820 ⇒ 01:00:03.099 Ryan Luke Daque: so. If you go to, you should go to light dash, like to our instance.
456 01:00:04.980 ⇒ 01:00:07.160 Uttam Kumaran: And if you click on the top right
457 01:00:09.770 ⇒ 01:00:12.699 Uttam Kumaran: or somewhere, there’s a thing to say.
458 01:00:16.320 ⇒ 01:00:18.309 Uttam Kumaran: Oh, if you click on the question mark.
459 01:00:20.280 ⇒ 01:00:22.739 Uttam Kumaran: there’s a join a slack community.
460 01:00:23.960 ⇒ 01:00:24.750 Ryan Luke Daque: Oh.
461 01:00:25.280 ⇒ 01:00:27.699 Uttam Kumaran: yeah, this is this is the invite. Yeah.
462 01:00:30.630 ⇒ 01:00:32.140 Ryan Luke Daque: Okay, there you go.
463 01:00:36.570 ⇒ 01:00:37.420 Uttam Kumaran: Cool.
464 01:00:41.640 ⇒ 01:00:42.750 Ryan Luke Daque: Nice.
465 01:00:43.640 ⇒ 01:00:48.450 Uttam Kumaran: Yeah. And the only thing I added you to another channel that’s like vendor light dash within us.
466 01:00:49.320 ⇒ 01:00:50.590 That’s
467 01:00:50.700 ⇒ 01:00:55.990 Uttam Kumaran: I mean a channel with like a bunch of people on the light dash side like the CEO, a bunch of people.
468 01:00:56.360 ⇒ 01:01:02.909 Uttam Kumaran: That way, if we need any help we can. I already submitted a bug last week about something. So
469 01:01:03.870 ⇒ 01:01:07.590 Ryan Luke Daque: okay. so most of these are from like, dash people.
470 01:01:08.160 ⇒ 01:01:09.350 Uttam Kumaran: Yeah.
471 01:01:10.440 ⇒ 01:01:11.260 Ryan Luke Daque: nice.
472 01:01:13.230 ⇒ 01:01:14.060 Ryan Luke Daque: cool.
473 01:01:14.630 ⇒ 01:01:19.460 Uttam Kumaran: cool. I know today was a lot. So I really appreciate it. No worries.
474 01:01:19.630 ⇒ 01:01:22.959 Uttam Kumaran: Let me know if I can answer anything else. And then.
475 01:01:23.010 ⇒ 01:01:30.730 Uttam Kumaran: yeah, I’ll look. I’ll look forward. I don’t know. I don’t. I don’t. I’ll be working on Sunday, maybe. But again, if you have any questions. In the meantime
476 01:01:30.820 ⇒ 01:01:33.270 Uttam Kumaran: I’m on slack. I’ll I’ll answer. And then
477 01:01:33.300 ⇒ 01:01:37.370 Uttam Kumaran: good touch base again on Monday. Primarily, yeah. Sounds good to me.
478 01:01:37.580 ⇒ 01:01:41.930 Ryan Luke Daque: I’m just yeah, I’ll I’ll probably update my slack.
479 01:01:42.280 ⇒ 01:01:54.140 Uttam Kumaran: Oh, away thing as well. So you’ll be able to do slacking anytime. I’ll just be able to reply. There’s not ton of chatter in slack, but I send stuff to internal
480 01:01:54.280 ⇒ 01:02:00.430 Ryan Luke Daque: announcements are engineering like I’ll I’ll add you to internal engineering channel.
481 01:02:01.070 ⇒ 01:02:10.939 Uttam Kumaran: I just talk about like if there’s stuff I’ve been reading or interesting things. so hopefully, more people will start talking because it’s mainly me talking to myself.
482 01:02:10.950 ⇒ 01:02:12.290 Ryan Luke Daque: Nice.
483 01:02:13.090 ⇒ 01:02:21.729 Ryan Luke Daque: I guess I’ll post the Dbt cast type here as well. Yeah, yeah, maybe you could post it in the internal engineering. So people can see that
484 01:02:24.090 ⇒ 01:02:24.930 Ryan Luke Daque: nice.
485 01:02:29.380 ⇒ 01:02:37.049 Uttam Kumaran: Okay. Alright, man! Well, I’ll let you go. I really appreciate the time again, and then let’s touch base on Monday.
486 01:02:37.230 ⇒ 01:02:40.540 Ryan Luke Daque: Thanks, thanks, Ethan. Have a nice rest of your day.
487 01:02:40.660 ⇒ 01:02:42.110 Uttam Kumaran: Yeah, thanks you, too.
488 01:02:42.240 ⇒ 01:02:43.100 Ryan Luke Daque: Bye-bye.