Meeting Title: ETL Tools Assessment walkthrough Date: 2025-12-09 Meeting participants: Awaish Kumar, Steve Sizer, Shivani Amar, Jason Wu, Andy Weist, Uttam Kumaran, Jason W’s iPhone
WEBVTT
1 00:02:16.390 ⇒ 00:02:17.390 Awaish Kumar: I know.
2 00:02:17.690 ⇒ 00:02:18.719 Steve Sizer: Hey, how’s it going?
3 00:02:19.540 ⇒ 00:02:20.950 Awaish Kumar: All good, how about you?
4 00:02:21.460 ⇒ 00:02:22.590 Steve Sizer: I’m good, thank you.
5 00:02:23.650 ⇒ 00:02:28.620 Shivani Amar: Hi, I’ll just be right back. I just have something I need to get out of the oven. I’ll be with you in a sec.
6 00:02:29.760 ⇒ 00:02:30.410 Awaish Kumar: Okay.
7 00:02:45.050 ⇒ 00:02:48.530 Jason Wu: For some reason, my camera’s doing funky things.
8 00:02:49.370 ⇒ 00:02:50.799 Jason Wu: Bless you, Steve.
9 00:02:52.900 ⇒ 00:02:54.040 Steve Sizer: Thank you, GSN?
10 00:04:27.690 ⇒ 00:04:29.180 Shivani Amar: Are we just waiting on Uttam?
11 00:04:33.270 ⇒ 00:04:35.840 Awaish Kumar: Yeah, he’s trying to join, but…
12 00:04:39.350 ⇒ 00:04:46.000 Awaish Kumar: Zoom is, like, reloading, so we’ll join in, like, maybe a few minutes.
13 00:05:07.660 ⇒ 00:05:13.259 Uttam Kumaran: Hey, everyone! Sorry, my Zoom just, like, updated right as I joined, and I don’t know, it started glitching in, let’s go.
14 00:05:14.790 ⇒ 00:05:17.149 Uttam Kumaran: Nice to meet everyone. Nice to meet you, Steve, Andy.
15 00:05:18.670 ⇒ 00:05:19.769 Steve Sizer: Good to meet you, too.
16 00:05:20.430 ⇒ 00:05:21.120 Uttam Kumaran: Ay.
17 00:05:21.660 ⇒ 00:05:22.720 Uttam Kumaran: Hey, Shivani.
18 00:05:23.210 ⇒ 00:05:24.140 Shivani Amar: Hi!
19 00:05:26.130 ⇒ 00:05:28.189 Uttam Kumaran: Great.
20 00:05:28.420 ⇒ 00:05:37.930 Uttam Kumaran: let’s get started. So, sort of the goal of this conversation is we put together a bit of a,
21 00:05:38.040 ⇒ 00:05:56.999 Uttam Kumaran: Zoom assessment, I mean, ETL assessment, basically going through, like, what, kind of, like, the end-to-end of, like, from our experience, you know, in implementing, data platforms and considering, you know, data movement tools, sort of, like.
22 00:05:57.060 ⇒ 00:06:16.100 Uttam Kumaran: what, you know, we would suggest, the path forward would be on, like, how to move data for reporting use cases, you know, here at Element. You know, kind of one of the goals, and I think a good piece of feedback from Shivani that we took yesterday is just to give a little bit more narrative from our experience on, you know.
23 00:06:16.110 ⇒ 00:06:21.150 Uttam Kumaran: Doing this, you know, day in and day out for several other clients.
24 00:06:21.230 ⇒ 00:06:24.930 Uttam Kumaran: So I just want to confirm if everyone has that doc, and…
25 00:06:25.190 ⇒ 00:06:29.420 Uttam Kumaran: We can kind of go through it end-to-end if that’s… Fair?
26 00:06:30.990 ⇒ 00:06:32.589 Jason Wu: Yep, we all have access to the dock.
27 00:06:33.200 ⇒ 00:06:34.000 Uttam Kumaran: Okay, cool.
28 00:06:34.390 ⇒ 00:06:39.309 Jason Wu: And then, sorry, my microphone is funky right now, so that’s the reason you can’t see me right now.
29 00:06:39.540 ⇒ 00:06:40.840 Uttam Kumaran: Oh, no problem.
30 00:06:42.960 ⇒ 00:06:52.760 Uttam Kumaran: Cool, so I guess if… did everyone have a chance to sort of give it a little bit of a read, or would we prefer I just kind of walk through from the top down?
31 00:06:53.850 ⇒ 00:06:59.620 Andy Weist: I think we should go top-down. I think there are some changes since some of us reviewed it, so it’d probably be better just go through the whole thing.
32 00:07:00.420 ⇒ 00:07:01.680 Uttam Kumaran: Great, okay, let’s do it.
33 00:07:01.960 ⇒ 00:07:05.970 Uttam Kumaran: And then, I guess, overall, as, as, you know.
34 00:07:06.210 ⇒ 00:07:24.699 Uttam Kumaran: I know I didn’t get to say… give a chance to say hi last time, but I run Brainforge. We do data and data platform work, setting up reporting systems for a lot of folks, you know, in a very similar ecosystem to y’all. So, want to make sure that you guys feel really comfortable with
35 00:07:24.710 ⇒ 00:07:40.789 Uttam Kumaran: the purpose of an ETL tool, where it fits into their overall strategy of accelerating, you know, the accuracy, of insights and the accuracy how many insights that the element team can get from our data, but also
36 00:07:40.790 ⇒ 00:07:49.679 Uttam Kumaran: Not just, like, cobbling together, you know, a bunch of random systems, like, actually taking the time to understand why, you know, ETL
37 00:07:49.730 ⇒ 00:08:08.140 Uttam Kumaran: is important in a broader, like, data platform solution. So at the top of this document, I basically highlighted some of, like, what the struggle is, you know, today. You know, just from our brief period and meeting some of the folks on the team. I don’t think anybody here is,
38 00:08:08.240 ⇒ 00:08:21.160 Uttam Kumaran: you know, everything, everybody, I’m sure, on the team understands some of, like, what happens on the reporting side of the business, whether it’s information coming from source medium, whether it’s information coming from source systems like Shopify, Amazon.
39 00:08:21.160 ⇒ 00:08:36.510 Uttam Kumaran: Whether it’s, like, actual manual ad reporting coming from other ad systems. So one of the core parts of a data, you know, data platform is loading and transforming data. And so this is, like, really the start of, like, okay.
40 00:08:36.840 ⇒ 00:08:43.909 Uttam Kumaran: Element uses several different vendors to run, you know, the business. This can be everything from
41 00:08:43.909 ⇒ 00:09:02.750 Uttam Kumaran: you know, Amazon to sell Amazon stuff, Shopify, so, you know, kind of just to even be very, very specific. It’s just, they’re systems that run Elements Business. That data in those systems is what y’all are reporting on. Right now, the team uses a mix of different ways of getting that data out, whether it’s
42 00:09:02.790 ⇒ 00:09:09.370 Uttam Kumaran: literally copying and pasting it from a UI, whether it’s manually exporting it out, and in some ways, whether it’s, like, programmatic.
43 00:09:09.390 ⇒ 00:09:25.099 Uttam Kumaran: And so part of our goal as a data team, commonly, is to centralize that data in order to combine it, and report on it, and actually provide a source of truth for information. For example, there are differences in the way Amazon reports on refunds than Shopify.
44 00:09:25.100 ⇒ 00:09:42.899 Uttam Kumaran: That is a nuance that, if you were owning Shopify, you would understand Shopify, you’re owning Amazon, you understand Amazon, but for anybody else in the business, it’s a very hard context to keep in your head. And those are just two systems, and just two parts of those systems. And so for any single system, there are…
45 00:09:42.900 ⇒ 00:09:57.910 Uttam Kumaran: Tons and tons of these nuances that typically the goal of a data team is to standardize those based on accepted metrics and accepted business definitions, and then make that available for reporting so that more and more people in the company can leverage data for their day-to-day.
46 00:09:58.270 ⇒ 00:09:59.490 Uttam Kumaran: That’s, like.
47 00:09:59.780 ⇒ 00:10:18.650 Uttam Kumaran: the gist of, you know, kind of, like, why data? In terms of, ETL, you know, ETL, extract, transform, load, often you see ELT. This is basically, how do we land data in a place for us to actually model it, and transform it for reporting.
48 00:10:18.820 ⇒ 00:10:23.240 Uttam Kumaran: And so, Tiki, can I even go a little bit, about, like.
49 00:10:23.280 ⇒ 00:10:38.999 Uttam Kumaran: a lot of the history here is there have always been a lot of vendors whose main job is to move data from one system to somewhere that you want. This is like a, you know, this is an industry that’s existed for a while. There have been a lot more players that have entered in that are offered cheaper, more reliable.
50 00:10:39.000 ⇒ 00:10:43.749 Uttam Kumaran: you know, integrations with systems, but what happens in the ETL world is
51 00:10:43.750 ⇒ 00:10:58.620 Uttam Kumaran: the number of tools grows. Like, as you guys know, if you’ve worked in e-commerce or technology, new systems are coming across, you know, the team all the time, and so one of the core parts of some of the tools that we’ve recommended here is their ability to, one, work with a wide
52 00:10:58.620 ⇒ 00:11:06.650 Uttam Kumaran: variety of systems. Two is, like, reliability. And three, if they see new systems, how do they actually approach and develop
53 00:11:06.670 ⇒ 00:11:20.020 Uttam Kumaran: you know, solution to move that data. What is the alternative to using an ETL tool? Well, you could build all this yourself, and so before ETL tools, data teams built Python, you know, Python or otherwise,
54 00:11:20.020 ⇒ 00:11:31.890 Uttam Kumaran: you know, data flows, right? Where you’re calling an REST endpoint, you’re taking a piece of data, and you’re moving it. But what happens, like, that is not just a one-time thing. You have to own that process, you have to monitor it.
55 00:11:31.890 ⇒ 00:11:45.699 Uttam Kumaran: And now you have to call several endpoints for one vendor, and then now you’re basically, running data flows. This is something that a lot of companies do manually, but the reason you go with a tool is, one, this is not a unique
56 00:11:45.770 ⇒ 00:11:52.300 Uttam Kumaran: advantage point for Element to own this process. There is not much alpha in
57 00:11:52.580 ⇒ 00:12:08.879 Uttam Kumaran: you know, hiring a bunch of data engineers to rewrite Shopify pipelines to land data into Snowflake. Moving data from several of these vendors into a data warehouse is now, you know, I would describe as, like, commoditized. Meaning, many people do this, many people offer
58 00:12:08.880 ⇒ 00:12:24.000 Uttam Kumaran: these data pipelines as a service, and it’s really actually a lot more cost-effective even now than it was 5 or 10 years ago to do this. So part of this is, like, truly, okay, we want to offload the ownership and the management of these
59 00:12:24.030 ⇒ 00:12:27.410 Uttam Kumaran: these data movement workloads to a vendor.
60 00:12:27.660 ⇒ 00:12:37.349 Uttam Kumaran: and then focus on the actual meat of the bone for Element, which is on managing KPIs, data modeling, and then making sure reporting gets adoption. So that’s a little bit about, like.
61 00:12:37.590 ⇒ 00:12:38.730 Uttam Kumaran: ETL.
62 00:12:39.130 ⇒ 00:12:43.439 Uttam Kumaran: Any questions there? And I’ll just keep going, but any thoughts or questions?
63 00:12:45.740 ⇒ 00:12:46.879 Jason Wu: Well, no questions answered.
64 00:12:46.880 ⇒ 00:12:47.410 Uttam Kumaran: Right.
65 00:12:48.750 ⇒ 00:12:58.119 Uttam Kumaran: Cool, so basically, we’re talking about, like, you know, why this matters now, and I think, you know, kind of a couple things that we’re looking at on the team is, one,
66 00:12:58.210 ⇒ 00:13:10.879 Uttam Kumaran: we’re starting to see a couple of symptoms of, like, why a data platform could be helpful. One is you don’t want your team to be focused on data entry and data movement. That is not what their superpower is.
67 00:13:10.880 ⇒ 00:13:28.980 Uttam Kumaran: And that is something you want to offload, whether it’s to a team or to a service, but you don’t want someone like Carlos, with his ultimate wisdom to be spending time copying and pasting from source media and potentially getting something wrong, right? I think we can probably all agree on that. So part of that is just making sure that teams can focus on where
68 00:13:28.980 ⇒ 00:13:41.460 Uttam Kumaran: you know, their superpower is. The second thing is Element is growing, and growing the number of data sources, right? As you guys grow into different retail channels, as you’re leveraging more tools to run the business.
69 00:13:41.460 ⇒ 00:13:51.639 Uttam Kumaran: These are all sources of data that the operators in the business want to leverage to report. And so that’s growing, right? The number of tools, you know, I’m sure as Jason, you guys, and the team.
70 00:13:51.640 ⇒ 00:13:57.709 Uttam Kumaran: you know, are aware of that. The other piece is, you know, NetSuite. So, NetSuite is coming up.
71 00:13:57.720 ⇒ 00:14:17.030 Uttam Kumaran: This is something that I know, the team is thinking about, and so this is going to be a huge source of data that the team is going to want to report on outside of NetSuite, to combine NetSuite data with other sources, and so we’re going to have to find a solution to move that data into a single, area. And then the last piece is just, like.
72 00:14:17.370 ⇒ 00:14:24.569 Uttam Kumaran: being able to actually look at data in a combined fashion within a timely manner. If something happens, you know, last month.
73 00:14:24.570 ⇒ 00:14:39.619 Uttam Kumaran: you don’t want to have to cobble together CSVs, put together a report, and then sort of have half confidence. You want to actually be able to have reliable reporting, and you want to have that scale to people that may not have the skill set to, like, do all of that manual data,
74 00:14:39.620 ⇒ 00:14:51.969 Uttam Kumaran: you know, pulling together work. So this is part of the reason this matters, and so I’ll kind of go through the recommendation and investment and stuff, but maybe I’ll just talk a little bit about kind of, like, what we’ve learned about
75 00:14:52.260 ⇒ 00:14:56.170 Uttam Kumaran: CPG in a lot of our work. One is, like.
76 00:14:56.560 ⇒ 00:15:03.400 Uttam Kumaran: commonly, all of these sources have different levels of lag. Even in talking to Carlos, we saw that, like.
77 00:15:03.400 ⇒ 00:15:21.410 Uttam Kumaran: there’s lag in Amazon that, like, takes 7 days to close, or other things close, so there are just these nuances about every single data source, that require attention. There’s also a lot, you know, that happens during, like, promotional periods, where there’s huge volume surges, that we see commonly across a lot of our
78 00:15:21.470 ⇒ 00:15:33.879 Uttam Kumaran: companies. You know, we work with another flower company, and so they do, like, 80% of their sales on, like, two weekends, Mother’s Day and Valentine’s Day. So it’s like an… there are just, like, these data challenges that are unique to
79 00:15:33.910 ⇒ 00:15:47.940 Uttam Kumaran: to, you know, e-commerce, and retail. Revenue definitions, so, you know, things like sales or customer acquisition costs, there are… there’s a lot of nuances depending on who’s using it, and what they’re using it for.
80 00:15:48.040 ⇒ 00:15:50.369 Uttam Kumaran: Settlement, so there’s just, like, hey.
81 00:15:50.440 ⇒ 00:16:04.159 Uttam Kumaran: for example, when a refund comes in on Amazon, does it get booked and actually adjust the month in which the order was placed, or does it actually get booked to the month, in where the refund came in? These are just, like, nuances about each system that
82 00:16:04.210 ⇒ 00:16:12.599 Uttam Kumaran: You may find one or two people know, but they’re… that knowledge doesn’t get scaled, and that… that isn’t, like, widely understood, and its impact.
83 00:16:12.650 ⇒ 00:16:21.450 Uttam Kumaran: And then the last piece is, like, wholesale fragmentation. So, as you guys start to do things in wholesale, in retail.
84 00:16:21.470 ⇒ 00:16:35.590 Uttam Kumaran: And in industries where they oftentimes don’t have great digital ecosystems, you’re gonna start to have to deal with CSVs, SFTP, like, kind of, like, sometimes garbage-in type
85 00:16:35.590 ⇒ 00:16:44.359 Uttam Kumaran: data systems. And so it’s really, really important for us, like, when we’re choosing a data ingestion tool, to understand that not everything is going to have a perfect, like.
86 00:16:44.400 ⇒ 00:16:59.920 Uttam Kumaran: REST API endpoint. We may be dealing with situations where we have to land flat files. In fact, that is, like, one of the requirements, you know, even for spins data, I think, that we leverage flat files, flat files being, like, CSVs or… or Parquet files, and so…
87 00:17:00.020 ⇒ 00:17:15.450 Uttam Kumaran: you kind of want to understand that there are systems like Shopify that perfect APIs, we can call them, and we can get data out, and there’s a ton of vendors that support points for Shopify, but we also need to support, like, the nth, you know, vendor that Element may bring on, and so…
88 00:17:15.450 ⇒ 00:17:22.250 Uttam Kumaran: It’s… this is sort of why, you know, you’re gonna see that we recommend a couple tools, but it’s mainly to account for the fact that
89 00:17:22.280 ⇒ 00:17:26.490 Uttam Kumaran: part of choosing an ETL tool in all of our experience is that
90 00:17:26.490 ⇒ 00:17:44.229 Uttam Kumaran: not any vendor is gonna have all of it. You’re gonna… you’re gonna… for example, Emerson is something that’s new to us, like, we haven’t seen another client with Emerson. We have seen similar systems, but, you know, it’s just something new, and so we want to make sure to choose a vendor that works for you, that can actually say, great, I can…
91 00:17:44.230 ⇒ 00:17:50.330 Uttam Kumaran: let’s say Emerson had an API, I can support building that, and I can actually support hosting and maintaining that.
92 00:17:50.430 ⇒ 00:18:04.220 Uttam Kumaran: In the event they can’t, what does that mean? It’s like, Element has to own the creation of a data pipeline to own that, the maintenance of that, when it goes down, who’s gonna own that, like… and these are not things that, like, Element wants to spend time or money
93 00:18:04.220 ⇒ 00:18:20.179 Uttam Kumaran: running data pipelines. You know, we want to spend time and money understanding the business and understanding how to grow faster. So, kind of like our philosophy and sort of how we think about UTL is written here. But we really always recommend using managed services.
94 00:18:20.180 ⇒ 00:18:24.259 Uttam Kumaran: In particular, unless you’re in, like, an extremely regulated environment.
95 00:18:24.350 ⇒ 00:18:31.669 Uttam Kumaran: Where there’s a lot of PII, or, you know, oftentimes for some of our health clients, there may be restrictions here.
96 00:18:31.740 ⇒ 00:18:36.200 Uttam Kumaran: We’re often like, hey, leverage these managed services for as much as ingestion as possible.
97 00:18:36.270 ⇒ 00:18:47.409 Uttam Kumaran: And you really want to think of ETL, as, like, piping in the wall. Ideally, after this sort of decision, we don’t want to spend time talking about these vendors.
98 00:18:47.410 ⇒ 00:18:58.560 Uttam Kumaran: like, they are not gonna be the heroes of the system, they’re just gonna be moving data from one place to another. So you kind of want… you don’t… oftentimes, and we’ll talk about, like.
99 00:18:58.560 ⇒ 00:19:13.769 Uttam Kumaran: a lot of the options that are in this space, oftentimes the ones that we don’t recommend are… are a lot of the systems that we’ve been called into clients to rip out because they’ve either been sunset, or they’ve been bought by another company and then sort of, like, left to die, or there’s no option for support.
100 00:19:14.090 ⇒ 00:19:15.260 Uttam Kumaran: And so, like…
101 00:19:15.490 ⇒ 00:19:25.939 Uttam Kumaran: This is where we just, you know, we work a lot with a lot of these vendors, and so these two are the ones that, when we come in, we stamp that we feel are great,
102 00:19:26.130 ⇒ 00:19:45.629 Uttam Kumaran: And of course, we work with a lot of vendors whose cost may be a factor, reliability, speed may be a factor, so this is all sort of dependent on, you know, the ecosystem, but these are ones that we know will allow us to go focus on the issues that actually matter for the company, and not sit right, you know, Python data pipelines that
103 00:19:45.760 ⇒ 00:19:47.269 Uttam Kumaran: You know, we have to maintain.
104 00:19:47.550 ⇒ 00:19:54.819 Uttam Kumaran: So, maybe I’ll pause there just before talking a little bit about assumptions. Any questions on, like.
105 00:19:55.570 ⇒ 00:20:01.090 Uttam Kumaran: ETL, data movement… I haven’t spoken at all about, like, storing the data.
106 00:20:01.220 ⇒ 00:20:07.670 Uttam Kumaran: I can go into that, you know, probably towards the end, but, like, Any questions on, like.
107 00:20:08.490 ⇒ 00:20:12.020 Uttam Kumaran: this type of data movement. And also, are we doing anything that, like.
108 00:20:12.470 ⇒ 00:20:14.499 Uttam Kumaran: Looks like this today, by the way.
109 00:20:14.700 ⇒ 00:20:16.599 Uttam Kumaran: Yeah, that’s a question for my side.
110 00:20:18.910 ⇒ 00:20:27.649 Andy Weist: We are… we’re not doing a ton of, like, ETL-type stuff today. We did employ Soligo as an IPaaS platform for…
111 00:20:27.650 ⇒ 00:20:28.180 Uttam Kumaran: Hey.
112 00:20:28.180 ⇒ 00:20:42.660 Andy Weist: an ERP integration that we then abandoned, so we’re moving to the NetSuite one now, so we don’t really currently have Soligo in any critical path for anything currently. We are moving some of our backend stuff to more of an event-driven architecture. We do have
113 00:20:42.720 ⇒ 00:20:51.870 Andy Weist: queuing systems going between our Shopify store and our 3PL, things like that, and we are moving more towards, like, a.
114 00:20:51.870 ⇒ 00:20:56.980 Uttam Kumaran: a queuing Kafka event-based architecture in general. Right.
115 00:20:56.980 ⇒ 00:21:06.859 Andy Weist: Those are gonna be more custom systems, back-end systems, rather than, like, putting them on… at least the current plan is not to employ, like.
116 00:21:07.270 ⇒ 00:21:10.350 Andy Weist: hosted ETL platforms for things like that.
117 00:21:10.450 ⇒ 00:21:19.259 Andy Weist: One thing I’m not sure if we were going to get to it, but on the ETL end, is the decision to…
118 00:21:19.340 ⇒ 00:21:33.410 Andy Weist: you know, the recommendation of having multiple systems versus trying to simplify to one single one, and what the trade-offs are. I know, you know, you spoke to not everything has a connector for every system.
119 00:21:33.430 ⇒ 00:21:50.849 Andy Weist: But most platforms do have the ability to write connectors, whether we employ their professional services or write them ourselves. I just want to focus a little bit on the trade-off of trying to manage two systems, employ two systems, pay two systems, versus trying to consolidate into one.
120 00:21:51.670 ⇒ 00:21:59.759 Uttam Kumaran: Definitely, yeah, like, maybe if we can, you know, let me just speak about that briefly. I’m also in a very similar light, like, I…
121 00:21:59.870 ⇒ 00:22:14.680 Uttam Kumaran: I don’t really like at all to say we need two people to come do this job for us. I would much rather consolidate. Part of the reason why we recommended both of these, is one, and, you know, I think
122 00:22:14.830 ⇒ 00:22:23.239 Uttam Kumaran: if this is the primary factor or not, I think I’ll let the team decide, but the cost is quite significant, quite significantly less
123 00:22:23.320 ⇒ 00:22:43.140 Uttam Kumaran: For Polytomic than it is for Fivetran. So there is a… and, you know, for that… for basically a very, very similar service. You know, we find very similar up times and, you know, actually quite a lot better support from Polytomic, so it’s one of the reasons why we commonly recommend them.
124 00:22:43.170 ⇒ 00:22:51.939 Uttam Kumaran: the thing with Fivetrans, it’s sort of the, like, number one in this world, both in, like, marketing spend.
125 00:22:51.940 ⇒ 00:23:07.029 Uttam Kumaran: But also in, like, how much of the market they’ve taken up. However, there’s also still a lot of other competitors, like Matillion, Informatica, like, there’s still a lot of, like, legacy providers, but I would say in terms of, like, what’s commonly recommended, FiveTrain is there.
126 00:23:07.030 ⇒ 00:23:12.779 Uttam Kumaran: You just have sources that we’re seeing within the Element ecosystem that they don’t directly support.
127 00:23:12.780 ⇒ 00:23:32.400 Uttam Kumaran: And so we have two options as a data team at that point. One, I can go to Fivetran and basically ask, hey, like, is this on the roadmap? And they typically come back with two… couple options. One, they’re like, oh, we do have, like, a private beta for that connector, but it’s gonna come with these caveats, blah blah blah. Second, oh, we don’t, but it’s on the roadmap, and here’s…
128 00:23:32.400 ⇒ 00:23:36.310 Uttam Kumaran: when it’s gonna be built, often that’s, like, not, like… it’s usually not…
129 00:23:36.310 ⇒ 00:23:43.459 Uttam Kumaran: oh yeah, it’s coming out next week, that’s, like, at some point it’ll be out. And the third is, like, oh, that’s not on the roadmap, and happy to, like.
130 00:23:43.960 ⇒ 00:24:00.620 Uttam Kumaran: you can submit a ticket, basically. So they’ve… when I started using Fivetron was almost, 10 years ago now, and they’re a lot different company, and they’re not building net new connectors nearly as often. Their business has shifted primarily towards enterprise and building enterprise. So.
131 00:24:00.620 ⇒ 00:24:14.619 Uttam Kumaran: for a system like NetSuite, Salesforce, you know, any type of SAP, they’re really, really strong, but for net new connectors, for use cases, like, if I was to have them build something for spins, for example, I doubt that they would
132 00:24:15.110 ⇒ 00:24:34.199 Uttam Kumaran: you know, make that happen. On the other side, like, given the fact that that’s where they are, that in a market like ETL, you know, there comes new entrants. And so Polytomic is one of the ones that, when I started the business, we went into the market to basically find, like, okay, we’re gonna come into several companies, and we’re gonna be implementing ETL,
133 00:24:34.210 ⇒ 00:24:50.379 Uttam Kumaran: I want us to have a really opinion on several different options that work, of course, including the option of, like, let’s just build stuff ourselves. And so these guys are folks that I spent a long time working with, have worked really, really well for almost, like, 5 or 6 of our clients.
134 00:24:50.380 ⇒ 00:25:01.879 Uttam Kumaran: are extremely responsive in Slack on the support side, and have built several net new connectors in, quite, quick turnaround time with no additional cost.
135 00:25:01.880 ⇒ 00:25:15.900 Uttam Kumaran: So for that reason, and I… and again, like, I think that was really what was… sealed the deal for me in recommending them, is that their support is way better than Fivetran. If Fivetran goes down, we just submit… we have to submit a ticket.
136 00:25:15.900 ⇒ 00:25:23.319 Uttam Kumaran: usually for these folks, we have them in a channel with their CEO and their core, you know, technical engineering team.
137 00:25:23.320 ⇒ 00:25:39.240 Uttam Kumaran: Second is the pricing is extremely favorable. You know, they’ve kept their expenses low on, you know, a lot of, like, what Five Trainer spends on marketing and G&A, and they’ve been able to pass that to their customers. And then I’m not seeing any issues on reliability.
138 00:25:39.270 ⇒ 00:25:56.049 Uttam Kumaran: So that’s, like, kind of the reason we… we recommend two. Part of this whole breakdown is that we are… we would like to start with Fivetran. We can do a lot of these on Fivetran and show the cost outlays, but of course, like, part of this is, like, as I mentioned, it’s a piping in the wall.
139 00:25:56.220 ⇒ 00:26:11.540 Uttam Kumaran: you really don’t want to talk about these folks. So, for example, if, like, something happens to a Fivetran connector, that’s… that, like, cuts off everything downstream of it. And so we… part of, like, why we recommend Polytomic is because of their support is, like.
140 00:26:11.740 ⇒ 00:26:15.699 Uttam Kumaran: really, really great. I don’t know, Wish, if you have any other, like.
141 00:26:15.940 ⇒ 00:26:18.270 Uttam Kumaran: Thoughts there, or anything to add?
142 00:26:18.950 ⇒ 00:26:27.449 Awaish Kumar: Yeah, I agree with what you said, and yeah, like… The 5chan is really…
143 00:26:29.400 ⇒ 00:26:36.200 Awaish Kumar: costly, and we can, like, optimize our cost if we are using Paradomic. And second thing, also.
144 00:26:36.500 ⇒ 00:26:41.180 Awaish Kumar: what Utam said, like, if one of the tools has downtime.
145 00:26:41.310 ⇒ 00:26:48.550 Awaish Kumar: Anywhere, and, like, we can just spin up the connectors from another tool, like, we have the backup.
146 00:26:51.170 ⇒ 00:27:03.850 Andy Weist: Okay, so there’s some semblance of redundancy there as well. And just coming back to… so, to be clear, though, there is the ability to write custom connectors with an SDK in Fivetran, right? So if we need to connect.
147 00:27:03.850 ⇒ 00:27:05.679 Uttam Kumaran: Similarly, in polyatomic style.
148 00:27:05.680 ⇒ 00:27:06.210 Andy Weist: bill.
149 00:27:06.680 ⇒ 00:27:07.470 Andy Weist: Okay.
150 00:27:07.950 ⇒ 00:27:09.090 Uttam Kumaran: Yeah. If…
151 00:27:09.200 ⇒ 00:27:13.540 Andy Weist: Cost was no object. Would you just move everything to Fivetran?
152 00:27:17.110 ⇒ 00:27:25.560 Uttam Kumaran: I still think, like, the support, like, to be honest, the support has gone worse over the years. And so this is the thing, like, as a, as, like.
153 00:27:26.180 ⇒ 00:27:35.600 Uttam Kumaran: as the, you know, if I’m giving my input here, it’s really nice to have, like, the full technology team for any vendor that we’re using, like, on speed dial.
154 00:27:36.080 ⇒ 00:27:44.180 Uttam Kumaran: And so, I… I oftentimes have found that that’s incredibly effective, and for ETL, which is just, again.
155 00:27:44.260 ⇒ 00:27:57.290 Uttam Kumaran: somewhat of, like, you just need it to work all the time, like, you just can’t have the power go out. It’s really nice. I’ve found that over the last, like, 5-6 years, like, the quality of service and support from Fivetran has gone down. While their pricing has, like.
156 00:27:57.500 ⇒ 00:28:03.380 Uttam Kumaran: basically gone up, like, 3X. It just changed, like, last week, actually, again.
157 00:28:03.510 ⇒ 00:28:19.580 Uttam Kumaran: So… but again, if price isn’t an option, and I would love to consolidate everything to 5 train. I don’t work for these guys. Like, I work for you guys. So I don’t… I don’t have… I don’t, I don’t care about either of them much. I, in fact, just care about redundancy, and I care about the fact that, like.
158 00:28:19.940 ⇒ 00:28:29.309 Uttam Kumaran: this is something we… we just know works all the time. I would… if Fivetrain was more… more open to building new connectors for us, I would probably just go with them, you know, no problem.
159 00:28:30.010 ⇒ 00:28:35.260 Shivani Amar: Does the 5TREN cost include, like, DBT because they acquired DBT?
160 00:28:36.090 ⇒ 00:28:36.919 Shivani Amar: Is that why the price.
161 00:28:36.920 ⇒ 00:28:42.749 Uttam Kumaran: No, not, not, no, not, not, they haven’t done, like, any sort of cost integration yet.
162 00:28:43.460 ⇒ 00:28:43.820 Shivani Amar: Hmm.
163 00:28:43.900 ⇒ 00:28:49.399 Uttam Kumaran: And dbt’s pricing is on… currently is on a user basis. So you’re… you’re…
164 00:28:49.950 ⇒ 00:28:59.360 Uttam Kumaran: It’s… yeah, it’s more of, like, if we have, like, 5 users using dbt, we pay, like, 50 or 100 bucks a month per user. They haven’t done any consolidation. I assume…
165 00:28:59.500 ⇒ 00:29:02.210 Uttam Kumaran: They will start to do that.
166 00:29:02.490 ⇒ 00:29:12.809 Uttam Kumaran: But there’s other ways for us to offset the DBT cost. And frankly, the bulk of the cost in this entire data platform system is going to come from ETL and BI.
167 00:29:13.020 ⇒ 00:29:21.250 Uttam Kumaran: Warehousing and… Dbt are the lowest two of the cost centers in the entire system.
168 00:29:23.230 ⇒ 00:29:27.500 Jason Wu: He had to, like… I understand, kind of, the redundancy, but…
169 00:29:28.930 ⇒ 00:29:33.490 Jason Wu: Why… so, given that, like, the focus… Well…
170 00:29:34.140 ⇒ 00:29:42.169 Jason Wu: the focus is on, kind of, like, the Shopify component first. Like, does Polytomic have, like, an Amazon connector already?
171 00:29:42.450 ⇒ 00:29:48.830 Jason Wu: Like, I guess the question is, like, why look at Fivetran? I know, I understand the redundancy, but, like, if we’re starting small.
172 00:29:49.070 ⇒ 00:29:53.369 Jason Wu: Polyatomic seems like… The one that’s more nimble?
173 00:29:53.380 ⇒ 00:30:11.780 Jason Wu: If we need to switch out, we could. And I’m having trouble kind of understanding the redundancy anyways, because we’re going to use specific connect… the recommendation is to use specific ETLs for specific connectors, right? So, it’s only redundant from the perspective that if one goes down, we gotta spin up another one in a different system.
174 00:30:13.440 ⇒ 00:30:33.169 Uttam Kumaran: Yeah, I would say… I would say, for one is… it’s for me to understand, you know, what is… what is the price sensitivity here. You know, second is, both of these we can start as trials and go month to month, so they kind of fit our procurement criteria in that we don’t… we don’t necessarily need to,
175 00:30:33.450 ⇒ 00:30:37.210 Uttam Kumaran: you know, sign any long-term agreements with.
176 00:30:37.400 ⇒ 00:30:47.620 Uttam Kumaran: I would prefer just go with one. The reason why I would suggest both is, for example, like, where to go, potentially spins and others.
177 00:30:47.820 ⇒ 00:30:54.170 Uttam Kumaran: I… we’re not gonna have much luck going to these guys and asking them to build it. Like, they are…
178 00:30:54.540 ⇒ 00:30:58.099 Uttam Kumaran: think of them like the NetSuite of, like, ETL, like, it’s…
179 00:30:58.270 ⇒ 00:31:01.679 Uttam Kumaran: they have a roadmap, and it’s pretty set. For PolyTom.
180 00:31:01.680 ⇒ 00:31:03.960 Andy Weist: We can build it ourselves, right?
181 00:31:03.960 ⇒ 00:31:05.760 Uttam Kumaran: We can also build it, yeah, we can totally build it.
182 00:31:05.760 ⇒ 00:31:13.209 Andy Weist: I want to keep that on the table, just saying. Like, we do have resources to be able to build these, if that factors into the long-term decision.
183 00:31:13.860 ⇒ 00:31:20.859 Uttam Kumaran: No, totally, and so this is another thing. If supporting a data source means calling, like, one endpoint and moving
184 00:31:20.980 ⇒ 00:31:23.480 Uttam Kumaran: like… one,
185 00:31:23.690 ⇒ 00:31:38.919 Uttam Kumaran: data frame over. That’s like… yeah, I guess those are… those aren’t as the things I would just basically kick over to them. It’s more like, hey, let’s say where to go is actually pretty complicated to support, and there’s tons of tons of volume.
186 00:31:39.290 ⇒ 00:31:46.420 Uttam Kumaran: You know, it’s up to us to decide, okay, do we support that? And, like, what is the engineering cost, the maintenance cost, and just, like.
187 00:31:46.740 ⇒ 00:31:52.050 Uttam Kumaran: having that as something to make… to kind of contemplate versus going to one of these vendors.
188 00:31:52.740 ⇒ 00:32:00.810 Uttam Kumaran: I would, you know, for me, if… ultimately, I would like to consolidate all to one. I think if Polytomic can handle all of these sources, then
189 00:32:01.170 ⇒ 00:32:05.460 Uttam Kumaran: consolidating there and having Fivetran as, like, a turn-on as backup.
190 00:32:05.940 ⇒ 00:32:11.289 Uttam Kumaran: would be best. But I wanted to sort of, like, show kind of, like, what all the options are.
191 00:32:12.890 ⇒ 00:32:32.100 Uttam Kumaran: the cost… the cost, difference in Polyatomic is extremely significant. And also, this isn’t, you know, I do… I do know that, like, Fivetran, Matillion are probably, like, the top two in this world. There are some vendors that we ruled out, like, AirByte, HEVO, Matillion, Stitch, Rudderstack.
192 00:32:32.270 ⇒ 00:32:45.389 Uttam Kumaran: For example, there’s another one called Portable. So we, like, are constantly in this market looking for these tools. A lot of these, like, come across really great, but extremely high maintenance, extremely fragile.
193 00:32:45.650 ⇒ 00:32:59.900 Uttam Kumaran: tool like Hivo and Stitch have both been bought and basically left to die. And then Matillion is just really, really hard to configure, so we’ve sort of arrived at these both as, like, typically what we
194 00:33:00.540 ⇒ 00:33:07.110 Uttam Kumaran: what we recommend, and Polytomic has a lot of pretty large enterprise customers, like the NFL, Okta, you know.
195 00:33:07.380 ⇒ 00:33:11.709 Uttam Kumaran: customers like that as well. They just don’t do any… they just don’t do good at marketing at all.
196 00:33:12.470 ⇒ 00:33:26.359 Shivani Amar: So, so, question for you with them, because it’s like, I would say price sensitivity, like, I know I was pushing a little bit on, like, give me a sense of the total cost, more to, like, talk about, like, landing the plane of the analysis, less about, like, cost is gonna be the lever that we go with, or something like that.
197 00:33:26.360 ⇒ 00:33:33.259 Shivani Amar: So, like, let’s just play the world for a second of, like, let’s say we only go polyatomic. What are the downsides?
198 00:33:34.550 ⇒ 00:33:35.980 Uttam Kumaran: Yeah, I mean,
199 00:33:37.710 ⇒ 00:33:55.610 Uttam Kumaran: I think the only downside to Polytomic, and this is after, you know, we’ve been implementing them for 3 years at the company, is just that they’re not… they’re not as well-known in the market, and then second is their larger, like, their NetSuite-style connectors may not be…
200 00:33:56.170 ⇒ 00:34:00.699 Uttam Kumaran: the most sophisticated in that they just haven’t been running NetSuite connectors for probably more.
201 00:34:00.700 ⇒ 00:34:01.100 Shivani Amar: Right.
202 00:34:01.100 ⇒ 00:34:02.089 Uttam Kumaran: One or two years.
203 00:34:02.090 ⇒ 00:34:17.130 Shivani Amar: And we know we’re gonna want to run NetSuite Connector, like, so if we just think about future state stack, we’re like, we know that we’re gonna want data flowing into a warehouse, data flowing from warehouse into NetSuite, data flowing from NetSuite into warehouse. So if you
204 00:34:17.719 ⇒ 00:34:19.429 Shivani Amar: Fivetran and Net.
205 00:34:19.429 ⇒ 00:34:19.919 Jason W’s iPhone: And net…
206 00:34:19.920 ⇒ 00:34:21.779 Shivani Amar: go the best together, then…
207 00:34:22.480 ⇒ 00:34:31.709 Shivani Amar: I think I’m echoing. But if you think Fivetran and NetSuite go the best tailor, then that’s, like, a helpful framing for, like, the future state stack, I think.
208 00:34:31.719 ⇒ 00:34:37.819 Uttam Kumaran: I would say for anything that is enterprise, like a NetSuite, or if we’re doing any other large ERP,
209 00:34:37.899 ⇒ 00:34:46.279 Uttam Kumaran: or, like, SAP Salesforce, 5chan has been doing those integrations for years. Like, that’s their bread and butter.
210 00:34:46.279 ⇒ 00:34:59.329 Uttam Kumaran: Polytomic is, as Jason mentioned, they’re nimble, and so we’ve gone to them before, and like, for example, we had a client that uses GoHighLevel for something, and we were like, hey guys, we need a GoHighLevel connector, and they’re like, cool, we’ll build it for you in, like, 2 weeks.
211 00:34:59.930 ⇒ 00:35:02.930 Uttam Kumaran: That was, like, huge, because my team was gonna have to…
212 00:35:03.430 ⇒ 00:35:15.790 Uttam Kumaran: either build that myself, or I was gonna have to go to the… to the next rung of options, you know? Because that is not something that we should be maintaining. So that was a great situation in where
213 00:35:15.920 ⇒ 00:35:27.840 Uttam Kumaran: they were a great partner, and they built it for us. Similarly, I don’t know, Wish, like, we’ve probably had them build almost, like, 5 or 6 other… 5, 6, 7 other connectors for us, and they’ve done a… they’ve done, like, a bang-up job. It’s been… it’s been awesome.
214 00:35:27.970 ⇒ 00:35:29.820 Uttam Kumaran: You know, so…
215 00:35:30.410 ⇒ 00:35:34.800 Awaish Kumar: Yeah, they have been, like, one week turnaround time.
216 00:35:35.700 ⇒ 00:35:38.380 Uttam Kumaran: Yeah, unless even about the speed, it’s just that, like.
217 00:35:38.790 ⇒ 00:35:45.080 Uttam Kumaran: there’s not often where we have vendors that, like, listen, and actually, like, are supportive. Most of the time, you buy software, and it’s like.
218 00:35:45.310 ⇒ 00:35:46.440 Uttam Kumaran: Oh, that’s just what it is.
219 00:35:46.440 ⇒ 00:35:48.069 Shivani Amar: It is what it is, yeah.
220 00:35:48.530 ⇒ 00:35:57.709 Uttam Kumaran: that’s why it’s been nice. And for all the vendors you’ll see us recommend, usually I try to just make sure that if something happens, we have a through line into support.
221 00:35:57.820 ⇒ 00:36:02.519 Uttam Kumaran: Like, whether that’s, like, something that we’ve been able to get, or that’s available for everyone, but, like.
222 00:36:03.200 ⇒ 00:36:08.690 Uttam Kumaran: we’re not just, like, I don’t want to just throw bad tools in the stack, like, it’ll… it’ll really affect us, and so both of these
223 00:36:08.890 ⇒ 00:36:16.190 Uttam Kumaran: we’ve been… 5Train in particular, like, I’ve used for most of my career, and then Polytomic over the last 2-3 years has been really, really great.
224 00:36:17.270 ⇒ 00:36:20.719 Uttam Kumaran: So if I was to sort of, like, round this out,
225 00:36:20.890 ⇒ 00:36:25.899 Uttam Kumaran: I… one thing we can do is, I would suggest we have both for redundancy.
226 00:36:26.020 ⇒ 00:36:42.710 Uttam Kumaran: we can totally get a better sense of the cost. I think you’re gonna find that Polyatomic is quite dramatically cheaper, but I want to also give this team a little bit of understanding for, like, NetSuite, and for Shopify, and for Amazon, like, for where our core volume is and the core complexity is.
227 00:36:42.710 ⇒ 00:36:47.510 Uttam Kumaran: how sophisticated their endpoints are, and I think a waste, that’s for us to compare.
228 00:36:47.660 ⇒ 00:37:00.929 Uttam Kumaran: But for tools like Where2Go, and for other tools that I know are coming down the stack, we’re gonna need a partner like Polyatomic to support, or we’re gonna have to, you know, build it ourselves, and that will be sort of source-by-source dependent.
229 00:37:01.450 ⇒ 00:37:10.770 Uttam Kumaran: In a situation where, like, for example, Spins is giving us, like, you know, retail files, we can just throw them to a data lake.
230 00:37:10.890 ⇒ 00:37:14.550 Uttam Kumaran: Or something on DigitalOcean, and then pipe that into Snowflake.
231 00:37:14.700 ⇒ 00:37:22.719 Uttam Kumaran: that’s not a problem, right? So there’s… it’s not like everything has to go through these guys, and like, if these guys don’t support it, we’re screwed. We can always just do it
232 00:37:22.950 ⇒ 00:37:29.300 Uttam Kumaran: The usual way, which is rewrite the scripts and load it in, but… Yeah.
233 00:37:32.840 ⇒ 00:37:43.920 Uttam Kumaran: So I guess, like, what’s… you know, is there any other… I know we kind of just jumped to, like, this, but, like, is there any other helpful context here? I mean, happy to talk… like, we basically went through each of the sources, and we kind of got a sense of the volume.
234 00:37:44.030 ⇒ 00:37:48.999 Uttam Kumaran: in ETL world, The way cost works is they scale by rows.
235 00:37:49.270 ⇒ 00:37:56.589 Uttam Kumaran: kind of a weird pricing, because I don’t think really, like, the cost to maintain these connectors goes up by row, but this is just, like, what the industry sort of arrived at.
236 00:37:56.740 ⇒ 00:37:59.090 Uttam Kumaran: I don’t see, like, typically.
237 00:37:59.220 ⇒ 00:38:11.460 Uttam Kumaran: we don’t see many, like, real-time use cases, so oftentimes we can say, like, the data team will try to guarantee at least, like, 4 hours of freshness, because between landing data, modeling it, getting it to BI,
238 00:38:11.640 ⇒ 00:38:26.080 Uttam Kumaran: So unless there’s, like, any real-time, super real-time use cases, I feel like at least we’re comfortable supporting all of the existing commercial data sources. And then I think it’s just a decision on which ETL tool to go with.
239 00:38:26.210 ⇒ 00:38:31.700 Uttam Kumaran: you know, and I think that’s… that’s really what it’s gonna come down to.
240 00:38:32.990 ⇒ 00:38:44.910 Andy Weist: Others can lend their opinion, but to me, a BI tool is not supposed to be real-time anyway, so I wouldn’t be concerned about… I don’t know of any, and again, correct me if I’m wrong, anyone on our side, I don’t know of any…
241 00:38:45.450 ⇒ 00:38:46.800 Andy Weist: critical path.
242 00:38:47.010 ⇒ 00:38:55.979 Andy Weist: tools we need for real-time, real-time data. We do have, you know, Shopify reports and stuff like that are a little more real-time than I would expect our BI tool would be.
243 00:38:55.980 ⇒ 00:38:57.120 Uttam Kumaran: So… Yeah.
244 00:38:57.120 ⇒ 00:38:58.779 Andy Weist: I don’t think that’s a huge concern.
245 00:38:59.360 ⇒ 00:38:59.890 Uttam Kumaran: Okay.
246 00:39:07.520 ⇒ 00:39:13.860 Uttam Kumaran: Then the other… the other piece we talked about here is on what we call reverse ETL, or basically, like.
247 00:39:14.260 ⇒ 00:39:32.859 Uttam Kumaran: activating data. This is common in marketing, where you, for example, like, a really good use case is, like, calculating, like, a lead score. Hey, this lead is, like, really ripe for an upsell, let’s send them an email. That piece of knowledge, that calculation, has to then move into another system. This is basically, like, posting.
248 00:39:32.990 ⇒ 00:39:38.299 Uttam Kumaran: data to an endpoint. This is something that now both of these vendors are now offering.
249 00:39:38.570 ⇒ 00:39:44.960 Uttam Kumaran: again, like, I don’t think is really unique to any vendor, and both are pretty comfortable doing this, so…
250 00:39:45.370 ⇒ 00:39:55.389 Uttam Kumaran: But this is something that I know the marketing team… we see this across the board on all of our clients that we support, that once we start calculating things in the warehouse that are, like, not possible to calculate.
251 00:39:55.620 ⇒ 00:40:08.490 Uttam Kumaran: within the tool, like, propensity to spend. For example, if we’re doing identity resolution, like, hey, we saw this customer start at Amazon, and we’re able to identify them on Shopify, and we want to then send them an email.
252 00:40:08.650 ⇒ 00:40:14.720 Uttam Kumaran: Those are things that I’m sure the marketing team will want to do, and so reverse ETL is that
253 00:40:14.830 ⇒ 00:40:20.850 Uttam Kumaran: Method of actually sending data to another system for Marketing activation, typically.
254 00:40:23.110 ⇒ 00:40:25.760 Uttam Kumaran: Or, like, for example, if you want to go into your…
255 00:40:25.930 ⇒ 00:40:28.939 Uttam Kumaran: CRM and put in the total spend for a customer.
256 00:40:29.310 ⇒ 00:40:36.929 Uttam Kumaran: it’s… it’s not as easy as, like, moving from Shopify to there. You may have to combine a bunch of sources and then move it, so that’s this process.
257 00:40:43.070 ⇒ 00:41:01.309 Uttam Kumaran: So we kind of go through a little bit of, like, you know, coverage and things like that. Again, like, FiveTrans has been in the market for a long time, so they have a lot of coverage. They just are now a lot slower on, like, building new connectors. That’s the net-net, the trade-off. There’s a lot of… there is… there is quite a long tail of people that do this work.
258 00:41:01.410 ⇒ 00:41:03.140 Uttam Kumaran: However, most of them suck.
259 00:41:03.280 ⇒ 00:41:04.480 Uttam Kumaran: And, like.
260 00:41:04.640 ⇒ 00:41:16.839 Uttam Kumaran: just… you have to trust us a little bit in that, like, all we do is buy and implement these tools. So part of the reason we’re recommending these two is because that makes our job really easy. Like, our team… you don’t want our team
261 00:41:16.900 ⇒ 00:41:28.180 Uttam Kumaran: basically having to deal with connector downtime. And if we go with some of these tools, that is, you know, often a problem that we face. We have a client that’s on Hivo and Stitch, and it’s, like, an absolute nightmare.
262 00:41:28.310 ⇒ 00:41:33.980 Uttam Kumaran: But, like, they’re locked in on the long-term contract, and we couldn’t… We couldn’t do anything, but…
263 00:41:34.010 ⇒ 00:41:45.480 Uttam Kumaran: we actually were able to… their NetSuite, one of their use cases, this is the flower company, one of their use cases for the data is they actually do have a real-time use case, because during those periods of time.
264 00:41:45.480 ⇒ 00:41:56.959 Uttam Kumaran: like, Mother’s Day and Valentine’s Day, they almost need within-the-hour reporting. And so Polyatomic actually built a direct ODBC connector to NetSuite.
265 00:41:57.080 ⇒ 00:42:11.580 Uttam Kumaran: That supported, like, basically a real-time sync process of that data. That was a huge win with the vendor, and I don’t think, like, not… wouldn’t… I don’t think Fivetran supports that, and if so, it would have been their enterprise tier, so it was totally, like.
266 00:42:11.800 ⇒ 00:42:15.200 Uttam Kumaran: not possible without them. That’s an example of, like.
267 00:42:15.560 ⇒ 00:42:26.739 Uttam Kumaran: our team got a challenge of, like, hey, you have to enable our 60-minute or less reporting during these peak sales periods. Okay, like, figure it out, you know? And so, a vendor was really, really clutch.
268 00:42:26.840 ⇒ 00:42:32.330 Uttam Kumaran: And enabling that for us, you know, so…
269 00:42:32.440 ⇒ 00:42:40.839 Uttam Kumaran: Yeah, let me just, like… again, I would say the real ROI when we talk about leveraging ETL is just, like, not having to build
270 00:42:41.110 ⇒ 00:42:53.999 Uttam Kumaran: connectors to Shopify. One person, a couple of these vendors are doing it, they’re doing it for all these folks, there’s nothing really unique about that, necessarily. What’s unique is just the cost and the stability of it.
271 00:42:55.920 ⇒ 00:42:57.750 Jason W’s iPhone: Yeah, so in terms of…
272 00:42:57.750 ⇒ 00:42:58.300 Uttam Kumaran: Yeah.
273 00:42:58.690 ⇒ 00:43:10.099 Jason W’s iPhone: Sorry, I was gonna say, no, I mean, I think we’re already sold on, kind of, like, the ETL tool. I think the question more is… is understanding from you, and I appreciate you breaking it down, is, like, the rationale behind why we want to do two.
274 00:43:10.360 ⇒ 00:43:20.950 Jason W’s iPhone: tools versus one. I think just, like, original impression was one ETL tool that kind of does it all, so you’re giving us kind of, like, that room for thought in terms of kind of, like.
275 00:43:21.060 ⇒ 00:43:25.460 Jason W’s iPhone: You know, what does it mean to have, like, a robust, like, enterprise-ready.
276 00:43:25.630 ⇒ 00:43:30.640 Jason W’s iPhone: integration like a Fivetran would offer to an enterprise tool, like NetSuite.
277 00:43:30.760 ⇒ 00:43:32.100 Jason W’s iPhone: You know, and then…
278 00:43:32.460 ⇒ 00:43:45.400 Jason W’s iPhone: you know, the other part is, like, all of the long-tail connectors that we’re gonna need in the long term, it’s like, what is that strategy? And what I’m hearing is polyatomic versus
279 00:43:45.520 ⇒ 00:43:53.309 Jason W’s iPhone: Like, you know, having to kind of, like, articulate… utilize our time, you know, for that, understanding that the cost for it seemed rather low.
280 00:43:54.560 ⇒ 00:43:56.809 Uttam Kumaran: You’re, you’re, you’re exactly right.
281 00:43:57.660 ⇒ 00:44:02.899 Jason W’s iPhone: I want to be mindful of time, actually, just because I know we only have, like, maybe 15 minutes left for this call here.
282 00:44:03.680 ⇒ 00:44:21.589 Jason W’s iPhone: team, I don’t know if there’s any question you have for that specifically, but I also want to talk a little bit more, because I saw it in one of the sections of the doc, how it sounds like you’re also, like, recommending that we use Snowflake right now as, like, kind of an assumption. Like, was there that exercise in terms of, like, Snowflake, BigQuery? Because I think that was something that we were… we were discussing prior as well.
283 00:44:23.200 ⇒ 00:44:33.340 Uttam Kumaran: Yeah, so this is part of our, like, we do have to do, you know, we’ll have a very similar conversation about warehouse. Snowflake offers some very unique
284 00:44:33.460 ⇒ 00:44:48.429 Uttam Kumaran: ETL solutions. In fact, one of which we’re actively engaging with, with the Emerson data. So I just wanted to kind of, like, have that highlighted here, which is both to Snowpipe and Snowflake Private Share.
285 00:44:48.550 ⇒ 00:45:03.929 Uttam Kumaran: Snowflake Private Share, for example, is the fact that, like, Snowflake, you know, is a… Snowflake is a digital cloud on top of, S3 and on top of Azure Blob. And so, if your vendor that you’re taking data from is a Snowflake customer.
286 00:45:03.930 ⇒ 00:45:11.239 Uttam Kumaran: they can share data with you directly through this product called Snowflake Share. Salesforce does this, Stripe does this.
287 00:45:11.240 ⇒ 00:45:27.969 Uttam Kumaran: company like Emerson does this, where their product data is there, and so they can just basically click share, and it shares it with you. Unique part of Snowflake, because it’s this layer. What does that mean is that there’s no ETL required. It’s like a direct replication. So just something I wanted to call out, but we’re not…
288 00:45:28.040 ⇒ 00:45:43.359 Uttam Kumaran: like, the reason why I mentioned that for these types of tools, for example, if we decide to not go with Snowflake, then we will have to consider whether the ETL vendor… there is an ETL vendor that we have that can build it, or we build it ourselves. So…
289 00:45:43.500 ⇒ 00:45:56.360 Uttam Kumaran: for Emerson, for example, I’ll have to understand, like, hey, what are their API options, and how can we go direct if we can’t go through this method? BigQuery has a sort of similar product to Snowpipe.
290 00:45:56.360 ⇒ 00:46:03.589 Uttam Kumaran: Nothing, you know, kind of similar to private share, to my knowledge. But that’s the only reason why we highlighted these two, is because we do have
291 00:46:03.850 ⇒ 00:46:04.620 Uttam Kumaran: Bye.
292 00:46:04.890 ⇒ 00:46:08.730 Uttam Kumaran: you know, Emerson in particular, we do have that Snowflake engagement right now, but…
293 00:46:08.830 ⇒ 00:46:18.000 Uttam Kumaran: I would say, if you… to talk about warehouse, both of these tools support the long… the kind of, like, core set of warehouses that
294 00:46:18.050 ⇒ 00:46:28.850 Uttam Kumaran: you know, I’m predicting that we go with, which are, you know, either Snowflake, BigQuery, and there’s a couple more that we’ll definitely highlight. But no risk on either of these
295 00:46:29.090 ⇒ 00:46:35.640 Uttam Kumaran: For those, really the risk for those warehouses are the long-tail connectors on whether they can support
296 00:46:35.750 ⇒ 00:46:38.930 Uttam Kumaran: Them directly, or we can build an endpoint to it.
297 00:46:39.080 ⇒ 00:46:41.999 Uttam Kumaran: Got it. Does that make sense?
298 00:46:42.000 ⇒ 00:46:51.279 Jason W’s iPhone: Yeah, no, it makes sense. When I saw that assumption and it mentioned Snowflake already, I didn’t know if you were already making that recommendation now, or if that was still kind of an exercise that was still in progress.
299 00:46:51.280 ⇒ 00:46:59.960 Uttam Kumaran: It’s just sort of like an ETL option, you know, if Snowflake ends up in the picture. So one thing I think a way we can highlight, just here, we can just put, like, if
300 00:47:00.360 ⇒ 00:47:13.450 Uttam Kumaran: Because this is one advantage to Snowflake, is this, like, no zero ETL data share, but it falls sort of under them, like, trying to enter into this ETL world, so I wanted to highlight that here.
301 00:47:13.450 ⇒ 00:47:21.100 Awaish Kumar: Yeah, but just to mention that we are working on, like, the comparative analysis of all the different data warehouses.
302 00:47:22.050 ⇒ 00:47:22.440 Uttam Kumaran: Yeah.
303 00:47:23.340 ⇒ 00:47:26.930 Shivani Amar: Yeah, so I… Oops, my camera turned off,
304 00:47:27.080 ⇒ 00:47:40.799 Shivani Amar: My understanding is that this is just an initial exercise, Jason, and that our way in on, like, what we think about BigQuery, like, context for you with Phil today was talking about the touting the benefits of, like, having Gemini sit over everything, so he…
305 00:47:40.800 ⇒ 00:47:41.349 Uttam Kumaran: Yeah, yeah.
306 00:47:41.350 ⇒ 00:47:52.910 Shivani Amar: if we’re using Google Suite of services, then, like, why not do BigQuery? So, I think that’s just, like, we’re doing one tool, and then hoping to chip away at the other question next.
307 00:47:52.910 ⇒ 00:48:03.840 Uttam Kumaran: like, even on your, like, you know, we just completed a spike for another client on AI on top of data, actually, and we evaluated maybe 6 or 7 different vendors, in addition to, like.
308 00:48:03.970 ⇒ 00:48:06.899 Uttam Kumaran: Snowflake native AI, BigQuery native AI,
309 00:48:07.060 ⇒ 00:48:13.320 Uttam Kumaran: But that’s something we’ll highlight in the warehouse piece, is like, does this unlock additional AI? Snowflake…
310 00:48:13.340 ⇒ 00:48:32.059 Uttam Kumaran: has, you know, very… like, a lot of these guys are now both coming out with a lot of these similar AI tools. Like, one… for example, one advantage of BigQuery is that you can land GA data, like, pretty seamlessly, because they’re both Google products. But there are also benefits to Snowflake, you know, in many different ways, so…
311 00:48:32.210 ⇒ 00:48:38.330 Uttam Kumaran: Yeah, something I think we’ll highlight, like, in that data warehouse. And then, yeah, Shivani, if, like, if you want us to go deeper on, like.
312 00:48:38.590 ⇒ 00:48:53.050 Uttam Kumaran: the AI piece, like, that was something that when we talk about, like, the BI layer, sort of just, like, what we call, like, data access layer, we’ll talk about, like, both data access through Tableau, Looker, like, those types of traditional BI tools, as well as
313 00:48:53.200 ⇒ 00:48:57.799 Uttam Kumaran: like, chat with data type, makes sense. You know, interfaces, yeah.
314 00:48:58.330 ⇒ 00:49:02.819 Shivani Amar: Yeah, and I think holding that lens when you’re thinking about the warehouse itself is helpful.
315 00:49:03.610 ⇒ 00:49:09.509 Uttam Kumaran: Yeah, because that’ll be… we’ll do a lot of show and tell during that piece, because we’ll have the data landed, you know, when we’re starting.
316 00:49:09.510 ⇒ 00:49:10.370 Shivani Amar: Perfect.
317 00:49:10.750 ⇒ 00:49:18.710 Shivani Amar: Yeah, but I just mean if one is more conducive, like, if BigQuery is more conducive to that, then just holding that in your assessment will be helpful.
318 00:49:20.460 ⇒ 00:49:21.070 Steve Sizer: Giovanni?
319 00:49:21.070 ⇒ 00:49:21.590 Shivani Amar: Cool.
320 00:49:21.590 ⇒ 00:49:23.840 Steve Sizer: You’re doing a… if we… what…
321 00:49:24.090 ⇒ 00:49:27.010 Steve Sizer: they would expect him to move away from Emerson.
322 00:49:29.860 ⇒ 00:49:46.700 Shivani Amar: I’m not sure… like, so when I be hearing that, and somebody else can correct me here, when I’m hearing we’re moving away from Emerson, my understanding is we’re moving away from Emerson doing some of the, like, administrative stuff for us that they’ve been doing, but it’s not necessarily that…
323 00:49:46.880 ⇒ 00:49:57.619 Shivani Amar: this is my understanding, Jason, you can correct me. It’s not my understanding that Emerson being the one feeding us data, would stop. Jason, do you know more than I do on that piece?
324 00:49:59.370 ⇒ 00:50:07.439 Jason W’s iPhone: I don’t have an update on where we are with that, but what I was gonna say was, I don’t know how much we should be
325 00:50:07.570 ⇒ 00:50:14.529 Jason W’s iPhone: investing into… like, Emerson, knowing that there might be some transitions later, so, like.
326 00:50:14.530 ⇒ 00:50:15.050 Shivani Amar: Yeah.
327 00:50:15.050 ⇒ 00:50:26.460 Jason W’s iPhone: you know, in an ideal situation, like, I mean, all Emerson’s doing is grabbing the Walmart data from their APIs and loading it to their Snowflake, right? So, depending on how that relationship forms out in the future.
328 00:50:26.460 ⇒ 00:50:36.290 Jason W’s iPhone: you know, whether or not we’d have direct access to the Walmart data in the future, or the Target data, it’s… that’s unclear to me. I think that’s something me and Siobhani have to, like, talk to Phil a little bit more about. So…
329 00:50:36.310 ⇒ 00:50:42.099 Jason W’s iPhone: you know, I guess that’s a long way of saying, like, let’s not put too much emphasis and weight
330 00:50:42.230 ⇒ 00:50:50.020 Jason W’s iPhone: you know, on… on the Snowflake private share, specifically for Emerson, just because I don’t know if that’s even something that’s kind of, like… Right.
331 00:50:50.310 ⇒ 00:50:50.820 Jason W’s iPhone: super.
332 00:50:50.820 ⇒ 00:51:07.900 Shivani Amar: But I think with them, this is, like, a helpful thing, like, if you were to tee up the questions you have about Emerson, let me, like, ping that to Phil, because, like, rather than just telling you, don’t hold that, like, let’s actually get you an answer. So, if you’re like, hey, Phil, I want to understand the future state of Emerson from a data perspective, then I can’.
333 00:51:07.900 ⇒ 00:51:08.320 Uttam Kumaran: Perfect.
334 00:51:08.320 ⇒ 00:51:17.749 Shivani Amar: conversation up with him, or he can, like, you know, he can comment async. He’s, like, pretty into async comms, so if you want to ping him, or you want to just comment…
335 00:51:17.750 ⇒ 00:51:28.039 Uttam Kumaran: Feel free to do that. Yeah, we have, something that’s on my desk to review, which is, like, our overview of everything in Emerson. So as part of that, I’ll put in, like.
336 00:51:28.270 ⇒ 00:51:35.299 Uttam Kumaran: this is what we found in there. Like, does this sort of, like, match, like, what you guys expect? And, like, yeah, tell me about the relationship and, like.
337 00:51:35.410 ⇒ 00:51:46.269 Uttam Kumaran: what do they promise? Are we getting everything we promised? Like, a lot of those questions. Because we can also go out into the market and see, like, what other options are for this identical data. Just would love to know, like, what the relationship is, so…
338 00:51:46.610 ⇒ 00:51:47.360 Shivani Amar: Yeah.
339 00:51:48.540 ⇒ 00:51:49.090 Uttam Kumaran: Okay.
340 00:51:50.170 ⇒ 00:51:50.640 Shivani Amar: Cool.
341 00:51:57.000 ⇒ 00:52:01.249 Uttam Kumaran: And then, maybe just in the last few minutes, sort of like…
342 00:52:01.770 ⇒ 00:52:18.970 Uttam Kumaran: You know, both of these sort of fit, like, our criteria for procurement. So this is one thing, Jason, that I, like, heard you loud and clear on, like, we don’t want to sign super long-term stuff, we want to have the flexibility, and so, in terms of, like, the data stack, like.
343 00:52:19.440 ⇒ 00:52:31.459 Uttam Kumaran: Of course, like as any tool, they’re gonna try to offer annual discounts. You can totally do both of these month-to-month, and we can evaluate, you know, different phases of contracts. Both of these forks are very flexible.
344 00:52:31.640 ⇒ 00:52:33.599 Uttam Kumaran: Like, their salespeople just wanna…
345 00:52:33.850 ⇒ 00:52:39.990 Uttam Kumaran: get money. So we’ve seen them structure many types of deals. To give you the opposite, like, in BI,
346 00:52:40.140 ⇒ 00:52:46.390 Uttam Kumaran: very rarely have we seen BI tools do month-to-month deals. So, when we get to BI,
347 00:52:46.730 ⇒ 00:52:49.430 Uttam Kumaran: it could be something we fight for, right? But,
348 00:52:49.780 ⇒ 00:53:09.280 Uttam Kumaran: well, I can tell you who… who’s worth fighting versus not, you know, so that’s, like, when we get to there, I can… I can give you a little bit more context, but at least in the warehouse world and ETL world, shouldn’t be a problem to adhere to that. Both of these have both… both of these have great, like, security constraints and things like that, so I wouldn’t worry too much there.
349 00:53:09.990 ⇒ 00:53:20.419 Uttam Kumaran: So I think, like, probably a next step, you know, here’s… as part of our just, like, top-level summary, Shivani, I’m just gonna highlight a little bit more about, like, why two tools.
350 00:53:20.430 ⇒ 00:53:33.919 Uttam Kumaran: very clearly, like, both for redundancy reasons, and to support the long tail of connectors. I will, you know, kind of agree with Andy that supporting long-tail connectors could be going with these, or building ourselves.
351 00:53:34.020 ⇒ 00:53:38.679 Uttam Kumaran: But that is the ultimate, like, I think, net-net here. I think…
352 00:53:38.830 ⇒ 00:53:53.350 Uttam Kumaran: what we can do is, for both of these tools, we can start to load data, and neither of them will force us to pay until they get an understanding of what our monthly expectation is. So we won’t have, like… FiveTrain has a 14-day trial, and we can go get that extended.
353 00:53:53.350 ⇒ 00:54:02.929 Uttam Kumaran: Polytomic, also, similarly, they typically offer, folks that, like, work with us, like, a month-long trial. So, it’s both things that we… we won’t have to, like.
354 00:54:02.960 ⇒ 00:54:07.759 Uttam Kumaran: Basically, be like, cool, here’s, like, 10 grand, and start, like, we’ll… we’ll slow… we can slow roll into these.
355 00:54:07.940 ⇒ 00:54:12.900 Uttam Kumaran: And then I think, Shivani, as soon as we get, like, an estimate of cost, we could probably make a…
356 00:54:13.800 ⇒ 00:54:16.450 Uttam Kumaran: better decisions. Does that seem like…
357 00:54:17.090 ⇒ 00:54:19.290 Uttam Kumaran: It’s kind of, like, a fair way to go.
358 00:54:21.080 ⇒ 00:54:23.610 Uttam Kumaran: And that still kind of keeps us on timeline of, like.
359 00:54:24.280 ⇒ 00:54:33.329 Uttam Kumaran: basically trying to land data… start landing data before Christmas. For the rest of the team, the reason being is for some of these sources, like Amazon, Shopify.
360 00:54:33.570 ⇒ 00:54:45.119 Uttam Kumaran: We’ve seen, like, one to two week times to land data, just given, like, rate limits and volume limits on Shopify and Amazon’s APIs.
361 00:54:45.410 ⇒ 00:54:51.330 Uttam Kumaran: And so, just want to make sure that we can do that before January, if possible, but…
362 00:54:51.790 ⇒ 00:54:53.540 Uttam Kumaran: Yeah, we can also kick that off.
363 00:54:53.650 ⇒ 00:54:56.320 Uttam Kumaran: Next month, if we… if we don’t want to rush, so…
364 00:54:56.960 ⇒ 00:55:01.980 Andy Weist: When you say land data, are we backfilling historical data from Shopify?
365 00:55:01.980 ⇒ 00:55:04.059 Uttam Kumaran: For instance, like, for example. That’s correct.
366 00:55:04.120 ⇒ 00:55:05.060 Andy Weist: Okay.
367 00:55:05.470 ⇒ 00:55:09.210 Andy Weist: Okay, so you would backfill basically all of our Shopify historicals?
368 00:55:09.920 ⇒ 00:55:26.009 Shivani Amar: That is correct. And that’s where, like, we’re gonna get the juice, right? Like, we had a conversation with Dan today, and he was like, oh, I feel like there’s so much analysis I would want to do if I could, like, look at Amazon historicals instead of just taking, like, monthly snapshots of data. So, like, it’ll unlock… I’m excited for what it eventually unlocks.
369 00:55:26.840 ⇒ 00:55:43.040 Uttam Kumaran: Yeah, and that’s exactly it. Like, we’ve gone to clients where we’ve been able to get their entire historical Amazon data, and so not only, like, seasonality analysis, but, like, for example, we have another client where we’re doing a lot of PO order-related analyses. It’s really, really rich, especially for your guys’ type level of order volume.
370 00:55:43.270 ⇒ 00:55:48.079 Uttam Kumaran: Like, there’s a ton in there, so we’ll go back, it’ll grab all of that.
371 00:55:49.600 ⇒ 00:55:52.960 Uttam Kumaran: And then that becomes basically your owned, right? So even if, like.
372 00:55:53.170 ⇒ 00:55:59.809 Uttam Kumaran: we go with a different ETL tours, like, that data doesn’t go anywhere, like, at least you have a copy, you know, of all of that.
373 00:56:00.230 ⇒ 00:56:01.750 Uttam Kumaran: And so, yeah.
374 00:56:02.930 ⇒ 00:56:11.150 Uttam Kumaran: And then in the data warehouse memo, we’ll talk about, like, the cost of storage and things like that across each of the warehouse tools as well.
375 00:56:12.200 ⇒ 00:56:12.880 Uttam Kumaran: Yeah.
376 00:56:15.220 ⇒ 00:56:16.500 Shivani Amar: Sounds good.
377 00:56:17.300 ⇒ 00:56:29.870 Jason W’s iPhone: Awesome. So, decision-wise, I think you said by, like, you know, obviously, like, next week is when we want to have that call. Is that when you expect us to have the data warehouse decision as well? So, are we… are we going to get another doc for…
378 00:56:30.650 ⇒ 00:56:35.479 Uttam Kumaran: We are gonna get another dock for the warehouse, ideally next week, as well.
379 00:56:35.660 ⇒ 00:56:42.310 Uttam Kumaran: I… I want, like, basically… basically, like, we… I don’t expect us to go…
380 00:56:42.430 ⇒ 00:57:01.289 Uttam Kumaran: off-kilter on the warehouse side, so, like, if we choose one of the major players, like, I’m not worried about either of these two supporting that. But yes, I would like to make both of those decisions before Christmas, ideally, so that we can, you know, our team can land that, start to land that data.
381 00:57:01.420 ⇒ 00:57:04.490 Uttam Kumaran: You know, in that time period.
382 00:57:04.600 ⇒ 00:57:08.370 Uttam Kumaran: That would be, like, Amazing.
383 00:57:08.840 ⇒ 00:57:10.770 Jason W’s iPhone: Yep, we’re on the same page, I just want to confirm that.
384 00:57:11.660 ⇒ 00:57:13.219 Uttam Kumaran: Yeah, that’s exactly right.
385 00:57:15.640 ⇒ 00:57:22.890 Uttam Kumaran: The… yeah, and I think on the warehouse piece, we’ll talk a lot about, like, some of the stuff we’re seeing on the AI side, some of the interesting things about
386 00:57:23.180 ⇒ 00:57:27.030 Uttam Kumaran: BigQuery and GA and some of the other options,
387 00:57:27.420 ⇒ 00:57:34.319 Uttam Kumaran: It’s a bit of a more nuanced com… it’s a… yeah, there’s gonna be some decisions for us to make, depending on our ergonomics there.
388 00:57:34.640 ⇒ 00:57:40.019 Uttam Kumaran: The AI stuff, Shiovanni, is tough, because it’s just moving so fast. I don’t know…
389 00:57:40.870 ⇒ 00:57:45.279 Uttam Kumaran: who’s gonna end up, like, way better than the other.
390 00:57:45.280 ⇒ 00:57:45.600 Shivani Amar: Yeah.
391 00:57:45.600 ⇒ 00:57:49.820 Uttam Kumaran: Warehouse is a decision really hard to… Go back on.
392 00:57:50.110 ⇒ 00:57:54.120 Uttam Kumaran: But… Both are really, really great, so,
393 00:57:54.550 ⇒ 00:57:57.000 Uttam Kumaran: You know, but we’ll put a bunch of options in front of y’all.
394 00:57:57.590 ⇒ 00:57:58.850 Shivani Amar: Yeah, perfect.
395 00:57:59.190 ⇒ 00:58:04.770 Awaish Kumar: Yeah, most of the, like, data warehouses can provide similar features, but
396 00:58:04.930 ⇒ 00:58:10.279 Awaish Kumar: For context, like, it would be nice if, if, if we know, like, if…
397 00:58:10.700 ⇒ 00:58:19.090 Awaish Kumar: Other parts of Element are considering any of the cloud providers, like GCP or AWS or anything.
398 00:58:19.780 ⇒ 00:58:26.430 Uttam Kumaran: Yeah, I think the team has just mentioned DigitalOcean, so I don’t think there’s, like, a… there’s, like, a push one way or another away.
399 00:58:26.610 ⇒ 00:58:29.930 Uttam Kumaran: Which, like, would toughly… Pushed us one way, but…
400 00:58:30.340 ⇒ 00:58:35.700 Uttam Kumaran: Like, we have another client who’s like, oh, we just want to procure as much as possible through AWS.
401 00:58:35.930 ⇒ 00:58:37.970 Uttam Kumaran: And so, okay, like, it makes…
402 00:58:38.100 ⇒ 00:58:45.660 Uttam Kumaran: it kind of puts BigQuery off the table, because you can buy Snowflake through AWS Marketplace, and they’re like, we want to procure everything through AWS Marketplace.
403 00:58:45.920 ⇒ 00:58:49.329 Uttam Kumaran: So there’s, like, things like that that I don’t think is the case here, so…
404 00:58:49.560 ⇒ 00:58:54.589 Uttam Kumaran: But we’ll kind of put some of the trade-offs there, and we can come to a decision.
405 00:58:58.280 ⇒ 00:59:07.480 Uttam Kumaran: I think also, Shivani, and we’re gonna… I’ll put what we learned from Source Medium about configuring and things like that in the data warehouse, because they’re using BigQuery behind the hood, so…
406 00:59:09.060 ⇒ 00:59:09.800 Shivani Amar: Perfect.
407 00:59:10.550 ⇒ 00:59:12.600 Uttam Kumaran: Great. Any feedback?
408 00:59:12.980 ⇒ 00:59:18.650 Uttam Kumaran: on, like, this meeting, this doc, like, I think we’re gonna… this will sort of be living as we, like.
409 00:59:19.310 ⇒ 00:59:22.750 Uttam Kumaran: You know, start to make this decision, but… Like…
410 00:59:22.990 ⇒ 00:59:28.609 Uttam Kumaran: I don’t know, I love talking about this stuff, there’s a lot of history in each of these different parts of the stack, so as you can tell, like, we…
411 00:59:29.630 ⇒ 00:59:42.709 Uttam Kumaran: we just think about these a lot, and we’ve tried to partner with the best tools, like, really don’t want to choose some of these guys on the bottom, because they’ve just been tough to work with sometimes. So, if there’s any feedback on, like, this type of meeting or this talk, let me know.
412 00:59:44.310 ⇒ 00:59:45.940 Shivani Amar: Sounds great, thank you, Utham.
413 00:59:46.510 ⇒ 00:59:47.130 Uttam Kumaran: Okay.
414 00:59:47.600 ⇒ 01:00:05.010 Uttam Kumaran: Okay, so yeah, we’ll plan on having a very similar warehouse conversation next week. I think, Shivani, I’ll organize that with you. And then, yeah, if there’s any questions on these, I think also, you know, on the Polytomic side, I’m happy to have their team come talk to our team and just say hi, so you put a face. Five Train is a much more sales-intensive process.
415 01:00:05.010 ⇒ 01:00:10.799 Uttam Kumaran: But yeah, that’s probably… that’ll probably be nice, so… so I’ll coordinate that with you, Shivani.
416 01:00:11.460 ⇒ 01:00:13.310 Shivani Amar: Okay, perfect, thank you.
417 01:00:13.810 ⇒ 01:00:15.849 Uttam Kumaran: Okay, alright. Thank you, everyone.
418 01:00:15.850 ⇒ 01:00:16.600 Steve Sizer: Appreciate the time.
419 01:00:16.600 ⇒ 01:00:18.030 Shivani Amar: Thank you! Bye.
420 01:00:18.030 ⇒ 01:00:19.279 Uttam Kumaran: Thank you. Bye.