Meeting Title: Weekly-Sprint-Review Date: 2024-01-18 Meeting participants: Ryan Luke Daque, Uttam Kumaran
WEBVTT
1 00:01:23.930 ⇒ 00:01:24.760 Ryan Luke Daque: Hello.
2 00:01:33.110 ⇒ 00:01:35.520 Ryan Luke Daque: Ayotan! Good morning! Can you hear me.
3 00:02:10.490 ⇒ 00:02:11.680 Uttam Kumaran: hey? Can you hear me?
4 00:02:12.020 ⇒ 00:02:14.670 Uttam Kumaran: Yep. Hey? How’s it going?
5 00:02:15.200 ⇒ 00:02:16.660 Ryan Luke Daque: Going? Great! How are you
6 00:02:17.060 ⇒ 00:02:20.010 Uttam Kumaran: doing? Well. yeah, it’s been a good week.
7 00:02:21.460 ⇒ 00:02:22.360 Ryan Luke Daque: Nice.
8 00:02:24.460 ⇒ 00:02:30.770 Uttam Kumaran: I I went to like an AI meetup last night here in Texas. It’s kind of fun.
9 00:02:30.930 ⇒ 00:02:31.880 Uttam Kumaran: Yeah.
10 00:02:32.860 ⇒ 00:02:36.300 Ryan Luke Daque: that’s cool like. AI is like the most
11 00:02:36.840 ⇒ 00:02:38.880 Ryan Luke Daque: yeah, it’s in the most recent.
12 00:02:39.020 ⇒ 00:02:40.050 Ryan Luke Daque: you know.
13 00:02:40.060 ⇒ 00:02:41.650 Fuzz all around.
14 00:02:41.840 ⇒ 00:02:47.780 Uttam Kumaran: Yeah, I’m trying to. You know, I’m trying to find ways for us to use it, you know, in our
15 00:02:48.310 ⇒ 00:02:53.089 Uttam Kumaran: in our like development process and like automating.
16 00:02:53.220 ⇒ 00:03:01.300 Uttam Kumaran: you know, like communication and stuff. So it’s been really cool, you know. That’s what you know. A lot of my time I want to spend making sure that
17 00:03:01.880 ⇒ 00:03:05.759 Uttam Kumaran: you know, folks that are working can spend as much time
18 00:03:06.010 ⇒ 00:03:11.480 Uttam Kumaran: doing development work. And then anything that’s a communication and things like that. We should try to leverage AI. And
19 00:03:11.540 ⇒ 00:03:16.689 Ryan Luke Daque: and also, you know, I try to. I try to use it for sales stuff and marketing. So yeah.
20 00:03:16.700 ⇒ 00:03:33.220 Ryan Luke Daque: yeah, that makes sense, like, are we? It’s always good to use the latest technology like AI can help us with that. Yeah, exactly. No, you’re you’re totally right. So we’ll see it’s it’s been. It was. It was cool just to meet a lot of people here that were working and stuff. And though
21 00:03:33.400 ⇒ 00:03:36.349 Ryan Luke Daque: some people did some demos, some cool stuff. So
22 00:03:36.900 ⇒ 00:03:38.200 Ryan Luke Daque: that’s cool.
23 00:03:39.290 ⇒ 00:03:49.040 Uttam Kumaran: are there like? Is there? Is there a huge like developer like community in the Philippines at like? Have you? Have you been able to meet other a lot of other people that are doing like
24 00:03:49.150 ⇒ 00:03:55.839 Ryan Luke Daque: data engineering and software development stuff. I don’t really know. I don’t think so. Or maybe there is. I just
25 00:03:56.080 ⇒ 00:04:01.569 Ryan Luke Daque: haven’t been, you know, very active and trying to meet people stuff like that. So
26 00:04:02.230 ⇒ 00:04:04.650 Ryan Luke Daque: I’ll try to look around. Maybe there are.
27 00:04:05.150 ⇒ 00:04:11.440 Ryan Luke Daque: Yeah, yeah, a lot. A lot of people that I met actually in data here I met through like meet ups and stuff like that.
28 00:04:11.470 ⇒ 00:04:14.770 Uttam Kumaran: because you know how there’s like the Dbt slack channel
29 00:04:15.320 ⇒ 00:04:26.229 Ryan Luke Daque: like in there. They host events sometimes. And so I mean, I actually met like a lot of friends just like going to those meetups and stuff. And it’s nice talking to data people, you know. So yeah, like.
30 00:04:26.310 ⇒ 00:04:45.829 Ryan Luke Daque: is you, you talk to anybody else. They don’t really understand you. No, exactly like I yeah, I mean, when do when you spend like 8 HA day doing data work, you want to talk to somebody about it like II have fun talking to you. And I’m working with some other people on some stuff.
31 00:04:45.910 ⇒ 00:04:51.649 Uttam Kumaran: But hopefully, you know, it’s it’s we’re we’re making some really good progress on the data provider side. And then.
32 00:04:51.740 ⇒ 00:05:09.140 Uttam Kumaran: you know, I’m gonna hopefully try to loop you in on some of that stuff as this stuff kind of like smoothly progresses. But I mean, it’s just I want to talk to people about like what’s cool and data and some of the new advancements. And yeah, so you know, that’s the big thing. Yeah, that’s that’s great.
33 00:05:09.490 ⇒ 00:05:10.340 Uttam Kumaran: Yeah.
34 00:05:10.900 ⇒ 00:05:14.020 Uttam Kumaran: cool. So I
35 00:05:14.420 ⇒ 00:05:20.970 Uttam Kumaran: in github projects. I just put together a new view.
36 00:05:21.110 ⇒ 00:05:30.129 Uttam Kumaran: So if you go to backlog, you’re gonna see all tickets. And I’m just gonna actually rename this to all tickets.
37 00:05:30.370 ⇒ 00:05:35.949 Uttam Kumaran: And if you go to current sprint. You’ll see just the things tagged with
38 00:05:36.190 ⇒ 00:05:44.110 Uttam Kumaran: sprint starting on Jan. 15. So for now we’re just doing stuff like one week. And I actually think that’s perfect because
39 00:05:44.580 ⇒ 00:05:48.420 Uttam Kumaran: we kind of start the week I have. I do stuff on Friday. We have feedback. So
40 00:05:48.670 ⇒ 00:05:52.540 Uttam Kumaran: and then we have all tickets kinda here. So
41 00:05:52.700 ⇒ 00:05:57.890 Uttam Kumaran: okay, just finding ways to like, kind of get a little bit more organized. And I see everything that’s been working on.
42 00:05:58.030 ⇒ 00:06:02.730 Uttam Kumaran: Yeah. So I can go
43 00:06:02.900 ⇒ 00:06:03.880 Uttam Kumaran: first.
44 00:06:04.140 ⇒ 00:06:11.129 Uttam Kumaran: So I followed up on this. This got closed out. We got about like $900 back in credits.
45 00:06:11.230 ⇒ 00:06:13.559 Uttam Kumaran: and because it was an issue on their side.
46 00:06:13.690 ⇒ 00:06:21.499 Uttam Kumaran: So that’s really good. And I communicated that. I got email back from 5 train about this
47 00:06:21.780 ⇒ 00:06:24.280 Uttam Kumaran: just like this morning.
48 00:06:24.430 ⇒ 00:06:29.509 Ryan Luke Daque: So this is now unblocked. I will be working on this later today.
49 00:06:30.150 ⇒ 00:06:33.940 Ryan Luke Daque: And then I have some time carved out today
50 00:06:34.130 ⇒ 00:06:38.000 Uttam Kumaran: to do a lot of documentation on these things.
51 00:06:38.220 ⇒ 00:06:44.839 Uttam Kumaran: so I’ll be closing that out today as well, and we’ll push and probably write everything
52 00:06:45.150 ⇒ 00:06:50.250 Uttam Kumaran: and probably write everything in Markdown in the repo
53 00:06:50.940 ⇒ 00:07:04.760 Uttam Kumaran: and then link out to get to figma, or wherever if I need to. But try to keep all the documentation here and the Github Actions documentation. I’ll keep in the work post folder. So yeah, that’s a big stuff for me. The only other thing is
54 00:07:05.240 ⇒ 00:07:09.940 Uttam Kumaran: I pushed up pr yesterday.
55 00:07:10.240 ⇒ 00:07:15.110 Uttam Kumaran: and
56 00:07:16.060 ⇒ 00:07:23.929 Uttam Kumaran: the main change was removing product class from all orders.
57 00:07:24.020 ⇒ 00:07:27.360 Ryan Luke Daque: because
58 00:07:27.760 ⇒ 00:07:34.900 Uttam Kumaran: we can’t, because multiple orders have multiple products. So I’ve removed it from there. And then
59 00:07:35.040 ⇒ 00:07:47.159 Uttam Kumaran: the second. I added some group labels. and then I remove product class from shopify orders. And the last thing is I removed a few tests on these
60 00:07:47.670 ⇒ 00:07:48.880 Uttam Kumaran: columns.
61 00:07:49.720 ⇒ 00:07:52.770 Which are the ups stuff. Cause I’m gonna be cleaning those up.
62 00:07:52.940 ⇒ 00:08:01.059 Uttam Kumaran: And so some of them were like, we’re freaking out. So yeah, that’s okay, like the main things to care about are these 2.
63 00:08:01.090 ⇒ 00:08:03.560 Uttam Kumaran: And then I’m gonna add more to more to these. So
64 00:08:04.400 ⇒ 00:08:06.399 Ryan Luke Daque: sure, cool, yeah, sounds good.
65 00:08:06.850 ⇒ 00:08:11.690 Uttam Kumaran: So that’s amazing. So just make sure you pull from main of a chance.
66 00:08:12.390 ⇒ 00:08:13.200 Ryan Luke Daque: Sure.
67 00:08:16.570 ⇒ 00:08:21.990 Uttam Kumaran: Okay, great. Do you wanna go? Yeah. Sure. Let me share my screen step.
68 00:08:22.830 ⇒ 00:08:24.839 Ryan Luke Daque: Can you see my screen now? Yes.
69 00:08:25.720 ⇒ 00:08:27.690 Ryan Luke Daque: So for me.
70 00:08:29.100 ⇒ 00:08:33.229 Ryan Luke Daque: so yeah, I’ve worked on these 2 tickets. First off is the
71 00:08:33.710 ⇒ 00:08:37.579 Ryan Luke Daque: versus the shipping cost issue. So it took me a while to really
72 00:08:38.200 ⇒ 00:08:40.030 dig into this.
73 00:08:40.150 ⇒ 00:08:44.550 Ryan Luke Daque: But essentially what I did was a
74 00:08:44.770 ⇒ 00:08:48.600 Ryan Luke Daque: like what we discussed here like it’s gonna be split by
75 00:08:48.840 ⇒ 00:08:53.549 Ryan Luke Daque: the order line, item by weight. If there’s a weight.
76 00:08:54.270 ⇒ 00:08:59.839 Ryan Luke Daque: weight, times quantity. So it was actually like for Amazon, it’s just wait
77 00:09:00.530 ⇒ 00:09:03.120 Uttam Kumaran: times quantity, because
78 00:09:03.220 ⇒ 00:09:06.110 Ryan Luke Daque: the older line items in Amazon doesn’t have
79 00:09:06.330 ⇒ 00:09:11.129 Ryan Luke Daque: the weight. So I had to pull in the product item table to get the weight
80 00:09:11.550 ⇒ 00:09:13.379 Ryan Luke Daque: like the item dimension.
81 00:09:13.660 ⇒ 00:09:16.110 Uttam Kumaran: yeah.
82 00:09:16.450 ⇒ 00:09:17.520 Ryan Luke Daque: and
83 00:09:19.570 ⇒ 00:09:21.160 Ryan Luke Daque: I think it’s
84 00:09:23.850 ⇒ 00:09:30.249 Ryan Luke Daque: yes, I pulled in the product man mentioned table should be here somewhere.
85 00:09:44.660 ⇒ 00:09:56.259 Ryan Luke Daque: but yeah, essentially that’s what I did. I and times monthly. But there were times that the wait was 0. So for those items, I just used
86 00:09:56.310 ⇒ 00:09:57.380 Ryan Luke Daque: quantity.
87 00:09:57.570 ⇒ 00:10:02.409 Ryan Luke Daque: So I yeah, I divided them, split them by quantity.
88 00:10:02.770 ⇒ 00:10:04.740 Ryan Luke Daque: So yeah.
89 00:10:04.810 ⇒ 00:10:16.389 Ryan Luke Daque: And that was like one of the issues I had when I was when I mentioned when it was pretty close, but sometimes we’re lower, and that was of the 0 weight items. For some reason there was 0.
90 00:10:16.810 ⇒ 00:10:29.669 Ryan Luke Daque: Walmart. We didn’t really have to do anything because there were no order line items for Walmart. So it was directly to orders. So mostly so it’s a bit. Changes were for Amazon and shuttle fine, basically.
91 00:10:30.070 ⇒ 00:10:30.820 Uttam Kumaran: Yes.
92 00:10:31.750 ⇒ 00:10:36.279 Ryan Luke Daque: Yeah. And then I also added the test to compare.
93 00:10:36.980 ⇒ 00:10:38.119 Ryan Luke Daque: Compare this.
94 00:10:40.310 ⇒ 00:10:43.840 Ryan Luke Daque: Aside from that, also the tests.
95 00:10:44.270 ⇒ 00:10:53.569 Ryan Luke Daque: yeah, like you mentioned you, you remove some of them. But yeah, I did create test 2. Verify the sum of the kpis
96 00:10:53.970 ⇒ 00:11:06.449 Ryan Luke Daque: between order items and orders. So that includes shopify Amazon and the Mics model for order items and orders. And then another another set of tests for the aggregate tables against the old
97 00:11:06.760 ⇒ 00:11:08.020 Ryan Luke Daque: order stable.
98 00:11:08.530 ⇒ 00:11:25.729 Ryan Luke Daque: yeah. So basically, the sales discount. So II didn’t do it by week, because it doesn’t really matter if it’s by week or not. It’s even. I guess it’s even better if it’s like all time. So if the sum of discounts all time matches, then that’s actually
99 00:11:26.330 ⇒ 00:11:27.490 Ryan Luke Daque: good. Alright
100 00:11:27.810 ⇒ 00:11:42.070 Ryan Luke Daque: but if you have, if you noticed, I just added 1% tolerance, just in case there’s like calculation rounding up issues and stuff like that. so if it’s within the 1% tolerance.
101 00:11:42.510 ⇒ 00:11:44.690 Ryan Luke Daque: then it should pass the test.
102 00:11:45.610 ⇒ 00:11:46.400 Uttam Kumaran: Okay?
103 00:11:47.220 ⇒ 00:11:48.730 Ryan Luke Daque: Yep. And then.
104 00:11:48.740 ⇒ 00:11:52.360 Ryan Luke Daque: yeah, I’ll be working on these 2 today. It should be
105 00:11:52.690 ⇒ 00:11:58.650 Ryan Luke Daque: pretty easy. I guess I haven’t started yet. But yeah, this is what I’ll be working on after our call
106 00:12:00.190 ⇒ 00:12:06.759 Ryan Luke Daque: creating zone State column and renaming and salesmet to make sure we’re like,
107 00:12:06.790 ⇒ 00:12:09.520 Ryan Luke Daque: let’s say, standardize the corrosol metrics.
108 00:12:09.980 ⇒ 00:12:10.780 Uttam Kumaran: Okay.
109 00:12:13.330 ⇒ 00:12:22.560 Uttam Kumaran: The only other change I wanted to share was, if you actually are able to open. Actually, I can share real quick, sure.
110 00:12:22.860 ⇒ 00:12:27.650 Uttam Kumaran: see?
111 00:12:31.290 ⇒ 00:12:35.259 Uttam Kumaran: So I made this change.
112 00:12:36.460 ⇒ 00:12:46.639 Uttam Kumaran: What this does is in shopify. We aren’t getting. There’s no shipment order. Wait for stuff that’s coming through Ltl.
113 00:12:47.480 ⇒ 00:12:48.580 Ryan Luke Daque: And
114 00:12:48.870 ⇒ 00:12:55.169 Uttam Kumaran: in ship station it comes in. The weight comes in as like 15 or like something like one pound.
115 00:12:55.380 ⇒ 00:13:00.499 Uttam Kumaran: So what I did is I have when the shipment code is Ltl, pull in
116 00:13:01.240 ⇒ 00:13:05.379 Uttam Kumaran: the actual product weight. And this solved a lot of issues.
117 00:13:05.820 ⇒ 00:13:08.359 Uttam Kumaran: The problem we’re having is like.
118 00:13:08.730 ⇒ 00:13:17.780 Uttam Kumaran: there’s multiple items in an order. And then there could be multiple shipments for order. So it’s like a many to many.
119 00:13:17.970 ⇒ 00:13:22.069 Ryan Luke Daque: So what I’m thinking about doing is potentially
120 00:13:22.340 ⇒ 00:13:25.879 Uttam Kumaran: creating a shipments first table.
121 00:13:26.150 ⇒ 00:13:28.290 Uttam Kumaran: So we start from the shipments
122 00:13:28.900 ⇒ 00:13:32.930 Uttam Kumaran: and then join in the order items directly to the shipments.
123 00:13:33.070 ⇒ 00:13:47.270 Uttam Kumaran: and we skip the concept of orders because for the shipping efficiency dashboard. we’re primarily looking at gross sales. We’re primarily looking, which we can get item, times quantity.
124 00:13:47.350 ⇒ 00:13:55.929 Uttam Kumaran: We’re looking at the shipping price, and we’re looking at a shipping weight. And then we’re looking at zones. So we don’t need a lot of like the order related information
125 00:13:56.520 ⇒ 00:14:00.399 Uttam Kumaran: which is like refunds blah, blah! Blah! I don’t need a lot of that.
126 00:14:01.430 ⇒ 00:14:09.890 Uttam Kumaran: So I don’t know something. We’re gonna consider longer term but I think we pretty much are a good place right now.
127 00:14:10.010 ⇒ 00:14:12.379 Uttam Kumaran: There are a couple of orders where
128 00:14:12.540 ⇒ 00:14:16.780 Uttam Kumaran: there’s 2 items per order.
129 00:14:17.810 ⇒ 00:14:20.209 Uttam Kumaran: But then 2 shipments for order.
130 00:14:20.370 ⇒ 00:14:23.160 Uttam Kumaran: and then it’s causing some duplication. But there’s not many.
131 00:14:23.760 ⇒ 00:14:36.190 Uttam Kumaran: So that’s okay we’ll we’ll hit that longer term. But yeah, that’s what I’m thinking of doing. Cause this has been like. it’s just been difficult to manage. Yeah,
132 00:14:36.460 ⇒ 00:14:46.729 Uttam Kumaran: I’m glad we have it all figured out. it’s just yeah. I would rather if we’re measuring just shipping stuff. Let’s just start from the shipments itself, you know. So
133 00:14:47.360 ⇒ 00:14:52.500 Ryan Luke Daque: yeah, I think that’s a good idea. I didn’t actually nice catch. I didn’t really
134 00:14:52.970 ⇒ 00:14:56.819 Ryan Luke Daque: see that like, I didn’t notice that there’s like 2
135 00:14:57.320 ⇒ 00:15:11.940 Ryan Luke Daque: like multiple shipments for like a single orders stuff like that. So yeah, yeah, I just thought about it. And then I was like, Let me go see if it’s happening. I know what’s happening. And I’ll when I create the ticket for that, I’ll share all the data points like the examples.
136 00:15:12.090 ⇒ 00:15:18.940 Uttam Kumaran: But I don’t know. We’ll probably push that off a little bit. I kinda want to see what con what
137 00:15:19.390 ⇒ 00:15:24.369 Uttam Kumaran: questions I have. And next week maybe more of like a a dashboarding type week.
138 00:15:24.670 ⇒ 00:15:27.359 Uttam Kumaran: so yeah.
139 00:15:27.580 ⇒ 00:15:28.720 Ryan Luke Daque: sounds good.
140 00:15:30.170 ⇒ 00:15:33.959 Uttam Kumaran: Okay, anything else I can help with?
141 00:15:35.190 ⇒ 00:15:38.299 Ryan Luke Daque: I think I’m good.
142 00:15:39.600 ⇒ 00:15:53.380 Uttam Kumaran: Okay. cool. So I may just have a couple of yeah, just shut me that Pr today and let me know where we end up at the end of the day. Today I’m gonna be working on the documentation. And then I’m gonna make some just like dashboard updates.
143 00:15:53.590 ⇒ 00:16:04.260 Uttam Kumaran: It’s pretty much what’s what I’m gonna be looking at. I may push a couple of things related to daily to vital signs. But yeah, I won’t be making any changes.
144 00:16:04.550 ⇒ 00:16:05.660 Ryan Luke Daque: Sounds good.
145 00:16:06.840 ⇒ 00:16:12.579 Uttam Kumaran: Okay, alright. Well, thanks, Ryan. I’ll talk to you. Slack you, too. Bye. Bye.