Meeting Title: Magic Spoon — Brainforge sync Date: 2026-01-06 Meeting participants: Demilade Agboola, Honey Nut, Uttam Kumaran, Ashwini Sharma
WEBVTT
1 00:00:15.920 ⇒ 00:00:17.379 Honey Nut: Oh, whoa, okay.
2 00:00:20.790 ⇒ 00:00:26.310 Honey Nut: A lot of doing this. Can we switch it? Can I do this?
3 00:00:26.650 ⇒ 00:00:31.770 Honey Nut: This is… this is a roller coaster. No mirror effect? Oh, is a responsive voice.
4 00:00:32.479 ⇒ 00:00:38.310 Honey Nut: Whatever it’s yet, I think it’s, like, an interesting idea, but it’s the most confusing time.
5 00:00:38.590 ⇒ 00:00:45.249 Honey Nut: Cinematography, gotta have a… compared to the students. Pretty much one person, they don’t talk, it seems.
6 00:00:45.620 ⇒ 00:00:48.390 Honey Nut: See if this works.
7 00:00:48.660 ⇒ 00:00:49.590 Honey Nut: Hey guys!
8 00:00:50.330 ⇒ 00:00:51.040 Uttam Kumaran: Hey!
9 00:00:52.170 ⇒ 00:00:52.940 Uttam Kumaran: How’s everyone?
10 00:00:54.130 ⇒ 00:00:54.930 Uttam Kumaran: Good!
11 00:00:55.230 ⇒ 00:00:58.190 Uttam Kumaran: Hope holidays were… Alright.
12 00:00:59.320 ⇒ 00:01:00.930 Honey Nut: Yeah, quite quick.
13 00:01:02.290 ⇒ 00:01:03.110 Uttam Kumaran: Nice.
14 00:01:03.260 ⇒ 00:01:17.520 Uttam Kumaran: Cool, yeah, I’m excited to kind of jump back in things, so maybe the demo, I can let you run today. We’ll kind of… we have a little bit of an agenda, and yeah, we can… I think we have a little bit of update on sort of the kind of core two work streams, so we can…
15 00:01:17.620 ⇒ 00:01:19.460 Uttam Kumaran: I can let you take a demo.
16 00:01:21.020 ⇒ 00:01:24.559 Demilade Agboola: Okay, sounds good. Let me share my screen.
17 00:01:29.350 ⇒ 00:01:33.450 Demilade Agboola: Alright, so, again, Happy New Year,
18 00:01:33.700 ⇒ 00:01:36.260 Demilade Agboola: Look forward to doing, like, amazing things this year.
19 00:01:37.300 ⇒ 00:01:39.550 Demilade Agboola: So, let’s start off.
20 00:01:40.090 ⇒ 00:01:48.150 Demilade Agboola: Alright, so the North Star and our end goal for, you know, everything that we’re doing this year and just in this project is…
21 00:01:48.260 ⇒ 00:01:49.830 Demilade Agboola: We need to create the spin…
22 00:01:49.950 ⇒ 00:01:56.140 Demilade Agboola: pipeline, we need to have the MMM data mount, and we also want to have, like, a lightweight data audit.
23 00:01:56.870 ⇒ 00:02:03.250 Demilade Agboola: And our agenda for today is we want to be able to have, like, we’re going to talk about the Spain’s API update.
24 00:02:03.360 ⇒ 00:02:07.499 Demilade Agboola: The audits, updates, and potential, like, roadblocks that we have seen so far.
25 00:02:08.220 ⇒ 00:02:12.839 Demilade Agboola: Alright, so… In terms of the Spain’s API audit.
26 00:02:13.780 ⇒ 00:02:18.620 Demilade Agboola: We’ve been updates, we’ve been able to get the perfect access, which has been done.
27 00:02:19.030 ⇒ 00:02:31.099 Demilade Agboola: There’s also, like, perfect discovery, so understanding, like, the current setup, how things are done in the environment, the current pipelines and everything, and the path to integrating spins, that has also been done.
28 00:02:31.250 ⇒ 00:02:35.000 Demilade Agboola: So right now, what we’re doing is the Spain’s API discovery.
29 00:02:35.120 ⇒ 00:02:40.589 Demilade Agboola: So we’re trying to understand the API architecture and what endpoints to hit to get the relevant data.
30 00:02:40.860 ⇒ 00:02:41.920 Demilade Agboola: And…
31 00:02:42.250 ⇒ 00:02:50.260 Demilade Agboola: In parallel, we’re also using that to obviously create the pipeline and implement that. So once that’s done, we will verify the data.
32 00:02:50.820 ⇒ 00:02:53.629 Demilade Agboola: And then we’ll now look at backfilling.
33 00:02:54.280 ⇒ 00:02:59.179 Demilade Agboola: From, like, the data that we, like, things that come… historical backfill.
34 00:02:59.910 ⇒ 00:03:03.039 Demilade Agboola: And so right now, this is the phase we’re in.
35 00:03:03.290 ⇒ 00:03:10.540 Demilade Agboola: And we look forward to being able to Verify, as well as, backfill anything.
36 00:03:12.130 ⇒ 00:03:12.680 Demilade Agboola: Anyhow…
37 00:03:12.680 ⇒ 00:03:13.740 Uttam Kumaran: Yeah, maybe…
38 00:03:14.160 ⇒ 00:03:24.039 Uttam Kumaran: Yeah, maybe one comment I’ll do here is, yeah, I think just because of holiday stuff, like, things just got a little slow last two weeks, so I feel like probably we’ll end up with landed data here.
39 00:03:24.170 ⇒ 00:03:33.690 Uttam Kumaran: like, hopefully something by the end of the week, and then, really, it’ll move to Devilate to sort of start to verify that landed in Redshift.
40 00:03:33.840 ⇒ 00:03:51.430 Uttam Kumaran: Kind of the reason for, like, the POC and the production ready is, we just kind of want to verify that we’re able to hit and get data and understand, how to hit the API, how to write, like, the batch script to get more data, and then the final point is, like, making sure the existing pipeline runs for about a week.
41 00:03:51.440 ⇒ 00:03:54.189 Uttam Kumaran: With, like, no hiccups, and then we’re also able to do a backfill.
42 00:03:54.250 ⇒ 00:04:04.110 Uttam Kumaran: So, by the time we get to, like, right, to the, like, POC, point, we can start modeling, basically, because we’ll have an understanding of the shape.
43 00:04:09.610 ⇒ 00:04:10.010 Demilade Agboola: Yes, sir.
44 00:04:10.010 ⇒ 00:04:25.469 Uttam Kumaran: And I would say that to the top two points, and we’ll probably get to this at the end, but maybe I’ll… I’ll spoil it. The Prefect OTP stuff is getting a little bit annoying, so I wanted to flag that. Sorry, go ahead.
45 00:04:26.320 ⇒ 00:04:31.099 Honey Nut: Yeah, no, thanks for the patience with all that, it’s annoying on our end as well.
46 00:04:31.100 ⇒ 00:04:32.369 Uttam Kumaran: So… so I think I just…
47 00:04:32.370 ⇒ 00:04:34.020 Honey Nut: I think too.
48 00:04:35.590 ⇒ 00:04:36.520 Honey Nut: What is that?
49 00:04:37.440 ⇒ 00:04:38.439 Uttam Kumaran: No, you’re gone.
50 00:04:38.440 ⇒ 00:04:51.210 Honey Nut: I was just asking, are we doing the auto-forward, or… Yeah, the GitHub needs it through the phone number, so… can Slack forward? I think so. It’s probably a Slack forward when I’m forwarding. If you have the locking code, let’s go there. Okay, cool.
51 00:04:51.530 ⇒ 00:04:52.280 Honey Nut: Yeah, that’s tough.
52 00:04:52.280 ⇒ 00:04:55.520 Uttam Kumaran: Yeah, because we sort of were… some people are working…
53 00:04:55.640 ⇒ 00:05:04.190 Uttam Kumaran: early, later, and so… but I feel like we’re… we’ll keep going, but just probably one thing to flag.
54 00:05:04.470 ⇒ 00:05:09.189 Uttam Kumaran: But again, it’s really just for this phase. As soon as we get this landed, and we won’t be spending
55 00:05:09.530 ⇒ 00:05:13.730 Uttam Kumaran: Too much time, like, in prefect, not nearly as much as we are now, so…
56 00:05:16.980 ⇒ 00:05:21.250 Honey Nut: Okay, that makes sense. And then, sorry, just back that slide one more.
57 00:05:21.600 ⇒ 00:05:26.769 Honey Nut: Just to confirm the timeline, you guys plan to be done the discovery by the end of this week?
58 00:05:27.870 ⇒ 00:05:34.879 Uttam Kumaran: Yeah, so we should have discovery, and basically we’ll be in implementation. These two are kind of, like, wrapped together, because…
59 00:05:34.990 ⇒ 00:05:41.980 Uttam Kumaran: We’ll be hitting the API locally, like, testing the endpoint, and then starting to basically put it into the prefect
60 00:05:42.760 ⇒ 00:05:53.590 Uttam Kumaran: like, environment. The prefect environment is… is sort of, like, figuring out how do we even… how does Magic Spoon run jobs, and how does it… how do we do the environment? Like, what is the prefect setup?
61 00:05:53.750 ⇒ 00:05:58.569 Uttam Kumaran: So yeah, I don’t know if we need… I feel like by the end of the week, we should have… we should have this, basically.
62 00:05:59.270 ⇒ 00:06:03.810 Uttam Kumaran: out the door, and we’ll be driving towards this. Like, I would like to see us kind of wrap
63 00:06:04.550 ⇒ 00:06:17.940 Uttam Kumaran: get to the end of this by, like, the end of next week. At least, I would like to see us get here, and then if we need to monitor the pipeline for about a week before, closing this out, then it would be…
64 00:06:18.310 ⇒ 00:06:22.560 Uttam Kumaran: You know, buy another, like, so, like, two and a half weeks following year.
65 00:06:24.050 ⇒ 00:06:34.109 Uttam Kumaran: And then within… but the modeling doesn’t, like, start at the end of this, meaning as soon as we have a shape, and we have a schema landed in Redshift.
66 00:06:34.240 ⇒ 00:06:44.220 Uttam Kumaran: Which is really, like, at this point, we’ll begin modeling. And in the meantime, Demi has been working on the, sort of some stuff around the audit, so…
67 00:06:47.180 ⇒ 00:06:52.919 Honey Nut: Okay, that sounds good. And then, just for, if you all… if we can be…
68 00:06:53.270 ⇒ 00:06:56.379 Honey Nut: of use at all in the discovery. I know that my goal provides…
69 00:06:58.300 ⇒ 00:07:09.639 Honey Nut: on my best. About the, Spins API and the certain queries and tables and fields that we need, so we’re… the entire team’s happy to help out there, if there’s any additional context you need.
70 00:07:10.340 ⇒ 00:07:16.689 Uttam Kumaran: Okay, yeah, I was just gonna ask, maybe at this point, if anything’s changed on my timeline. We’re gonna…
71 00:07:17.470 ⇒ 00:07:24.310 Uttam Kumaran: bust through this as fast as we can, but if there’s any, like, deadlines we need to hit that you want us to be aware of, let us know.
72 00:07:25.230 ⇒ 00:07:43.170 Honey Nut: Yeah, I think just, yeah, don’t feel… like, don’t be afraid to set up time on, like, my calendar as well. I was in… I’ve been in the Spins API a lot the past, like, week and a half or so, so it’s pretty top of mind. Happy to just set up a half hour so we can… can you still hear me? We’re having some issues on our end.
73 00:07:43.700 ⇒ 00:07:44.819 Demilade Agboola: Yeah, we can hear you.
74 00:07:45.310 ⇒ 00:08:01.270 Honey Nut: Very cool. Yeah, new conference room setup after the year. Anyway, yeah, feel free to set up some time on my calendar. I just want to make sure, like, when we do kind of transition from that, POC to, like, the model creation phase, like.
75 00:08:01.270 ⇒ 00:08:16.960 Honey Nut: we’re doing it right the first time, just so we save any sort of, like, refactoring later on. So, happy to just, like, walk through some of the specs together. Yeah, just let me know, tap me in when you feel like you’re kind of, like, over the hump with discovery, and we can get to the modeling phase a little bit quicker.
76 00:08:18.990 ⇒ 00:08:22.769 Uttam Kumaran: Yeah, for sure. Sweeney, it’s on you, so I’ll probably let you schedule time.
77 00:08:23.140 ⇒ 00:08:25.509 Ashwini Sharma: Sure, yeah, yeah, I’ll book some time.
78 00:08:28.040 ⇒ 00:08:34.279 Demilade Agboola: Okay, sounds good. So moving to the next part, which is the infra audit.
79 00:08:34.760 ⇒ 00:08:42.210 Demilade Agboola: So, so far, I have been able to look through the GitHub, And take… put, like, mine…
80 00:08:42.429 ⇒ 00:08:43.960 Demilade Agboola: observations down.
81 00:08:44.140 ⇒ 00:08:46.479 Demilade Agboola: So there are strengths, where…
82 00:08:46.680 ⇒ 00:08:56.249 Demilade Agboola: There’s a lot of data. There’s a lot of data available for transformation, and obviously that’s great, because we can also use that data for, like, a lot of insights across the business.
83 00:08:56.870 ⇒ 00:09:06.239 Demilade Agboola: In terms of, like, optimization, we have, like, explicit distribution and sort keys, which allows for good building and warehouse optimization when querying.
84 00:09:06.550 ⇒ 00:09:11.350 Demilade Agboola: But then, on the other hand, we also have, like, some weaknesses, and…
85 00:09:12.910 ⇒ 00:09:25.700 Demilade Agboola: first things was… first thing I noticed was, like, almost every single model. So I didn’t go through every single model, but I went through most of the models across. But a lot of models are tables, and obviously with that, that comes a lot of materialization costs.
86 00:09:25.740 ⇒ 00:09:35.700 Demilade Agboola: Because every single time dbt runs, he has to rebuild every single table. And so, obviously, that’s not necessarily the best way to…
87 00:09:35.860 ⇒ 00:09:42.100 Demilade Agboola: Build our tables, and if we want to save some time, and as well as save some compute.
88 00:09:42.310 ⇒ 00:09:49.950 Demilade Agboola: We would want to, switch some things to views, switch some things to incremental models, and have that go faster.
89 00:09:50.300 ⇒ 00:09:59.859 Demilade Agboola: I also noticed there were no YAML files at all. So with that came a lack of documentation, as well as testing, like native dbt testing.
90 00:10:00.090 ⇒ 00:10:09.799 Demilade Agboola: And, well, part of the first part was also no incremental models, because, again, we don’t want to be building every single thing from scratch, every single dbt run.
91 00:10:10.540 ⇒ 00:10:15.780 Demilade Agboola: And then also, there were no explicit tagging, so because models were not tagged.
92 00:10:15.950 ⇒ 00:10:28.070 Demilade Agboola: if things break, it’s kind of harder to rerun selective builds, so with tags, it’s easy to say, hey, I just want to rerun Amazon models, or I want to rerun finance models.
93 00:10:28.280 ⇒ 00:10:32.349 Demilade Agboola: But without those explicit tags, that makes it harder to do.
94 00:10:34.050 ⇒ 00:10:39.829 Demilade Agboola: Also, I just wanted to note that I haven’t necessarily gone into the long-running models, I will still do that.
95 00:10:40.020 ⇒ 00:10:57.219 Demilade Agboola: Once I go into long-running models, I’ll figure out, like, you know, how can we make it faster? Do we need to split certain models? Do we need to change the materialization strategy? And how else can we just make it go from, you know, 10, 30 minutes to half the time, or a third of the time?
96 00:10:57.320 ⇒ 00:11:02.729 Demilade Agboola: So that will be the next phase of the audit, but this is what I’ve seen so far.
97 00:11:03.570 ⇒ 00:11:05.430 Demilade Agboola: Any questions?
98 00:11:08.020 ⇒ 00:11:17.219 Honey Nut: No, I think a lot of that makes sense, nothing that we’re too surprised about. I think, again, moving forward here, don’t be afraid to kind of tag us in to help,
99 00:11:17.420 ⇒ 00:11:24.690 Honey Nut: Help give some of that business context on what tables can be switched to an incremental build, what couldn’t be,
100 00:11:25.200 ⇒ 00:11:32.719 Honey Nut: any, like, grouping of, like, data sources or things that we can help out with there. We’re happy to help provide some of that context.
101 00:11:33.120 ⇒ 00:11:33.550 Demilade Agboola: Okay.
102 00:11:33.550 ⇒ 00:11:41.209 Honey Nut: Do you have a list of all the tables that are incremental versus full table materials or sheets?
103 00:11:41.500 ⇒ 00:11:48.119 Honey Nut: I know for BC we have it, but do we have it for everything, or is there an easy way to pull it out? Not money. Knowledge.
104 00:11:49.480 ⇒ 00:11:54.750 Uttam Kumaran: I think we more… We could put a list of that together. Yeah. Yeah, it’d be pretty easy for us to do that.
105 00:11:55.190 ⇒ 00:11:58.850 Uttam Kumaran: But what we’ve done in the past is, like… Yeah, yeah, go ahead, go ahead.
106 00:12:00.010 ⇒ 00:12:08.650 Honey Nut: Shopify, Amazon, Klaviyo, Like, there’s a… we have a lot of tables which end up being…
107 00:12:09.380 ⇒ 00:12:12.700 Honey Nut: Smaller, where it’s, like, it’s not deep.
108 00:12:12.700 ⇒ 00:12:13.100 Demilade Agboola: Yes.
109 00:12:13.100 ⇒ 00:12:17.320 Honey Nut: like, the Shopify, Amazon, Klaviyo.
110 00:12:18.710 ⇒ 00:12:27.139 Honey Nut: And any of your BC tables are probably… BC tables. It might be easier if we send you, like, a list, and then you let us know, like, the build type.
111 00:12:27.450 ⇒ 00:12:30.190 Honey Nut: Just because we don’t need you to run through anything, and…
112 00:12:30.560 ⇒ 00:12:47.999 Honey Nut: tell us on a bunch of tables we don’t care about that they’re… I hope you did a good job on the VC tables, making sure that they go on the increment dollars. Does that reduce their error rate, too, right? Yeah, there was… but there are certain ones that just need to pay. Like the sales order.
113 00:12:48.000 ⇒ 00:12:55.970 Uttam Kumaran: What we’ve done in the past, basically, is, like, we’ll put together, like, a quick Google Sheet of all the models alongside their runtime.
114 00:12:56.080 ⇒ 00:13:10.840 Uttam Kumaran: And, like, things like row count, and, like, the mark they’re part of, and then it’ll be easy if… for y’all to say, like, okay, this is totally safe to switch to incremental. These, like, are… these maybe we should even drop, or, like, are no longer used, things like that.
115 00:13:11.800 ⇒ 00:13:15.819 Uttam Kumaran: And in addition, we’ll also put the number of times they’ve been queried.
116 00:13:15.860 ⇒ 00:13:33.489 Uttam Kumaran: So in Redshift, you can look at the query frequency for every table, so you… there’s… what happens often is, like, you may have created models a while ago, nobody… the BI tools, or nobody’s going direct and querying them, we should just remove them, you know, or basically archive them within the dbt project, so we can look at that.
117 00:13:34.510 ⇒ 00:13:40.749 Honey Nut: Okay, awesome. Yeah, that would be perfect. In that structure, you can tag anything, like, all the important ones that are…
118 00:13:41.160 ⇒ 00:13:53.860 Uttam Kumaran: Yeah, it’ll be easy to just… basically, it’s like a… we’ll just, in Google Sheet, you can just put the little, like, archive, yes or no, incremental, yes or no, and then there’ll be somewhere, maybe we debate, you know, as… but the most important, or the most…
119 00:13:53.990 ⇒ 00:13:57.160 Uttam Kumaran: Impactful ones, it’ll be easy to kind of turn those on.
120 00:13:57.440 ⇒ 00:14:16.160 Demilade Agboola: Yeah, and also, like, just, like, going forward with tagging, we can tag them within dbt as, like, legacy, and so we can have our dbt runs excluding legacy tag models, so that would also make it easier to selectively build out the models that we care for, while excluding models that we do not care for, so…
121 00:14:16.250 ⇒ 00:14:23.309 Demilade Agboola: You know, like, that’s part of the weakness of tagging, because tagging helps so much, instead of having to run
122 00:14:24.570 ⇒ 00:14:36.140 Demilade Agboola: like, trying to figure out how to run different models in a convoluted way. When you have tags, you can just say, hey, I want to run these tags specifically, or I want to run everything downstream of this tag, so upstream of these tags.
123 00:14:36.140 ⇒ 00:14:46.839 Demilade Agboola: Or I want to run everything excluding these tags. So that way allows you to, if things break, it allows you to be able to jump in and kind of run the necessary models you need to run.
124 00:14:46.870 ⇒ 00:14:49.689 Demilade Agboola: Without having to, like, select individually, so…
125 00:14:53.310 ⇒ 00:14:54.310 Demilade Agboola: Okay…
126 00:14:54.310 ⇒ 00:14:54.659 Uttam Kumaran: No, no.
127 00:14:54.810 ⇒ 00:15:03.510 Uttam Kumaran: I think, Demi, also, when you, when you go into, when you go into Omni, see if you can also pull, like, the usage of the views.
128 00:15:03.790 ⇒ 00:15:07.109 Uttam Kumaran: And then that’ll give you some more understanding there, too.
129 00:15:07.820 ⇒ 00:15:13.170 Demilade Agboola: Okay, yeah, sure, I’ll definitely talk into the Omni to supplement the stability audit so far.
130 00:15:13.810 ⇒ 00:15:16.740 Uttam Kumaran: Okay. If you want to refresh this? I added some…
131 00:15:17.140 ⇒ 00:15:18.940 Demilade Agboola: Okay, we’ll do…
132 00:15:28.960 ⇒ 00:15:29.650 Demilade Agboola: Okay.
133 00:15:31.250 ⇒ 00:15:39.119 Demilade Agboola: Yeah, so the… what we’re trying to do for January is we want to complete the spin API in the next two weeks.
134 00:15:39.840 ⇒ 00:15:43.269 Demilade Agboola: And they want to kick off, MRM dates are mat
135 00:15:43.910 ⇒ 00:15:52.600 Demilade Agboola: Obviously that will be dependent on the data being available, so that’s… but the idea is, yeah, we want to get the data available and then kick off.
136 00:15:52.940 ⇒ 00:16:04.059 Demilade Agboola: And, yeah, we want to complete the data audit, so that will be… so, like I said, a more comprehensive view. So these are your long-running models, these are the things you notice in your dbt infrastructure.
137 00:16:04.230 ⇒ 00:16:10.789 Demilade Agboola: this is how Omni also, you know, the models or the topics you have in Omni also play a role in that.
138 00:16:11.040 ⇒ 00:16:15.830 Demilade Agboola: this might be too complex anomaly, maybe we might move it to DBT, things like that.
139 00:16:16.160 ⇒ 00:16:20.849 Demilade Agboola: We will be able to put everything together and have an audio ready for you this month.
140 00:16:21.140 ⇒ 00:16:34.569 Demilade Agboola: And then scoping for February, we can start looking at, like, new connector buildouts, so things like Attentive, things like TikTok Shops, and retailer APIs. So that is the plan for this month.
141 00:16:35.320 ⇒ 00:16:38.330 Demilade Agboola: Before we go to any blockers, do you have any questions on that?
142 00:16:38.750 ⇒ 00:16:40.549 Demilade Agboola: Is everyone okay with the plan?
143 00:16:42.480 ⇒ 00:16:47.730 Honey Nut: Yeah, I think it makes sense. I think in regards for the data audit, I just imagine there’s gonna be some…
144 00:16:47.770 ⇒ 00:17:02.439 Honey Nut: want of knowledge transfer, so we’re happy to, do some working sessions to go over what the existing structure is, who’s using what and why, which I think will help refine what we want to put in scope for February, because I think
145 00:17:02.440 ⇒ 00:17:13.810 Honey Nut: Likely, instead of new connector build-outs, we might be more likely to want to update some of our existing tables with some projects that we’ve been working on with our previous partner.
146 00:17:14.119 ⇒ 00:17:18.290 Honey Nut: Yes. That’s my call, too, the transition project, but that’d be right.
147 00:17:19.280 ⇒ 00:17:22.909 Demilade Agboola: Okay, and who, who would I be working these, like, working sessions with?
148 00:17:23.500 ⇒ 00:17:27.640 Honey Nut: I think the team here, then we will… whoever is…
149 00:17:27.790 ⇒ 00:17:30.059 Honey Nut: Available, we’ll be able to be there.
150 00:17:30.060 ⇒ 00:17:30.710 Demilade Agboola: Oh, okay.
151 00:17:30.710 ⇒ 00:17:33.500 Honey Nut: I’ll be there.
152 00:17:34.360 ⇒ 00:17:36.850 Demilade Agboola: Alright, sounds good. So I think…
153 00:17:37.150 ⇒ 00:17:45.220 Demilade Agboola: I’ll figure out… I’ll message the group maybe Fridays, potentially Fridays, maybe Thursdays. We’ll figure out a time where we can have working sessions.
154 00:17:45.360 ⇒ 00:18:01.279 Demilade Agboola: Where we just go over things within the infrastructure, figure out what’s working, figuring out why things are built that way, and just use that knowledge to be able to plan the roadmap ahead and integrate that knowledge into the audits, so that… Yeah. Yeah.
155 00:18:02.190 ⇒ 00:18:03.329 Honey Nut: Awesome, sounds great.
156 00:18:03.540 ⇒ 00:18:04.620 Honey Nut: Sounds good.
157 00:18:05.990 ⇒ 00:18:20.389 Demilade Agboola: Okay, and the final thing is just, like, you know, Otam kind of mentioned it, but, you know, there’s a time zone spread, so, you know, if we need OTPs at different times, it can be a bit of a blocker, especially if…
158 00:18:20.570 ⇒ 00:18:33.529 Demilade Agboola: you know, trying to get stuff done prior to the US team waking up, you might lose, like, two, three hours waiting for the OTP. So if there’s a way, like, we can make it more seamless, that would be greatly appreciated.
159 00:18:34.430 ⇒ 00:18:44.040 Demilade Agboola: I know, like, you have everything in one place, so obviously we can’t necessarily get that channel, because we see… there are more OTPs than we need to see. But even if it’s just a way we can have, like,
160 00:18:44.280 ⇒ 00:18:53.369 Demilade Agboola: OTP channel for Brainforge, or a simple… a simpler way to build things for us to be able to see the OTPs that we need. Like I said, that would be greatly appreciated.
161 00:18:54.140 ⇒ 00:19:00.529 Honey Nut: Yeah, and quick, Gobana, are you getting the forwarded emails for, like, Refix and Omni?
162 00:19:01.230 ⇒ 00:19:08.470 Demilade Agboola: Yes, for… I logged into Omni today using that, so that works. It’s the… it’s things for GitHub and dbt.
163 00:19:10.120 ⇒ 00:19:10.650 Demilade Agboola: them.
164 00:19:10.650 ⇒ 00:19:29.899 Honey Nut: Great. Yeah, we just set up, like, a forwarding rule during this meeting, so try to, like, log into GitHub and, like, do the one-time password, and we’ll see if that forwards. We’ll finish that today. It goes to our shared Slack channel. So, we’ll just try to forward it, like Josh said.
165 00:19:33.830 ⇒ 00:19:44.410 Uttam Kumaran: Cool, so I think, like, probably two meetings, right, Ashmini? You’ll grab some time to go through Spin’s API once you’re… have some questions this week, and then… Demi, yeah, let’s… let’s set up our, like, typical
166 00:19:44.620 ⇒ 00:19:49.340 Uttam Kumaran: Like, dbt audit, like, template, like, where we go through every model.
167 00:19:49.480 ⇒ 00:19:53.380 Uttam Kumaran: And then that way, for the working session, you can… you can go do that with team.
168 00:19:55.850 ⇒ 00:19:57.900 Demilade Agboola: Okay, sounds good.
169 00:19:58.940 ⇒ 00:20:05.460 Demilade Agboola: I know we’ve talked for most of it, I don’t know if you have anything you’d like to say, any questions or any feedback.
170 00:20:06.230 ⇒ 00:20:06.800 Honey Nut: Bill.
171 00:20:07.490 ⇒ 00:20:18.479 Honey Nut: No, I think the plan seems good. I think the main feedback on our end, I know that we’ve been through the holidays and everything, we’d just like to be as helpful and involved as we can be, so…
172 00:20:18.560 ⇒ 00:20:32.249 Honey Nut: Just to emphasize, please don’t hesitate to… to include us in… in any… when you have any questions, we don’t want to be a blocker there, and we’re happy to, help give you guys the context, so there’s no guessing game, because we know that it can be a little messy.
173 00:20:32.960 ⇒ 00:20:33.609 Demilade Agboola: Okay, sounds.
174 00:20:33.610 ⇒ 00:20:34.170 Uttam Kumaran: But…
175 00:20:34.390 ⇒ 00:20:36.840 Demilade Agboola: I plan to be able to, like.
176 00:20:37.430 ⇒ 00:20:41.879 Demilade Agboola: Regularly reach out to the team, let you know what’s going on, and whatever blockers we’re having.
177 00:20:41.930 ⇒ 00:20:43.560 Honey Nut: So…
178 00:20:43.690 ⇒ 00:20:51.379 Demilade Agboola: You know, we’re working with you, and so it’s very important that you see what’s going on as well, so we’ll definitely try and keep you in the loop of everything that’s going on.
179 00:20:51.550 ⇒ 00:20:59.589 Demilade Agboola: And we’ll definitely have way more meetings, like, going forward with, like, the working sessions, and Ashwini will definitely book time to be able to…
180 00:20:59.870 ⇒ 00:21:05.139 Demilade Agboola: Let you know, or ask… bring up questions about the… Spain’s API, so…
181 00:21:07.360 ⇒ 00:21:22.669 Honey Nut: Okay, awesome. Yeah, and I just pulled, like, the three endpoints that’ll be most useful. I just added those and linked the documentation in our onboarding packet, so definitely start there. I’m not sure if I passed this off previously, but I’ll tag in that right now as well.
182 00:21:23.430 ⇒ 00:21:32.079 Honey Nut: it’s… yeah, there’s, like, a market trend mutation, and there’s, like, a market insights query, as well as, like, an extract, so… I’ll just tag in that.
183 00:21:32.380 ⇒ 00:21:41.169 Ashwini Sharma: Oh, sorry, sorry. Is that the only two endpoints from where you need data, or… there’s a bunch of other endpoints, not exactly endpoints, but
184 00:21:41.680 ⇒ 00:21:46.219 Ashwini Sharma: the other query objects, right? One there.
185 00:21:46.940 ⇒ 00:21:57.929 Honey Nut: Yeah, these… those two mutations were what were recommended by the product team, from Spins. We’ve been, like, what I’ve hit in the past at just these two endpoints, one is for…
186 00:21:57.930 ⇒ 00:22:11.859 Honey Nut: like, like, just a snapshot view of, like, timeframes from, like, backed off from a specific date, and the other one’s, like, trends, like, multiple periods, so you can get, like, week over week with the trends mutation, and then with the main
187 00:22:11.970 ⇒ 00:22:27.170 Honey Nut: Market Insights mutation, you can just get, like, kind of our standard view. I think either of those will work for our current use cases. So yeah, just focus in there. I wouldn’t get too into the weeds on the queries, because that’s what their product team pointed us towards.
188 00:22:27.720 ⇒ 00:22:29.440 Ashwini Sharma: Okay, got it, yeah.
189 00:22:29.820 ⇒ 00:22:36.429 Ashwini Sharma: Can you add, the client ID and client secret to the perfect environment?
190 00:22:36.780 ⇒ 00:22:43.409 Ashwini Sharma: I’m not able to see where it was added for the other… Other connectors that you have.
191 00:22:44.310 ⇒ 00:22:48.210 Honey Nut: Cool, yeah, I can, let me log in and see if I can add that. It’s a good call out.
192 00:22:51.500 ⇒ 00:22:52.710 Honey Nut: Oh, it’s not even.
193 00:22:56.520 ⇒ 00:22:57.090 Uttam Kumaran: Okay.
194 00:22:58.150 ⇒ 00:22:58.760 Honey Nut: Nope.
195 00:22:59.560 ⇒ 00:23:08.390 Honey Nut: Yeah, feel free to Slack us if anything else… you need anything else about, like, related to Prefect or these endpoints. Again, like, happy to keep passing off this kind of context.
196 00:23:10.110 ⇒ 00:23:10.770 Uttam Kumaran: Okay.
197 00:23:13.660 ⇒ 00:23:14.430 Uttam Kumaran: Alright.
198 00:23:14.740 ⇒ 00:23:15.819 Uttam Kumaran: Appreciate it, everyone.
199 00:23:17.100 ⇒ 00:23:19.839 Honey Nut: Awesome, speak to you guys soon. Thank you.
200 00:23:19.840 ⇒ 00:23:21.120 Demilade Agboola: Thank you. Bye.
201 00:23:21.750 ⇒ 00:23:22.560 Honey Nut: Renee.