Meeting Title: Snapshot Data Integration Planning Date: 2025-08-28 Meeting participants: Emily Giant, Demilade Agboola
WEBVTT
1 00:01:07.640 ⇒ 00:01:08.610 Demilade Agboola: Hi, Moon.
2 00:01:10.650 ⇒ 00:01:11.910 Emily Giant: How’s it going?
3 00:01:12.840 ⇒ 00:01:14.159 Demilade Agboola: Pretty well, how are you?
4 00:01:14.760 ⇒ 00:01:18.889 Emily Giant: I’m good. Thank you for moving the meeting. I had to take Matt to the airport at, like.
5 00:01:19.110 ⇒ 00:01:33.790 Emily Giant: 5 AM today, and… but when came around, I was like, I literally need a giant thing of coffee and a nap, and I cannot think right now. So… I was just… can you see this cat on the couch behind me? Wait.
6 00:01:34.180 ⇒ 00:01:45.210 Emily Giant: Oh, it’s too blurry. He’s just, like, a blob. Anyway, I really appreciate it. I was gonna be, like, too dumb for words if we met then, so… yeah. ….
7 00:01:45.210 ⇒ 00:01:45.900 Demilade Agboola: It’s all good.
8 00:01:49.800 ⇒ 00:01:50.830 Emily Giant: How are you?
9 00:01:51.590 ⇒ 00:01:54.880 Demilade Agboola: I’m doing okay. I can’t complain.
10 00:01:55.030 ⇒ 00:01:58.859 Demilade Agboola: I have a trip to Nigeria coming up on Saturday.
11 00:01:58.860 ⇒ 00:02:01.260 Emily Giant: Oh, that’s great! See the fans?
12 00:02:02.460 ⇒ 00:02:08.569 Demilade Agboola: No, actually, a lot of my family’s actually outside in India. But, my friend’s getting married.
13 00:02:08.810 ⇒ 00:02:13.550 Demilade Agboola: So it’ll be nice to… and I’m his best man, so I’ll be there for that.
14 00:02:14.630 ⇒ 00:02:15.660 Demilade Agboola: Do you have to give speed?
15 00:02:17.850 ⇒ 00:02:25.599 Demilade Agboola: To be fair, I’m not sure. I probably will… there’s a lot that goes on behind the scenes, so, like, helping organize things.
16 00:02:25.720 ⇒ 00:02:30.970 Demilade Agboola: The bachelor’s party, all that stuff, like, just being there, so…
17 00:02:31.280 ⇒ 00:02:38.410 Demilade Agboola: That’s kind of why I’m… it’s on the 12th, I believe? It’s the Friday, not the Saturday.
18 00:02:39.160 ⇒ 00:02:41.219 Emily Giant: Oh, you’ve gotta be there for a while!
19 00:02:41.500 ⇒ 00:02:54.230 Demilade Agboola: Yeah, so I’ll be there… so that’s why I’m going there, this Saturday. Well, I land on Sunday. So it’s a different city, it’s… if I land in the capital of Nigeria, I land in Abuja.
20 00:02:54.440 ⇒ 00:03:00.540 Demilade Agboola: And then I’ll be heading off to Kaduna, so Kaduna is a train ride away, like a two-hour train ride away.
21 00:03:01.260 ⇒ 00:03:09.470 Demilade Agboola: And then I will be there for a couple of weeks before I go back to Abuja for another, like, week, and then I come back to Malta.
22 00:03:10.480 ⇒ 00:03:15.809 Emily Giant: Nice. I know you said weddings are, like, a really big deal in Nigeria.
23 00:03:16.130 ⇒ 00:03:17.320 Emily Giant: Would be a big one.
24 00:03:18.090 ⇒ 00:03:22.499 Demilade Agboola: I mean, small by Nigerian standards, but we’re still looking at 500 people.
25 00:03:24.030 ⇒ 00:03:33.630 Emily Giant: That is so wild. Like, I don’t even know if I invited my family and my family’s friends if it would make 500 people.
26 00:03:33.650 ⇒ 00:03:35.389 Demilade Agboola: That’s so wild.
27 00:03:35.950 ⇒ 00:03:39.859 Demilade Agboola: Yeah, it’s a small item, but it’s, like, 500 people.
28 00:03:40.170 ⇒ 00:03:42.590 Emily Giant: How does anyone afford that?
29 00:03:45.250 ⇒ 00:03:46.680 Demilade Agboola: I mean, to be fair.
30 00:03:46.680 ⇒ 00:03:51.259 Emily Giant: Or, like, how did… like, I could never. It would wipe me out for 10 years.
31 00:03:52.300 ⇒ 00:03:56.709 Demilade Agboola: I mean, to be fair, it’s… you know, I think it’s one of those things where…
32 00:03:56.890 ⇒ 00:04:11.420 Demilade Agboola: you know, I know, like, here in the US, for instance, that the same place you might get, once they head to a wedding, they have wedding prices and stuff, like, I think, like, in Nigeria, it’s not that bad in terms of pricing.
33 00:04:11.560 ⇒ 00:04:16.930 Demilade Agboola: … Plus, people do enjoy…
34 00:04:17.149 ⇒ 00:04:31.280 Demilade Agboola: it’s a cultural thing, so when everyone does it that way, you kind of want to do it that way. My friend’s partner, his fiancee, she wanted a larger wedding. It’s my friend that’s pushing for the smaller wedding, which is why we have 500.
35 00:04:31.460 ⇒ 00:04:35.099 Demilade Agboola: Because she’s more… she’s more of an out-there person.
36 00:04:35.350 ⇒ 00:04:36.010 Emily Giant: Hmm.
37 00:04:36.010 ⇒ 00:04:41.539 Demilade Agboola: our friends are more, you know, out there. And so she wants… she wanted, like, a larger party.
38 00:04:41.880 ⇒ 00:04:48.860 Demilade Agboola: And he’s just like, I’m not, I can’t, like, I’m not doing… I’m doing that large a party. The 500 is the compromise.
39 00:04:49.220 ⇒ 00:04:51.619 Emily Giant: Yeah. Do they know that 500 would be, like.
40 00:04:51.920 ⇒ 00:05:00.340 Emily Giant: the biggest wedding in the U.S. that’s ever been. Like, I don’t think anyone has a wedding that is that big. Like, celebrities don’t.
41 00:05:01.400 ⇒ 00:05:02.159 Demilade Agboola: Yeah, you know.
42 00:05:03.230 ⇒ 00:05:03.950 Emily Giant: So, 500….
43 00:05:03.950 ⇒ 00:05:04.389 Demilade Agboola: I mean, like.
44 00:05:04.390 ⇒ 00:05:07.580 Emily Giant: Biggest wedding I’ve ever heard of. Ever.
45 00:05:08.090 ⇒ 00:05:10.269 Demilade Agboola: My sister’s wedding was a little over $1,000.
46 00:05:11.120 ⇒ 00:05:13.510 Emily Giant: That’s like… that’s like a fucking name.
47 00:05:13.620 ⇒ 00:05:22.500 Emily Giant: At a high school. Like, that’s too many. You’d have to have, like, an… I don’t even know what kind of facility outside of a stadium could have that many people.
48 00:05:23.220 ⇒ 00:05:29.389 Demilade Agboola: I mean, yeah, we have event centers. Again, because of the culture, we have event centers that, like, can seat that many people.
49 00:05:29.720 ⇒ 00:05:30.410 Emily Giant: Oh my gosh.
50 00:05:30.410 ⇒ 00:05:33.689 Demilade Agboola: 700-700 seaters, …
51 00:05:34.050 ⇒ 00:05:38.109 Demilade Agboola: I don’t know, my sister probably has pictures of it, but it was big. There was, like, a huge, like.
52 00:05:38.720 ⇒ 00:05:42.039 Demilade Agboola: what’s that thing called? It’s not a red carpet, but the….
53 00:05:42.730 ⇒ 00:05:44.030 Demilade Agboola: The same idea of, like….
54 00:05:44.030 ⇒ 00:05:45.370 Emily Giant: thing, where, yeah, the.
55 00:05:45.370 ⇒ 00:05:45.810 Demilade Agboola: Yeah, hu.
56 00:05:45.810 ⇒ 00:05:46.540 Emily Giant: That’s cool.
57 00:05:46.540 ⇒ 00:05:55.660 Demilade Agboola: So then it led to where they were sitting, and there was, like, a throne being, and then there were different chairs. So, like, even myself, like, we all had different tables for our guests.
58 00:05:55.930 ⇒ 00:06:06.160 Demilade Agboola: So I had, like, 15 people. That’s just me. I brought, like, 15 people, like, to my friends. And that’s the beauty of, like, how Nigerian weddings are, is that you don’t necessarily need to know
59 00:06:07.000 ⇒ 00:06:09.010 Demilade Agboola: the, ….
60 00:06:09.010 ⇒ 00:06:09.950 Emily Giant: The bride and groom.
61 00:06:09.950 ⇒ 00:06:13.009 Demilade Agboola: Right, exactly. It’s just, like, you need to know someone.
62 00:06:13.610 ⇒ 00:06:21.290 Demilade Agboola: who can bring you in. That’s kind of how it is. It’s like… it’s like… it calls for celebration, so it’s different, the mentality’s different.
63 00:06:21.450 ⇒ 00:06:22.900 Demilade Agboola: So, like, literally, my friends….
64 00:06:22.900 ⇒ 00:06:26.470 Emily Giant: Oh, it sounds, like, so fun to be a guest. It sounds like.
65 00:06:26.470 ⇒ 00:06:27.010 Demilade Agboola: Thank you.
66 00:06:27.010 ⇒ 00:06:29.890 Emily Giant: Nightmare to be the people getting married.
67 00:06:30.880 ⇒ 00:06:34.939 Demilade Agboola: I mean, so you’re not there to… you’re not there to, like, necessarily…
68 00:06:35.630 ⇒ 00:06:42.659 Demilade Agboola: So, you know, I’ve seen… I’ve been to a couple of American weddings, or, like, more American weddings than intern weddings when I was in the US.
69 00:06:42.980 ⇒ 00:06:46.889 Demilade Agboola: And it’s, like, generally quite intimate, so you kind of know everybody.
70 00:06:46.890 ⇒ 00:06:47.230 Emily Giant: Yeah.
71 00:06:47.230 ⇒ 00:07:05.570 Demilade Agboola: You know, does anybody could give a speech and talk about, you know, oh, I remember back in our days, and I look at you all married. That’s not how my internal weddings work. Some people literally, in no context, right? But they’re just there to have a good time, and you know what?
72 00:07:05.570 ⇒ 00:07:06.930 Emily Giant: event.
73 00:07:06.930 ⇒ 00:07:08.649 Demilade Agboola: That’s part of it, that’s part of it.
74 00:07:08.650 ⇒ 00:07:15.550 Emily Giant: I love it. I think that’s awesome. I love it. Like, I would love to go. It’s so fun to go to, like.
75 00:07:15.830 ⇒ 00:07:31.409 Emily Giant: weddings of other cultures, it’s just… they’re usually so much more joyous than American weddings, too. Like, I’ve gone to, like, an Indian wedding. It was, in New York City, but it was, like, a traditional Indian family, and, …
76 00:07:31.440 ⇒ 00:07:52.930 Emily Giant: they had a band, but they also had, like, a drum, like, that sound that you hear in, like, all the Bollywood movies, so, like, they’d be playing, like, all the single ladies, all the… but there would be, like, the… like, the drum beat to everything. I was like, this is the coolest thing ever. Just so, like, different, and, like, the colors, like, there wasn’t, like, the white and the black, it was, like…
77 00:07:52.980 ⇒ 00:07:55.980 Emily Giant: So colorful, so fun.
78 00:07:55.980 ⇒ 00:07:59.800 Demilade Agboola: Nigerian wedding is next on my list.
79 00:07:59.800 ⇒ 00:08:07.180 Emily Giant: Is it, like, specific to Nigeria, or are there other, like, countries in that area that also have huge weddings?
80 00:08:07.670 ⇒ 00:08:13.040 Demilade Agboola: To be fair, I’m not sure, to be honest. I haven’t really done any other, like, West African wedding.
81 00:08:13.270 ⇒ 00:08:13.780 Emily Giant: Mmm.
82 00:08:13.780 ⇒ 00:08:21.359 Demilade Agboola: So I cannot say. But I will say, when you said the music, I just remember that one of the huge things they do is, like, you would have…
83 00:08:21.520 ⇒ 00:08:23.600 Demilade Agboola: So the after, like, the parties…
84 00:08:23.770 ⇒ 00:08:40.710 Demilade Agboola: are, like, crazy. Like, Nigerian after-parties are crazy, because again, you have so many people. It’s basically like having a club. Like, towards the end, the adults want to go home. Like, when I mean adults, I mean people over, like, 50, 40. They just want to go home. They’re done, they’ve had the meals, they’re good, they…
85 00:08:40.710 ⇒ 00:08:44.380 Demilade Agboola: You know, giving gifts, they prayed for whatever, they just want to go home.
86 00:08:44.390 ⇒ 00:08:50.960 Demilade Agboola: So go ahead. For your left for people who are, like, under 40, between their 20s and their 40s, who are just, like, there to have fun.
87 00:08:50.960 ⇒ 00:09:05.039 Demilade Agboola: DJs go crazy, it’s a party, drinks are… because everyone’s been drinking, drinks are flowing, the vibe is great, people are just on their phones, like, recording, like, snaps and, like, it’s just… it’s just so much fun. And then…
88 00:09:05.330 ⇒ 00:09:20.039 Demilade Agboola: initially, so, like, usually there’s, like, a live band, so they have, like, the talking drum, so that’s, like, a Nigerian sort of drum. They’re just, like, a lot of instruments, they have the regular drums, they have their saxophones and whatever, and they’re playing music, and they’re, like.
89 00:09:20.160 ⇒ 00:09:26.869 Demilade Agboola: Literally, like, coming up with So usually, they have, like, routine songs that they will sing, normally.
90 00:09:27.480 ⇒ 00:09:33.749 Demilade Agboola: Some of them are twists on, like, popular name trend songs, or, like, popular, like, whatever songs that are trending right then.
91 00:09:34.050 ⇒ 00:09:50.860 Demilade Agboola: But then they also will freestyle, like, they would… your song comes on a stage, and they know the person’s name, they start singing the person’s name in a song, and then the person starts praying their money. It’s, it’s, it’s quite something, like, it’s honestly… Oh my god. It’s honestly quite….
92 00:09:50.980 ⇒ 00:09:53.670 Emily Giant: Ridiculous. That sounds so fun.
93 00:09:54.060 ⇒ 00:09:54.970 Emily Giant: That’s it.
94 00:09:55.280 ⇒ 00:09:58.660 Demilade Agboola: like, Nigerian parties are… are really top tier.
95 00:09:59.080 ⇒ 00:10:00.850 Emily Giant: Like, I get so, like.
96 00:10:00.890 ⇒ 00:10:17.159 Emily Giant: I’m a theater kid, so, like, if you give me a party or a stage, like, I don’t even need to drink. I will, but people will think that I’m, like, super, super drunk, and I’ll have had, like, one drink, because I get so excited that I start acting like a complete idiot. …
97 00:10:17.160 ⇒ 00:10:30.819 Emily Giant: But the last wedding I went to, it was a couple of my friends getting married in Charleston, and they both love Whitney Houston, so they had, like, a really cool soul band that also would do, like, Whitney Houston covers, and they had us do a dance-off.
98 00:10:30.990 ⇒ 00:10:50.280 Emily Giant: And I… I should send you this video, because I don’t think I’d have any drinks at all. And I was acting insane in front of, like… it was such a beautiful, classic wedding, and I was just trashing it up. I was acting like it was midnight, and it was, like, 6 in the evening, but…
99 00:10:50.830 ⇒ 00:10:57.139 Emily Giant: I won, I won the dance-off, but it was… too much. Too much, like…
100 00:10:57.470 ⇒ 00:11:10.209 Emily Giant: I’m not even that good of friends with them, and I think they just invited me because they were like, Emily will be… wild. She will make sure people are dancing, no worries there, but anyway.
101 00:11:10.210 ⇒ 00:11:12.130 Demilade Agboola: Q1, that’s what matters.
102 00:11:12.280 ⇒ 00:11:17.370 Emily Giant: I did, and then the next day, I was like, you know, I have that personality where, like.
103 00:11:17.750 ⇒ 00:11:28.979 Emily Giant: in the moment, I have so much fun, and then the next day, I’m like, oh my god, did I offend them? Did I make a fool of myself? And I’ll worry about it the entire next day. And no one else is thinking about it, ever. No one cares, because it’s all in the mic.
104 00:11:28.980 ⇒ 00:11:29.460 Demilade Agboola: Exactly.
105 00:11:29.460 ⇒ 00:11:30.670 Emily Giant: But that’s….
106 00:11:30.670 ⇒ 00:11:40.439 Demilade Agboola: Another thing I heard about American weddings, which I never knew was a huge, big deal, was that you’re not supposed to wear white to the wedding. Like, never, ever. And I’m just like…
107 00:11:41.030 ⇒ 00:11:44.510 Demilade Agboola: I know. Never. Literally never.
108 00:11:44.510 ⇒ 00:11:53.729 Emily Giant: Like, I have friends that will have burnt a bridge with another person for having worn white to their wedding, and I’m like, really? Do you care that much? Like…
109 00:11:53.840 ⇒ 00:12:00.200 Emily Giant: who cares? But I have not had a wedding, and if I do, it will be at a courthouse. So, …
110 00:12:00.300 ⇒ 00:12:17.609 Emily Giant: that’s, like, my, mostly because I just don’t want to spend money on it, and I don’t like being the center of attention in that way. Like, I do need the center of attention at a dance-off at not my wedding, but I don’t want to be, like, standing in front of people. I don’t know, that just seems…
111 00:12:17.610 ⇒ 00:12:24.670 Emily Giant: It gives me the, like, some… letting people into something that personal makes me, like, no, no, don’t look at me.
112 00:12:24.670 ⇒ 00:12:26.350 Demilade Agboola: Fair, fair enough.
113 00:12:26.680 ⇒ 00:12:39.159 Emily Giant: That’s just theater people, though. I feel like they all feel that way. Okay, so, actual work, which I’m not enthused about at this point, because I’ve worked too much, and burnt myself out. …
114 00:12:39.360 ⇒ 00:12:50.440 Emily Giant: I wanted to go over the snapshots in Looker, like, how I can add them to Looker, and, like, how… I don’t think… we don’t even need to add them to Looker, I just want to see, like, how…
115 00:12:50.950 ⇒ 00:12:53.830 Emily Giant: To teach people to use them.
116 00:12:53.970 ⇒ 00:13:02.460 Emily Giant: because I’ve never used a snapshot before, like, I understand the point of them, but, like, the reason people would want to use them is…
117 00:13:02.480 ⇒ 00:13:19.080 Emily Giant: that they’re gonna want to look at why orders shipped suboptimally, or why somebody didn’t convert on the website. Like, it’s gonna be all questions of, like, at this moment in time, why didn’t we get the customer, or why did we ship
118 00:13:19.080 ⇒ 00:13:23.269 Emily Giant: something from California to Florida, and so…
119 00:13:23.320 ⇒ 00:13:26.090 Emily Giant: Like, can you show me, like…
120 00:13:26.160 ⇒ 00:13:34.279 Emily Giant: because I’m not used to snapshot data, like, how you would query that, because I can set up the look, I just need to see a couple examples of…
121 00:13:34.720 ⇒ 00:13:38.849 Emily Giant: Like, how to join this with other information, and yadda yadda yadda.
122 00:13:39.900 ⇒ 00:13:43.789 Demilade Agboola: Okay, so what table do we have snapshots of, so can you….
123 00:13:43.790 ⇒ 00:13:44.370 Emily Giant: We….
124 00:13:44.370 ⇒ 00:13:45.120 Demilade Agboola: Okay.
125 00:13:46.010 ⇒ 00:13:52.279 Emily Giant: Let’s use the one that you had set up for the inventory lot balance, because that’s going to be the main one.
126 00:13:53.690 ⇒ 00:13:54.420 Demilade Agboola: Okay.
127 00:13:54.420 ⇒ 00:13:57.719 Emily Giant: Probably the only one that,
128 00:13:58.100 ⇒ 00:14:09.919 Emily Giant: Really neat snapshots, because it has, like, the available for sale at specific moments in time, and the moment that available for sale changes to zero is going to be really important.
129 00:14:10.770 ⇒ 00:14:11.360 Demilade Agboola: Okay.
130 00:14:13.920 ⇒ 00:14:18.129 Emily Giant: It is raining, it is so lovely out. I love when it rains.
131 00:14:18.610 ⇒ 00:14:24.729 Emily Giant: it makes me, like, just work and not worry about whether or not I’m supposed to be playing outside and weeding my driveway.
132 00:14:26.730 ⇒ 00:14:28.050 Demilade Agboola: One lesson to worry about.
133 00:14:28.050 ⇒ 00:14:28.790 Emily Giant: Yep.
134 00:14:31.030 ⇒ 00:14:38.210 Demilade Agboola: when you said it’s raining, I don’t know, for some reason, I thought it’s raining cats and dogs, and I just was wondering, why do we get cats? Like, what happened to….
135 00:14:38.420 ⇒ 00:14:46.260 Emily Giant: Yeah, why… where did that scene come from? It makes me think of, like… this is really dark. It makes me think of Hurricane Katrina, like, when…
136 00:14:46.420 ⇒ 00:14:51.300 Emily Giant: It rained so much that it… there were probably a lot of cats and dogs, left over.
137 00:14:52.840 ⇒ 00:14:55.470 Demilade Agboola: And they’re like, oh, I know what we’re gonna see now.
138 00:14:55.470 ⇒ 00:14:55.980 Emily Giant: Yah.
139 00:14:55.980 ⇒ 00:14:57.630 Demilade Agboola: Extreme rain.
140 00:14:57.630 ⇒ 00:14:59.329 Emily Giant: How do we make light of this?
141 00:15:04.400 ⇒ 00:15:06.419 Demilade Agboola: Okay, so have you actually looked at the snapshot?
142 00:15:07.140 ⇒ 00:15:09.119 Emily Giant: Not since you made them.
143 00:15:09.770 ⇒ 00:15:12.300 Demilade Agboola: Okay, alright, …
144 00:15:17.330 ⇒ 00:15:20.029 Demilade Agboola: Let me share my screen, so we can have it.
145 00:15:20.450 ⇒ 00:15:23.139 Demilade Agboola: An idea of what’s going on.
146 00:15:24.130 ⇒ 00:15:27.050 Demilade Agboola: Alright, so we have our snapshots in this.
147 00:15:27.260 ⇒ 00:15:28.740 Demilade Agboola: Part of our…
148 00:15:35.330 ⇒ 00:15:41.220 Demilade Agboola: In this part of our, … What’s it called? Schema?
149 00:15:42.000 ⇒ 00:15:49.800 Demilade Agboola: We have the analytics, we have an analytics database, the snapshot schema, and then we have tables here for that.
150 00:15:50.730 ⇒ 00:15:56.629 Demilade Agboola: So, if we look at the tables, we will see that it’s basically the same table.
151 00:15:56.990 ⇒ 00:15:59.599 Demilade Agboola: But we just have some new columns.
152 00:15:59.860 ⇒ 00:16:02.709 Demilade Agboola: That helps us… that help us keep track of what
153 00:16:03.100 ⇒ 00:16:12.940 Demilade Agboola: … is going on in the table. So if we run this… Let’s limited to 50…
154 00:16:32.900 ⇒ 00:16:35.810 Demilade Agboola: Personally, it’s taking that long. …
155 00:16:37.790 ⇒ 00:16:47.089 Demilade Agboola: Yeah, so basically what’s going on here is we have… we’re keeping track of… so can you give me a, like, a number? Like, an inventory number?
156 00:16:47.560 ⇒ 00:16:48.769 Emily Giant: Yes, let’s.
157 00:16:48.770 ⇒ 00:16:52.060 Demilade Agboola: Oh, NetSuite, NetSuite Lot ID, whichever one works.
158 00:16:52.440 ⇒ 00:16:54.089 Emily Giant: Yeah, I’ll pull a recent one.
159 00:17:01.340 ⇒ 00:17:03.660 Emily Giant: Alright, sending it in the chat…
160 00:17:06.700 ⇒ 00:17:08.189 Emily Giant: It’s a NetSuite lot.
161 00:17:11.680 ⇒ 00:17:15.929 Demilade Agboola: Here’s my… my mobile, like, my key, so I have a keyboard.
162 00:17:16.599 ⇒ 00:17:17.880 Demilade Agboola: That I use.
163 00:17:19.380 ⇒ 00:17:23.659 Demilade Agboola: It appears it’s low. Yeah, I like it. It’s quite, like, convenient.
164 00:17:26.040 ⇒ 00:17:32.560 Emily Giant: I need to get one for traveling. I always use my laptop, but it’s not as, like, good for my wrists.
165 00:17:33.480 ⇒ 00:17:36.809 Demilade Agboola: Funny enough, I actually like my laptop a lot.
166 00:17:37.120 ⇒ 00:17:41.790 Demilade Agboola: Like, if… if I am… If I’m in a zone….
167 00:17:42.270 ⇒ 00:17:54.700 Demilade Agboola: I tend to bring my computer from up here, I put it down here on the table itself, and just use the second, like, just use the computer directly with the monitor, because sometimes it’s just, like, I do enjoy it quite a bit.
168 00:17:54.970 ⇒ 00:18:11.930 Emily Giant: I agree, like, I need to move around during the day, and I have a travel monitor that weighs nothing, and is, like, super thin, and I’ll just move around my house with that. But it’s just with my laptop, and I, like, hook it in, it’s great. But for meetings, this one’s so much better, because it’s just, like…
169 00:18:12.040 ⇒ 00:18:23.349 Emily Giant: everything huge and right in front of me, so I can see other people’s screens, because otherwise, I can never see people’s screen share if we’re on Google Meets. On Zoom, it’s fine, but Google Meets, I’m, like, blind.
170 00:18:35.620 ⇒ 00:18:42.000 Demilade Agboola: Hmm… oh, I think I must have… Remove some change…
171 00:18:45.150 ⇒ 00:18:47.909 Demilade Agboola: Alright, let’s see, let’s see, let’s see…
172 00:18:54.180 ⇒ 00:18:59.830 Demilade Agboola: Okay, so, … This is recent, right?
173 00:19:01.450 ⇒ 00:19:02.010 Demilade Agboola: Training.
174 00:19:05.690 ⇒ 00:19:12.190 Demilade Agboola: So if we come to the end right here… So, the valid prompt…
175 00:19:12.730 ⇒ 00:19:21.239 Demilade Agboola: means this is the first time, like, dbt processed this data. So that means, on the 7th of this month.
176 00:19:22.110 ⇒ 00:19:29.059 Demilade Agboola: These were the values that we, like, we could calculate of that, inventory number, or that lot IU.
177 00:19:29.610 ⇒ 00:19:33.350 Demilade Agboola: And that, it was valued up until… 18.
178 00:19:34.500 ⇒ 00:19:45.150 Demilade Agboola: Right? And then on the 18th till now, this is the new calculation that we have. So, for instance, the calculation is…
179 00:19:45.430 ⇒ 00:19:48.399 Demilade Agboola: So part of what we have is that
180 00:19:48.910 ⇒ 00:19:56.159 Demilade Agboola: Between the 8 and the 18, the lot balance was 8, or 0, sorry, and now it’s a negative 2.
181 00:19:56.700 ⇒ 00:20:04.600 Demilade Agboola: Yeah, we’ll need to look into that. But, like, the available for sale before was $30.
182 00:20:05.010 ⇒ 00:20:09.329 Demilade Agboola: And then from the 18th, So now, it’s 28.
183 00:20:10.440 ⇒ 00:20:22.719 Demilade Agboola: Right? Obviously, if the things are changing really quickly, every time dbt runs, it would… because basically what the dbt snapshot is, is every time dbt runs the model, it takes a snapshot.
184 00:20:22.990 ⇒ 00:20:25.770 Demilade Agboola: And then it now processes the new data.
185 00:20:26.090 ⇒ 00:20:40.560 Demilade Agboola: And then, so we can keep seeing the delta as DVC is running, so… so it’s possible that you can have multiple, like, a lot of deltas, basically, but in this scenario, there’s literally only been one delta, so it’s not really changing a lot right now.
186 00:20:41.450 ⇒ 00:20:43.990 Demilade Agboola: So yeah, we can see…
187 00:20:44.210 ⇒ 00:20:47.669 Demilade Agboola: The available vessel was 30, it’s now 28.
188 00:20:48.240 ⇒ 00:20:54.090 Demilade Agboola: And then… what else? Committed silk until it was 0 before, now it’s 2.
189 00:20:55.670 ⇒ 00:20:59.380 Demilade Agboola: I mean, that currency sold, it was 0 before, now it’s 2.
190 00:21:01.240 ⇒ 00:21:04.149 Demilade Agboola: We can kind of see all that’s going on, basically.
191 00:21:05.360 ⇒ 00:21:07.349 Emily Giant: Okay, that’s great. …
192 00:21:07.520 ⇒ 00:21:15.809 Emily Giant: So when I add this to Looker, does it need to be its own Explorer, or does it need… or can it live alongside the other lot data?
193 00:21:16.980 ⇒ 00:21:23.010 Demilade Agboola: So what I would always suggest, actually, to make it usable for a BI tool, is that you flatten it.
194 00:21:23.010 ⇒ 00:21:23.660 Emily Giant: Yeah.
195 00:21:23.820 ⇒ 00:21:25.930 Emily Giant: So, what that means is….
196 00:21:28.240 ⇒ 00:21:29.410 Demilade Agboola: …
197 00:21:30.590 ⇒ 00:21:41.620 Demilade Agboola: you would need a date, something called a date spine, or you would need to figure out how you want to be able to, like, flatten it. Because as it is, it’s basically aggregated everything that’s happened from the 8th.
198 00:21:41.710 ⇒ 00:21:51.129 Demilade Agboola: to the… or from the 7th to the 18th of this month in one single line, which can be hard to visualize. What you want to do is you want to have
199 00:21:51.270 ⇒ 00:21:56.819 Demilade Agboola: each day, for instance, or each… I know PK mentioned each hour, the value for each hour.
200 00:21:57.220 ⇒ 00:22:00.289 Demilade Agboola: So you want every hour, full hour, between
201 00:22:03.360 ⇒ 00:22:07.700 Demilade Agboola: Between this time, this point in time, till this point in time.
202 00:22:08.090 ⇒ 00:22:08.420 Emily Giant: every….
203 00:22:08.420 ⇒ 00:22:13.139 Demilade Agboola: for our… Let’s find, like, that… did that value be represented.
204 00:22:13.520 ⇒ 00:22:24.740 Demilade Agboola: And then once the value 2 expires, and this is the new value, from that hour forward, every hour is now this… is now, like, represented by these values.
205 00:22:24.990 ⇒ 00:22:33.610 Demilade Agboola: That way, yeah, until it changes, or between whatever changes are occurring. That way, what ends up happening is that you have
206 00:22:34.880 ⇒ 00:22:51.189 Demilade Agboola: an hour… an hour-by-hour report on what’s going on. It’s there in your table, and it’s easy to visualize in your dashboard. You can just basically say, hey, this… but, like, if you have it this way, it’s kind of tricky to use and visualize, because it’s just one row.
207 00:22:52.720 ⇒ 00:22:54.150 Emily Giant: Yeah, yeah.
208 00:22:54.470 ⇒ 00:23:03.350 Emily Giant: Okay, that makes sense to me, the date spine. Where the date spine would happen in DBT?
209 00:23:04.220 ⇒ 00:23:11.100 Demilade Agboola: Yes, I would advise that, like, what we do … T….
210 00:23:16.000 ⇒ 00:23:27.920 Emily Giant: I know that they said they only need a snapshot once a day, but I think the way you have it in this table is so much better. Like, every time it runs on the hour, to see the changes are… is, to me, like, incredible.
211 00:23:28.410 ⇒ 00:23:29.110 Demilade Agboola: Yeah.
212 00:23:30.330 ⇒ 00:23:33.259 Emily Giant: So helpful. We’ve never had this.
213 00:23:36.080 ⇒ 00:23:41.510 Demilade Agboola: … I know you’ve had snapshots, but I don’t think you might have used them the same way, to be honest.
214 00:23:41.510 ⇒ 00:23:43.139 Emily Giant: It didn’t work.
215 00:23:45.280 ⇒ 00:23:51.610 Emily Giant: Trust me, I was really good friends with the person that built them, and he was like, yeah, those never worked, did they? And I was like, they sure didn’t.
216 00:23:55.260 ⇒ 00:24:00.210 Demilade Agboola: Alright, so for inventory, what we can now do is…
217 00:24:00.980 ⇒ 00:24:03.160 Demilade Agboola: We can now create a new model.
218 00:24:03.160 ⇒ 00:24:05.099 Emily Giant: This is just me.
219 00:24:05.490 ⇒ 00:24:13.650 Demilade Agboola: It’ll be, like, maybe… hourly… we’re talking about hourly, right? So, hourly… marked.
220 00:24:14.620 ⇒ 00:24:16.830 Demilade Agboola: hourly inventory.
221 00:24:18.410 ⇒ 00:24:19.520 Demilade Agboola: Lots.
222 00:24:20.660 ⇒ 00:24:21.780 Demilade Agboola: Cable.
223 00:24:22.750 ⇒ 00:24:24.559 Demilade Agboola: Changes or something, right?
224 00:24:25.920 ⇒ 00:24:28.860 Demilade Agboola: And then, once we have this model.
225 00:24:29.110 ⇒ 00:24:33.950 Demilade Agboola: We’re basically going to be creating… so we have a… with DateSpying…
226 00:24:35.110 ⇒ 00:24:39.670 Demilade Agboola: Oh, our spine, actually, because it’s not just the date or time step. Timestamp spine.
227 00:24:40.110 ⇒ 00:24:43.740 Demilade Agboola: Cause we don’t… it’s not just the date that we’re using, …
228 00:24:44.260 ⇒ 00:24:46.610 Demilade Agboola: How’s this, we will create that.
229 00:24:46.900 ⇒ 00:24:51.020 Demilade Agboola: And then we will take this snapshot.
230 00:24:51.510 ⇒ 00:24:57.260 Demilade Agboola: As… and then we’ll refer to the snapshot that we have here.
231 00:24:57.450 ⇒ 00:25:00.870 Demilade Agboola: Then, basically, we’ll flatten it.
232 00:25:02.930 ⇒ 00:25:10.070 Demilade Agboola: as… whatever. So basically, at this point, we’re going to now see Mickey’s soul.
233 00:25:10.340 ⇒ 00:25:11.700 Demilade Agboola: This point leads.
234 00:25:12.510 ⇒ 00:25:15.870 Demilade Agboola: represent… itch.
235 00:25:17.290 ⇒ 00:25:20.910 Demilade Agboola: … Lot.
236 00:25:23.820 ⇒ 00:25:29.050 Demilade Agboola: From the first… dbt valid.
237 00:25:29.360 ⇒ 00:25:30.160 Demilade Agboola: bomb.
238 00:25:31.070 ⇒ 00:25:33.030 Demilade Agboola: Till… date.
239 00:25:33.410 ⇒ 00:25:34.380 Demilade Agboola: Bye.
240 00:25:35.610 ⇒ 00:25:36.880 Demilade Agboola: Future power.
241 00:25:38.820 ⇒ 00:25:42.909 Demilade Agboola: And then select all from Platinum.
242 00:25:44.910 ⇒ 00:25:49.360 Demilade Agboola: I mean, not necessarily all, because I like to list out the name… the names of the columns.
243 00:25:49.530 ⇒ 00:25:52.960 Emily Giant: But yeah, so it would have a timestamp, which would now be….
244 00:25:53.770 ⇒ 00:25:55.010 Demilade Agboola: No.
245 00:25:56.880 ⇒ 00:25:59.800 Demilade Agboola: Okay, from a joint…
246 00:26:04.430 ⇒ 00:26:06.950 Demilade Agboola: Wrong timestamp is fine.
247 00:26:10.330 ⇒ 00:26:12.109 Demilade Agboola: Two snapshots.
248 00:26:16.980 ⇒ 00:26:23.669 Demilade Agboola: So… Do you have an idea of, like, what… what’s going on here?
249 00:26:24.230 ⇒ 00:26:30.310 Emily Giant: I do, I’m just wondering where the timestamps that it is joining to come from.
250 00:26:30.850 ⇒ 00:26:31.819 Demilade Agboola: Because I don’t.
251 00:26:31.820 ⇒ 00:26:33.779 Emily Giant: Didn’t the snapshot come from, but…
252 00:26:34.060 ⇒ 00:26:35.770 Emily Giant: I don’t know where the other ones come from.
253 00:26:36.130 ⇒ 00:26:39.120 Demilade Agboola: Alright, so for the timestamp, we’ll have to do…
254 00:26:39.500 ⇒ 00:26:42.940 Demilade Agboola: I think you guys have a date spine, so it’s just going to be dates.
255 00:26:44.380 ⇒ 00:26:52.079 Demilade Agboola: Fine… … I know you guys have a date span in the open catalog.
256 00:26:54.200 ⇒ 00:27:01.200 Demilade Agboola: It’ll just be basically that, but… It’s mine.
257 00:27:05.700 ⇒ 00:27:07.650 Demilade Agboola: I guess we have a marker for it.
258 00:27:12.910 ⇒ 00:27:14.980 Demilade Agboola: You have so many dead animals.
259 00:27:14.980 ⇒ 00:27:19.699 Emily Giant: Yeah, not only do we have one, we have 10!
260 00:27:21.250 ⇒ 00:27:22.730 Demilade Agboola: …
261 00:27:28.810 ⇒ 00:27:33.930 Demilade Agboola: Alright, so… This is one way to create a dead spine.
262 00:27:34.630 ⇒ 00:27:40.590 Emily Giant: … Look at those notes. That’s hilarious.
263 00:27:41.000 ⇒ 00:27:41.659 Demilade Agboola: What’s that?
264 00:27:41.660 ⇒ 00:27:43.770 Emily Giant: Look at those commented-out notes.
265 00:27:50.990 ⇒ 00:27:52.560 Demilade Agboola: I mean, Steve was having fun.
266 00:27:52.560 ⇒ 00:27:56.210 Emily Giant: Yeah, I think he was like, no one will ever look at this.
267 00:27:58.680 ⇒ 00:28:04.609 Demilade Agboola: So there are different ways to generate the data buying, but yeah, effectively, what you just need to do is…
268 00:28:05.500 ⇒ 00:28:09.380 Demilade Agboola: … That’s our pond.
269 00:28:11.810 ⇒ 00:28:13.749 Demilade Agboola: It’s kind of one of those things that, like.
270 00:28:14.020 ⇒ 00:28:18.270 Demilade Agboola: I can do it, but, like, I don’t always, like, store the logic in my head, because…
271 00:28:18.430 ⇒ 00:28:19.569 Demilade Agboola: What’s the point?
272 00:28:21.650 ⇒ 00:28:24.770 Demilade Agboola: …
273 00:29:24.790 ⇒ 00:29:28.390 Demilade Agboola: Okay, so effectively…
274 00:29:43.860 ⇒ 00:29:44.660 Demilade Agboola: Excellent.
275 00:31:27.690 ⇒ 00:31:31.630 Emily Giant: Sorry, I’m, like, deep in some Reddit posts about dbt date spines.
276 00:31:32.320 ⇒ 00:31:37.410 Demilade Agboola: Yeah, so you could… this is an example of something, how sometimes this would work.
277 00:32:19.080 ⇒ 00:32:21.570 Emily Giant: There’s a dbt utils thing….
278 00:32:22.100 ⇒ 00:32:24.569 Demilade Agboola: Yeah, that creates a spying.
279 00:32:24.570 ⇒ 00:32:25.370 Emily Giant: Yeah.
280 00:32:58.640 ⇒ 00:33:04.950 Emily Giant: For what it’s worth, and I don’t even know if this is relevant, but this is the one that apparently works with Postgres.
281 00:33:05.250 ⇒ 00:33:06.360 Emily Giant: Redshift.
282 00:33:16.580 ⇒ 00:33:19.709 Demilade Agboola: Oh, so there’s, … I guess there’s a macro.
283 00:33:23.090 ⇒ 00:33:25.939 Demilade Agboola: Yeah, so the reason why…
284 00:33:35.910 ⇒ 00:33:43.060 Demilade Agboola: Sure, so the thing… so one of the things, like, to make it a bit more dynamic, is ultimately…
285 00:33:43.200 ⇒ 00:33:44.120 Demilade Agboola: Honestly.
286 00:33:44.930 ⇒ 00:33:48.200 Demilade Agboola: One of the things that helps me keep it more dynamic is…
287 00:33:49.920 ⇒ 00:33:52.070 Demilade Agboola: Most likely, left in cold.
288 00:33:52.480 ⇒ 00:33:54.090 Demilade Agboola: When you have…
289 00:34:02.610 ⇒ 00:34:07.350 Demilade Agboola: When you have, like… yeah, so, because… Alright, wrong….
290 00:34:35.570 ⇒ 00:34:38.290 Emily Giant: Wow, that post had 10,000 views.
291 00:35:31.280 ⇒ 00:35:31.935 Demilade Agboola: …
292 00:35:35.620 ⇒ 00:35:41.670 Demilade Agboola: I could shoot that over to you, like, if you need a… I could create a… if you need a macro for that, I can create a macro.
293 00:35:42.450 ⇒ 00:35:43.919 Demilade Agboola: So what I would do….
294 00:35:44.350 ⇒ 00:35:45.310 Emily Giant: Sorry, go ahead.
295 00:35:45.500 ⇒ 00:35:48.440 Demilade Agboola: So what the macro will do will be, it will take
296 00:35:48.600 ⇒ 00:35:52.750 Demilade Agboola: the minimum value from the dbt value 2.
297 00:35:54.050 ⇒ 00:35:59.179 Demilade Agboola: And then it will start to use that to create a timestamp for every hour from that point going forward.
298 00:36:00.610 ⇒ 00:36:01.350 Emily Giant: Okay.
299 00:36:02.400 ⇒ 00:36:06.679 Demilade Agboola: So, what you would just do is you would pick it will be…
300 00:36:08.230 ⇒ 00:36:14.030 Demilade Agboola: It’ll be, like, you’ll just call the macro and just put the column that you’re referring to.
301 00:36:14.420 ⇒ 00:36:16.799 Emily Giant: Which, in this case, would be dbt valid from.
302 00:36:16.950 ⇒ 00:36:24.729 Demilade Agboola: And then it will then try to find the minimum, and then start using that value from that point forward to create
303 00:36:25.450 ⇒ 00:36:35.919 Demilade Agboola: The timestamps that you need. So I just don’t want to generate, like, from 2019, when maybe the snapshots only start from 2023 or 2024. No, actually, the snapshots start from 2025.
304 00:36:36.090 ⇒ 00:36:45.419 Demilade Agboola: So, we don’t need, you know, data from all… from the past, but I also want to make it a macro in such a way that it’s dynamic, so if you do want to use it for something else.
305 00:36:46.290 ⇒ 00:36:53.800 Demilade Agboola: All you just need to do is put that column in, and it’ll find the minimum, just use it to create the… the snapshot… the snapshot, the spine.
306 00:36:55.440 ⇒ 00:36:56.220 Emily Giant: Okay.
307 00:36:56.490 ⇒ 00:36:59.830 Demilade Agboola: Yeah, so I could just quit YouTube, but that would be a quick task.
308 00:37:00.280 ⇒ 00:37:03.580 Demilade Agboola: Knock that out in the next, like, Hour or so?
309 00:37:04.080 ⇒ 00:37:04.680 Emily Giant: Okay.
310 00:37:05.060 ⇒ 00:37:07.869 Emily Giant: So, then how do you use that to flatten it?
311 00:37:08.730 ⇒ 00:37:12.359 Demilade Agboola: So, once you have that, once you have the timestamp.
312 00:37:12.600 ⇒ 00:37:17.570 Demilade Agboola: The macro. It would… eventually, you just… you call it, it would create the timestamp.
313 00:37:17.570 ⇒ 00:37:19.319 Emily Giant: Then you have a snapshot.
314 00:37:19.340 ⇒ 00:37:23.899 Demilade Agboola: And so now you need to join the timestamp.
315 00:37:24.120 ⇒ 00:37:25.500 Demilade Agboola: To the snapshots.
316 00:37:25.640 ⇒ 00:37:31.500 Demilade Agboola: Basically, you’re going to say where the dbt valid from is between this value and this value.
317 00:37:33.070 ⇒ 00:37:38.139 Demilade Agboola: And the time… the snap… the time… timestamp spine matches it.
318 00:37:39.490 ⇒ 00:37:41.570 Demilade Agboola: Like, let’s equate it to that.
319 00:37:41.980 ⇒ 00:37:47.360 Demilade Agboola: So, like, if we come back here to this… It would basically be saying…
320 00:37:50.970 ⇒ 00:37:52.340 Demilade Agboola: We’re joining…
321 00:37:55.110 ⇒ 00:38:01.969 Demilade Agboola: on the things between value from and value 2. So now, it’s saying, hey, if the time is between
322 00:38:02.400 ⇒ 00:38:06.789 Demilade Agboola: the first valid is from 7th of August.
323 00:38:07.260 ⇒ 00:38:13.979 Demilade Agboola: 14, so, like, it has to be greater than 14 and 15, and then it goes, okay, so if it’s between that, then it’s 14.
324 00:38:14.300 ⇒ 00:38:18.019 Demilade Agboola: Right? So at 4th, like… or if you’re saying, like.
325 00:38:18.340 ⇒ 00:38:21.429 Demilade Agboola: So, everything in between, basically, it starts to go.
326 00:38:21.730 ⇒ 00:38:23.720 Demilade Agboola: Before that, it doesn’t care.
327 00:38:24.260 ⇒ 00:38:30.590 Demilade Agboola: between these values and the 18th of August, it’s going to have to say, oh, for every hour, this is the value.
328 00:38:32.360 ⇒ 00:38:41.830 Demilade Agboola: And that’s what you will keep tying it back to. It will keep saying, hey, this is the value at the hour, this is the value at the hour, this is the… so you have to join from the timestamp.
329 00:38:42.460 ⇒ 00:38:46.550 Demilade Agboola: between the values of dbt from and dbt value to.
330 00:38:46.820 ⇒ 00:38:47.520 Emily Giant: Okay.
331 00:38:48.570 ⇒ 00:38:51.740 Emily Giant: And so that way, it’s only ever returning one row.
332 00:38:52.610 ⇒ 00:38:55.790 Demilade Agboola: Yes, yeah, you only return one. One row.
333 00:38:55.790 ⇒ 00:38:56.220 Emily Giant: Sense.
334 00:38:56.220 ⇒ 00:38:57.240 Demilade Agboola: per hour.
335 00:38:57.350 ⇒ 00:38:59.880 Demilade Agboola: for each water ID.
336 00:39:00.190 ⇒ 00:39:07.530 Emily Giant: So, okay, so flattening it essentially means, like, if there were 10 rows, and the valid, the updated from, and to.
337 00:39:08.130 ⇒ 00:39:14.589 Emily Giant: By… if we just did it this way, it would return all 5 rows. But when you flatten it, it just…
338 00:39:15.740 ⇒ 00:39:21.340 Emily Giant: aggregates those values, or, like, what is… it gives you the max value of.
339 00:39:22.260 ⇒ 00:39:24.519 Demilade Agboola: So, like, for instance, I mean, I mean….
340 00:39:25.190 ⇒ 00:39:26.850 Emily Giant: Let’s have a….
341 00:39:27.200 ⇒ 00:39:29.800 Demilade Agboola: There she’s not new.
342 00:39:31.410 ⇒ 00:39:36.180 Demilade Agboola: So right now, we have… lot ID.
343 00:39:37.500 ⇒ 00:39:42.830 Demilade Agboola: And we have the dishes available for sale, because we don’t need to… it does the same thing across all the roads.
344 00:39:44.600 ⇒ 00:39:47.730 Demilade Agboola: All the call, I’m sorry, although for sale.
345 00:39:48.080 ⇒ 00:39:54.740 Demilade Agboola: And then we have dbt, valid, … dbt valid.
346 00:39:54.920 ⇒ 00:39:55.830 Demilade Agboola: 2.
347 00:39:56.870 ⇒ 00:39:59.020 Demilade Agboola: That’s what we have, let’s see, ABC.
348 00:39:59.810 ⇒ 00:40:00.910 Demilade Agboola: ABC.
349 00:40:01.250 ⇒ 00:40:05.910 Demilade Agboola: So right now, what we have is the available for sale was 30, now it’s 28.
350 00:40:08.520 ⇒ 00:40:12.149 Demilade Agboola: And it was valid from… so I’m just copying that.
351 00:40:35.520 ⇒ 00:40:39.099 Demilade Agboola: So when we flatten it, what ends up happening is this. We will still have
352 00:40:39.720 ⇒ 00:40:46.060 Demilade Agboola: Something similar, but now instead of having valid from, you might have like, timestamp.
353 00:40:50.870 ⇒ 00:40:54.210 Demilade Agboola: And so now… for ABC.
354 00:40:55.770 ⇒ 00:40:57.600 Demilade Agboola: This is value of 30.
355 00:40:57.840 ⇒ 00:40:59.970 Demilade Agboola: It’s basically going to now be…
356 00:41:00.990 ⇒ 00:41:03.860 Demilade Agboola: Let’s see, the value adds 15.
357 00:41:04.280 ⇒ 00:41:05.749 Demilade Agboola: At the hour, basically.
358 00:41:06.980 ⇒ 00:41:07.770 Demilade Agboola: Is this?
359 00:41:09.010 ⇒ 00:41:09.960 Demilade Agboola: Right?
360 00:41:10.360 ⇒ 00:41:14.930 Demilade Agboola: the value… At the next hour.
361 00:41:21.200 ⇒ 00:41:22.179 Demilade Agboola: Use this.
362 00:41:23.750 ⇒ 00:41:24.790 Demilade Agboola: Right?
363 00:41:25.280 ⇒ 00:41:29.929 Demilade Agboola: And so now, let’s see if we can get a quick extension.
364 00:41:30.580 ⇒ 00:41:34.580 Demilade Agboola: Alright, so yeah, it continues all the way up until the 18th.
365 00:41:35.830 ⇒ 00:41:45.060 Demilade Agboola: But what is happening now is we can start to visualize that every hour, This was the value.
366 00:41:45.960 ⇒ 00:41:50.199 Demilade Agboola: So now I can create… easily create a chart and see what is the change
367 00:41:51.290 ⇒ 00:41:53.050 Demilade Agboola: In this, I don’t know, at all.
368 00:41:53.560 ⇒ 00:41:54.710 Demilade Agboola: I’ll give you both back.
369 00:41:56.850 ⇒ 00:41:57.590 Demilade Agboola: time.
370 00:42:03.130 ⇒ 00:42:04.160 Demilade Agboola: That’s very fascinating.
371 00:42:08.960 ⇒ 00:42:13.159 Demilade Agboola: Yeah, so we can kind of see at every hour what the change is. Now, this is not really…
372 00:42:13.830 ⇒ 00:42:16.169 Demilade Agboola: But are you able to… you’re able to visualize it now.
373 00:42:19.340 ⇒ 00:42:24.080 Demilade Agboola: And so, we can keep going this way up until… Boost D.
374 00:42:24.310 ⇒ 00:42:26.080 Emily Giant: Where we can now say, hey.
375 00:42:26.080 ⇒ 00:42:27.490 Demilade Agboola: On this day, though.
376 00:42:28.560 ⇒ 00:42:32.520 Demilade Agboola: You know, at the 16, R.
377 00:42:36.620 ⇒ 00:42:41.129 Demilade Agboola: it became 28. So now I can easily plot a graph
378 00:42:41.450 ⇒ 00:42:43.130 Demilade Agboola: At the hour for every day.
379 00:42:43.420 ⇒ 00:42:48.500 Demilade Agboola: And say, at this date, this is when the available for sale dropped to this value.
380 00:42:48.880 ⇒ 00:42:50.120 Emily Giant: Nice, okay.
381 00:42:50.120 ⇒ 00:42:54.629 Demilade Agboola: slot ID. Yeah, so that’s kind of what I mean about, like, flat minutes. You’re just joining it from….
382 00:42:54.700 ⇒ 00:42:57.670 Emily Giant: Okay, that’s great, yeah. I get it.
383 00:42:58.060 ⇒ 00:42:58.760 Demilade Agboola: Yeah.
384 00:42:59.290 ⇒ 00:43:06.419 Emily Giant: Awesome. Yeah, I can see how stakeholders would, like, super dig having this in Looker. Like, even just a chart…
385 00:43:06.840 ⇒ 00:43:09.300 Emily Giant: That, and, like, being able to see
386 00:43:09.960 ⇒ 00:43:18.389 Emily Giant: like, when it… when it stocks out. Like, our inability to, like, notice and capture stock outs is so lacking.
387 00:43:19.050 ⇒ 00:43:21.189 Emily Giant: But… yeah.
388 00:43:21.190 ⇒ 00:43:27.620 Demilade Agboola: Yeah, so that’s kind of what I mean by flattening it. So instead of having it… so you could do it by day, but I know Pickey said he wanted it by hour.
389 00:43:27.930 ⇒ 00:43:29.960 Emily Giant: I think ours… perfect.
390 00:43:30.300 ⇒ 00:43:35.909 Demilade Agboola: Alright, so basically, we’re just going to truncate… we’re basically trunking what’s happening between each
391 00:43:36.630 ⇒ 00:43:47.530 Demilade Agboola: from and value to, or just chunking it to the hour. So, obviously, this is a very large chunk right now, because, you know, not a lot happened, but I’m sure there are some that are very, like, dynamic.
392 00:43:47.750 ⇒ 00:43:50.939 Demilade Agboola: Very much. In that case, yeah, they’ll have smaller chunks.
393 00:43:51.170 ⇒ 00:43:55.570 Demilade Agboola: And then when it’s null, the null value just means it’s the current version.
394 00:43:55.690 ⇒ 00:44:00.809 Demilade Agboola: So we will replace that, we’ll do the coalesce, replace that with the current timestamp.
395 00:44:01.520 ⇒ 00:44:03.800 Demilade Agboola: And that’s how we know what we currently have.
396 00:44:04.590 ⇒ 00:44:09.990 Demilade Agboola: Sweet. Yeah. So that’s… you kind of kind of see the history of the lot ID over time.
397 00:44:10.830 ⇒ 00:44:15.290 Emily Giant: Okay, cool. Yeah, this is great, and it doesn’t sound that hard.
398 00:44:15.290 ⇒ 00:44:16.019 Demilade Agboola: That’s not.
399 00:44:16.800 ⇒ 00:44:17.340 Emily Giant: Awesome.
400 00:44:17.340 ⇒ 00:44:21.579 Demilade Agboola: I feel for the other clients, I had to do something like this for them.
401 00:44:21.860 ⇒ 00:44:26.870 Demilade Agboola: I had to… the one I had to be able to see …
402 00:44:27.520 ⇒ 00:44:32.430 Demilade Agboola: The new customers per day, as at the beginning of the day.
403 00:44:32.960 ⇒ 00:44:43.239 Demilade Agboola: And that kind of changes over time, because some customers convert, some don’t. So just having that fixed value was very important for them, and it allows for better analytics, and yeah.
404 00:44:43.420 ⇒ 00:44:45.200 Demilade Agboola: It’s very helpful. It’s very, very helpful.
405 00:44:45.200 ⇒ 00:44:55.359 Emily Giant: Totally. Okay, so as far as, like, getting this deployed so that I can put it in Looker, because I think the use is very, very plain to see once you see it out like this.
406 00:44:55.710 ⇒ 00:45:02.150 Emily Giant: Are you able to do it? I know you mentioned that it doesn’t take very long, or do you want me to do it?
407 00:45:02.480 ⇒ 00:45:05.549 Demilade Agboola: I could do it, if you… when do you need it back?
408 00:45:05.770 ⇒ 00:45:08.020 Emily Giant: It doesn’t matter. Like….
409 00:45:09.980 ⇒ 00:45:14.090 Demilade Agboola: I’m asking because, like, just helps me prioritize work. …
410 00:45:14.650 ⇒ 00:45:17.870 Demilade Agboola: All right, so let’s… here’s what we’ll do. I’ll try and get it done by tomorrow.
411 00:45:17.870 ⇒ 00:45:26.020 Emily Giant: Okay, I think getting it done sooner rather than later would be good, just to show stakeholders, like, progress, and, like, to get them…
412 00:45:26.280 ⇒ 00:45:36.849 Emily Giant: Yeah, so let me know if, like, you get bulldozed with other projects, and I can jump in and help, because right now I’m just saying, some of the stuff I was working on last week.
413 00:45:37.080 ⇒ 00:45:44.649 Emily Giant: So… I’m not as overloaded right now, starting today, as I was for the last 3 weeks, solid.
414 00:45:44.790 ⇒ 00:45:52.440 Emily Giant: But yeah, it’s still a lot. I know, like, you’re right there with me, with all the stuff, so…
415 00:45:52.570 ⇒ 00:46:01.559 Emily Giant: But yeah, this is great. I’m thinking that this will be, like, one of the biggest value adds that we give to stakeholders when it comes to inventory, outside of, like, you know.
416 00:46:01.680 ⇒ 00:46:03.960 Emily Giant: accuracy at all.
417 00:46:03.960 ⇒ 00:46:04.440 Demilade Agboola: Yeah.
418 00:46:04.440 ⇒ 00:46:05.400 Emily Giant: before.
419 00:46:06.200 ⇒ 00:46:12.720 Emily Giant: So, okay, cool. This is really all I wanted to chat through, so, did you have anything you wanted to go over?
420 00:46:13.560 ⇒ 00:46:23.510 Demilade Agboola: No, that’s all. For me, I’m just trying to finish up some of the other, like, locked orders, and not the flexed orders, so…
421 00:46:23.690 ⇒ 00:46:24.919 Demilade Agboola: So far, so good.
422 00:46:25.570 ⇒ 00:46:36.039 Emily Giant: Let me just show you real quick, so that, like, we’re not duplicating work. Mostly I’m working on all historical stuff, because I know that you’re working on, like, future states, so, like.
423 00:46:36.040 ⇒ 00:46:46.660 Emily Giant: I’ll probably have to tweak what I’m doing, but in my branch… you know what? Let’s not. Let’s not even. I don’t even want to spin anything. What I was doing was trying to, like, historically align
424 00:46:47.010 ⇒ 00:46:56.249 Emily Giant: ComponentsXF and everything before it, so that, like, when you launch the new revenue stuff, it’s super easy to, like, plug in the historical revenue stuff.
425 00:46:56.660 ⇒ 00:46:57.540 Demilade Agboola: Okay.
426 00:46:57.540 ⇒ 00:47:03.449 Emily Giant: But yeah, it’s about, like, HEVO split line items, line item strikethrough. There’s just so much, like.
427 00:47:04.210 ⇒ 00:47:09.159 Emily Giant: institutional crap that I’ve had to, like, know to…
428 00:47:09.190 ⇒ 00:47:29.129 Emily Giant: do it accurately, that I’m like, I don’t know how you could do those models without knowing all the BS of, like, urban stems through time. But you’re doing it in Shopify, it looks like, the lineup. Yeah. So don’t even worry about it, because it’s not relevant. I’m just doing historical stuff.
429 00:47:29.650 ⇒ 00:47:34.799 Demilade Agboola: Alright, that’s cool. So you can look up the PR, and if it looks good, just approve it, and I’ll merge it.
430 00:47:35.160 ⇒ 00:47:50.769 Emily Giant: Okay, perfect. Alright, I’ll give you your time back. Thank you so much for the snapshot stuff. Do you want me to make a ticket, or should I just… Yeah, you can make a ticket, so at least Amber has some context on what’s going on. Okay, cool, I’ll do that, and assign it to you.
431 00:47:51.550 ⇒ 00:47:52.700 Demilade Agboola: Okay, sounds good.
432 00:47:52.700 ⇒ 00:47:54.990 Emily Giant: Alright, you’re traveling when? Saturday?
433 00:47:55.300 ⇒ 00:48:02.880 Demilade Agboola: Yeah, but it’s, like, a 25-hour flight. Well, it’s not 25 hours, I have, I have, like, a 17-hour allele, that’s the, that’s the real issue.
434 00:48:03.660 ⇒ 00:48:05.990 Emily Giant: Oh my god, just get drunk. That’s my best advice.
435 00:48:05.990 ⇒ 00:48:06.650 Demilade Agboola: That’s all I could say.
436 00:48:06.650 ⇒ 00:48:13.419 Emily Giant: Just overindulge, and then don’t remember any of that layover. That’s way too long. Way too long.
437 00:48:13.420 ⇒ 00:48:22.479 Demilade Agboola: But when I’m coming back, it’s 11 hours. It’s just, like, again, that’s with the layover. I just couldn’t find any shorter turnaround time, and I was just like, oh, damn it.
438 00:48:22.710 ⇒ 00:48:26.460 Emily Giant: Oh man, I honestly get so much work done at airports. You’ll probably get a lot.
439 00:48:26.460 ⇒ 00:48:27.260 Demilade Agboola: Welcome.
440 00:48:27.260 ⇒ 00:48:27.979 Emily Giant: Just find a good place.
441 00:48:29.090 ⇒ 00:48:29.610 Emily Giant: I….
442 00:48:29.610 ⇒ 00:48:30.170 Demilade Agboola: Exactly.
443 00:48:30.170 ⇒ 00:48:34.600 Emily Giant: get wasted. My second was like, just work. So, maybe some work.
444 00:48:34.600 ⇒ 00:48:44.379 Demilade Agboola: I’ll figure something out. It’s an Istanbul airport ahead, it’s quite big. Just maybe explore, or just, you know, yeah. We’ll see.
445 00:48:44.380 ⇒ 00:48:46.500 Emily Giant: Have you done medicine before? I haven’t.
446 00:48:46.500 ⇒ 00:48:47.160 Demilade Agboola: Okay.
447 00:48:47.160 ⇒ 00:48:48.689 Emily Giant: I’m dying to go. It looks so cool.
448 00:48:50.520 ⇒ 00:48:52.279 Demilade Agboola: Yeah, we’ll see, we’ll see.
449 00:48:52.280 ⇒ 00:48:54.610 Emily Giant: I think it’s got, what, like.
450 00:48:55.350 ⇒ 00:49:00.110 Emily Giant: 14 million people, or something, like, insane like that?
451 00:49:00.110 ⇒ 00:49:01.000 Demilade Agboola: Oh, really?
452 00:49:01.000 ⇒ 00:49:03.080 Emily Giant: It’s HUGE.
453 00:49:03.310 ⇒ 00:49:08.169 Emily Giant: Let me see… how many… our population in Istanbul.
454 00:49:14.800 ⇒ 00:49:17.609 Emily Giant: 16.7 million.
455 00:49:19.670 ⇒ 00:49:20.799 Demilade Agboola: Festival of people.
456 00:49:21.090 ⇒ 00:49:30.459 Emily Giant: That’s too big. It’s far too big. They need to break it into different cities. Alright, I’ll let you go. Thank you so much, and I will talk to you soon.
457 00:49:30.960 ⇒ 00:49:32.200 Demilade Agboola: Okay, sounds good.
458 00:49:32.650 ⇒ 00:49:33.270 Emily Giant: I….
459 00:49:33.270 ⇒ 00:49:33.960 Demilade Agboola: Take care.