Meeting Title: PP2G-Standup Date: 2024-06-14 Meeting participants: Nicolas Sucari, Ryan Luke Daque, Patrick Trainer


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1 00:01:07.470 00:01:08.419 Ryan Luke Daque: Hello! Hello!

2 00:01:12.180 00:01:12.740 Patrick Trainer: Go.

3 00:01:15.150 00:01:17.650 Ryan Luke Daque: Hey, man! Nice, nice, nice one, with a

4 00:01:18.030 00:01:20.157 Ryan Luke Daque: training period of thing.

5 00:01:20.860 00:01:21.650 Patrick Trainer: Yeah, it’s, it’s.

6 00:01:21.650 00:01:25.329 Ryan Luke Daque: The other one, though, like the detection period. So it’s.

7 00:01:25.330 00:01:25.870 Patrick Trainer: Said that.

8 00:01:25.870 00:01:26.769 Ryan Luke Daque: Looks like.

9 00:01:26.770 00:01:28.780 Patrick Trainer: The detection period.

10 00:01:29.460 00:01:30.190 Ryan Luke Daque: Get.

11 00:01:30.190 00:01:30.990 Patrick Trainer: It’s

12 00:01:32.310 00:01:36.700 Patrick Trainer: it’s pretty confusing, honestly like.

13 00:01:37.690 00:01:40.830 Patrick Trainer: cause I like it’ll use

14 00:01:41.050 00:01:42.910 Patrick Trainer: the last week.

15 00:01:43.310 00:01:48.569 Patrick Trainer: Ask the bounds of like of finding the anomaly right.

16 00:01:48.570 00:01:49.820 Ryan Luke Daque: Right so, and then the.

17 00:01:49.820 00:01:53.140 Patrick Trainer: Training period is how far it looks back

18 00:01:53.320 00:01:57.329 Patrick Trainer: to understand. Like the the yeah, the pattern.

19 00:01:57.330 00:01:58.979 Ryan Luke Daque: Calculate the upper lower.

20 00:01:58.980 00:02:02.279 Patrick Trainer: Yeah, and then, and then it counts each row

21 00:02:03.360 00:02:05.629 Patrick Trainer: in into a time bucket.

22 00:02:06.840 00:02:07.650 Ryan Luke Daque: Right.

23 00:02:08.289 00:02:09.409 Patrick Trainer: So it’s

24 00:02:09.869 00:02:10.429 Patrick Trainer: like.

25 00:02:10.430 00:02:12.170 Ryan Luke Daque: Shouldn’t. Shouldn’t we? Like.

26 00:02:12.910 00:02:14.010 Ryan Luke Daque: yeah, yeah, go ahead.

27 00:02:14.010 00:02:16.539 Patrick Trainer: So I was, gonna say, the 3 are

28 00:02:16.770 00:02:18.110 Patrick Trainer: really like

29 00:02:18.790 00:02:20.330 Patrick Trainer: interconnected.

30 00:02:20.500 00:02:22.889 Patrick Trainer: And it’s like, if you change one.

31 00:02:22.920 00:02:24.929 Patrick Trainer: it kind of changes

32 00:02:25.370 00:02:28.979 Patrick Trainer: the like. The the other 2,

33 00:02:29.100 00:02:30.909 Patrick Trainer: or at least it it’s

34 00:02:31.660 00:02:32.730 Patrick Trainer: there’s

35 00:02:33.990 00:02:36.240 Patrick Trainer: I’m thinking of it as like

36 00:02:38.000 00:02:40.550 Patrick Trainer: there’s 3 variables in this function.

37 00:02:40.900 00:02:41.540 Patrick Trainer: and.

38 00:02:41.540 00:02:42.010 Ryan Luke Daque: Right.

39 00:02:42.010 00:02:43.350 Patrick Trainer: The output

40 00:02:44.400 00:02:52.159 Patrick Trainer: which is like a standard deviation, right is going to be impacted by one of those 3 variables.

41 00:02:52.160 00:02:53.050 Ryan Luke Daque: Right.

42 00:02:53.050 00:02:54.219 Patrick Trainer: And so

43 00:02:54.970 00:03:00.370 Patrick Trainer: we have the time buckets which is going, which is pretty much like

44 00:03:00.520 00:03:02.829 Patrick Trainer: the line, and like.

45 00:03:02.830 00:03:03.420 Nicolas Sucari: Guys.

46 00:03:03.960 00:03:04.420 Patrick Trainer: Hey.

47 00:03:04.740 00:03:06.980 Patrick Trainer: yeah, it’s yeah. It’s the

48 00:03:07.090 00:03:10.339 Patrick Trainer: the X axis. Yeah, where it’s plotted on the X,

49 00:03:10.650 00:03:13.330 Patrick Trainer: and then the training period

50 00:03:13.630 00:03:15.060 Patrick Trainer: is

51 00:03:16.889 00:03:22.159 Patrick Trainer: irrelevant to that. Yeah, it’s it’s just like how how long

52 00:03:22.640 00:03:26.809 Patrick Trainer: the time buck or or how long they’re going to bucket time

53 00:03:28.290 00:03:32.690 Patrick Trainer: and then the detection period is, it’s it’s like a moving

54 00:03:33.428 00:03:36.430 Patrick Trainer: like a moving period of 7 days.

55 00:03:37.040 00:03:40.059 Patrick Trainer: or like a moving period of X amount of

56 00:03:40.450 00:03:43.359 Patrick Trainer: days, weeks, months, etc.

57 00:03:43.650 00:03:46.190 Patrick Trainer: And so like, if you

58 00:03:46.540 00:03:49.099 Patrick Trainer: increase the detection period.

59 00:03:51.730 00:03:54.559 Patrick Trainer: it’s going to be less sensitive

60 00:03:55.220 00:03:56.400 Patrick Trainer: to

61 00:03:56.720 00:03:58.090 Patrick Trainer: anomalies.

62 00:03:58.960 00:04:00.910 Patrick Trainer: Because, yeah, that.

63 00:04:01.780 00:04:02.880 Patrick Trainer: And

64 00:04:03.940 00:04:05.520 Patrick Trainer: and then the

65 00:04:07.400 00:04:16.819 Patrick Trainer: right. And so then the anomaly sensitivity is where you’re setting either 1, 2, or 3 standard deviations away.

66 00:04:16.940 00:04:19.889 Patrick Trainer: So that’s gonna set the like. The gray.

67 00:04:20.170 00:04:20.980 Ryan Luke Daque: Right.

68 00:04:21.500 00:04:25.140 Patrick Trainer: Okay, I need to. I need to draw this out. I think.

69 00:04:25.140 00:04:26.920 Ryan Luke Daque: Yeah, I, think I I think.

70 00:04:26.920 00:04:30.300 Patrick Trainer: Figured it out, or at least I understand it now.

71 00:04:31.000 00:04:36.679 Ryan Luke Daque: The detection period. Looks like I’m I’m reading like what the it’s saying here. So like, currently

72 00:04:36.710 00:04:39.759 Ryan Luke Daque: you moved it up from 2 days to 7 days. Right?

73 00:04:40.080 00:04:41.359 Ryan Luke Daque: So it does. Detection.

74 00:04:41.360 00:04:45.440 Patrick Trainer: Yeah. Or I moved it to one week. So it’s always good. Yeah.

75 00:04:45.460 00:04:47.829 Patrick Trainer: it’s always gonna move one week at a time.

76 00:04:48.580 00:04:53.689 Ryan Luke Daque: Yeah, but that would mean that if there was any an anomaly within that week.

77 00:04:53.960 00:04:55.679 Ryan Luke Daque: then it would always be

78 00:04:56.280 00:04:58.350 Ryan Luke Daque: flagged as an anomaly.

79 00:04:59.220 00:05:02.370 Ryan Luke Daque: right, even even if today was within the bounds.

80 00:05:02.976 00:05:05.400 Patrick Trainer: So. So let’s say

81 00:05:05.440 00:05:06.620 Patrick Trainer: it’s

82 00:05:07.230 00:05:09.659 Patrick Trainer: like we’ll start the week on

83 00:05:09.820 00:05:16.667 Patrick Trainer: one and go through 7 right? If we’re only on day 2 or day. 3.

84 00:05:17.500 00:05:19.020 Patrick Trainer: It will

85 00:05:19.240 00:05:20.500 Patrick Trainer: do like

86 00:05:20.760 00:05:27.160 Patrick Trainer: day, minus one, minus 2, minus 3, until you get a period of one week.

87 00:05:27.160 00:05:28.410 Ryan Luke Daque: 7, right.

88 00:05:28.410 00:05:29.140 Patrick Trainer: Right?

89 00:05:30.050 00:05:35.380 Patrick Trainer: And so and so there’s kind of like a hierarchy. So it’s gonna go.

90 00:05:35.440 00:05:36.930 Patrick Trainer: what training

91 00:05:37.960 00:05:38.940 Patrick Trainer: period?

92 00:05:39.050 00:05:41.970 Patrick Trainer: That’s kinda like the the the top level.

93 00:05:42.290 00:05:44.020 Patrick Trainer: And then it’s gonna go

94 00:05:44.580 00:05:46.160 Patrick Trainer: detection period

95 00:05:47.890 00:05:49.000 Patrick Trainer: detect.

96 00:05:49.320 00:05:52.019 Patrick Trainer: And then and then it’s going to go

97 00:05:53.460 00:05:55.039 Patrick Trainer: to the time bucket.

98 00:05:58.510 00:05:59.859 Patrick Trainer: I think that’s how it’s

99 00:06:00.120 00:06:00.930 Patrick Trainer: yeah.

100 00:06:01.980 00:06:03.360 Patrick Trainer: And so

101 00:06:03.690 00:06:09.670 Patrick Trainer: I’m I’m like drawing on a sticky note right now, and and also thinking out loud. So

102 00:06:10.210 00:06:13.359 Patrick Trainer: yeah, the training period relates to

103 00:06:13.950 00:06:16.410 Patrick Trainer: the actual data in the table.

104 00:06:17.480 00:06:18.300 Patrick Trainer: Brian.

105 00:06:18.510 00:06:20.740 Patrick Trainer: And then the time bucket.

106 00:06:22.330 00:06:25.020 Patrick Trainer: Okay? So yeah, there’s like.

107 00:06:25.400 00:06:28.840 Patrick Trainer: training period is over the entire thing.

108 00:06:29.030 00:06:31.089 Patrick Trainer: And then there’s the

109 00:06:31.240 00:06:32.510 Patrick Trainer: the plots

110 00:06:32.970 00:06:36.020 Patrick Trainer: on the days like the circles.

111 00:06:40.370 00:06:43.649 Patrick Trainer: And then so that’s the time bucket.

112 00:06:44.250 00:06:44.920 Ryan Luke Daque: Right.

113 00:06:45.060 00:06:46.250 Patrick Trainer: And then

114 00:06:47.790 00:06:49.749 Patrick Trainer: we have the

115 00:06:50.170 00:06:51.520 Patrick Trainer: but anomaly

116 00:06:51.940 00:06:55.260 Patrick Trainer: sensitivity, which is like the gray.

117 00:06:55.260 00:06:57.350 Ryan Luke Daque: Standard. That’s standard TV.

118 00:06:57.350 00:07:01.040 Patrick Trainer: Yeah, the standard deviation. So like the acceptable

119 00:07:01.430 00:07:03.330 Patrick Trainer: range of values.

120 00:07:03.330 00:07:03.775 Ryan Luke Daque: Yeah.

121 00:07:04.780 00:07:06.430 Patrick Trainer: And then the

122 00:07:06.440 00:07:08.179 Patrick Trainer: look back period.

123 00:07:08.390 00:07:10.679 Patrick Trainer: The detection period

124 00:07:12.210 00:07:12.890 Patrick Trainer: is

125 00:07:13.010 00:07:15.270 Patrick Trainer: that, like orange

126 00:07:17.330 00:07:19.449 Patrick Trainer: kind of like highlighted area.

127 00:07:19.450 00:07:20.140 Ryan Luke Daque: Right.

128 00:07:20.380 00:07:21.310 Patrick Trainer: So

129 00:07:22.460 00:07:23.590 Patrick Trainer: I don’t know

130 00:07:24.210 00:07:26.070 Patrick Trainer: how well you can

131 00:07:26.500 00:07:29.929 Patrick Trainer: see this. Yeah. But like that.

132 00:07:30.120 00:07:31.280 Patrick Trainer: that’s it.

133 00:07:32.500 00:07:38.160 Patrick Trainer: So here. And I’m gonna label it. Now, wait. I gotta draw some

134 00:07:39.050 00:07:41.270 Patrick Trainer: lines on there.

135 00:07:48.990 00:07:52.690 Ryan Luke Daque: Says here that the detection peer, if the detection period

136 00:07:52.810 00:07:58.550 Ryan Luke Daque: is set to 2 days, only data points in the last 2 days will be included in the detection

137 00:07:58.930 00:08:01.599 Ryan Luke Daque: period, and could be flagged, anomalous.

138 00:08:01.600 00:08:02.570 Patrick Trainer: Right.

139 00:08:02.570 00:08:05.359 Ryan Luke Daque: So if it’s 7 days, or if it’s 1 week, then.

140 00:08:06.080 00:08:08.439 Patrick Trainer: It’s actually giving a chance to look

141 00:08:09.460 00:08:18.260 Patrick Trainer: over the past 7 days. So like, I’m kind of thinking of it like a moving average. So art is is the moving average is the moving average.

142 00:08:18.260 00:08:18.850 Ryan Luke Daque: -

143 00:08:18.850 00:08:26.529 Patrick Trainer: In the last 2 days? Or is the moving average in the last 7 days? And where are we in relation to that moving moving average.

144 00:08:26.530 00:08:27.270 Ryan Luke Daque: Versions.

145 00:08:27.520 00:08:30.530 Ryan Luke Daque: So it’s not the exact point within the

146 00:08:31.080 00:08:33.230 Ryan Luke Daque: within the range, but the

147 00:08:33.799 00:08:35.800 Ryan Luke Daque: average of the last 7 days.

148 00:08:35.809 00:08:37.019 Patrick Trainer: Right, right.

149 00:08:37.020 00:08:38.139 Ryan Luke Daque: Give me the link

150 00:08:39.299 00:08:40.250 Ryan Luke Daque: makes sense.

151 00:08:40.250 00:08:41.120 Patrick Trainer: Right?

152 00:08:41.440 00:08:43.210 Patrick Trainer: Yeah, because, like.

153 00:08:44.220 00:08:45.990 Patrick Trainer: if it used

154 00:08:49.810 00:08:52.700 Patrick Trainer: the and I’m trying to think of like the

155 00:08:52.950 00:08:58.139 Patrick Trainer: the, the the counter to it, like, if it used the entire data set.

156 00:09:03.030 00:09:04.520 Ryan Luke Daque: It’s always, if you really use.

157 00:09:04.520 00:09:05.170 Patrick Trainer: Yeah, it.

158 00:09:05.170 00:09:06.839 Ryan Luke Daque: It’s it’s always the mean right.

159 00:09:06.840 00:09:09.549 Patrick Trainer: Yeah, it’s it. Yeah, it’s always gonna be in the middle. Yeah.

160 00:09:09.550 00:09:10.220 Ryan Luke Daque: Yeah.

161 00:09:10.760 00:09:15.130 Patrick Trainer: Yeah, because you’re not able to compare any period to

162 00:09:15.250 00:09:16.650 Patrick Trainer: another period.

163 00:09:17.030 00:09:18.280 Patrick Trainer: Perfect sense.

164 00:09:18.310 00:09:19.450 Patrick Trainer: So

165 00:09:20.460 00:09:21.720 Patrick Trainer: training.

166 00:09:23.330 00:09:24.120 Patrick Trainer: I’m late

167 00:09:24.250 00:09:26.220 Patrick Trainer: thinking out rubber ducking

168 00:09:26.690 00:09:27.470 Patrick Trainer: and

169 00:09:27.640 00:09:28.470 Patrick Trainer: all

170 00:09:29.840 00:09:30.610 Patrick Trainer: donna

171 00:09:31.693 00:09:32.719 Patrick Trainer: and then

172 00:09:33.430 00:09:34.530 Patrick Trainer: the

173 00:09:37.020 00:09:38.190 Patrick Trainer: detection.

174 00:09:39.920 00:09:42.494 Nicolas Sucari: It was a tricky one to figure out right.

175 00:09:42.940 00:09:47.970 Patrick Trainer: Yeah, there, like, I thought their docs were good.

176 00:09:48.790 00:09:51.615 Patrick Trainer: But they’re they’re actually not.

177 00:09:52.180 00:09:52.950 Nicolas Sucari: Half level.

178 00:09:52.950 00:09:58.790 Patrick Trainer: Like some some of them are, but some of them are, are actually really bad.

179 00:09:59.450 00:10:02.609 Ryan Luke Daque: Training the parameters is like, not as

180 00:10:03.910 00:10:05.109 Ryan Luke Daque: where I am.

181 00:10:05.500 00:10:10.569 Patrick Trainer: Right? So let’s there’s like a crazy example. It was.

182 00:10:12.570 00:10:14.520 Patrick Trainer: I think it’s detection period.

183 00:10:14.930 00:10:15.890 Patrick Trainer: Laugh.

184 00:10:18.166 00:10:20.713 Patrick Trainer: Yeah, how it works

185 00:10:21.770 00:10:27.106 Patrick Trainer: detection period defines the detection period like Oh, no, like

186 00:10:29.338 00:10:31.609 Patrick Trainer: you don’t say like.

187 00:10:32.140 00:10:32.670 Ryan Luke Daque: Thanks.

188 00:10:33.405 00:10:33.780 Patrick Trainer: Thing

189 00:10:36.860 00:10:38.200 Patrick Trainer: honestly like

190 00:10:39.775 00:10:43.570 Patrick Trainer: wanna like raise an issue on that and be like this

191 00:10:43.880 00:10:45.290 Patrick Trainer: sucks.

192 00:10:50.630 00:10:54.470 Nicolas Sucari: Okay, I don’t think anyone else is gonna join

193 00:10:54.640 00:10:56.150 Nicolas Sucari: to this meeting today.

194 00:10:56.901 00:11:05.208 Nicolas Sucari: But just to understand Ryan, you already fixed like Kim’s report right like that. She’s working fine. I can see the tables.

195 00:11:05.990 00:11:16.703 Nicolas Sucari: so that’s working perfectly. I can. Probably we can mark that task that we have there in progress? Can we move it to done? Or, yeah, right?

196 00:11:17.100 00:11:18.709 Ryan Luke Daque: We can move that to done.

197 00:11:19.100 00:11:20.310 Nicolas Sucari: Home which is done

198 00:11:20.620 00:11:22.419 Nicolas Sucari: and the one that you have.

199 00:11:22.460 00:11:31.009 Nicolas Sucari: Patrick, about these errors. What do you want to do about that? Are you still like. Try trying to send something, you know, for request, or it’s already.

200 00:11:31.010 00:11:35.420 Patrick Trainer: Yeah, I’ve I’ve got a pull request open and I’m just like

201 00:11:35.870 00:11:38.390 Patrick Trainer: making the changes on

202 00:11:39.260 00:11:45.620 Patrick Trainer: all of the the volume and not or volume tests that are already there, so that, like

203 00:11:45.650 00:11:48.380 Patrick Trainer: that should be done in like the next half hour.

204 00:11:49.230 00:11:50.380 Nicolas Sucari: Excellent. Okay.

205 00:11:50.680 00:12:05.239 Nicolas Sucari: great and then I don’t know if you have the chance. Yesterday to grab some time with each of that with each of us. Well, I don’t need product to like set that key authorization for Snowflake. I think it was.

206 00:12:06.430 00:12:07.229 Nicolas Sucari: Thanks for what it was.

207 00:12:07.230 00:12:08.940 Patrick Trainer: Yes, I

208 00:12:10.290 00:12:18.839 Patrick Trainer: we’ll probably just reach out to you, you and Ryan today. The rest I can hit like Monday or Tuesday.

209 00:12:19.510 00:12:20.130 Patrick Trainer: Okay.

210 00:12:20.130 00:12:21.650 Nicolas Sucari: Depending, depending on.

211 00:12:21.650 00:12:23.700 Patrick Trainer: Yeah, depending on when people are back.

212 00:12:23.700 00:12:27.159 Nicolas Sucari: This is for Snowflake, or for what is that? Do we need.

213 00:12:27.160 00:12:29.729 Patrick Trainer: Yeah, it’s for Snowflake. It’s like, instead of

214 00:12:30.738 00:12:34.030 Patrick Trainer: like, when you’re developing like, Dbt.

215 00:12:34.450 00:12:34.830 Nicolas Sucari: Yeah.

216 00:12:34.830 00:12:39.149 Patrick Trainer: Meeting to connect to to Snowflake. You’ll use

217 00:12:40.250 00:12:47.190 Patrick Trainer: a a key pair like a a graphic key instead of a password. It’s just more secure.

218 00:12:48.010 00:12:51.051 Nicolas Sucari: Perfect. Okay, I don’t think.

219 00:12:52.290 00:12:56.260 Nicolas Sucari: yeah, I don’t know if I’m gonna need. I’m I mean, like, I’m using

220 00:12:56.430 00:13:01.310 Nicolas Sucari: Utam’s user to snowflake because I I don’t have like a snowflake user. I think.

221 00:13:01.310 00:13:04.770 Patrick Trainer: Oh, you don’t! I’ll create. I’ll create you user. You should.

222 00:13:04.770 00:13:05.460 Nicolas Sucari: Yeah, okay.

223 00:13:05.460 00:13:06.559 Patrick Trainer: Yeah, you should have your own.

224 00:13:06.896 00:13:09.590 Nicolas Sucari: I was using you them because I was.

225 00:13:09.590 00:13:09.940 Patrick Trainer: Yeah.

226 00:13:09.940 00:13:14.420 Nicolas Sucari: Recording or doing anything yet. So, but yeah, if you can create me a user, and it doesn’t like.

227 00:13:14.550 00:13:15.379 Nicolas Sucari: yeah, yeah.

228 00:13:15.380 00:13:16.689 Patrick Trainer: Yeah. Will you?

229 00:13:16.690 00:13:17.040 Nicolas Sucari: Or something.

230 00:13:17.040 00:13:20.439 Patrick Trainer: Create like just a a ticket for that.

231 00:13:20.710 00:13:22.490 Patrick Trainer: So I don’t lose it.

232 00:13:23.960 00:13:24.935 Nicolas Sucari: Totally.

233 00:13:26.050 00:13:27.920 Nicolas Sucari: I’m gonna add even backlog

234 00:13:28.880 00:13:32.070 Nicolas Sucari: brain with disease brain for objections. Probably

235 00:13:40.020 00:13:41.210 Nicolas Sucari: we. Okay.

236 00:13:42.070 00:13:43.250 Nicolas Sucari: and then.

237 00:13:45.000 00:13:48.604 Nicolas Sucari: yeah, I’m gonna assign to you, but

238 00:13:51.260 00:13:52.590 Nicolas Sucari: excellent.

239 00:13:53.780 00:14:02.389 Nicolas Sucari: Yeah. And then I don’t. I don’t know if we have like anything else for now, Ryan. I don’t know if you have like any other thing that you need to be working on.

240 00:14:03.090 00:14:05.150 Nicolas Sucari: Are you working on something else?

241 00:14:05.520 00:14:08.150 Ryan Luke Daque: I’m I’m like, Yeah, I’m working on the

242 00:14:08.290 00:14:09.260 Ryan Luke Daque: slack.

243 00:14:10.440 00:14:10.970 Nicolas Sucari: Yeah, okay.

244 00:14:10.970 00:14:17.149 Ryan Luke Daque: Files integration. I’m still like trying to research like what the best way to do that. So I was like thinking, maybe

245 00:14:17.870 00:14:27.050 Ryan Luke Daque: creating a lambda function or something, just to load all the Csp data into Snowflake. But yeah, that’s I’m still researching, basically the best

246 00:14:27.700 00:14:33.110 Ryan Luke Daque: way to do that. Unless maybe, Patrick, you have any ideas like what? What the best way to do that would be.

247 00:14:33.684 00:14:38.029 Patrick Trainer: Sorry I missed the i i i missed the question.

248 00:14:38.030 00:14:42.189 Ryan Luke Daque: Oh, no problem. It’s like the I’m starting to research on

249 00:14:42.270 00:14:48.390 Ryan Luke Daque: like integrating the slack files that we are getting. The Csv files excel, S files into

250 00:14:49.665 00:14:50.589 Ryan Luke Daque: snowflake.

251 00:14:50.890 00:14:53.229 Ryan Luke Daque: Create that data pipeline for that one.

252 00:14:53.230 00:14:53.650 Patrick Trainer: Right

253 00:14:54.490 00:14:59.119 Ryan Luke Daque: Like thinking, maybe just create a lambda function, or whatever be below that. But.

254 00:14:59.450 00:15:02.089 Patrick Trainer: So where are the

255 00:15:02.160 00:15:04.140 Patrick Trainer: files like?

256 00:15:04.630 00:15:05.999 Patrick Trainer: Where did those live?

257 00:15:06.220 00:15:08.330 Ryan Luke Daque: They’re in the Channel. They they’re

258 00:15:08.490 00:15:11.060 Ryan Luke Daque: and internal.

259 00:15:11.060 00:15:18.790 Nicolas Sucari: Process today is, I think, like full parts. We are receiving these from post pilot. They already like they.

260 00:15:19.060 00:15:26.520 Nicolas Sucari: There is like an automatic email that it is sent to us, and we receive that Csv file through a Channel slack channel. So we have that.

261 00:15:26.520 00:15:28.790 Patrick Trainer: Yeah, yeah, there’s it’s an email. That’s right.

262 00:15:28.790 00:15:29.130 Nicolas Sucari: Yeah.

263 00:15:29.465 00:15:35.160 Patrick Trainer: Can we? Can we, from post pilot? Can we just dump those into an S. 3 bucket.

264 00:15:35.800 00:15:42.159 Ryan Luke Daque: I’m not sure if the all of these are from post Pilot. Maybe these are. Some of these are like scheduled reports.

265 00:15:43.030 00:15:43.520 Ryan Luke Daque: February.

266 00:15:43.520 00:15:45.010 Nicolas Sucari: Where they are coming from.

267 00:15:45.460 00:15:47.418 Ryan Luke Daque: Like one is from attentive

268 00:15:48.210 00:15:51.560 Patrick Trainer: Right? I mean, we can like it

269 00:15:52.800 00:15:56.040 Patrick Trainer: regardless of like the the specifics like

270 00:15:56.120 00:16:00.060 Patrick Trainer: is, can we get those? If we can get those Csv’s

271 00:16:00.110 00:16:10.879 Patrick Trainer: into an S. 3 bucket we can set up snow pipe, or just like a an S. 3 integration, and then, whenever anything is loaded.

272 00:16:12.330 00:16:16.489 Patrick Trainer: or or like dropped into into S. 3. It’ll trigger.

273 00:16:16.490 00:16:16.890 Nicolas Sucari: Create it.

274 00:16:16.890 00:16:19.789 Patrick Trainer: Snow pipe, and it’ll load it. Load it into snowflake.

275 00:16:20.225 00:16:23.240 Patrick Trainer: That’s like that’s like snowflake bread and butter.

276 00:16:23.670 00:16:31.349 Ryan Luke Daque: Yeah, that’s what I was thinking, maybe because there’s no easy way to get like this from slack to an S 3 bucket. So maybe I’ll have to create this.

277 00:16:31.350 00:16:32.910 Patrick Trainer: They’re they’re they’re

278 00:16:33.600 00:16:34.520 Patrick Trainer: might

279 00:16:35.530 00:16:38.132 Patrick Trainer: the like. There’s

280 00:16:45.640 00:16:47.840 Patrick Trainer: There might be like either.

281 00:16:48.970 00:16:59.409 Nicolas Sucari: But like like the the best way to do this is like to understand if we can do it directly from post pipelot, attentive, or any other tool directly into Snowflake. Right like that’s like the.

282 00:16:59.410 00:17:03.210 Patrick Trainer: Yeah, yeah, that’s that’s that’s that’s the right way to do it. Yeah.

283 00:17:03.490 00:17:04.020 Patrick Trainer: yeah,

284 00:17:05.390 00:17:07.421 Ryan Luke Daque: Yeah, otherwise, yeah, we there’s.

285 00:17:07.760 00:17:11.769 Patrick Trainer: Like, I imagine. I imagine there’s some like aws.

286 00:17:12.089 00:17:13.040 Patrick Trainer: the.

287 00:17:13.490 00:17:14.740 Nicolas Sucari: Trick, yeah, yeah.

288 00:17:14.740 00:17:19.720 Patrick Trainer: Yeah, like service. Like, I was thinking, like ses, like, simple email service.

289 00:17:19.720 00:17:20.919 Ryan Luke Daque: Or like the queue.

290 00:17:20.920 00:17:21.920 Patrick Trainer: New service.

291 00:17:22.069 00:17:24.820 Patrick Trainer: or even SMS,

292 00:17:27.869 00:17:30.989 Patrick Trainer: or we can just like hack something together that

293 00:17:31.030 00:17:35.140 Patrick Trainer: horses emails. And then just like forwards

294 00:17:36.880 00:17:49.868 Patrick Trainer: like, I love using app script for these sorts of things, but like, if it just, we send it to a gmail inbox that only receives those we can filter that, and then.

295 00:17:50.554 00:17:50.870 Ryan Luke Daque: Mean.

296 00:17:51.270 00:17:54.720 Patrick Trainer: And and then like route it to to S. 3.

297 00:17:54.720 00:17:55.840 Ryan Luke Daque: S. 3. Because that.

298 00:17:55.840 00:17:58.240 Patrick Trainer: Like, that’s yeah. That’s pretty simple.

299 00:17:59.430 00:18:01.939 Patrick Trainer: Yeah, we just like post it to S, 3.

300 00:18:02.890 00:18:04.090 Nicolas Sucari: And and without.

301 00:18:04.490 00:18:07.729 Patrick Trainer: Plan B or plan C, so.

302 00:18:08.490 00:18:13.529 Ryan Luke Daque: But then we’ll have to do some data transformation to make sure there’s no duplicates and stuff like that right?

303 00:18:13.690 00:18:15.858 Ryan Luke Daque: Or we do that in Snowflake, in in.

304 00:18:16.130 00:18:20.500 Patrick Trainer: Yeah, yeah, I think we would do that and stuff like, yeah, we we dump it raw.

305 00:18:20.620 00:18:22.959 Patrick Trainer: And then, yeah, and then stage it.

306 00:18:23.690 00:18:24.340 Ryan Luke Daque: Okay.

307 00:18:25.010 00:18:26.019 Ryan Luke Daque: yeah, I’ll I’ll.

308 00:18:26.020 00:18:26.450 Patrick Trainer: Or.

309 00:18:26.450 00:18:27.080 Ryan Luke Daque: And mark.

310 00:18:27.250 00:18:28.290 Ryan Luke Daque: or.

311 00:18:29.370 00:18:32.539 Patrick Trainer: If we could send it to 5 Tran, because there’s a.

312 00:18:32.820 00:18:34.237 Ryan Luke Daque: Oh, right! Connector.

313 00:18:35.450 00:18:39.150 Patrick Trainer: Ye? Oh, yeah, like we can. There’s a connector, and we can

314 00:18:39.550 00:18:40.940 Patrick Trainer: like if there’s a post.

315 00:18:41.880 00:18:47.819 Patrick Trainer: If there’s a post pilot connector, and then we can set like an SS. 3 as a destination.

316 00:18:50.460 00:19:03.000 Nicolas Sucari: So we need to understand, like the documentation from post pilots and intent to understand, if that is possible, right like, let’s try to do it like the right way directly from both pilots attentive to free and then to Snowflake.

317 00:19:03.170 00:19:10.280 Nicolas Sucari: And if that’s not possible, we can go with Plan B, and see how we can do it in Google Drive, or something like that. What do you think.

318 00:19:10.800 00:19:11.340 Patrick Trainer: Right.

319 00:19:11.340 00:19:12.100 Nicolas Sucari: Yeah, there’s also.

320 00:19:12.100 00:19:13.580 Patrick Trainer: Some other products.

321 00:19:15.290 00:19:18.750 Patrick Trainer: that we can go through. But yeah, let’s let’s start with that step

322 00:19:19.100 00:19:21.399 Patrick Trainer: before we yeah get ahead of ourselves.

323 00:19:22.750 00:19:23.409 Nicolas Sucari: Like sense.

324 00:19:23.990 00:19:24.840 Nicolas Sucari: Okay.

325 00:19:26.270 00:19:40.850 Nicolas Sucari: cool. I don’t think we have like anything else for now, probably for next week, where we need to start working on having the teams report Ryan on real. Probably like we need to start understanding, if

326 00:19:41.040 00:19:43.920 Nicolas Sucari: is something possible to do. Like to have like the

327 00:19:43.990 00:19:48.320 Nicolas Sucari: light dash report, but instead of using light dash, move everything to real.

328 00:19:48.640 00:19:49.110 Ryan Luke Daque: You mean.

329 00:19:49.110 00:19:49.800 Nicolas Sucari: That happened.

330 00:19:49.800 00:19:51.310 Ryan Luke Daque: Weekly report right.

331 00:19:51.660 00:20:00.559 Nicolas Sucari: Yeah, so that we can show her how we are using real and like, if she has all the information there, probably she will start using it.

332 00:20:01.420 00:20:02.340 Ryan Luke Daque: Sounds good.

333 00:20:02.740 00:20:05.589 Ryan Luke Daque: Yeah, we can. We can do that for next week, I guess.

334 00:20:06.040 00:20:06.760 Ryan Luke Daque: Yeah.

335 00:20:07.110 00:20:10.518 Nicolas Sucari: Excellent. Okay, and then,

336 00:20:11.210 00:20:12.460 Nicolas Sucari: yeah, I think.

337 00:20:13.048 00:20:17.429 Nicolas Sucari: Patrick, we have, like 3 or 4 tickets about security

338 00:20:17.470 00:20:21.869 Nicolas Sucari: on ready this week. Probably we should like continue with that right.

339 00:20:23.604 00:20:27.240 Patrick Trainer: Yeah, I’m gonna reach out to you and Ryan.

340 00:20:27.460 00:20:28.420 Patrick Trainer: Excellent.

341 00:20:30.820 00:20:31.500 Nicolas Sucari: Okay.

342 00:20:31.700 00:20:32.729 Patrick Trainer: Should be today.

343 00:20:34.120 00:20:41.410 Nicolas Sucari: Perfect. Okay, thank you, guys, I don’t have like anything else, for now Jacob is out today. Utam, I really don’t know.

344 00:20:42.920 00:20:47.670 Nicolas Sucari: I sent him a message before, but he didn’t answer, so I don’t know if he’s around or not.

345 00:20:48.340 00:20:55.230 Nicolas Sucari: and we are having a meeting with Ben and Dan later today. So probably we’ll gather some more requirements or something. Okay.

346 00:20:55.660 00:20:56.350 Nicolas Sucari: cool.

347 00:20:58.110 00:21:01.434 Nicolas Sucari: excellent. Thank you. Guys talk to you.

348 00:21:01.910 00:21:02.600 Ryan Luke Daque: Someday.

349 00:21:02.710 00:21:03.550 Ryan Luke Daque: See, y’all.

350 00:21:03.550 00:21:04.300 Nicolas Sucari: Bye, bye.