Meeting Title: Data Engineering Standup Date: 2026-05-05 Meeting participants: Awaish Kumar, Ashwini Sharma
WEBVTT
1 00:03:06.200 ⇒ 00:03:07.300 Ashwini Sharma: I wish.
2 00:03:08.480 ⇒ 00:03:09.310 Awaish Kumar: Hello.
3 00:03:09.750 ⇒ 00:03:10.720 Ashwini Sharma: Hello.
4 00:03:15.070 ⇒ 00:03:16.390 Ashwini Sharma: Hello? Yeah, hello.
5 00:03:19.610 ⇒ 00:03:20.849 Ashwini Sharma: Hey, can you hear me?
6 00:03:31.540 ⇒ 00:03:32.970 Ashwini Sharma: I, I can hear you.
7 00:03:37.920 ⇒ 00:03:38.790 Ashwini Sharma: Hello?
8 00:03:44.260 ⇒ 00:03:44.970 Awaish Kumar: Hello?
9 00:03:45.610 ⇒ 00:03:48.399 Ashwini Sharma: Yeah, I can hear you. How about you? Can you hear me?
10 00:03:52.060 ⇒ 00:03:53.229 Ashwini Sharma: Can you hear me?
11 00:03:56.230 ⇒ 00:03:58.220 Ashwini Sharma: Okay, let me rejoin.
12 00:04:08.380 ⇒ 00:04:09.240 Awaish Kumar: Okay.
13 00:04:16.170 ⇒ 00:04:17.040 Awaish Kumar: Hello.
14 00:04:18.240 ⇒ 00:04:19.089 Ashwini Sharma: Hello?
15 00:04:19.670 ⇒ 00:04:20.140 Awaish Kumar: Yep.
16 00:04:20.149 ⇒ 00:04:20.919 Ashwini Sharma: horrible.
17 00:04:21.110 ⇒ 00:04:24.829 Awaish Kumar: Yeah, yeah, now I can hear you. Okay. Okay, so I…
18 00:04:25.420 ⇒ 00:04:28.019 Awaish Kumar: By the way, how you doing?
19 00:04:28.530 ⇒ 00:04:29.400 Ashwini Sharma: All good.
20 00:04:29.600 ⇒ 00:04:34.000 Awaish Kumar: Okay, yeah, so I just wanted to go over…
21 00:04:34.100 ⇒ 00:04:37.600 Awaish Kumar: Some of the tickets, and yeah, clear any…
22 00:04:38.050 ⇒ 00:04:44.129 Awaish Kumar: Like, if you have any questions. So, I know the tickets are not…
23 00:04:44.300 ⇒ 00:04:46.609 Awaish Kumar: In the ideal format, as…
24 00:04:47.010 ⇒ 00:04:53.649 Awaish Kumar: Yeah, I didn’t have time to, like, go over each ticket, so it’s… basically, what we have to do is…
25 00:04:54.130 ⇒ 00:05:00.650 Awaish Kumar: Yeah, reference the… Meetings that we are in, or…
26 00:05:01.040 ⇒ 00:05:04.799 Awaish Kumar: Use the cursor for, like, for each meeting we do.
27 00:05:05.560 ⇒ 00:05:09.289 Awaish Kumar: all the transcript, goes to the BrainForge platform.
28 00:05:09.840 ⇒ 00:05:15.689 Awaish Kumar: So, if you ask Khasser that, what is the context for this topic.
29 00:05:15.790 ⇒ 00:05:20.130 Awaish Kumar: It should give you the information about that, and the meeting link, and everything.
30 00:05:20.350 ⇒ 00:05:24.309 Awaish Kumar: And also, if you remember… yeah, if you remember, or if,
31 00:05:24.990 ⇒ 00:05:35.270 Awaish Kumar: if we added the description that… what meeting it is in, then it’s… it’s easier, but otherwise, like, Cursor can help you with third, because it has all the transcripts already in the…
32 00:05:35.380 ⇒ 00:05:36.650 Awaish Kumar: I mean, an apple.
33 00:05:38.080 ⇒ 00:05:40.679 Ashwini Sharma: Okay, we just need to pull that… this thing, right?
34 00:05:40.680 ⇒ 00:05:41.780 Awaish Kumar: That’s platforming.
35 00:05:42.530 ⇒ 00:05:49.029 Awaish Kumar: you just have to pull the latest main, like, from the BrainForge platform, for the BrainForge platform repo. It should…
36 00:05:49.690 ⇒ 00:05:53.660 Awaish Kumar: Download all the… transcripts.
37 00:05:54.180 ⇒ 00:05:55.350 Awaish Kumar: Yeah.
38 00:05:55.350 ⇒ 00:05:56.250 Ashwini Sharma: weekend.
39 00:06:02.740 ⇒ 00:06:06.009 Ashwini Sharma: Brainforce platform main.
40 00:06:15.690 ⇒ 00:06:21.089 Ashwini Sharma: You have divergent branches and need to specify how to reconcile them.
41 00:06:21.240 ⇒ 00:06:26.360 Ashwini Sharma: Do a rebase… What changes did I add?
42 00:06:33.940 ⇒ 00:06:34.630 Awaish Kumar: Sorry?
43 00:06:35.450 ⇒ 00:06:37.829 Ashwini Sharma: Now it’s asking me to rebase.
44 00:06:38.570 ⇒ 00:06:39.340 Awaish Kumar: Okay, then we’re.
45 00:06:39.340 ⇒ 00:06:40.689 Ashwini Sharma: Do a rebase, yeah.
46 00:06:41.190 ⇒ 00:06:49.019 Awaish Kumar: Yeah, like, you can do that afterwards. We can go with this stand-up right now. Okay.
47 00:06:49.770 ⇒ 00:06:50.300 Ashwini Sharma: Yeah.
48 00:06:50.920 ⇒ 00:06:51.680 Awaish Kumar: Yeah, I’m…
49 00:06:51.680 ⇒ 00:06:53.769 Ashwini Sharma: what? This is CTA?
50 00:06:55.090 ⇒ 00:06:57.080 Awaish Kumar: So this is basically all the tickets that are assigned.
51 00:06:57.080 ⇒ 00:06:58.760 Ashwini Sharma: Oh, okay, okay. Yeah.
52 00:06:58.950 ⇒ 00:07:00.560 Awaish Kumar: We will go over everything.
53 00:07:03.370 ⇒ 00:07:10.699 Awaish Kumar: So, like, not every client is relevant here, but it’s just… It’ll show all the tickets.
54 00:07:10.800 ⇒ 00:07:11.660 Awaish Kumar: Okay.
55 00:07:12.910 ⇒ 00:07:14.259 Awaish Kumar: So, you can look at the CRM.
56 00:07:14.260 ⇒ 00:07:18.490 Ashwini Sharma: Let’s go to CTA, this is what… these are newly assigned.
57 00:07:18.750 ⇒ 00:07:22.309 Awaish Kumar: So this is the ticket that I already…
58 00:07:22.730 ⇒ 00:07:25.250 Awaish Kumar: I assigned to you on Friday, right?
59 00:07:25.250 ⇒ 00:07:26.140 Ashwini Sharma: Okay, yeah.
60 00:07:26.140 ⇒ 00:07:28.640 Awaish Kumar: during the meeting, in the middle of meeting, in the Slack.
61 00:07:29.080 ⇒ 00:07:31.090 Ashwini Sharma: Okay, okay, yeah.
62 00:07:32.890 ⇒ 00:07:34.429 Ashwini Sharma: Friday or Monday?
63 00:07:35.140 ⇒ 00:07:37.950 Awaish Kumar: I don’t remember, right? It’s 4… from 4 days ago, or whatever.
64 00:07:37.950 ⇒ 00:07:39.569 Ashwini Sharma: Okay, Friday only, yeah.
65 00:07:40.270 ⇒ 00:07:45.119 Ashwini Sharma: validated raw versus Martin numbers after the expo card fixes, okay?
66 00:07:45.570 ⇒ 00:07:48.129 Ashwini Sharma: I’ll take a look at this, yeah.
67 00:07:48.700 ⇒ 00:07:51.330 Awaish Kumar: It’s coming from that meeting, and…
68 00:07:52.190 ⇒ 00:07:54.349 Awaish Kumar: I tagged you there, and I think you…
69 00:07:54.610 ⇒ 00:07:57.110 Awaish Kumar: Obviously, you saw this message, you have a…
70 00:07:57.860 ⇒ 00:08:02.350 Awaish Kumar: hands up on my message, so I assume that you read it.
71 00:08:02.500 ⇒ 00:08:07.180 Awaish Kumar: And Amber and Catherine were raising some issues with their
72 00:08:07.840 ⇒ 00:08:13.009 Awaish Kumar: Data, but, yeah, you can use that transcript, like, what they were talking.
73 00:08:13.580 ⇒ 00:08:15.909 Awaish Kumar: And, then based on that.
74 00:08:16.830 ⇒ 00:08:22.060 Awaish Kumar: You can do some validation. If there are issues, you can fix it, or if… yeah, that’s all.
75 00:08:22.540 ⇒ 00:08:23.190 Ashwini Sharma: Okay.
76 00:08:23.450 ⇒ 00:08:24.850 Awaish Kumar: Regarding Expo Kitmal.
77 00:08:25.360 ⇒ 00:08:27.100 Ashwini Sharma: This is for CTA, yeah.
78 00:08:27.630 ⇒ 00:08:28.200 Awaish Kumar: Yes.
79 00:08:28.400 ⇒ 00:08:32.590 Ashwini Sharma: Go to others. These are the newly created ones.
80 00:08:32.590 ⇒ 00:08:36.489 Awaish Kumar: These are newly created. This is from yesterday’s meeting.
81 00:08:36.900 ⇒ 00:08:38.820 Awaish Kumar: This is Unified Event Mod.
82 00:08:39.179 ⇒ 00:08:44.280 Awaish Kumar: Like, it’s all from Monday’s meeting. Obviously, you will get context from that meeting.
83 00:08:44.920 ⇒ 00:08:46.140 Awaish Kumar: I have a little bit of…
84 00:08:46.320 ⇒ 00:08:53.600 Awaish Kumar: like, I remember a little bit is that we need to create a… even… You can say dim event.
85 00:08:53.700 ⇒ 00:08:54.950 Awaish Kumar: Table? Rev?
86 00:08:55.170 ⇒ 00:09:00.580 Awaish Kumar: Which has all the events that are… that happened at CTA.
87 00:09:02.540 ⇒ 00:09:10.280 Awaish Kumar: It might include, including events that are… like, including CES events. CES is just one event per year.
88 00:09:10.450 ⇒ 00:09:22.239 Awaish Kumar: But we need a country for each year, right? So we don’t have to create it, we have to figure out where we can get this data. There are, like, there is also… there is a…
89 00:09:22.590 ⇒ 00:09:24.280 Awaish Kumar: A table called,
90 00:09:25.110 ⇒ 00:09:33.370 Awaish Kumar: dim CES show events or something. So it’s… or, like, other events, like, we can get data from Cvent.
91 00:09:33.570 ⇒ 00:09:44.230 Awaish Kumar: if you go into Cvent, and we get what all the events are happening using Cvent, you can get a list of all the events, right? And also, she mentioned about Zoom. Zoom data is not there yet.
92 00:09:44.460 ⇒ 00:09:51.780 Awaish Kumar: But, Yeah, so it’s kind of a… I don’t know, it’s…
93 00:09:52.520 ⇒ 00:09:57.970 Awaish Kumar: You can, like, prioritize it after other tickets, because Zoom is something we are working on right now.
94 00:09:58.630 ⇒ 00:10:02.480 Ashwini Sharma: Okay, let’s do the other two, CS and C event.
95 00:10:02.840 ⇒ 00:10:04.120 Awaish Kumar: Yeah, so…
96 00:10:05.040 ⇒ 00:10:14.139 Awaish Kumar: Basically, this is kind of a… we don’t need any, like, event attendees or all that data, it’s just events, like.
97 00:10:14.140 ⇒ 00:10:16.209 Ashwini Sharma: Just the event name, right? Event name and event name.
98 00:10:16.210 ⇒ 00:10:16.619 Awaish Kumar: Amy Fant.
99 00:10:16.620 ⇒ 00:10:17.550 Ashwini Sharma: Yeah.
100 00:10:17.550 ⇒ 00:10:21.080 Awaish Kumar: By the event, the dates, and whatever relevant to event.
101 00:10:21.490 ⇒ 00:10:22.100 Ashwini Sharma: Right.
102 00:10:22.500 ⇒ 00:10:24.330 Awaish Kumar: event, itself.
103 00:10:26.300 ⇒ 00:10:32.559 Awaish Kumar: Either if it is a conference, or webinar, or if it is whatever it is, and, like, all the…
104 00:10:32.660 ⇒ 00:10:40.700 Awaish Kumar: information related to that, so… She wanted that, so Zoom data is not there, we can say that.
105 00:10:40.900 ⇒ 00:10:45.470 Awaish Kumar: Chrome data is not… Other than Snowflake yet.
106 00:10:45.850 ⇒ 00:10:46.690 Awaish Kumar: So…
107 00:10:47.680 ⇒ 00:10:54.780 Awaish Kumar: you can create a… if you are done with these two, you can create a downstream, like, a ticket for Zoom, right?
108 00:10:54.780 ⇒ 00:10:55.240 Ashwini Sharma: Okay.
109 00:10:55.240 ⇒ 00:10:59.220 Awaish Kumar: So we can track, like, what is missing, and close this ticket.
110 00:11:01.060 ⇒ 00:11:01.940 Ashwini Sharma: Alright, yeah.
111 00:11:04.800 ⇒ 00:11:12.970 Awaish Kumar: Same goes with this. This is what I was kind of suggesting, but it does not have all the… that’s why I don’t ask clients, it’s because
112 00:11:13.160 ⇒ 00:11:28.889 Awaish Kumar: It’s like we have Salesforce Marketing Cloud. What is in the Salesforce Marketing Cloud is just the emails, right? But we have a table called events inside of SMC. There’s also, like, a table that has all the, like.
113 00:11:29.180 ⇒ 00:11:34.680 Awaish Kumar: Not the event as a conference or webinar, it’s more the events that are happening, like,
114 00:11:35.100 ⇒ 00:11:38.689 Awaish Kumar: email sent, email received, right? So you…
115 00:11:39.020 ⇒ 00:11:41.980 Awaish Kumar: We have, I think we have the granular data of
116 00:11:42.620 ⇒ 00:11:47.800 Awaish Kumar: which… who received the email, right? And their… their email addresses, their names.
117 00:11:47.910 ⇒ 00:11:52.570 Awaish Kumar: So we have a… we have this, like, granular data.
118 00:11:54.170 ⇒ 00:11:58.469 Awaish Kumar: What needs to be done is, we need to find who converted, basically.
119 00:11:58.630 ⇒ 00:12:05.030 Awaish Kumar: So, out of, like, 100 people we sent the emails to, How many?
120 00:12:05.700 ⇒ 00:12:13.569 Awaish Kumar: And who, actually, who were? Like, not just how many. We… we want to… the ons… the question one answer is… might be that how many were…
121 00:12:13.730 ⇒ 00:12:17.510 Awaish Kumar: Converted, but what we need is basically want to join this
122 00:12:17.830 ⇒ 00:12:26.620 Awaish Kumar: data with the CES data, so you can identify, right? So, for example, in the email event, you find, okay, we sent an
123 00:12:27.420 ⇒ 00:12:28.950 Awaish Kumar: We sent an email.
124 00:12:29.100 ⇒ 00:12:31.659 Awaish Kumar: to Ashwini for CES event, right?
125 00:12:31.910 ⇒ 00:12:34.120 Awaish Kumar: Okay. You will combine it
126 00:12:34.340 ⇒ 00:12:41.659 Awaish Kumar: You will try to join it on, maybe, email address, or whatever identifier you think is… could work.
127 00:12:41.830 ⇒ 00:12:48.010 Awaish Kumar: To see events, registration… Plus attendance data.
128 00:12:48.390 ⇒ 00:12:53.089 Awaish Kumar: to see… Sorry, but I’m going.
129 00:12:57.320 ⇒ 00:13:04.240 Awaish Kumar: Adler’s data to see if, the person actually… Attended, right?
130 00:13:07.790 ⇒ 00:13:16.249 Awaish Kumar: So this is conversion. So… We sent… maybe we sent email to 100 people, only 50 of them actually
131 00:13:16.470 ⇒ 00:13:22.999 Awaish Kumar: attended the event. So… We need a kind of a table where you can have this flag.
132 00:13:23.470 ⇒ 00:13:24.300 Awaish Kumar: Sold.
133 00:13:24.430 ⇒ 00:13:25.120 Awaish Kumar: I’m really happy.
134 00:13:25.120 ⇒ 00:13:25.650 Ashwini Sharma: Thank you.
135 00:13:25.650 ⇒ 00:13:35.100 Awaish Kumar: You can see, for the CES 2026 events, we sent emails to 10,000 people, and out of those, 5,000 attended, right?
136 00:13:35.100 ⇒ 00:13:35.920 Ashwini Sharma: Okay, yeah.
137 00:13:35.920 ⇒ 00:13:38.920 Awaish Kumar: Or you will have this, flag, or whatever.
138 00:13:39.050 ⇒ 00:13:40.000 Awaish Kumar: That’s…
139 00:13:40.370 ⇒ 00:13:42.039 Ashwini Sharma: Yeah, got it.
140 00:13:47.060 ⇒ 00:13:53.159 Ashwini Sharma: This is just a mod table, right? One more table that… a report table, basically, not even MART table.
141 00:13:54.750 ⇒ 00:13:55.520 Ashwini Sharma: Okay.
142 00:13:56.630 ⇒ 00:14:05.700 Awaish Kumar: What else, so… This is for agents, I don’t know if we… So this…
143 00:14:05.700 ⇒ 00:14:07.790 Ashwini Sharma: Let’s see for multi-agent work.
144 00:14:08.410 ⇒ 00:14:09.980 Awaish Kumar: like, this…
145 00:14:10.590 ⇒ 00:14:19.410 Awaish Kumar: These, these are all, like, 1, 2, 3, like, some of it we can do it, this week, right? This needs to be done, anyhow.
146 00:14:19.580 ⇒ 00:14:23.560 Awaish Kumar: This is what is multi-agent framework we discussed yesterday.
147 00:14:24.610 ⇒ 00:14:28.999 Awaish Kumar: I don’t know how familiar are you with Cortex agents, or if you’re… if you want to take it.
148 00:14:29.690 ⇒ 00:14:33.489 Ashwini Sharma: I haven’t done anything on Vortex till now, but what needs to be done over here?
149 00:14:34.130 ⇒ 00:14:35.280 Awaish Kumar: We have, like…
150 00:14:35.840 ⇒ 00:14:43.279 Awaish Kumar: the… we have… we have a… we are creating agents in Snowflake, right? The task is that…
151 00:14:43.400 ⇒ 00:14:50.629 Awaish Kumar: We want to come up with a multi-agent framework where you will have a master agent, kind of a distributed computing.
152 00:14:51.060 ⇒ 00:14:55.269 Awaish Kumar: concept, you have a master agent, where we will provide the
153 00:14:55.790 ⇒ 00:15:03.089 Awaish Kumar: The user will ask the questions, and based on the rule we set for that agent, it will route your request to the
154 00:15:03.770 ⇒ 00:15:05.520 Awaish Kumar: child agents.
155 00:15:05.720 ⇒ 00:15:06.700 Ashwini Sharma: So… Okay.
156 00:15:06.700 ⇒ 00:15:07.910 Awaish Kumar: That’s mood.
157 00:15:08.070 ⇒ 00:15:15.800 Awaish Kumar: So… The customer… client will… stakeholder will come to the master agent and ask a question.
158 00:15:16.250 ⇒ 00:15:19.390 Awaish Kumar: And the master agent will decide which
159 00:15:19.950 ⇒ 00:15:24.499 Awaish Kumar: child agent I should give this question to, so I can get a better result.
160 00:15:24.830 ⇒ 00:15:31.159 Awaish Kumar: Right? So it will basically decide… decides on, given our set of rules.
161 00:15:33.160 ⇒ 00:15:33.720 Awaish Kumar: And .
162 00:15:33.720 ⇒ 00:15:36.819 Ashwini Sharma: Have we created any agents for this one CTA?
163 00:15:36.820 ⇒ 00:15:37.530 Awaish Kumar: Yes, yes.
164 00:15:37.530 ⇒ 00:15:42.300 Ashwini Sharma: In Snowflake, can you share some of the PR, or link me to some code that.
165 00:15:42.630 ⇒ 00:15:49.900 Awaish Kumar: Oh, it is in Kutzer, in the CTA, like, if you just… it is in main, if you just pull latest main.
166 00:15:50.150 ⇒ 00:15:50.820 Ashwini Sharma: Okay.
167 00:15:52.060 ⇒ 00:15:56.299 Awaish Kumar: under… CTA DataOps, if you go to the scripts.
168 00:15:56.440 ⇒ 00:15:59.579 Awaish Kumar: And the Codex Agents is a folder for Codex Agents.
169 00:16:00.260 ⇒ 00:16:00.980 Ashwini Sharma: Okay.
170 00:16:00.980 ⇒ 00:16:07.170 Awaish Kumar: This is the code for creating. So basically, this is not just creating agent, it is creating the full flow of
171 00:16:07.820 ⇒ 00:16:14.620 Awaish Kumar: what… gets fed into the agent, so it has… it creates semantic views, it creates,
172 00:16:14.950 ⇒ 00:16:22.670 Awaish Kumar: set of golden questions. It also creates the search service, but one of the
173 00:16:24.550 ⇒ 00:16:26.889 Awaish Kumar: Which one, actually? Let me find…
174 00:16:27.060 ⇒ 00:16:31.939 Awaish Kumar: Cortex create agent. So, this one is… this file is actually creating the agent.
175 00:16:33.840 ⇒ 00:16:36.910 Ashwini Sharma: Okay, create or replace agent directly, okay.
176 00:16:36.910 ⇒ 00:16:38.080 Awaish Kumar: Yeah.
177 00:16:38.080 ⇒ 00:16:39.250 Ashwini Sharma: What’s this agent doing?
178 00:16:39.380 ⇒ 00:16:45.060 Ashwini Sharma: Instructions here. Okay, so you’re just, yeah, creating an agent using these instructions.
179 00:16:45.060 ⇒ 00:16:47.820 Awaish Kumar: It combines these two tools.
180 00:16:48.040 ⇒ 00:16:50.730 Awaish Kumar: These tools are for… one is semantic view.
181 00:16:51.800 ⇒ 00:16:54.750 Awaish Kumar: or dock search service. So it is…
182 00:16:54.750 ⇒ 00:16:59.230 Ashwini Sharma: Do we have a semantic view created on the tables? Yes, we do.
183 00:17:00.110 ⇒ 00:17:01.060 Ashwini Sharma: Okay.
184 00:17:02.040 ⇒ 00:17:06.530 Ashwini Sharma: And this multi-agent is going to work on those semantic views?
185 00:17:09.200 ⇒ 00:17:17.439 Awaish Kumar: Multi-agent has to work on the agent, so this one, you say, this is one agent. This, we can call it a child agent.
186 00:17:18.020 ⇒ 00:17:18.599 Ashwini Sharma: Okay.
187 00:17:18.609 ⇒ 00:17:26.289 Awaish Kumar: It only supports questions for CES data.
188 00:17:26.799 ⇒ 00:17:27.689 Awaish Kumar: Right?
189 00:17:27.939 ⇒ 00:17:32.499 Awaish Kumar: only the questions related to CES, attendance, and registrations.
190 00:17:32.619 ⇒ 00:17:50.459 Awaish Kumar: This is able to answer that only. And then we have provided some docs related to CES event data, and it will read some context from there as well. But, for example, in a master agent, if I come in and I should ask, okay, for the CES, maybe this… in this CES, I have missed something, right?
191 00:17:50.659 ⇒ 00:17:55.439 Awaish Kumar: I’m creating a semantic view for CES events, but it’s possible that we just…
192 00:17:55.529 ⇒ 00:18:15.459 Awaish Kumar: Based on current requirements, we just joined a few tables that only gives you answers about attendance and registrations, and it misses one of the tables that has some information. And the user asks a question, which is relevant to that table. So now what happens is, if you come to this agent and ask a question, it will look at the semantic view.
193 00:18:15.519 ⇒ 00:18:16.059 Awaish Kumar: It will look.
194 00:18:16.060 ⇒ 00:18:16.500 Ashwini Sharma: cons…
195 00:18:16.500 ⇒ 00:18:20.090 Awaish Kumar: the talks, and it will say, okay, I don’t find any answer for that, right?
196 00:18:20.380 ⇒ 00:18:28.360 Awaish Kumar: Either it will give you a garbage, or it will… or it will just say that it didn’t find any relevant information. So what happens is.
197 00:18:29.050 ⇒ 00:18:41.210 Awaish Kumar: now, like, kind of this is failing. So what Catherine wants is that we should have an agent, a separate agent, that can actually go to the prod march tables, not just.
198 00:18:41.210 ⇒ 00:18:41.800 Ashwini Sharma: That’s normal.
199 00:18:42.120 ⇒ 00:18:44.759 Awaish Kumar: To become… just read from the semantic view.
200 00:18:45.090 ⇒ 00:18:49.990 Awaish Kumar: Right? But it also goes into the tables. This is restricted.
201 00:18:50.150 ⇒ 00:18:56.739 Awaish Kumar: The scope of this agent is completely restricted to just use this semantic view.
202 00:18:56.930 ⇒ 00:19:11.219 Awaish Kumar: Yeah. And this search service. That’s all. It’s not going beyond that, so this is kind of a restrictive way to do that, but the problem is, currently, what she’s facing is, we don’t have this, like, semantic view in a
203 00:19:11.500 ⇒ 00:19:12.640 Awaish Kumar: You know, very…
204 00:19:13.020 ⇒ 00:19:23.269 Awaish Kumar: like, ideal shape, right? Because we are iterating over it, we don’t know what user will ask, so we need to continuously iterate over it until it satisfies all the requests from user.
205 00:19:23.790 ⇒ 00:19:30.799 Awaish Kumar: So, the problem with initial phase is, when a user comes in, they ask a question, and they don’t go get an answer.
206 00:19:30.950 ⇒ 00:19:33.080 Awaish Kumar: The adoption will drop.
207 00:19:33.270 ⇒ 00:19:38.589 Awaish Kumar: they won’t go again to the agent, because they know, like, it’s garbage, it is not giving…
208 00:19:38.840 ⇒ 00:19:44.299 Awaish Kumar: So what she wants is… What she wants is that we should have an agent
209 00:19:44.800 ⇒ 00:19:52.470 Awaish Kumar: A master agent that decides, okay, if it is a question for which we can answer from
210 00:19:53.240 ⇒ 00:19:58.560 Awaish Kumar: From this agent, from semantic view, then use this… if…
211 00:19:58.750 ⇒ 00:20:09.130 Awaish Kumar: this agent is not able to support the answer for that specific question, then, okay, use the instead another sub-child, like, sub-agent.
212 00:20:09.310 ⇒ 00:20:14.060 Awaish Kumar: which can actually read from ProdMart’s, like, the tables.
213 00:20:14.310 ⇒ 00:20:26.290 Awaish Kumar: without using semantic view, and try to grab information, and then might succeed, because there is… the table was missing in the semantic view, but it is in there in ProdMarts.
214 00:20:26.390 ⇒ 00:20:30.120 Awaish Kumar: So basically, that’s what we are trying to… Catch.
215 00:20:30.120 ⇒ 00:20:31.569 Ashwini Sharma: Okay, okay.
216 00:20:31.610 ⇒ 00:20:32.230 Awaish Kumar: So not…
217 00:20:32.230 ⇒ 00:20:36.190 Ashwini Sharma: We don’t have a… we don’t have that agent, right, that can look into ProdMarts right now.
218 00:20:36.880 ⇒ 00:20:50.049 Awaish Kumar: We have… we have Agent, so, like, I don’t know what the latest data is in here, because it’s Amber’s… Amber is working on it. We have Agent, which is looking into ProdMart, so Amber has created it, you can ask, you can sync with her… sync with her.
219 00:20:50.150 ⇒ 00:20:56.630 Awaish Kumar: We have an agent which can look into semantic view. We don’t have a router agent, the master agent, that can…
220 00:20:56.650 ⇒ 00:21:00.369 Ashwini Sharma: Got it. So, the POC is for creating that router agent, right?
221 00:21:00.370 ⇒ 00:21:01.580 Awaish Kumar: Yes, yes.
222 00:21:01.580 ⇒ 00:21:02.109 Ashwini Sharma: Go ahead.
223 00:21:02.110 ⇒ 00:21:08.590 Awaish Kumar: is that you have to be a master agent, Which can basically… Based on the… the…
224 00:21:08.760 ⇒ 00:21:23.699 Awaish Kumar: the questions asked. Question asked, it can route you to use either semantic view, or use, like, either use the agent which has semantic view, or use the agent which has access to tables, or even
225 00:21:24.140 ⇒ 00:21:31.199 Awaish Kumar: And it is just one scenario, right? We can add multiple agents then, okay? An agent for…
226 00:21:31.510 ⇒ 00:21:41.960 Awaish Kumar: Cortex adoption, an agent for X4K data, an agent for SFMC data, and then your master agent will… what will do, it will, based on the rules defined.
227 00:21:42.250 ⇒ 00:21:49.320 Awaish Kumar: it will decide which agent should I invoke, right? If a question is related to ExpoCade, it should not ask for, like.
228 00:21:49.830 ⇒ 00:21:52.290 Awaish Kumar: answering to the CS agent, right?
229 00:21:52.800 ⇒ 00:21:54.680 Ashwini Sharma: Okay, okay, okay, yeah.
230 00:21:55.170 ⇒ 00:21:56.069 Ashwini Sharma: Got it.
231 00:22:00.480 ⇒ 00:22:07.489 Awaish Kumar: So, yeah, that’s POC. If you are… want to look at it, like, this… these things are much…
232 00:22:08.870 ⇒ 00:22:15.910 Awaish Kumar: Yeah, basically, this all needs to be done in a week, so I’m not sure. If you don’t feel confident, let me know. I will take it.
233 00:22:16.920 ⇒ 00:22:24.670 Ashwini Sharma: Which all… whatever we discussed right now, validation, Unified Event Mart, and IMC plus CES.
234 00:22:25.020 ⇒ 00:22:25.790 Awaish Kumar: Yes.
235 00:22:25.790 ⇒ 00:22:27.750 Ashwini Sharma: All of these? Okay.
236 00:22:28.400 ⇒ 00:22:30.999 Ashwini Sharma: Yeah, I…
237 00:22:31.000 ⇒ 00:22:33.759 Awaish Kumar: Because this one is, I think, I don’t know, we… we do need.
238 00:22:33.760 ⇒ 00:22:37.780 Ashwini Sharma: Yeah, we’re still… no, we’re still waiting on some data to work on this.
239 00:22:37.990 ⇒ 00:22:42.379 Awaish Kumar: Yeah, but that’s… that’s okay, but I just want to, like, be…
240 00:22:42.860 ⇒ 00:22:48.599 Awaish Kumar: just follow up with Catherine on the data, what’s going on, the trade, that’s all.
241 00:22:48.740 ⇒ 00:22:53.090 Awaish Kumar: Why not? It’s not there, so it’s okay, but just… Just…
242 00:22:53.090 ⇒ 00:22:54.330 Ashwini Sharma: I’ll follow up.
243 00:22:54.330 ⇒ 00:23:02.320 Awaish Kumar: We are actually… we know we are tracking the work, and we know what we are doing, so she don’t feel like, okay, we just…
244 00:23:02.780 ⇒ 00:23:03.830 Awaish Kumar: Forgotable.
245 00:23:03.830 ⇒ 00:23:04.410 Ashwini Sharma: Yep.
246 00:23:07.200 ⇒ 00:23:11.530 Awaish Kumar: And so, this… no work needs to be done here, but just an…
247 00:23:12.880 ⇒ 00:23:13.319 Ashwini Sharma: up here.
248 00:23:13.320 ⇒ 00:23:13.930 Awaish Kumar: Cool.
249 00:23:14.260 ⇒ 00:23:23.370 Awaish Kumar: this, I will work on it. This… yeah, but all these four… yeah, five, basically, but this is… I’m working on. These 4 needs to be closed.
250 00:23:23.840 ⇒ 00:23:24.470 Ashwini Sharma: Okay.
251 00:23:25.580 ⇒ 00:23:30.900 Awaish Kumar: So if you… And there is some hidden work might come up. So let’s see, like.
252 00:23:31.980 ⇒ 00:23:34.230 Awaish Kumar: Start with the other things.
253 00:23:34.580 ⇒ 00:23:42.209 Awaish Kumar: And, leave… Maybe… it’s up to you how you want to work on it, but
254 00:23:42.410 ⇒ 00:23:48.860 Awaish Kumar: I’m just… I’m just saying that some other written work will come if Jasmine starts to work on dashboards.
255 00:23:49.150 ⇒ 00:23:55.279 Awaish Kumar: So, let’s see, like, because the models you have created, she might have some feedback on those.
256 00:23:55.600 ⇒ 00:23:57.219 Awaish Kumar: Outside, yep.
257 00:23:57.220 ⇒ 00:23:58.579 Ashwini Sharma: For the Eden, yeah, okay.
258 00:23:59.700 ⇒ 00:24:01.550 Awaish Kumar: So, but for now, this is the…
259 00:24:01.690 ⇒ 00:24:04.770 Awaish Kumar: case. Let me know if you feel,
260 00:24:05.280 ⇒ 00:24:08.089 Awaish Kumar: There’s a lot of work, so, yeah, that’s all.
261 00:24:08.880 ⇒ 00:24:09.580 Ashwini Sharma: Alright.
262 00:24:11.150 ⇒ 00:24:16.019 Awaish Kumar: Apart from that, we have these tickets, but I don’t think any of these.
263 00:24:16.020 ⇒ 00:24:19.319 Ashwini Sharma: No, these are all blocked, not doable right now.
264 00:24:19.670 ⇒ 00:24:23.510 Awaish Kumar: Okay, let’s… Okay,
265 00:24:27.490 ⇒ 00:24:31.770 Awaish Kumar: friends, no problem, just close it, close CTA.
266 00:24:32.170 ⇒ 00:24:39.859 Awaish Kumar: Okay, then… On the default, we don’t have anything.
267 00:24:40.710 ⇒ 00:24:47.130 Awaish Kumar: for Eden, just close your tickets, which, I don’t know, What is this?
268 00:24:48.870 ⇒ 00:24:50.540 Ashwini Sharma: What is Adam Fitbeth?
269 00:24:53.760 ⇒ 00:24:56.090 Awaish Kumar: Okay, I have created this ticket.
270 00:24:56.230 ⇒ 00:24:57.110 Ashwini Sharma: Okay.
271 00:24:59.420 ⇒ 00:25:05.169 Awaish Kumar: This is for… Yeah, he just created it using, AI.
272 00:25:05.680 ⇒ 00:25:07.949 Awaish Kumar: But I think he just needs one more table.
273 00:25:08.440 ⇒ 00:25:11.840 Awaish Kumar: And I don’t know why he wants to have one more table, but…
274 00:25:12.070 ⇒ 00:25:15.089 Awaish Kumar: We can even have it in our DIM customer table.
275 00:25:17.160 ⇒ 00:25:18.070 Awaish Kumar: Let’s go.
276 00:25:18.070 ⇒ 00:25:19.460 Ashwini Sharma: customer order beers.
277 00:25:19.460 ⇒ 00:25:27.240 Awaish Kumar: Yeah, he… what he does is just, like, he has shared some query, so he just needs to see the… this is kind of POC.
278 00:25:28.580 ⇒ 00:25:35.730 Awaish Kumar: So we need to make sure that the data which is coming in using this is correct, and if it is possible to…
279 00:25:36.120 ⇒ 00:25:43.290 Awaish Kumar: And… but I think we need this data from both systems, so it should be… A unified table for…
280 00:25:44.390 ⇒ 00:25:46.420 Ashwini Sharma: Okay, ordered.
281 00:25:46.420 ⇒ 00:25:50.039 Awaish Kumar: and HealthOS, or sorry, Indian OS and PASC.
282 00:25:50.450 ⇒ 00:25:56.369 Ashwini Sharma: Yeah. Can you mention that somewhere on the top, that it’s a unified table?
283 00:25:56.370 ⇒ 00:25:57.340 Awaish Kumar: Yikes.
284 00:25:57.730 ⇒ 00:26:01.339 Awaish Kumar: Should be a… Couldn’t find two booth.
285 00:26:06.330 ⇒ 00:26:11.640 Awaish Kumar: But, yeah, we can, like, we already have a DIM customer, which includes BASC data.
286 00:26:11.910 ⇒ 00:26:14.740 Awaish Kumar: So, we can even use that, we can…
287 00:26:15.180 ⇒ 00:26:19.960 Awaish Kumar: in the customer, we can just feed this… data into…
288 00:26:20.140 ⇒ 00:26:22.600 Awaish Kumar: EDN OS data into same DIM customer.
289 00:26:23.060 ⇒ 00:26:24.810 Awaish Kumar: What all the fields we have.
290 00:26:24.950 ⇒ 00:26:31.390 Awaish Kumar: Along with a few fields, he… He wants, like, These are all the ones.
291 00:26:31.720 ⇒ 00:26:35.319 Awaish Kumar: he’s specifically looking for? Like, these are the fields that…
292 00:26:35.730 ⇒ 00:26:40.760 Awaish Kumar: is looking for, so I’m not sure if we… yeah, you can decide on…
293 00:26:40.960 ⇒ 00:26:48.239 Awaish Kumar: What is the… what should be the best, like, if we include… because this table is one… one row per customer.
294 00:26:48.760 ⇒ 00:26:50.020 Ashwini Sharma: So, for footing.
295 00:26:50.370 ⇒ 00:26:52.729 Awaish Kumar: Customer, we need these phones.
296 00:26:53.080 ⇒ 00:26:56.300 Awaish Kumar: So, DIM customer is already kind of one roper customer.
297 00:26:57.210 ⇒ 00:27:01.670 Ashwini Sharma: Is it just one row per customer? Fact customer order?
298 00:27:04.650 ⇒ 00:27:05.350 Awaish Kumar: This is one.
299 00:27:05.350 ⇒ 00:27:07.120 Ashwini Sharma: It’s only one row per customer.
300 00:27:09.110 ⇒ 00:27:16.159 Awaish Kumar: This is, because what he’s trying to do is, for a single customer, he just wants to know what was the first order cohort month.
301 00:27:16.560 ⇒ 00:27:18.220 Ashwini Sharma: Okay, first order date, first.
302 00:27:18.220 ⇒ 00:27:21.699 Awaish Kumar: In the second order things, and that’s all.
303 00:27:22.090 ⇒ 00:27:22.610 Ashwini Sharma: Okay.
304 00:27:22.610 ⇒ 00:27:23.380 Awaish Kumar: Edge.
305 00:27:27.630 ⇒ 00:27:32.529 Awaish Kumar: And… and it… I don’t know if this query will work or not, I don’t know, it’s…
306 00:27:32.760 ⇒ 00:27:34.570 Ashwini Sharma: I’ll check that in BigQuery here.
307 00:27:34.750 ⇒ 00:27:37.250 Awaish Kumar: Yeah, use router, outer counters,
308 00:27:37.420 ⇒ 00:27:43.910 Awaish Kumar: He’s saying it should be 2, and also it should be 1, and yeah.
309 00:27:50.120 ⇒ 00:27:51.809 Ashwini Sharma: What’s the priority for this?
310 00:27:51.810 ⇒ 00:28:01.159 Awaish Kumar: Yeah, like, it… yeah, I think it is restricting it to actually, for each customer, we… it will restrict. The query will restrict that, we need to…
311 00:28:01.630 ⇒ 00:28:05.539 Awaish Kumar: That for this customer, we need to see if there is a second order.
312 00:28:05.960 ⇒ 00:28:08.110 Awaish Kumar: Given it has a first order.
313 00:28:08.220 ⇒ 00:28:14.760 Awaish Kumar: Right? So it means only those customers will show up, which actually have two orders, at least.
314 00:28:17.730 ⇒ 00:28:18.659 Awaish Kumar: In this goodie.
315 00:28:20.040 ⇒ 00:28:21.090 Ashwini Sharma: Mmm…
316 00:28:21.340 ⇒ 00:28:29.439 Ashwini Sharma: Now, the second order, if the… look at the statement below that, right? If a customer has not placed a second order, they will get null for O2 fields.
317 00:28:30.100 ⇒ 00:28:34.000 Ashwini Sharma: So it… even if the customer has only one order, it will still appear.
318 00:28:34.560 ⇒ 00:28:38.860 Awaish Kumar: Yeah, this is the description, but, like, the curious is something…
319 00:28:38.860 ⇒ 00:28:40.410 Ashwini Sharma: What is wrong, right?
320 00:28:41.160 ⇒ 00:28:44.800 Awaish Kumar: So… But that’s exactly what I want, right?
321 00:28:44.800 ⇒ 00:28:48.680 Ashwini Sharma: No, but it’s just a join, join, so query is right, yeah, yeah, it will appear.
322 00:28:48.990 ⇒ 00:28:53.289 Ashwini Sharma: It’s just… it will be null. It’s a left join on the other fact transactions, right?
323 00:28:53.930 ⇒ 00:28:57.059 Awaish Kumar: Left join, where the counter is 2.
324 00:28:57.230 ⇒ 00:29:01.530 Ashwini Sharma: Yeah, so if the counter is not 2, if the joint condition does not come, the first
325 00:29:01.970 ⇒ 00:29:04.089 Ashwini Sharma: Order will still be there.
326 00:29:09.630 ⇒ 00:29:11.189 Awaish Kumar: Yeah, but then it includes…
327 00:29:15.200 ⇒ 00:29:20.559 Awaish Kumar: I don’t know, I’m… This… From this table, we will have everything.
328 00:29:21.010 ⇒ 00:29:22.619 Awaish Kumar: Yeah. Then we’re saying…
329 00:29:22.820 ⇒ 00:29:26.709 Ashwini Sharma: From the other table, you’ll only have those records which have a second order.
330 00:29:26.710 ⇒ 00:29:33.040 Awaish Kumar: making order, right? So… And then we are left training. So, basically, we are… we will have everything.
331 00:29:34.540 ⇒ 00:29:38.149 Ashwini Sharma: Right, everything from the first table. Everything means, like.
332 00:29:38.320 ⇒ 00:29:40.529 Ashwini Sharma: Any customer who has at least one order.
333 00:29:42.350 ⇒ 00:29:44.929 Awaish Kumar: Yeah, but fact, transaction is not a customer table, it’s an order table.
334 00:29:44.930 ⇒ 00:29:47.689 Ashwini Sharma: Right, yeah, so basically, yeah.
335 00:29:48.870 ⇒ 00:29:50.670 Ashwini Sharma: At least, yeah.
336 00:29:51.620 ⇒ 00:29:52.160 Awaish Kumar: But…
337 00:29:52.160 ⇒ 00:29:54.480 Ashwini Sharma: It will have customer who has placed an order.
338 00:29:55.160 ⇒ 00:30:01.629 Awaish Kumar: So… Yeah, like… So, if the same customer has 10 orders, like, what will happen?
339 00:30:02.250 ⇒ 00:30:12.869 Ashwini Sharma: If the same customer as 10 orders, then the join condition, what that is going to do is it’s going to ignore the other, other orders, and only focus on first and second.
340 00:30:16.580 ⇒ 00:30:21.100 Awaish Kumar: Okay, so… okay, this will join everything, but then this will only keep the one…
341 00:30:21.100 ⇒ 00:30:21.780 Ashwini Sharma: Yeah.
342 00:30:22.110 ⇒ 00:30:24.990 Awaish Kumar: Okay.
343 00:30:26.880 ⇒ 00:30:33.290 Awaish Kumar: Yeah, but that’s what we can do in this DIM customer as well, like… Why, why are we housed?
344 00:30:33.670 ⇒ 00:30:37.699 Awaish Kumar: We don’t need to create a, like, order pair of table.
345 00:30:38.310 ⇒ 00:30:41.719 Awaish Kumar: So, in the DIM customer, we already have one work per order.
346 00:30:41.850 ⇒ 00:30:45.419 Awaish Kumar: So, and we can just enrich that with these fields, right?
347 00:30:46.320 ⇒ 00:30:48.310 Ashwini Sharma: With the second order, yeah, we can do that.
348 00:30:48.800 ⇒ 00:30:57.470 Awaish Kumar: And we can include Eden West data in the DIM customers, and we can then try to enrich that table with these fields.
349 00:30:58.710 ⇒ 00:31:05.429 Awaish Kumar: Bringing the data from these, like, bring the first order, bring the second order, And that’s all.
350 00:31:08.650 ⇒ 00:31:11.320 Awaish Kumar: And, I think that’s… that’s the…
351 00:31:11.930 ⇒ 00:31:14.320 Awaish Kumar: That’s what God has wanted here.
352 00:31:14.500 ⇒ 00:31:19.790 Awaish Kumar: I’m not sure, like, I really want to work on this, hmm.
353 00:31:20.300 ⇒ 00:31:24.789 Awaish Kumar: Actually, I have… Let me confirm. So, I will confirm.
354 00:31:25.120 ⇒ 00:31:31.099 Awaish Kumar: Today, maybe focus on CDA. For this ticket, I will confirm with Robert if we want to
355 00:31:31.710 ⇒ 00:31:35.690 Awaish Kumar: Work on it right now, or want to… Pause.
356 00:31:35.860 ⇒ 00:31:37.210 Awaish Kumar: But, okay.
357 00:31:38.260 ⇒ 00:31:44.670 Awaish Kumar: Yeah, but the… yeah, you got the context, but if… if it is okay from Robert, we…
358 00:31:44.930 ⇒ 00:31:46.390 Awaish Kumar: What we have to do, right?
359 00:31:46.840 ⇒ 00:31:47.430 Ashwini Sharma: Yep.
360 00:31:50.750 ⇒ 00:31:56.329 Awaish Kumar: So… So, and also close whatever you have on PR reviews.
361 00:31:59.630 ⇒ 00:32:04.129 Awaish Kumar: I don’t know what these are, but if you think you have done… you’re done with it, because I’m…
362 00:32:04.370 ⇒ 00:32:09.350 Awaish Kumar: I think I’m already merging PDRs, so…
363 00:32:10.030 ⇒ 00:32:12.909 Awaish Kumar: This one, I just want you to review, because…
364 00:32:14.080 ⇒ 00:32:16.760 Awaish Kumar: There are, there are some comments, like,
365 00:32:19.210 ⇒ 00:32:22.990 Awaish Kumar: Which says, like, there are, like, raw differences.
366 00:32:23.520 ⇒ 00:32:27.980 Awaish Kumar: broad rows has more rows than the staging ones. It could be…
367 00:32:29.340 ⇒ 00:32:30.269 Awaish Kumar: It could be… It will be.
368 00:32:30.270 ⇒ 00:32:32.570 Ashwini Sharma: more, right? It’s a unified table, so…
369 00:32:33.260 ⇒ 00:32:33.980 Awaish Kumar: Yeah, Broad Road.
370 00:32:34.520 ⇒ 00:32:40.349 Awaish Kumar: The staging one is unified. It should have… it is… has lower… less rows than the prod one.
371 00:32:43.600 ⇒ 00:32:46.170 Ashwini Sharma: Okay, let me know compared.
372 00:32:47.730 ⇒ 00:32:51.230 Ashwini Sharma: Yeah, I delete… oh, hold on a second.
373 00:32:51.720 ⇒ 00:32:56.679 Ashwini Sharma: This is running on what? This is running on staging, or this is running on.
374 00:32:56.680 ⇒ 00:32:59.090 Awaish Kumar: It is comparing both, right? Staging and prod.
375 00:32:59.810 ⇒ 00:33:18.920 Awaish Kumar: So this is the staging, these are prod rows. So it’s saying, from prod, for this table, it got this many rows. From staging, it got these rows. What logic changed? Whatever changed, I don’t know. It might be correct, it might be wrong, and that’s why we have this PR data diff, so we can just…
376 00:33:19.000 ⇒ 00:33:26.019 Awaish Kumar: validate. So you just go and validate if the number of rows you are looking at are actually correct, and there’s no issues.
377 00:33:26.300 ⇒ 00:33:39.990 Awaish Kumar: And, like, this is… this is what you were expecting. Basically, if in this PR, if you have tightened any filters or something that can cause lower… less number of rows, then
378 00:33:40.030 ⇒ 00:33:52.120 Awaish Kumar: it’s okay, right? But I… we don’t know right now. We just got… I just saw this, and I see, okay, like, we have 25,000 less rows. Like, let’s figure out why. If it is correct, it’s margin.
379 00:33:52.230 ⇒ 00:33:55.440 Awaish Kumar: If it is not, then we need to fix it, but that’s all.
380 00:33:55.840 ⇒ 00:33:59.549 Awaish Kumar: For the other ones, I am… I have just merged everything for now.
381 00:33:59.650 ⇒ 00:34:04.769 Awaish Kumar: And anything comes up, if anything comes up from the strategy team, we just help them.
382 00:34:06.460 ⇒ 00:34:10.760 Awaish Kumar: help them so that they can build those unified dashboards. That’s all for now.
383 00:34:11.280 ⇒ 00:34:11.980 Ashwini Sharma: Okay.
384 00:34:14.020 ⇒ 00:34:15.820 Awaish Kumar: So, for Eden, I think…
385 00:34:15.820 ⇒ 00:34:19.170 Ashwini Sharma: Right now, I’ll focus on CTA, and Aiden.
386 00:34:19.760 ⇒ 00:34:21.650 Awaish Kumar: Yeah, yeah, close this PR.
387 00:34:21.889 ⇒ 00:34:29.300 Awaish Kumar: First, This one, like this. So you can give me go ahead, like, should I merge it or not?
388 00:34:29.659 ⇒ 00:34:30.209 Ashwini Sharma: Okay.
389 00:34:30.210 ⇒ 00:34:37.919 Awaish Kumar: Validate this first, then you can focus on CTA until we have a feedback from Robert to do anything else.
390 00:34:38.330 ⇒ 00:34:38.980 Ashwini Sharma: Okay.
391 00:34:38.980 ⇒ 00:34:43.040 Awaish Kumar: They should… and also you can close your tickets if you think they are done.
392 00:34:43.429 ⇒ 00:34:45.190 Awaish Kumar: You can close this out.
393 00:34:46.920 ⇒ 00:34:49.539 Awaish Kumar: I don’t know what is Adam’s feedback?
394 00:34:51.989 ⇒ 00:34:55.850 Awaish Kumar: Yeah, that is what, I created the ticket, okay, we just discussed.
395 00:34:56.090 ⇒ 00:34:58.690 Awaish Kumar: Okay, this is, this is all.
396 00:35:01.640 ⇒ 00:35:05.070 Awaish Kumar: Okay, let’s… let’s… Move on.
397 00:35:05.420 ⇒ 00:35:06.540 Awaish Kumar: And there are…
398 00:35:11.390 ⇒ 00:35:12.070 Awaish Kumar: Whoa.
399 00:35:12.310 ⇒ 00:35:14.659 Awaish Kumar: Do we have so many. What are these?
400 00:35:15.250 ⇒ 00:35:25.660 Awaish Kumar: modified actions and data from not. I don’t know what these are for, like, these are old tickets, like, let’s see, Ashwin, if you’ve already fixed… worked on them, just close them.
401 00:35:26.160 ⇒ 00:35:26.750 Ashwini Sharma: Okay.
402 00:35:28.050 ⇒ 00:35:30.190 Awaish Kumar: Just close… Just close…
403 00:35:30.190 ⇒ 00:35:32.020 Ashwini Sharma: These are relatively older tickets.
404 00:35:32.020 ⇒ 00:35:38.620 Awaish Kumar: Yeah, just look at them, see if you’ve already worked on that, just close that out. And GHL, let’s just host a segment, this is…
405 00:35:40.290 ⇒ 00:35:43.050 Awaish Kumar: This is… I had done it already, so we don’t need it.
406 00:35:45.940 ⇒ 00:35:48.910 Awaish Kumar: Joinage to treatment data, what that is.
407 00:35:58.110 ⇒ 00:36:01.950 Awaish Kumar: So we don’t need this. Please, like, cancel this, it was…
408 00:36:02.760 ⇒ 00:36:11.210 Awaish Kumar: These are created by Henry. So let’s… yeah, you create… you close out the… Tickets that…
409 00:36:11.580 ⇒ 00:36:17.039 Awaish Kumar: you have worked on, so I can just cancel other… That are not needed anymore.
410 00:36:18.380 ⇒ 00:36:23.479 Awaish Kumar: For Eden, yeah, a few more tickets will come up. I have a few projects.
411 00:36:23.860 ⇒ 00:36:29.220 Awaish Kumar: There are… maybe we might have to execute, but for now, it’s… there’s nothing, so…
412 00:36:31.020 ⇒ 00:36:35.520 Awaish Kumar: for Eden Ops, we… yeah, we just… I just merged all of them, so…
413 00:36:35.520 ⇒ 00:36:36.100 Ashwini Sharma: Okay.
414 00:36:36.750 ⇒ 00:36:40.499 Awaish Kumar: Let’s close these out, like, any new feedback will be a new ticket.
415 00:36:40.720 ⇒ 00:36:43.770 Awaish Kumar: Since we have been waiting for them so long.
416 00:36:46.460 ⇒ 00:36:54.289 Awaish Kumar: So if there will be any ticket, we can work on… yeah. I don’t know if you’ve done… done… if you are done with this, or it’s still in to-do.
417 00:36:55.380 ⇒ 00:36:57.049 Ashwini Sharma: I’ll check that here. Alright.
418 00:36:57.840 ⇒ 00:37:00.760 Awaish Kumar: No, no, I’m asking, is it still in there? Like, it’s from…
419 00:37:00.760 ⇒ 00:37:03.750 Ashwini Sharma: I’m still in, still in Toro, I have not yet, done on this.
420 00:37:05.040 ⇒ 00:37:08.820 Ashwini Sharma: I think I have done that 46. 46 is not yet done.
421 00:37:10.230 ⇒ 00:37:13.420 Awaish Kumar: Okay, like, this is… Yeah, let’s not…
422 00:37:13.420 ⇒ 00:37:20.629 Ashwini Sharma: Maybe 56 is done. The other one is not done, yeah. I’ll check and, you know, comment on this one. Close it if it is already done.
423 00:37:20.900 ⇒ 00:37:21.530 Awaish Kumar: Okay.
424 00:37:26.250 ⇒ 00:37:31.830 Awaish Kumar: You don’t then… We have… element.
425 00:37:33.340 ⇒ 00:37:37.529 Awaish Kumar: So, for Element, it’s mostly memos.
426 00:37:38.840 ⇒ 00:37:40.440 Awaish Kumar: Condition blocked.
427 00:37:40.440 ⇒ 00:37:46.790 Ashwini Sharma: Yeah, I have that, facts and dimensions modeled for the supply chain, right? She had some basic questions.
428 00:37:47.060 ⇒ 00:37:47.920 Ashwini Sharma: Yeah.
429 00:37:48.110 ⇒ 00:37:54.389 Ashwini Sharma: And I had some basic questions on… on… on the kind of KPIs that she wants us to address, Jasmine.
430 00:37:55.110 ⇒ 00:37:56.410 Awaish Kumar: But, yeah, they respond to your.
431 00:37:56.410 ⇒ 00:38:12.950 Ashwini Sharma: But yeah, most of the, you know, initial set of questions that they want to answer can be done using those facts and dimensions that we have created. But as more KPI comes, I think we’ll get more clarity on what kind of mod we need to be creating.
432 00:38:13.950 ⇒ 00:38:17.900 Awaish Kumar: Okay, great. So, if you want to split it out, do that.
433 00:38:17.900 ⇒ 00:38:24.960 Ashwini Sharma: Yeah, it needs to be split out. Like, what I’ll do is, based on the questions that she has asked, right, initial set of KPIs.
434 00:38:25.260 ⇒ 00:38:31.869 Ashwini Sharma: I’ll… I’ll, you know, create some queries on top of facts and dimensions that can answer those questions.
435 00:38:32.610 ⇒ 00:38:33.540 Ashwini Sharma: And…
436 00:38:33.720 ⇒ 00:38:34.740 Awaish Kumar: That’s okay, buddy.
437 00:38:34.740 ⇒ 00:38:35.430 Ashwini Sharma: Yeah.
438 00:38:36.050 ⇒ 00:38:55.309 Awaish Kumar: What I’m telling you right now is I just created this as a placeholder, because I don’t know what is going to come up. Now that you have more requirements, create more tickets for yourself on supply chain. Whatever you are working on, whatever your new tables, or validation, or metric definition, or requirements gathering, or whatever.
439 00:38:55.430 ⇒ 00:38:58.850 Awaish Kumar: Needs to be done, just create those tickets here.
440 00:38:58.850 ⇒ 00:39:02.210 Ashwini Sharma: Let’s close this, I’ll create new tickets for each new requirement.
441 00:39:02.520 ⇒ 00:39:07.349 Awaish Kumar: Okay, just create others, we can close it… you can close it after they are done.
442 00:39:07.350 ⇒ 00:39:08.200 Ashwini Sharma: Hold on.
443 00:39:08.200 ⇒ 00:39:10.560 Awaish Kumar: Don’t want to lose this placeholder before we…
444 00:39:10.790 ⇒ 00:39:14.069 Awaish Kumar: have something. So, after that, you can just cancel it.
445 00:39:14.850 ⇒ 00:39:21.709 Awaish Kumar: And… Yeah, and then also, like, if you’re, communicating with Jasmine.
446 00:39:22.050 ⇒ 00:39:26.320 Awaish Kumar: Do that in channel, so you are visible.
447 00:39:27.990 ⇒ 00:39:32.180 Awaish Kumar: Right? So we know that, like, whatever is going on with the supply chain.
448 00:39:33.870 ⇒ 00:39:40.570 Awaish Kumar: And also, try to be visible, like, that whatever you are working on, what give your updates, so…
449 00:39:40.780 ⇒ 00:39:44.380 Awaish Kumar: Put them can actually see that… what you’re doing.
450 00:39:44.920 ⇒ 00:39:45.550 Ashwini Sharma: Okay.
451 00:39:47.700 ⇒ 00:39:52.719 Awaish Kumar: Basically, yeah, that’s the thing. In a remote work, you just have to be visible.
452 00:39:54.240 ⇒ 00:39:58.179 Awaish Kumar: even if, like, you’re in progress, like, I’m working on this, it’s in progress.
453 00:39:58.730 ⇒ 00:40:04.909 Awaish Kumar: Even if it’s not done, we have to just give an update that What’s the current status?
454 00:40:05.470 ⇒ 00:40:08.899 Awaish Kumar: Apart from that, this one is also needs to be done.
455 00:40:11.520 ⇒ 00:40:15.470 Ashwini Sharma: Which one is that? Yeah, data PR validation, yeah, I haven’t done that.
456 00:40:15.890 ⇒ 00:40:21.210 Ashwini Sharma: I think there was a similar PR for Magic Spoon, which we didn’t want to do.
457 00:40:21.460 ⇒ 00:40:24.890 Ashwini Sharma: This one, yeah, I’ll take care of this.
458 00:40:25.210 ⇒ 00:40:27.060 Ashwini Sharma: database PR validation, yeah.
459 00:40:27.300 ⇒ 00:40:30.070 Awaish Kumar: So why we didn’t want a tool for Magic Spoon?
460 00:40:31.000 ⇒ 00:40:36.620 Ashwini Sharma: And then Demilade was saying that the models are not yet optimized, let’s not add more complexity on top of that.
461 00:40:38.060 ⇒ 00:40:40.889 Awaish Kumar: But there’s no complexity on the models, right? It’s just…
462 00:40:40.890 ⇒ 00:40:47.499 Ashwini Sharma: Yeah, it’s taking a lot of time to run already. Adding this thing will further increase the…
463 00:40:49.630 ⇒ 00:40:52.940 Ashwini Sharma: compute time, right? So that’s why I think,
464 00:40:53.120 ⇒ 00:40:55.319 Ashwini Sharma: You wanted to delay this thing on…
465 00:40:56.020 ⇒ 00:40:56.480 Awaish Kumar: Oh, okay.
466 00:40:56.480 ⇒ 00:40:57.470 Ashwini Sharma: Magic Spawn.
467 00:40:58.510 ⇒ 00:41:03.580 Awaish Kumar: Okay, so then there is an issue with the how he’s running action, so we…
468 00:41:03.820 ⇒ 00:41:08.240 Awaish Kumar: I will… I will just… talk to you.
469 00:41:08.530 ⇒ 00:41:09.120 Ashwini Sharma: Okay.
470 00:41:09.120 ⇒ 00:41:10.700 Awaish Kumar: And we need to optimize that.
471 00:41:11.260 ⇒ 00:41:17.200 Awaish Kumar: But for… Basically, it’s happening for all the clients, so…
472 00:41:17.340 ⇒ 00:41:21.660 Awaish Kumar: What I’m doing is trying to split the data back actions by…
473 00:41:22.090 ⇒ 00:41:26.190 Awaish Kumar: Using state, and also by dividing it by mods, so…
474 00:41:27.270 ⇒ 00:41:33.669 Awaish Kumar: Each mod is, like, independent, so it doesn’t… like, if supply… if there is any model that is…
475 00:41:33.810 ⇒ 00:41:37.119 Awaish Kumar: for supply chain is breaking, we don’t want to break wholesale, right?
476 00:41:37.430 ⇒ 00:41:38.260 Awaish Kumar: No.
477 00:41:39.140 ⇒ 00:41:40.439 Awaish Kumar: That’s what I mean.
478 00:41:40.580 ⇒ 00:41:45.939 Awaish Kumar: trying… you’re doing for element, basically. So yeah, let’s create these tables for yourself.
479 00:41:46.270 ⇒ 00:41:48.900 Awaish Kumar: Sharing element, and also…
480 00:41:51.270 ⇒ 00:41:58.550 Awaish Kumar: create a ticket so we can actually see what’s going on. Supply chain is really, we want to close it, right?
481 00:41:58.820 ⇒ 00:42:01.309 Awaish Kumar: Whatever is there, like, push for it.
482 00:42:01.720 ⇒ 00:42:04.699 Awaish Kumar: So, if you are not getting something from Jasmine, like.
483 00:42:07.220 ⇒ 00:42:10.609 Awaish Kumar: Like, follow up and push on it, so that,
484 00:42:11.490 ⇒ 00:42:14.680 Awaish Kumar: Like, we need to close the modeling, at least.
485 00:42:15.300 ⇒ 00:42:16.540 Awaish Kumar: This week.
486 00:42:17.500 ⇒ 00:42:18.130 Ashwini Sharma: Okay.
487 00:42:20.630 ⇒ 00:42:26.219 Awaish Kumar: And then we have e-com modeling coming up, and then this is done.
488 00:42:26.970 ⇒ 00:42:33.340 Awaish Kumar: For Element, we… There is some ingestion work that I’m doing, right?
489 00:42:35.320 ⇒ 00:42:39.909 Awaish Kumar: Amazon needs to be ingested, and then these are still blocked.
490 00:42:40.660 ⇒ 00:42:41.470 Awaish Kumar: Okay.
491 00:42:41.780 ⇒ 00:42:46.610 Awaish Kumar: This one is actually… unblocked, but I’m…
492 00:42:47.900 ⇒ 00:42:53.619 Awaish Kumar: Yeah, let’s see how it goes. Apart from Element.
493 00:42:54.340 ⇒ 00:42:57.260 Awaish Kumar: Yeah, is there anything on Magic Spoon for you?
494 00:42:57.650 ⇒ 00:43:01.430 Ashwini Sharma: Magic Spoon, I’m documenting, how to…
495 00:43:01.570 ⇒ 00:43:04.390 Ashwini Sharma: Troubleshoot some of the failures in pipelines.
496 00:43:07.120 ⇒ 00:43:14.579 Awaish Kumar: Okay, for the pipeline, are we… are we done with those suggestions that I… that Utam actually gave us?
497 00:43:14.750 ⇒ 00:43:20.280 Awaish Kumar: Regarding, like, having this… Time-out thing only for…
498 00:43:22.000 ⇒ 00:43:29.759 Ashwini Sharma: No, the timeout needs to be investigated, right? I need to investigate that, timeout error that’s occurring on pipelines.
499 00:43:30.800 ⇒ 00:43:31.500 Ashwini Sharma: Wow.
500 00:43:31.500 ⇒ 00:43:36.980 Awaish Kumar: I’m asking about that error, that thing that we… like, any…
501 00:43:37.240 ⇒ 00:43:43.520 Awaish Kumar: pipeline that… that runs for more than 2 hours, it was actually failing, like, it was killing…
502 00:43:43.660 ⇒ 00:43:49.339 Awaish Kumar: We had workflow for scaling, so we wanted to increase time only for our Spins API, and not.
503 00:43:49.340 ⇒ 00:43:50.910 Ashwini Sharma: Oh, that is done, right? That is done.
504 00:43:50.910 ⇒ 00:43:52.590 Awaish Kumar: It’s done, and ain’t all of them?
505 00:43:53.420 ⇒ 00:43:56.270 Ashwini Sharma: All others die after 2 hours, pins will live for longer.
506 00:43:56.570 ⇒ 00:44:02.070 Awaish Kumar: for Sprint’s API, we already also had some optimization suggestions, right?
507 00:44:03.430 ⇒ 00:44:07.239 Awaish Kumar: like, in the Slack message, I don’t know if you…
508 00:44:08.110 ⇒ 00:44:15.570 Ashwini Sharma: Yeah, no, those are done, some of the basic optimizations, first things, like adding test cases and all those things, right?
509 00:44:15.780 ⇒ 00:44:18.980 Awaish Kumar: Okay, then now the question is, is it running smoothly, and…
510 00:44:18.980 ⇒ 00:44:26.510 Ashwini Sharma: See, it runs only once a month, right? So, we’ll check when it runs in May. This month, it’s going to run.
511 00:44:28.790 ⇒ 00:44:29.580 Awaish Kumar: Okay.
512 00:44:30.160 ⇒ 00:44:31.510 Awaish Kumar: But, like.
513 00:44:31.510 ⇒ 00:44:34.769 Ashwini Sharma: One click, let me check when it is going to run this one.
514 00:44:36.060 ⇒ 00:44:38.330 Ashwini Sharma: Spins, spin, spin, spins…
515 00:44:40.430 ⇒ 00:44:42.069 Awaish Kumar: Okay, so data loading.
516 00:44:42.210 ⇒ 00:44:42.870 Awaish Kumar: part.
517 00:44:42.870 ⇒ 00:44:43.710 Ashwini Sharma: No.
518 00:44:54.220 ⇒ 00:44:57.140 Ashwini Sharma: Yeah, I need to run the Spence pipeline today.
519 00:44:59.900 ⇒ 00:45:00.640 Awaish Kumar: Okay.
520 00:45:00.760 ⇒ 00:45:02.950 Awaish Kumar: So why you don’t have a ticket for that?
521 00:45:04.690 ⇒ 00:45:05.030 Awaish Kumar: Cutting?
522 00:45:05.030 ⇒ 00:45:07.009 Ashwini Sharma: Yeah, I should… I should create one ticket.
523 00:45:07.010 ⇒ 00:45:07.460 Awaish Kumar: Muslim?
524 00:45:07.460 ⇒ 00:45:14.079 Ashwini Sharma: I’ve not set it automatically. It’s not on a recurring schedule, right? There are different dates on which it should run.
525 00:45:16.080 ⇒ 00:45:17.779 Awaish Kumar: Hey, Pipeline 1.
526 00:45:18.000 ⇒ 00:45:20.890 Awaish Kumar: is being executed manually, that’s my question.
527 00:45:21.290 ⇒ 00:45:31.959 Ashwini Sharma: Yeah, right now it is manually, because there is no, either… like, what I can do is I can revert back to the original kind of schedule, where it runs every day, but it does not do anything.
528 00:45:32.360 ⇒ 00:45:35.550 Ashwini Sharma: It just checks for new data is there or not, and then.
529 00:45:35.550 ⇒ 00:45:38.180 Awaish Kumar: No, in the precept, don’t we have any…
530 00:45:38.500 ⇒ 00:45:41.369 Awaish Kumar: Like, we can schedule to run it once a month.
531 00:45:42.590 ⇒ 00:45:51.799 Ashwini Sharma: No, once a month we can do that, but it has to be on the… on the same date, right? So, if I’m running on 10th of every month, it should be on 10th of every month, right?
532 00:45:53.150 ⇒ 00:45:53.650 Awaish Kumar: We can…
533 00:45:53.650 ⇒ 00:45:59.460 Ashwini Sharma: But that is not how, spins works, right? So, for example, like.
534 00:45:59.880 ⇒ 00:46:04.770 Ashwini Sharma: on… on… in March, it was supposed to be run on 9th, right?
535 00:46:05.140 ⇒ 00:46:09.120 Ashwini Sharma: And in April, it was supposed to be run on 6th.
536 00:46:09.890 ⇒ 00:46:18.170 Ashwini Sharma: And similarly, like, in October, it will be run on 19, so it’s not a fixed date on which it should run. There are different dates.
537 00:46:18.170 ⇒ 00:46:18.510 Awaish Kumar: Yeah.
538 00:46:18.510 ⇒ 00:46:19.510 Ashwini Sharma: But every month.
539 00:46:19.810 ⇒ 00:46:23.129 Awaish Kumar: My question is, who is deciding these dates when it should run?
540 00:46:23.440 ⇒ 00:46:25.930 Ashwini Sharma: So they have published some information.
541 00:46:26.200 ⇒ 00:46:29.779 Ashwini Sharma: Spins on what date they will release the data.
542 00:46:30.090 ⇒ 00:46:32.680 Ashwini Sharma: Based on that, we’ll have to run it.
543 00:46:35.350 ⇒ 00:46:36.190 Awaish Kumar: Okay.
544 00:46:38.720 ⇒ 00:46:42.050 Awaish Kumar: And do we already have these dates, right?
545 00:46:42.070 ⇒ 00:46:45.310 Ashwini Sharma: We have it till the beginning of next year.
546 00:46:46.090 ⇒ 00:46:50.359 Awaish Kumar: So we can actually add the… in the cron job that…
547 00:46:50.850 ⇒ 00:46:53.320 Awaish Kumar: Exact dates when it should run, right?
548 00:46:54.690 ⇒ 00:46:57.360 Ashwini Sharma: Mmm… in the cron job…
549 00:46:58.690 ⇒ 00:47:02.279 Awaish Kumar: Yeah, like, in the con, there should… I don’t exactly…
550 00:47:02.620 ⇒ 00:47:10.009 Ashwini Sharma: It should have some kind of pattern, right? There is no pattern over here, based on which I can add that kind of date.
551 00:47:12.250 ⇒ 00:47:13.729 Awaish Kumar: Yeah, like, let me ask.
552 00:47:20.770 ⇒ 00:47:23.900 Ashwini Sharma: You can add a single date, definitely true.
553 00:47:23.900 ⇒ 00:47:24.650 Awaish Kumar: Let’s see…
554 00:47:24.650 ⇒ 00:47:29.209 Ashwini Sharma: You cannot add multiple dates on which Unless there is a…
555 00:47:29.590 ⇒ 00:47:34.730 Ashwini Sharma: Like, you can say run it on the second day of every month, right? That you can do in Chron.
556 00:47:36.340 ⇒ 00:47:38.960 Ashwini Sharma: But you cannot do something like run it on…
557 00:47:39.390 ⇒ 00:47:47.300 Ashwini Sharma: You know, 1st of June, and then run it on 29th of June, and then run it on 27th of July, right?
558 00:47:48.600 ⇒ 00:47:52.129 Awaish Kumar: Return crown first… what’d you say?
559 00:47:56.000 ⇒ 00:48:05.940 Awaish Kumar: Control… I’m gonna jump on a… D, you know… Month.
560 00:48:06.800 ⇒ 00:48:14.470 Awaish Kumar: But the date is… different for… Each month.
561 00:48:15.390 ⇒ 00:48:21.110 Awaish Kumar: Let’s see… Okay, grown can’t handle, right?
562 00:48:24.040 ⇒ 00:48:28.089 Awaish Kumar: But, Prefect doesn’t also not have this feature.
563 00:48:28.090 ⇒ 00:48:43.100 Ashwini Sharma: No, no, it’s different dates on which. So, what I can do is generally, like, what I had done earlier was the pipeline runs every day, right? It just executes some basic queries, and then decides not to run, because there is no new data that is released.
564 00:48:43.570 ⇒ 00:48:49.170 Ashwini Sharma: or what I can do is, like, I can feed these dates.
565 00:48:49.540 ⇒ 00:48:58.789 Ashwini Sharma: directly into the system, release dates, but this has to be updated every time new release dates are released from SPINs, right? Which is, again.
566 00:48:59.580 ⇒ 00:49:06.089 Ashwini Sharma: like, additional PRs and changes, like, manual intervention would be required, right? Somebody has to create a PR and do it.
567 00:49:07.550 ⇒ 00:49:14.019 Ashwini Sharma: Or, it’s just once a month, you can just click run, and then it should take care of it.
568 00:49:15.530 ⇒ 00:49:19.640 Awaish Kumar: But in any case, it is manual, right? You have to remember the date and run it.
569 00:49:19.640 ⇒ 00:49:21.230 Ashwini Sharma: Yeah, you have to remember the date, right?
570 00:49:21.230 ⇒ 00:49:26.560 Awaish Kumar: If you forgot today, right, you… they could have, escalated it.
571 00:49:27.840 ⇒ 00:49:36.699 Awaish Kumar: Now it’s, like, you remember it, it’s now in your hands that you can actually run it and inform the client. But what if you forgot today?
572 00:49:38.420 ⇒ 00:49:42.670 Ashwini Sharma: Yeah, so that’s an issue with the manual thing, right?
573 00:49:49.770 ⇒ 00:50:00.440 Awaish Kumar: But is there any issue? We run it every day and check if there is no data for release date, then, like, don’t execute everything, just check further if it is in the release date.
574 00:50:02.730 ⇒ 00:50:04.680 Awaish Kumar: Did you see any issues with that?
575 00:50:06.200 ⇒ 00:50:15.120 Ashwini Sharma: So, when the release dates change, right, somebody will have to update that, right? Right now, I have release dates only up till 2027, January.
576 00:50:15.380 ⇒ 00:50:16.170 Ashwini Sharma: Right?
577 00:50:16.320 ⇒ 00:50:22.909 Ashwini Sharma: Now, after 6 months, or maybe 8 months down the line, some Spence is going to release the new release dates, right?
578 00:50:23.550 ⇒ 00:50:30.740 Awaish Kumar: I get your point, but I’m saying, is there no way to… is there any API we can hit and figure out if there is new data?
579 00:50:33.210 ⇒ 00:50:36.150 Ashwini Sharma: Mmm… there could be,
580 00:50:37.220 ⇒ 00:50:41.820 Ashwini Sharma: that, like, it’s a static file, HTML file over there, where they publish the states.
581 00:50:41.820 ⇒ 00:50:42.510 Awaish Kumar: Okay.
582 00:50:42.510 ⇒ 00:50:46.860 Ashwini Sharma: You have to crawl it, and then get the dates from a table, and…
583 00:50:47.830 ⇒ 00:50:58.119 Awaish Kumar: Okay, so there’s no… nothing, okay, so let’s… Oh… Let’s do that,
584 00:50:58.550 ⇒ 00:51:01.870 Awaish Kumar: Let’s see if you… if there is any such kind of endpoint.
585 00:51:02.300 ⇒ 00:51:11.150 Awaish Kumar: There you can actually hit without running the full extract and load, if there is anything that can help us figure out the data, if we have data for this month.
586 00:51:11.660 ⇒ 00:51:12.290 Ashwini Sharma: Okay.
587 00:51:13.280 ⇒ 00:51:21.849 Awaish Kumar: That’s just a… quick spike on that, that’s all. There’s nothing else for magic spoon here, right?
588 00:51:22.130 ⇒ 00:51:22.780 Ashwini Sharma: No.
589 00:51:23.640 ⇒ 00:51:24.770 Awaish Kumar: Okay, so…
590 00:51:25.130 ⇒ 00:51:32.339 Awaish Kumar: just remember that there might be some tickets coming up. So, Element, you already have some tickets. For CTA, you already have something.
591 00:51:32.640 ⇒ 00:51:33.240 Ashwini Sharma: Yeah.
592 00:51:33.600 ⇒ 00:51:36.580 Awaish Kumar: I think just focus on that. For Eden, maybe I will…
593 00:51:37.340 ⇒ 00:51:40.189 Awaish Kumar: I will take it, if there is anything. Okay.
594 00:51:40.190 ⇒ 00:51:41.610 Ashwini Sharma: Okay, alright.
595 00:51:43.210 ⇒ 00:51:44.230 Awaish Kumar: Okay, thank you.
596 00:51:45.670 ⇒ 00:51:46.350 Ashwini Sharma: Right.