Meeting Title: Intro-to-Snowflake! Date: 2024-06-21 Meeting participants: Nicolas Sucari, Uttam Kumaran, Priyadharshini Kalidoss, Jared Patterson
WEBVTT
1 00:02:58.580 ⇒ 00:02:59.440 Uttam Kumaran: Hey, guys.
2 00:03:02.580 ⇒ 00:03:03.300 JARED PATTERSON: Hello!
3 00:03:04.050 ⇒ 00:03:04.540 Priyadharshini Kalidoss: Hey! Hi!
4 00:03:04.540 ⇒ 00:03:05.559 Uttam Kumaran: Hey? How’s it going?
5 00:03:06.140 ⇒ 00:03:07.690 Uttam Kumaran: Hi! How’s everyone?
6 00:03:10.140 ⇒ 00:03:11.340 Priyadharshini Kalidoss: That’s good. How are you?
7 00:03:12.230 ⇒ 00:03:13.145 Uttam Kumaran: I’m doing well.
8 00:03:15.870 ⇒ 00:03:16.420 Priyadharshini Kalidoss: Arctic.
9 00:03:16.420 ⇒ 00:03:16.820 Uttam Kumaran: Friday.
10 00:03:16.820 ⇒ 00:03:17.180 Priyadharshini Kalidoss: Yeah.
11 00:03:17.180 ⇒ 00:03:17.620 Uttam Kumaran: End up.
12 00:03:17.620 ⇒ 00:03:18.677 Priyadharshini Kalidoss: I was born
13 00:03:19.680 ⇒ 00:03:21.530 Priyadharshini Kalidoss: because I’m outside.
14 00:03:22.570 ⇒ 00:03:23.240 Uttam Kumaran: Okay.
15 00:03:23.870 ⇒ 00:03:24.450 Priyadharshini Kalidoss: Yeah.
16 00:03:27.738 ⇒ 00:03:32.899 Uttam Kumaran: Yeah, I guess today I just wanted to chat with everybody about how the
17 00:03:33.580 ⇒ 00:03:36.119 Uttam Kumaran: introduction course went and
18 00:03:36.586 ⇒ 00:03:40.449 Uttam Kumaran: answer any questions. And also I I would be great
19 00:03:40.740 ⇒ 00:03:45.189 Uttam Kumaran: folks can share kind of like what they when it’s been a while since I’ve
20 00:03:45.740 ⇒ 00:03:47.510 Uttam Kumaran: taking the course. So.
21 00:03:50.750 ⇒ 00:03:55.560 Priyadharshini Kalidoss: Yeah, I’ve started. I can go to the polls, have started a room with.
22 00:03:59.140 ⇒ 00:04:01.730 Uttam Kumaran: I I can’t. It’s hard to kind of hear you, Priya.
23 00:04:02.928 ⇒ 00:04:08.911 Priyadharshini Kalidoss: I enrolled the code that you shared the other day, and I started taking
24 00:04:09.830 ⇒ 00:04:11.210 Priyadharshini Kalidoss: backgrounds. Report.
25 00:04:14.030 ⇒ 00:04:17.999 Uttam Kumaran: I? Okay, I I think I you mentioned you said you started taking the course.
26 00:04:18.560 ⇒ 00:04:19.570 Priyadharshini Kalidoss: Yeah, yeah.
27 00:04:20.800 ⇒ 00:04:24.230 Uttam Kumaran: Okay, but you’re not completed yet. Did you like what? What?
28 00:04:24.620 ⇒ 00:04:26.439 Uttam Kumaran: What part are you on.
29 00:04:27.442 ⇒ 00:04:32.200 Priyadharshini Kalidoss: I’m into the I’m understanding the Workspace part
30 00:04:32.530 ⇒ 00:04:35.279 Priyadharshini Kalidoss: where it is, what is it?
31 00:04:35.300 ⇒ 00:04:37.010 Priyadharshini Kalidoss: And into the slot?
32 00:04:37.763 ⇒ 00:04:44.110 Priyadharshini Kalidoss: Sorry I’m not in front of my laptop, so I’m not able to write section of it. I will do the 3rd section on this.
33 00:04:45.640 ⇒ 00:04:53.310 Uttam Kumaran: Okay, yeah, it’s it should be pretty quick to complete the whole thing. So is there is anyone else on the call were able to make it through the whole thing.
34 00:04:55.570 ⇒ 00:04:56.095 JARED PATTERSON: I’m
35 00:04:56.860 ⇒ 00:04:59.430 JARED PATTERSON: I’m on the
36 00:04:59.460 ⇒ 00:05:04.470 JARED PATTERSON: lesson. 7. I was just gonna kind of finish that this morning.
37 00:05:05.072 ⇒ 00:05:07.649 Uttam Kumaran: Oh, great. Okay. How’s it been so far?
38 00:05:08.680 ⇒ 00:05:12.549 JARED PATTERSON: It’s it’s been pretty, I mean, a lot of it has to do with like
39 00:05:14.090 ⇒ 00:05:15.860 JARED PATTERSON: more so like
40 00:05:17.710 ⇒ 00:05:19.010 JARED PATTERSON: about
41 00:05:19.310 ⇒ 00:05:25.220 JARED PATTERSON: it’s more like generic at the beginning, and like a lot of a like identity access and stuff like that.
42 00:05:25.510 ⇒ 00:05:26.492 Uttam Kumaran: Yeah, yeah, yeah.
43 00:05:27.270 ⇒ 00:05:28.054 JARED PATTERSON: Whatever
44 00:05:28.840 ⇒ 00:05:29.460 Uttam Kumaran: Yeah.
45 00:05:29.800 ⇒ 00:05:32.800 JARED PATTERSON: But now I’m getting more into like from
46 00:05:33.270 ⇒ 00:05:38.080 JARED PATTERSON: I’d say for Lesson 4 and on you’re kind of working a little bit more with like
47 00:05:38.640 ⇒ 00:05:49.330 JARED PATTERSON: actual creating queries and like loading and data and stuff like that. So it’s a pretty interesting. I can definitely see why it’s a a benefit to use snowflake.
48 00:05:50.500 ⇒ 00:05:58.679 Uttam Kumaran: Yeah, I guess. Like, what have you been noticing so far? Like, what? What have they been explaining about the benefit? So the roles and things.
49 00:05:58.700 ⇒ 00:06:00.609 Uttam Kumaran: although it seems a little bit
50 00:06:00.770 ⇒ 00:06:05.100 Uttam Kumaran: like, Hey, we’re just like setting stuff up. That’s actually a huge part of
51 00:06:06.498 ⇒ 00:06:10.981 Uttam Kumaran: like, what we do is like role based access control.
52 00:06:11.780 ⇒ 00:06:13.780 Uttam Kumaran: you know everything from
53 00:06:13.970 ⇒ 00:06:17.599 Uttam Kumaran: what roles have access to, what? How do you do? Data governance? So
54 00:06:18.217 ⇒ 00:06:23.109 Uttam Kumaran: I’m sure they probably just walk through it just to set stuff up. But there’s a whole
55 00:06:23.420 ⇒ 00:06:26.979 Uttam Kumaran: like there’s a whole industry built on just
56 00:06:27.220 ⇒ 00:06:28.739 Uttam Kumaran: like that sort of stuff.
57 00:06:29.407 ⇒ 00:06:34.290 Uttam Kumaran: But yeah, I guess like, what have you been noticing so far about like the benefits.
58 00:06:35.983 ⇒ 00:06:42.169 JARED PATTERSON: I think the user interface is really easy to manage. And then the tutorial definitely gives like
59 00:06:43.920 ⇒ 00:06:46.930 JARED PATTERSON: a good give you a good understanding of like.
60 00:06:47.520 ⇒ 00:06:50.048 JARED PATTERSON: you know. Why, there’s
61 00:06:52.110 ⇒ 00:06:58.789 JARED PATTERSON: like how to use the schema. Use the tables, how that sits in a database, how the database uses the like
62 00:06:59.100 ⇒ 00:07:04.154 JARED PATTERSON: computing warehouse and all that stuff. I think it’s
63 00:07:07.270 ⇒ 00:07:10.339 JARED PATTERSON: It definitely makes it like easier for me to see how
64 00:07:11.140 ⇒ 00:07:22.129 JARED PATTERSON: using this in a project would be pretty easy to do. I don’t. I guess my one question would be like, what would the alternative to this be? Just like using like my sequel, or something like that.
65 00:07:23.060 ⇒ 00:07:31.710 Uttam Kumaran: Yeah. Good question. So the alternative to Snowflake, there is a couple of other options, you know. Now, there’s a lot more options. But
66 00:07:31.800 ⇒ 00:07:35.059 Uttam Kumaran: so so typically people use
67 00:07:35.070 ⇒ 00:07:42.520 Uttam Kumaran: databases in a few different ways. So there’s commonly production application databases which is like, let’s say you’re running like
68 00:07:42.580 ⇒ 00:07:43.605 Uttam Kumaran: your
69 00:07:45.240 ⇒ 00:07:53.150 Uttam Kumaran: Let’s say, for example, you, you’re building like a web conferencing software like Zoom, you actually have a production database that you use
70 00:07:53.190 ⇒ 00:08:00.710 Uttam Kumaran: to power your application. For example, when we all logged on this call, you can see that our names are showing up right? That’s a call to a database
71 00:08:01.160 ⇒ 00:08:04.890 Uttam Kumaran: right for it to get to your user profile. It’s a call to a database.
72 00:08:04.920 ⇒ 00:08:12.930 Uttam Kumaran: But those databases are tuned in order to optimize for quickly reading data. Transactionally.
73 00:08:13.020 ⇒ 00:08:19.460 Uttam Kumaran: however, the stuff that we do in analytics is actually running large. Select
74 00:08:19.650 ⇒ 00:08:20.930 Uttam Kumaran: over
75 00:08:21.501 ⇒ 00:08:24.209 Uttam Kumaran: like large data like large tables.
76 00:08:24.320 ⇒ 00:08:32.340 Uttam Kumaran: right? And that’s not commonly something that you do in an application setting meaning you’re not commonly looking up every single user
77 00:08:32.350 ⇒ 00:08:33.740 Uttam Kumaran: that uses. Zoom.
78 00:08:33.900 ⇒ 00:08:40.790 Uttam Kumaran: right? You’re looking for one specific user. So there’s there’s application databases that are like postgres is pretty common.
79 00:08:41.245 ⇒ 00:08:48.720 Uttam Kumaran: You may also see, like Maria dB, other different things. And then there’s analytics, warehouses. So analytics, warehouses is where snowflake
80 00:08:48.800 ⇒ 00:09:06.790 Uttam Kumaran: lives in so commonly you’ll have you can. You can use things like Databricks has like a data warehouse. Amazon redshift is one of the larger ones, and probably the one that was like the most popular for a while, and kind of the one I learned when I 1st started.
81 00:09:06.930 ⇒ 00:09:11.250 Uttam Kumaran: The difference is, and this is again kind of what I mentioned, where
82 00:09:11.550 ⇒ 00:09:16.293 Uttam Kumaran: you know. Maybe it’s beneficial for everybody to learn redshift, just to see kind of like
83 00:09:16.610 ⇒ 00:09:19.340 Uttam Kumaran: how bad the experience is, but
84 00:09:19.620 ⇒ 00:09:22.849 Uttam Kumaran: in redshift you have to start the cluster.
85 00:09:23.150 ⇒ 00:09:25.630 Uttam Kumaran: You then have to actually set up
86 00:09:25.710 ⇒ 00:09:35.479 Uttam Kumaran: like what your warehouse sizes, and it’s not a dropdown like you have to run commands to basically say, like, I, I want like this much memory stuff like that.
87 00:09:35.500 ⇒ 00:09:40.449 Uttam Kumaran: And then, you know, and stuff like, now, you notice that you can quickly just change that right.
88 00:09:40.460 ⇒ 00:09:44.789 Uttam Kumaran: that process. You have to bring down the entire cluster in redshift
89 00:09:44.920 ⇒ 00:09:47.140 Uttam Kumaran: in order to make those types of changes.
90 00:09:47.220 ⇒ 00:10:02.359 Uttam Kumaran: The other thing is, it’s also not very easy to create roles like you did in Snowflake. It takes quite a lot of like steps to do the same kind of role and schema creation.
91 00:10:02.590 ⇒ 00:10:03.670 Uttam Kumaran: though.
92 00:10:04.068 ⇒ 00:10:10.330 Uttam Kumaran: Yeah, it’s just way more complicated. There’s there’s a whole class of like study called database administration.
93 00:10:10.520 ⇒ 00:10:15.829 Uttam Kumaran: Database administration commonly deals with these sorts of path where you’re basically
94 00:10:15.880 ⇒ 00:10:21.330 Uttam Kumaran: creating roles, doing sizing things like that. Snowflake handles a ton of that.
95 00:10:21.640 ⇒ 00:10:29.170 Uttam Kumaran: And so for a company like mine and for many companies. You just don’t need a DVA, right? A database administrator anymore. You can
96 00:10:29.793 ⇒ 00:10:34.129 Uttam Kumaran: basically have everyone just get familiar with how Snowflake works.
97 00:10:34.539 ⇒ 00:10:48.150 Uttam Kumaran: And you don’t need that extra role. And it’s a lot less complicated because there’s not many used cases in my world where I need that level of fine detail. And so I’m willing to like get rid of some of that over complexity
98 00:10:48.320 ⇒ 00:10:50.679 Uttam Kumaran: in order to just make the things simpler.
99 00:10:51.295 ⇒ 00:11:02.124 Uttam Kumaran: So that’s an example. There’s so redshift is a is a really good example. If you want to look into that and I’m happy to, you know, kind of give an overview into that as well. But
100 00:11:02.980 ⇒ 00:11:03.740 Uttam Kumaran: yeah.
101 00:11:06.550 ⇒ 00:11:08.099 JARED PATTERSON: No, thank you. That makes sense.
102 00:11:08.700 ⇒ 00:11:09.440 Uttam Kumaran: Yeah.
103 00:11:09.440 ⇒ 00:11:14.944 JARED PATTERSON: I didn’t really have any other questions, just because I’m still like walking through it. I think a lot of
104 00:11:16.270 ⇒ 00:11:24.320 JARED PATTERSON: like the nice thing about doing the tutorial is that, like you get to practice your sequel a little bit as well. And I mean, that’s clearly like
105 00:11:24.990 ⇒ 00:11:26.860 JARED PATTERSON: the one thing I’ll have to
106 00:11:27.440 ⇒ 00:11:29.230 JARED PATTERSON: continue to work on.
107 00:11:29.490 ⇒ 00:11:34.050 JARED PATTERSON: cause I feel like once I get a good understanding of snowflake
108 00:11:34.350 ⇒ 00:11:35.230 JARED PATTERSON: like.
109 00:11:35.780 ⇒ 00:11:39.870 JARED PATTERSON: And the user interface, like, it’s pretty easy to, you know.
110 00:11:39.970 ⇒ 00:11:48.220 JARED PATTERSON: navigate and kind of even if you don’t know where to something is, it’s not hard to find, because it’s like a couple of buttons. You can click and see if that’s that’s what you’re looking for.
111 00:11:48.690 ⇒ 00:11:49.350 Uttam Kumaran: Yeah.
112 00:11:50.050 ⇒ 00:11:56.459 JARED PATTERSON: Yeah, I think just like a better understanding of sequel. And just like, keep grinding sequels is
113 00:11:56.640 ⇒ 00:11:58.010 JARED PATTERSON: next steps
114 00:11:58.370 ⇒ 00:11:59.599 JARED PATTERSON: at least for
115 00:11:59.820 ⇒ 00:12:00.890 JARED PATTERSON: Snowflake.
116 00:12:01.820 ⇒ 00:12:06.100 Uttam Kumaran: Yeah, do you? Do, you actually wanna share? Cause I’m interested. They’ve changed the learning
117 00:12:06.120 ⇒ 00:12:10.100 Uttam Kumaran: like courses a lot I would interested to see like how it looks like from
118 00:12:10.270 ⇒ 00:12:11.519 Uttam Kumaran: from your side.
119 00:12:12.970 ⇒ 00:12:13.620 JARED PATTERSON: Yeah.
120 00:12:16.860 ⇒ 00:12:18.321 Uttam Kumaran: Sorry to put you on the spot.
121 00:12:18.530 ⇒ 00:12:19.510 JARED PATTERSON: No, that’s fine.
122 00:12:24.390 ⇒ 00:12:26.239 JARED PATTERSON: You guys see this.
123 00:12:26.510 ⇒ 00:12:27.300 Uttam Kumaran: Yes.
124 00:12:29.080 ⇒ 00:12:30.399 JARED PATTERSON: Yeah, so like.
125 00:12:30.840 ⇒ 00:12:33.760 JARED PATTERSON: I don’t know. The one thing that I
126 00:12:33.780 ⇒ 00:12:35.106 JARED PATTERSON: had mentioned
127 00:12:36.620 ⇒ 00:12:37.920 JARED PATTERSON: for the
128 00:12:38.350 ⇒ 00:12:40.250 JARED PATTERSON: like daily report outs
129 00:12:40.270 ⇒ 00:12:41.670 JARED PATTERSON: was just that
130 00:12:42.904 ⇒ 00:12:45.100 JARED PATTERSON: like. If you were gonna do this again.
131 00:12:45.440 ⇒ 00:12:46.960 JARED PATTERSON: ma’am, I would I would have.
132 00:12:46.960 ⇒ 00:12:47.680 Uttam Kumaran: Yeah.
133 00:12:47.860 ⇒ 00:12:49.260 JARED PATTERSON: Doesn’t really matter. But
134 00:12:51.220 ⇒ 00:12:55.000 JARED PATTERSON: cause I’m I’m worried when I’m like clicking around on stuff on
135 00:12:55.320 ⇒ 00:12:58.140 JARED PATTERSON: Snowflake that I’m actually going to
136 00:12:58.150 ⇒ 00:12:59.590 JARED PATTERSON: mess something up
137 00:13:00.550 ⇒ 00:13:01.319 JARED PATTERSON: like has.
138 00:13:01.320 ⇒ 00:13:02.340 Uttam Kumaran: Enjoy the one.
139 00:13:02.620 ⇒ 00:13:04.760 JARED PATTERSON: Computing warehouse. Cause I’m like, I
140 00:13:04.990 ⇒ 00:13:09.462 JARED PATTERSON: basically had like, no idea what it was doing until
141 00:13:10.330 ⇒ 00:13:10.870 JARED PATTERSON: yeah,
142 00:13:12.100 ⇒ 00:13:14.070 JARED PATTERSON: yeah. So it’s basically just like
143 00:13:14.130 ⇒ 00:13:16.772 JARED PATTERSON: you just kinda click right through it.
144 00:13:17.150 ⇒ 00:13:17.710 Uttam Kumaran: Okay. Cool.
145 00:13:18.230 ⇒ 00:13:20.050 JARED PATTERSON: Questions that you can like.
146 00:13:20.750 ⇒ 00:13:25.949 JARED PATTERSON: So when you get to like sequel parts, it’s helpful to like you can copy and paste it in
147 00:13:27.007 ⇒ 00:13:31.660 JARED PATTERSON: so just like right now, loading in the data and then gonna be
148 00:13:32.120 ⇒ 00:13:34.009 JARED PATTERSON: kind of moving on to these ones.
149 00:13:34.140 ⇒ 00:13:34.679 JARED PATTERSON: Some of the.
150 00:13:34.680 ⇒ 00:13:35.650 Uttam Kumaran: Much quicker.
151 00:13:36.780 ⇒ 00:13:40.310 Uttam Kumaran: Some of these are complicated. So it’s actually interesting to see. Yeah, cause.
152 00:13:40.650 ⇒ 00:13:43.290 Uttam Kumaran: So up up to probably 5
153 00:13:43.360 ⇒ 00:13:44.690 Uttam Kumaran: is like.
154 00:13:44.860 ⇒ 00:13:51.720 Uttam Kumaran: really the 4, 6, 7. Actually, I don’t know. Can you click on 7 real quick? I just want to see what the store thing is.
155 00:13:51.720 ⇒ 00:13:52.290 JARED PATTERSON: Yeah.
156 00:13:52.724 ⇒ 00:13:53.159 Uttam Kumaran: Service!
157 00:13:53.160 ⇒ 00:13:53.980 JARED PATTERSON: Opted.
158 00:13:53.980 ⇒ 00:13:55.110 Uttam Kumaran: Oh, okay. Okay.
159 00:13:55.360 ⇒ 00:13:56.220 JARED PATTERSON: Choice.
160 00:13:59.490 ⇒ 00:14:12.789 Uttam Kumaran: Okay, cool. Yeah. I mean, it’s actually nice, because it’s going through it. I think the one thing I’ll say, and maybe I’ll also post. This in the Channel is that if you get stuck, just let me know. Some of these concepts actually are a lot harder. Let’s to beat this up a bit more.
161 00:14:13.203 ⇒ 00:14:20.070 Uttam Kumaran: And it may be a little bit tough to wrap your head around staging data, semi-structured data things like that.
162 00:14:20.554 ⇒ 00:14:32.939 Uttam Kumaran: So maybe what we’ll we? What I’ll do is I’m just gonna also enroll in the course just to see the curriculum, and then maybe just suggest a good place for everyone to pause because.
163 00:14:33.521 ⇒ 00:14:36.599 Uttam Kumaran: I didn’t know they actually had the semi-structured data
164 00:14:37.036 ⇒ 00:14:45.329 Uttam Kumaran: also included in there. You guys won’t need to go that far, for now, although it’s really helpful to see it. And the nice thing is, you guys get
165 00:14:45.750 ⇒ 00:14:50.750 Uttam Kumaran: well, the thing I also why, I kind of want you guys to go through these badge workshops.
166 00:14:50.790 ⇒ 00:14:53.690 Uttam Kumaran: It’s because you will actually see
167 00:14:54.012 ⇒ 00:14:59.660 Uttam Kumaran: you actually get like a badge at the end. And you know, that’s helpful to post on Linkedin, and then
168 00:14:59.690 ⇒ 00:15:06.357 Uttam Kumaran: kind of have for for reference. Later, when you’re when you’re gonna going for snowflake gigs.
169 00:15:06.780 ⇒ 00:15:11.530 Uttam Kumaran: so that’s kind of the reason I want you guys to get get that, because we’ll give you a little badge. But
170 00:15:11.680 ⇒ 00:15:18.409 Uttam Kumaran: maybe we’ll I’ll I’ll kind of look through it a good place to pause. And you guys complete it at any point. During the summer. So
171 00:15:19.910 ⇒ 00:15:21.469 Uttam Kumaran: Akshay, how’s it going.
172 00:15:25.000 ⇒ 00:15:35.329 Akshay kumar.G: Yeah, actually, I didn’t enroll in the course yet, like, as I told you, like some like intern that was happening in my call like that took a lot of time, and those things like right after the meeting I will try to enroll.
173 00:15:36.440 ⇒ 00:15:47.879 Uttam Kumaran: Okay, yeah, take your time. Yeah, I think, if you guys can just give a status on where you guys are at. So what we’re gonna be doing for next week is Nico and I are gonna be planning
174 00:15:49.470 ⇒ 00:15:55.940 Uttam Kumaran: kind of like small tasks for you guys to take on that actually, you know, involve things in Snowflake.
175 00:15:56.110 ⇒ 00:16:00.170 Uttam Kumaran: These will be creating tables or exploring other technologies.
176 00:16:00.511 ⇒ 00:16:04.730 Uttam Kumaran: You know I would. I would love for everybody to kind of walk through
177 00:16:06.390 ⇒ 00:16:08.279 Uttam Kumaran: to walk through this
178 00:16:08.310 ⇒ 00:16:11.449 Uttam Kumaran: this main like data warehouse workshop.
179 00:16:12.240 ⇒ 00:16:13.300 Uttam Kumaran: But again.
180 00:16:13.540 ⇒ 00:16:16.529 Uttam Kumaran: some of this stuff like it’ll come over time.
181 00:16:16.829 ⇒ 00:16:21.239 Uttam Kumaran: And I want you guys to get this badge. That’ll get you into the snowflake learning
182 00:16:21.280 ⇒ 00:16:22.339 Uttam Kumaran: course. And
183 00:16:23.170 ⇒ 00:16:25.706 Uttam Kumaran: you know you’ll you’ll kind of get a sense of
184 00:16:26.210 ⇒ 00:16:41.549 Uttam Kumaran: you know how Snowflake works. So I’m glad that you guys can see that. And each of these concepts, you know, you may not get a chance to see directly for a little bit. But everything that we’re doing we’re using every single thing that’s listed there one way or another.
185 00:16:42.640 ⇒ 00:16:48.809 Uttam Kumaran: So yeah, I think the biggest thing to mention is
186 00:16:49.080 ⇒ 00:16:50.699 Uttam Kumaran: for next week.
187 00:16:51.033 ⇒ 00:16:55.110 Uttam Kumaran: We’re gonna be planning, I think, for 3 of you on this call.
188 00:16:55.130 ⇒ 00:17:00.430 Uttam Kumaran: Primarily, we’re going to be planning tasks related to data analysis.
189 00:17:00.470 ⇒ 00:17:03.579 Uttam Kumaran: This is going to be tasks related to
190 00:17:05.130 ⇒ 00:17:10.040 Uttam Kumaran: using some of our business intelligence tools
191 00:17:10.109 ⇒ 00:17:13.368 Uttam Kumaran: to answer and put together.
192 00:17:14.390 ⇒ 00:17:22.050 Uttam Kumaran: you know, analyses for some clients in addition, I actually may have you guys learn 2 separate tools.
193 00:17:22.532 ⇒ 00:17:30.999 Uttam Kumaran: And we’ll take a little bit of time to do some local development. And basically, we’ll probably end up doing a session where we we all walk through.
194 00:17:32.270 ⇒ 00:17:35.509 Uttam Kumaran: we all walk through how to set things up and things like that. So
195 00:17:35.907 ⇒ 00:17:38.450 Uttam Kumaran: the 2 tools that will we kind of
196 00:17:38.670 ⇒ 00:17:40.489 Uttam Kumaran: are really enjoying
197 00:17:40.830 ⇒ 00:17:42.489 Uttam Kumaran: using here
198 00:17:42.560 ⇒ 00:17:44.779 Uttam Kumaran: on the data analysis side.
199 00:17:44.860 ⇒ 00:17:46.859 Uttam Kumaran: I will put that into the
200 00:17:47.490 ⇒ 00:17:50.279 Uttam Kumaran: chat here. It’s a tool called evidence.
201 00:17:51.460 ⇒ 00:17:54.899 Uttam Kumaran: And there’s a tool called Rel.
202 00:17:56.930 ⇒ 00:18:02.270 Uttam Kumaran: So both of these tools, we’re really investing a lot of time and resources do
203 00:18:02.410 ⇒ 00:18:03.760 Uttam Kumaran: into.
204 00:18:04.170 ⇒ 00:18:09.211 Uttam Kumaran: you know, making sure that we’re everybody’s familiar with this and
205 00:18:12.331 ⇒ 00:18:18.110 Uttam Kumaran: basically that, we can use this for our clients real. If you
206 00:18:18.660 ⇒ 00:18:23.890 Uttam Kumaran: I’ll even share my screen. And I can show you a little bit about what real looks like.
207 00:18:32.850 ⇒ 00:18:40.649 Uttam Kumaran: So this is real and real. We have a series of dashboards for a client pool parts to go that you guys will become familiar with
208 00:18:41.138 ⇒ 00:18:42.830 Uttam Kumaran: in this client. In this
209 00:18:43.320 ⇒ 00:18:49.989 Uttam Kumaran: software. We have a couple of different dashboards, that, and there’s some logic behind them. But I’ll kind of just leave it high level, for now
210 00:18:50.190 ⇒ 00:18:55.630 Uttam Kumaran: the nice thing about real is, it’s really just like a beautiful dashboard like this, where you can
211 00:18:55.670 ⇒ 00:19:02.370 Uttam Kumaran: filter to like the last 12 months. There’s a ton of different information. And it’s a great tool for analysis.
212 00:19:02.510 ⇒ 00:19:05.229 Uttam Kumaran: For example, if I want to look at
213 00:19:05.590 ⇒ 00:19:07.200 Uttam Kumaran: like, what was the
214 00:19:07.230 ⇒ 00:19:10.880 Uttam Kumaran: how many shipments we sent from Amazon ship station
215 00:19:10.970 ⇒ 00:19:13.370 Uttam Kumaran: via Usps.
216 00:19:13.600 ⇒ 00:19:17.020 Uttam Kumaran: I can see exactly how much that that was
217 00:19:17.501 ⇒ 00:19:26.119 Uttam Kumaran: and it’s really, really slick. And this is what we’re actually promoting for our clients to begin using. And we use this for analysis as well.
218 00:19:26.290 ⇒ 00:19:30.209 Uttam Kumaran: So you guys will be learning how to leverage this tool.
219 00:19:30.980 ⇒ 00:19:33.299 Uttam Kumaran: And then also, we’re using
220 00:19:33.520 ⇒ 00:19:35.790 Uttam Kumaran: evidence.
221 00:19:36.360 ⇒ 00:19:38.370 Uttam Kumaran: which is
222 00:19:39.320 ⇒ 00:19:40.000 Uttam Kumaran: huh?
223 00:19:40.370 ⇒ 00:19:42.000 Uttam Kumaran: Somewhere here.
224 00:19:49.657 ⇒ 00:19:55.702 Uttam Kumaran: So we’re using this tool evidence. That’s just a really really nice tool for building
225 00:19:56.260 ⇒ 00:19:57.610 Uttam Kumaran: data products.
226 00:19:57.820 ⇒ 00:19:59.540 Uttam Kumaran: I’ll give it a sec
227 00:19:59.900 ⇒ 00:20:01.299 Uttam Kumaran: the load.
228 00:20:04.240 ⇒ 00:20:04.920 Uttam Kumaran: Yep.
229 00:20:07.110 ⇒ 00:20:08.060 Uttam Kumaran: So
230 00:20:08.170 ⇒ 00:20:14.489 Uttam Kumaran: you know, if you guys have put together like analysis reports or other reports, you know, you’re probably used to doing this stuff in Google, Doc, or.
231 00:20:14.550 ⇒ 00:20:19.369 Uttam Kumaran: you know, another form factor where it’s really inflexible and doesn’t have.
232 00:20:19.510 ⇒ 00:20:24.349 Uttam Kumaran: you know, kind of like. Then how nice it is to just hold SQL. Directly into those.
233 00:20:24.380 ⇒ 00:20:28.760 Uttam Kumaran: This is all written in Markdown, and this is what we’ll be using to kind of put together analyses.
234 00:20:29.664 ⇒ 00:20:31.799 Uttam Kumaran: So I really like.
235 00:20:32.920 ⇒ 00:20:38.759 Uttam Kumaran: I really like having everything here because it allows us to have a conversation with the client about exactly what we’re seeing.
236 00:20:38.980 ⇒ 00:20:42.039 Uttam Kumaran: And there’s many different ways of of cutting this
237 00:20:42.756 ⇒ 00:20:43.790 Uttam Kumaran: and so
238 00:20:43.810 ⇒ 00:20:48.059 Uttam Kumaran: I’m urging that we do all of our downstream analyses.
239 00:20:48.230 ⇒ 00:20:53.220 Uttam Kumaran: Once we get to a point where we understand the problem. And we’ve done enough
240 00:20:53.430 ⇒ 00:21:00.920 Uttam Kumaran: sequel that you can basically move everything to here. So this is an example of where we were understanding returns and refunds.
241 00:21:00.930 ⇒ 00:21:03.579 Uttam Kumaran: And we basically were able to talk through
242 00:21:05.190 ⇒ 00:21:08.850 Uttam Kumaran: like the returns profile for this client.
243 00:21:11.200 ⇒ 00:21:13.749 Uttam Kumaran: and the nice thing is this continues so.
244 00:21:14.635 ⇒ 00:21:15.470 Uttam Kumaran: These
245 00:21:15.890 ⇒ 00:21:18.170 Uttam Kumaran: looks like these are cutting off at like.
246 00:21:18.370 ⇒ 00:21:22.697 Uttam Kumaran: I like complete months. So when June ends, it’ll update
247 00:21:23.260 ⇒ 00:21:36.239 Uttam Kumaran: But basically, this is how we want all of our analysts to end up living in a live document like this, so that we continue to iterate. And it looks really great, you know, versus just screenshots of charts.
248 00:21:36.440 ⇒ 00:21:39.149 Uttam Kumaran: So both of these tools, we’re gonna kind of
249 00:21:39.180 ⇒ 00:21:41.950 Uttam Kumaran: allow you guys to play around with next week.
250 00:21:44.200 ⇒ 00:21:47.009 Uttam Kumaran: and hopefully begin taking on
251 00:21:47.200 ⇒ 00:21:53.359 Uttam Kumaran: like one or 2 like 2 week long tickets that you guys can use both of these tools
252 00:21:53.500 ⇒ 00:21:54.846 Uttam Kumaran: to implement.
253 00:21:55.770 ⇒ 00:21:58.550 Uttam Kumaran: I will say, both of these tools are like, really
254 00:21:58.570 ⇒ 00:22:03.200 Uttam Kumaran: kind of like, brand new like, probably like one or 2 year old companies.
255 00:22:03.240 ⇒ 00:22:05.500 Uttam Kumaran: Very, very like cutting edge.
256 00:22:05.580 ⇒ 00:22:10.830 Uttam Kumaran: So I’m excited to kind of give you guys the opportunity to play around with this. This is like, totally
257 00:22:11.270 ⇒ 00:22:19.012 Uttam Kumaran: outside of probably what you would learn anywhere else. So for me, that’s very exciting to kind of like. Allow you to use these tools. And
258 00:22:19.630 ⇒ 00:22:26.180 Uttam Kumaran: you know, although it’s technically they’re both, you know, will be a learning curve. They’re both very, very nice.
259 00:22:29.270 ⇒ 00:22:34.700 Uttam Kumaran: great any questions about evidence, or real, or next week.
260 00:22:38.170 ⇒ 00:22:39.719 JARED PATTERSON: No, I I don’t have anything.
261 00:22:45.602 ⇒ 00:22:50.829 Priyadharshini Kalidoss: These. Both tools are kind of similar what they have with additionalities.
262 00:22:51.070 ⇒ 00:22:51.760 Priyadharshini Kalidoss: La.
263 00:22:52.947 ⇒ 00:22:57.460 Uttam Kumaran: I I think you said there, these tools like, are they used together?
264 00:22:58.567 ⇒ 00:23:03.040 Priyadharshini Kalidoss: Yeah, or bit similar is what we do. We decide
265 00:23:03.070 ⇒ 00:23:05.520 Priyadharshini Kalidoss: chunk capacity.
266 00:23:06.460 ⇒ 00:23:12.870 Uttam Kumaran: Yeah, I guess these are so. These are kind of separate, totally separate tools. But the this real is used more for.
267 00:23:12.910 ⇒ 00:23:21.009 Uttam Kumaran: like just understanding day to day. Weekly performance like, it’s a very operational. So think about like you wake up, and you just want to look at like how
268 00:23:21.060 ⇒ 00:23:23.930 Uttam Kumaran: the company is doing, or you get a specific question.
269 00:23:23.950 ⇒ 00:23:30.269 Uttam Kumaran: The real is really great. To quickly drill down right? You can quickly select things and understand data.
270 00:23:31.200 ⇒ 00:23:38.860 Uttam Kumaran: Evidence is like the place where we want to put the final analyses in basically with the story of like what we found.
271 00:23:39.564 ⇒ 00:23:43.189 Uttam Kumaran: additional findings, examples, things like that.
272 00:23:43.450 ⇒ 00:23:47.910 Uttam Kumaran: So everything that goes into here is like the final stage of analysis.
273 00:23:49.340 ⇒ 00:23:52.779 Priyadharshini Kalidoss: Yeah, okay, it’s more like a presentation. What do you want to present?
274 00:23:52.990 ⇒ 00:23:54.220 Priyadharshini Kalidoss: It’s evidence.
275 00:23:54.630 ⇒ 00:24:00.219 Uttam Kumaran: Exactly. So instead of like putting together a slide deck instead of putting together like a word document.
276 00:24:00.630 ⇒ 00:24:04.030 Uttam Kumaran: basically, I, I said nothing.
277 00:24:04.270 ⇒ 00:24:07.669 Uttam Kumaran: Everything has to go through evidence, because
278 00:24:07.780 ⇒ 00:24:14.400 Uttam Kumaran: this is gonna make us look really really great. The fact that you have this many, this much amount of visualization.
279 00:24:14.440 ⇒ 00:24:17.140 Uttam Kumaran: And if you go to evidence, dot dev.
280 00:24:17.200 ⇒ 00:24:18.679 Uttam Kumaran: if you go under
281 00:24:19.660 ⇒ 00:24:21.430 Uttam Kumaran: you go under examples.
282 00:24:21.500 ⇒ 00:24:29.049 Uttam Kumaran: You can see that they have a lot of great examples here. That you could take a look at. For example, if we look at
283 00:24:31.790 ⇒ 00:24:37.809 Uttam Kumaran: they have a ton of great examples where there’s a lot of great visualization charts. This is for like a single company, Apis.
284 00:24:40.190 ⇒ 00:24:46.850 Uttam Kumaran: And so, you know, my goal is to understand, like how we can get all analysis into a world like this.
285 00:24:46.920 ⇒ 00:24:50.559 Uttam Kumaran: And also, if you look at the amount of
286 00:24:50.680 ⇒ 00:24:53.009 Uttam Kumaran: like chart types that they
287 00:24:53.310 ⇒ 00:24:55.890 Uttam Kumaran: have, let me go to
288 00:24:56.150 ⇒ 00:24:57.140 Uttam Kumaran: dark.
289 00:24:59.440 ⇒ 00:25:04.893 Uttam Kumaran: If you go to their components. You can see that there’s so many different components that can be leveraged.
290 00:25:05.570 ⇒ 00:25:12.569 Uttam Kumaran: and you know this isn’t. You can’t. There’s not many tools in the market where you can do all these. And it looks this great
291 00:25:14.110 ⇒ 00:25:19.140 Uttam Kumaran: you know. And so I definitely, I’m I’m pushing that we leverage we leverage
292 00:25:19.710 ⇒ 00:25:21.760 Uttam Kumaran: evidence for as much as possible.
293 00:25:23.920 ⇒ 00:25:25.840 Priyadharshini Kalidoss: Yeah, this looks cool, actually.
294 00:25:27.590 ⇒ 00:25:31.070 Uttam Kumaran: Yeah, no, I’m it’s it’s a company that you know. I I just like.
295 00:25:31.290 ⇒ 00:25:39.030 Uttam Kumaran: you know, I I read about new companies and data all the time, and it’s company that I found, you know, recently, and I wanted to just leverage it. And
296 00:25:39.250 ⇒ 00:25:46.700 Uttam Kumaran: you know it’s really really great. So you know the folks on the analysis side, there’s a lot of really rich analysis problems that we have that.
297 00:25:46.750 ⇒ 00:25:48.059 Uttam Kumaran: you know. I’ll be
298 00:25:48.140 ⇒ 00:25:54.760 Uttam Kumaran: encouraging you guys to leverage and evidence. Because the work we do, you know, beyond SQL,
299 00:25:54.860 ⇒ 00:26:03.489 Uttam Kumaran: sometimes visually, it’s not great, and I don’t want the value to be lost because we just didn’t have the tools visually to explain what we were meaning.
300 00:26:03.680 ⇒ 00:26:05.050 Uttam Kumaran: And so
301 00:26:05.180 ⇒ 00:26:07.680 Uttam Kumaran: that’s the problem with some with.
302 00:26:07.690 ⇒ 00:26:20.359 Uttam Kumaran: you know, sometimes in data is like you work through data, engineering, analytics, engineering, you do data models, you get it to the analysis stage. And then you put together a Google Doc or an ugly presentation
303 00:26:20.420 ⇒ 00:26:29.839 Uttam Kumaran: like, we’re going to fall flat because the business guys, they’re not going to see all the background work. They’re just going to see what we share. And so the presentation layer
304 00:26:30.080 ⇒ 00:26:31.649 Uttam Kumaran: matters a lot.
305 00:26:31.920 ⇒ 00:26:37.450 Uttam Kumaran: just like every layer, you know. So I want to make sure that every layer we’re using the best in class tools.
306 00:26:44.060 ⇒ 00:26:51.140 Priyadharshini Kalidoss: I I got I googled the evidence, and I got Link for everything. Could you share the real fast because I’m not able to get a
307 00:26:51.170 ⇒ 00:26:53.520 Priyadharshini Kalidoss: right, I’ll move it.
308 00:26:54.290 ⇒ 00:26:56.698 Uttam Kumaran: Yes, I will. I’ll send
309 00:26:58.040 ⇒ 00:27:00.850 Uttam Kumaran: I’ll send both of these in slack.
310 00:27:10.610 ⇒ 00:27:16.709 Uttam Kumaran: so we’ll go through we’ll go through examples where you’ll be able to load these locally
311 00:27:17.030 ⇒ 00:27:20.379 Uttam Kumaran: and put together a piece of analysis to kind of present.
312 00:27:20.610 ⇒ 00:27:23.290 Uttam Kumaran: And then we can talk about, how do you deploy this
313 00:27:23.560 ⇒ 00:27:24.839 Uttam Kumaran: to production.
314 00:27:26.130 ⇒ 00:27:29.159 Uttam Kumaran: this is, gonna take some time to get the hang of it, but
315 00:27:29.280 ⇒ 00:27:35.959 Uttam Kumaran: we’ll cut the the work will actually walk through basically the snowflake some stuff in real and evidence.
316 00:27:36.110 ⇒ 00:27:40.529 Uttam Kumaran: And again, like, I want to start an analysis side using some tables we already have.
317 00:27:40.580 ⇒ 00:27:44.530 Uttam Kumaran: and then we’ll have you guys make some modifications to tables for what you need. So
318 00:27:44.930 ⇒ 00:27:46.170 Uttam Kumaran: yeah, I’m excited.
319 00:27:47.020 ⇒ 00:27:52.880 Priyadharshini Kalidoss: Yeah before one thing and mostly fast. I just want to understand whether the
320 00:27:53.120 ⇒ 00:27:58.979 Priyadharshini Kalidoss: they have a separate section for understanding the roles. Okay.
321 00:28:00.014 ⇒ 00:28:03.339 Priyadharshini Kalidoss: the electric system admin
322 00:28:03.440 ⇒ 00:28:06.320 Priyadharshini Kalidoss: the user account whatsoever.
323 00:28:07.225 ⇒ 00:28:07.969 Priyadharshini Kalidoss: Yeah.
324 00:28:08.160 ⇒ 00:28:13.599 Priyadharshini Kalidoss: yeah, is it really important to understand what it is? Completely, obviously just one.
325 00:28:14.310 ⇒ 00:28:19.524 Uttam Kumaran: Yeah, let me let me. Now that I know, kind of like what’s in the
326 00:28:19.940 ⇒ 00:28:26.290 Uttam Kumaran: what they’ve put into the new, the new badge. Now I’m gonna find a couple of more and begin to put together.
327 00:28:26.697 ⇒ 00:28:42.550 Uttam Kumaran: I’ll put. I’ll have a notion page with basically the the best, like quick start where you could learn about roles, learn about creating tables, and then kind of begin to create that page. So as you guys are working on different things. You’ll have the appropriate
328 00:28:42.720 ⇒ 00:28:45.849 Uttam Kumaran: like learning module to basically go through.
329 00:28:47.480 ⇒ 00:28:49.440 Priyadharshini Kalidoss: Yeah, that would be expensive.
330 00:28:50.070 ⇒ 00:28:50.670 Uttam Kumaran: Great.
331 00:28:56.850 ⇒ 00:28:57.960 Uttam Kumaran: Okay. Cool.
332 00:28:58.996 ⇒ 00:29:00.470 Uttam Kumaran: Anything else.
333 00:29:05.680 ⇒ 00:29:06.970 JARED PATTERSON: Good on my end. Here.
334 00:29:08.640 ⇒ 00:29:09.200 Uttam Kumaran: Cool.
335 00:29:09.979 ⇒ 00:29:13.210 Uttam Kumaran: So I will slack you guys a few things.
336 00:29:13.800 ⇒ 00:29:21.429 Uttam Kumaran: I think, Jared, I put some transcripts in there. Maybe we can catch up later today if you’re still gonna be on
337 00:29:23.693 ⇒ 00:29:24.540 JARED PATTERSON: I don’t.
338 00:29:25.000 ⇒ 00:29:31.857 JARED PATTERSON: I’ll I’ll probably be on in the morning. Not sure about the afternoon, but I was gonna ask you
339 00:29:32.940 ⇒ 00:29:37.489 JARED PATTERSON: like. Have you been marking down which ones you’ve recorded on that little table or no?
340 00:29:37.490 ⇒ 00:29:38.990 Uttam Kumaran: Yes, I have.
341 00:29:39.450 ⇒ 00:29:40.110 JARED PATTERSON: Okay.
342 00:29:45.820 ⇒ 00:29:47.440 Uttam Kumaran: Yeah. So I did some.
343 00:29:48.330 ⇒ 00:29:49.640 JARED PATTERSON: Oh, ye- so those.
344 00:29:51.850 ⇒ 00:29:55.460 Uttam Kumaran: So the 2 at the bottom. Oh, and then I also did this one on real.
345 00:29:56.930 ⇒ 00:29:57.940 JARED PATTERSON: Okay, perfect.
346 00:30:02.320 ⇒ 00:30:04.570 Uttam Kumaran: So yeah, let me know how it goes. And then I also
347 00:30:04.580 ⇒ 00:30:11.390 Uttam Kumaran: I also, you know, was able to. I I have exactly the flow of how to update the website with stuff.
348 00:30:11.620 ⇒ 00:30:17.129 Uttam Kumaran: So I’m also gonna record a little video of how to actually add a page, the site.
349 00:30:17.400 ⇒ 00:30:23.864 Uttam Kumaran: And I can, you know, get you into web flow, and we can collaborate on that, too. But okay, great. Yeah. Let me know when you log off, and then
350 00:30:24.240 ⇒ 00:30:25.580 Uttam Kumaran: catch up again next week.
351 00:30:26.890 ⇒ 00:30:28.169 JARED PATTERSON: Sounds good. Thank you.
352 00:30:28.700 ⇒ 00:30:29.380 Uttam Kumaran: Yeah.
353 00:30:29.870 ⇒ 00:30:42.590 Uttam Kumaran: okay, everyone. Let’s catch up again Monday, and we’re gonna kick off some work for the week hopefully allows you to kind of work on your own time. I know it’s been tough to meet for some of the folks in India, you know, kind of late.
354 00:30:42.620 ⇒ 00:30:44.590 Uttam Kumaran: so we’ll give you enough work?
355 00:30:46.220 ⇒ 00:30:50.389 Uttam Kumaran: to kind of take on. And then you’ll have basically have the entire team.
356 00:30:50.898 ⇒ 00:30:52.840 Uttam Kumaran: To kind of answer questions.
357 00:30:53.321 ⇒ 00:30:58.619 Uttam Kumaran: And yeah, I appreciate the hard work. So I’ll talk to you guys next week.
358 00:31:01.670 ⇒ 00:31:02.739 Priyadharshini Kalidoss: Yes. Yeah.
359 00:31:03.400 ⇒ 00:31:04.350 Priyadharshini Kalidoss: Okay. Yeah.
360 00:31:05.610 ⇒ 00:31:06.430 Uttam Kumaran: Thanks guys.
361 00:31:06.430 ⇒ 00:31:07.110 Nicolas Sucari: Thank you. Guys.
362 00:31:07.110 ⇒ 00:31:07.720 Priyadharshini Kalidoss: Yeah. No.
363 00:31:07.720 ⇒ 00:31:08.250 Nicolas Sucari: Hey bye.
364 00:31:09.630 ⇒ 00:31:10.279 Akshay kumar.G: Yeah. Boy.