The Exchanges

Q Okay, so that's Snowflake. Um, let's talk about Fivetran and ETL, and maybe just in one minute, uh, what is Fivetran and what is ETL? We had George, uh, Fraser, the CEO, uh, at this event online during the pandemic, but, uh, maybe as a refresher.

A So, okay, so five trend is like the far left of this diagram you all just saw. Um, it is, you got a bunch of data in third party sources or in data warehouses. You want to centralize it into your central warehouse, be it Snowflake or Databricks or BigQuery or whatever. Um, the way you had to do that before, the first data team I worked on in Silicon Valley did this. You had to basically write a bunch of stuff to scrape things out of APIs of these services. So you'd have to basically hire an engineer to scrape stuff out of Salesforce's API. It was an enormous pain. Uh, the API is actually decent, but it's still like, you have to manage it when things change, you have to fix it. Fivetrend does it all for you. So Fivetrend is basically like, pull data out of various services, they connect to a couple hundred now, I don't know how many. Um, you push a button, you say sync the data from this service into your warehouse, and they just do all of it for you. So it's essentially like a copy it from thing that doesn't quite look like a database into a database, and then you can build all the stuff you just saw on top of it.

AI assessment note: “Fivetrend is basically like, pull data out of various services”

Q Uh, and it's, uh, a company that's been around for, like, about 10 years, and is actually, uh, as far as I know, one, one of those, like, companies are over a hundred million in, in, in revenue. So what's, what's the case against, not necessarily them, but, like, that space?

A So the, the, to me, the potential question there is, it's like a little bit of an awkward thing for a company to be sitting as this middleman, where, what they essentially do is they sit in between, take Salesforce and Snowflake, they sit in between those two, They have to maintain a connection to Salesforce's APIs. When Salesforce changes it, which Salesforce doesn't care what Fivetrend does, like, I mean, Fivetrend's maybe big enough now that they do a little bit, but like, third party services aren't gonna go call Fivetrend and be like, hey, we're changing our API. Fix it. Um, so Fivetrend basically has to maintain that. The way they also get data out of it is, is they scrape it. They, some, some companies provide ways for like, we are making changes. They push it to, to other services. But a lot of times it's just like, run a script against the API, check the differences, and like, put the thing back into the database and batch. That's kind of a clunky way to do this. Like, it would be sort of more sensible, uh, if you could design this in a perfect world, that Salesforce just writes it to a database. Now obviously they didn't do that way back when because nobody wanted it, but now it's become such a thing to say, hey, we want our database, our data out of your SaaS software into a database, not for the sake of migrating away from Salesforce, but for the sake of all the ana…

AI assessment note: “it's like a little bit of an awkward thing for a company to be sitting as this middleman”

Q Have you seen people starting to do that?

A There are, so there are some companies that have done this before. Um, companies like Segment, basically event tracking services did this because that's kind of the product. Um, Stripe has a way to do this. Uh, there's a few that kind of have some Crude versions of this. The, I actually talked to George, like, a little bit after that post. The, his take is, which I think is probably fair, is it's a lot harder to build that than you think. That, like, the reason FiveTran is a six billion dollar company or whatever is because they did a bunch of awful work that we, none of us want to do. And so, as a SaaS business, like, Mode could do this. Mode could build a thing that syncs stuff to Snowflake. We're not going to because We have other things to build, and like, sure, we could monetize it, but it's not really worth it. We're not looking for something like marginally makes us more money. We need to make things that are gonna make us 10 X more money. Um, so I think that's the reason we don't. The one thing to me that changes that dynamic is if Snowflake or Databricks or whoever Start to say, hey, we want to make it really easy for people to be able to do this, and we build services that make it so that we can like, in a week, build that connection to Snowflake, so they have kind of a, an app layer essentially, but instead of it being something built on top of Snowflake, it's more o…

AI assessment note: “There are, so there are some companies that have done this before.”

Q to do it, so it's Ben, B-E-N-N, dot sub stack, Uh, and, and you write, uh, very prolifically every week. So, it's actually a great place to start for a lot of people who are in, uh, you know, technical roles, uh, or product roles in technical companies. Um, you know, there's been this rise of people writing interesting content, but, you know, professional content. So, why do you write?

A Uh, so, so when we first started Mode, it was three of us. Um, our CEO, who was presentable and could talk to investors and customers, uh, the guy who was our technical co-founder, who was our CTO, who was actually building the product, and me, who was either of those things, uh, and had no real job. Um, and so back then, what I did was I wrote a blog, like, and it was a blog that was, we had no product and nothing to sort of advertise, so it was basically a blog about Data adjacent things that was kind of, it was like pre-Five-Thirty-Eight, but it was kind of pre-Five-Thirty-Eight-ish stuff. The very, the very first blog on Mode's corporate blog is a post I did like three days after we started the company that was about Miley Cyrus and the VMAs. And so I like did that for like six months because I had no other job, and it actually worked reasonably well as like a, ok, this got some data people interested in what Mode was, they had no idea what the product was, it was like, These people are talking about stuff that seems sort of interesting, even if it's not terribly relevant to what I do day to day. Um, over the course of my time at Mode, you bounce on a bunch of different jobs. You did stuff in support and product and marketing and solutions and all these different things. Uh, at some point, basically, everybody at Mode realized I'm not good at any of those jobs, and I, like,…

AI assessment note: “I did that for like six months because I had no other job”

Q marketing and technical writing and all those things, do you, um, beyond your own entertainment, do you try to trace this back to any kind of, like, metrics or lead generation or any of those things? I mean, I can certainly vouch for the fact that, like, everybody in the data world reads this thing, so it's usually influential, but do you, do you have, uh, metrics attached to it?

A Uh, much to our marketing team's chagrin, we do not. Uh, so we have, like, Substack doesn't do a great job of helping you out here. Uh, we have metrics of, like, I follow how many people subscribe to it, and you can look at traffic to it, and it goes up on Fridays and goes down on Saturdays. Um, in terms of tying it back to, like, driving leads at mode, not really, and in a lot of ways, that's not the goal. Like, we sort of, I started doing it as a, let's see what happens. Um, now there is some push from As would make sense from folks in the marketing team and stuff to be like, all right, what do we, you need to, you need to actually deliver some value here. And so like we, a lot of though I think is, to me the value of it is it's not marketing content, it's not gonna be like at the end of it, and by the way, mode solves this problem by mode. Um, I don't want it to be that. That doesn't mean there aren't ways to turn it into something that's useful or turn sort of the brand into something useful or whatever, but that's a little bit of a work in progress. Like to us it was, And to me it was like, alright, write it. Do it for something that's interesting and fun and kind of see what happens. And then if it works, figure it out from there. If it doesn't work, you know, I guess I'll yell at my corner on the internet in order to pay attention.

AI assessment note: “much to our marketing team's chagrin, we do not.”

Q just again, like, to rephrase, like, all of this, like, I, I, all of this is done with love, and, uh, just as a, as a way to think through, Where our industry is going as opposed to like criticizing anyone in, in, in particular. Uh, but can you, the post on dbt has not come out. Can you, can you give us like a little bit of a preview?

A What's the preview of the dbt one? Uh, That, that it's fundamentally wrong, basically, that, like, dbt's a transformation tool. They're moving in, like, the semantic layer tool, so basically they're saying, give us raw data, and we will tell you, like, apply semantics to it. The way that they do that now is through SQL. Um, so, like, semantics are kind of, like, air quote semantics. It's basically, like, semantics as messy data to a clean data set. It's not really semantics. It's not really connected together in a real way. Like, it's not a model. Um, the analogy I've used for this before is, DBT is basically because you create a bunch of tables, the model is essentially like an animated movie where each shot is independent of the other one. Like, yeah, they're connected in like a DAG, but they're not really logically connected. Um, if you want to build a real model, you probably want something from like Pixar, or if you want to shoot a different shot, you actually can just say like, Point it from that direction, and you know it's gonna be the same thing, whereas in dbt's case, if you sort of point it from the other direction, you gotta make a new model, and that model could be different. Like, you could draw, you know, Aladdin with a hat on differently, or whatever. Um, to me, as they move in this, like, semantic direction, move towards things like metrics, move towards things…

AI assessment note: “That, that it's fundamentally wrong, basically, that, like, dbt's a transformation tool.”

Q Great. Um, I want to open up for questions in a minute, but maybe, um, sort of to close, let's actually talk about mode. We, we haven't, um, what does mode do today? What's the roadmap? What are you excited about?

A Yeah, so, so Mode is a, it's a BI analytics product, sits on top of your warehouse, uh, has like a SQL IDE, has a visualization tool similar to something like you get in Tableau, has some embedded notebooks. Um, the idea behind it is basically data teams have to provide reporting to businesses, like that is a core part of their function. They have traditionally not liked the way they've had to do it. Um, they don't want to, like, LookML and Looker is great, but a lot of analysts aren't, like, wanting to write LookML all day. They want to do tool, use tools that are sort of more native to them. Uh, but you still have to provide the dashboarding sort of experience. And so our view is, how do we get it so that, how do we build a tool that can solve the BI and self-serve reporting problem, uh, while also doing it in a way that is more comfortable for analysts, uh, and is comfortable for their end users as well. Um, and so for us, it's, it's about bringing those experiences together. Like, we don't see it as reinventing notebooks or reinventing visualizations. It's more of What are the best experiences that we can provide to people in those different sort of form function, form factors, uh, and then give them all in one, one kind of seamless way. Um, so what does that mean for the roadmap? It's largely about how do we think about bringing those tools together, um, and bringing the p…

AI assessment note: “Mode is a, it's a BI analytics product, sits on top of your warehouse”

Q And you were saying in the last couple of years in particular of a frothy, uh, VC environment, there was a little bit of, uh, data people in companies that actually knew what they were talking about left to their companies. To start a company, uh, and because all the data people left, the companies had to buy the product that those people left, uh, built?

A Yeah, so there was, there was, to me, like, this all kind of peaked in this, like, there was a conference in Austin, it's called Data Council, good conference, pro conference, pro that conference, like, no, no striking against that conference. The timing of it was just, like, too perfect, where it was this, the first big imperfect person data conference among kind of the modern data stack community, Uh, it was this big like celebration of the modern data stack. Airflow acquired, not Airflow, Astronomer acquired a company in the middle of it. Um, it was also like right as the market was teetering. And there was this kind of moment of like, I don't know, like dancing on the deck of the Titanic a little bit of like, wait a minute, this doesn't, is this gonna, are we gonna have this party next year? Cause I don't know if we're gonna have this party next year. Um, but anyway, in response to that conference, a couple of people were saying basically like, there were a lot of data practitioners there who'd become founders. And they viewed it as, these people kind of are inevitably going to be successful, because when data practitioners start companies, they create more of a market for more data people to sell to, and there are fewer data people to be able to build data products internally, so we have to go buy them, and it's like, how can this all fail? And it felt a little bit like, h…

AI assessment note: “it doesn't seem like it's going to really hold up”