Julia Schottenstein

dbt is an open-source data transformation tool that allows analysts to write SQL-based workflows to clean and prepare data for analysis.

So our founders, Tristan, Drew and Connor, they had a belief that the people who do data analysis work, that really work closely with their business stakeholders should also be the ones to contribute to creating clean data assets in production because that data prep work is a necessary prerequisite for any analysis that you do. So dbt was really this belief that if you know SQL, we want to invite you to do these workflows that were traditionally held by data engineers but you had to earn that.

dbt Labs is a data transformation software company that started as Fishtown Analytics, a data consulting firm.

And dbt Labs started as Fishtown Analytics, a consulting firm. So they worked really, really closely and hands-on with all of their consulting partners to get the pain point and really solve firsthand challenges that they saw. I think that combination of being at the right place at the right time and also getting to work really closely with people's day-to-day problems created a really special experience.

dbt is an open-source data transformation tool that helps analysts write SQL to clean and prepare data for analysis.

Whenever they encountered paper cuts or friction or the workflow was taking longer than they expected, they would build that into dbt. And that really matured the experience of the product because the people who were building it, the founders were also day-to-day working with these customers or clients that had pain points.

But what we didn't need at launch was a distributed scheduler with coworkers and RabbitMQ. We just didn't need it because we had no users.

Do fewer things and try to single thread the team as much as possible.

It's really to help me combat this perfectionism because perfect doesn't exist and you should instead go with good enough because when you ship, that's the moment when you get to learn a lot from your users and you just can't anticipate it.

It's can they not stop talking about it and that's the chatter about a product, they want to share it with their teammates or to other people at different companies. That just top of mind love and wanting to share what they've found with others is really a great sign that you're onto something.

Everyone who's listening hopefully either is interested in products or has a product background. So I won't say too much there, but can you talk to users or potential customers, are they building something that's really special, unique? Can you hear that, spark that enthusiasm and figure out if this is going to be special.

dbt Labs makes data transformation software that helps companies process and analyze data stored in cloud data warehouses like Snowflake or BigQuery.

And at dbt Labs we have this value, it's one of our core values that says we are more concerned with value creation than value capture. And we really mean this. When we talk about what is the value of dbt Labs to our customers, they often talk about how it's either 20 to 35% as valuable as what they spend on their cloud data warehouse. But what we charge our customers is a very small fraction of that 20 to 35% and that's by design.

Pricing and willingness to pay is such a hard conversation and lots of startups don't do this early enough in their company journey. Likely a side effect of zero interest rates where investors were happy funding GitHub stars and usage and companies never thought about how am I going to make revenue or make money, which is it's important.

And I try to remind the engineers, we would be so lucky to have tech debt because that means people are using the product.

RabbitMQ is a message broker software, and "coworkers" here refers to a background job processing system, not human colleagues.

But what we didn't need at launch was a distributed scheduler with coworkers and RabbitMQ. We just didn't need it because we had no users.

So I showed up to a team offsite with a spool of rope and sticky notes and I think my team looked at me crazy, went with two heads when I started to tie people up to create a graph. So each note of the graph was an engineer and the rope was the edges of the graph to connect them. And then we worked through the new algorithm extremely slowly, step by step.

You can't just solve these problems in spreadsheets. You have to go talk to customers, test the waters, understand where people's appetites are and sussing that out is really hard.

dbt is a data transformation tool that helps companies manage and structure data in cloud data warehouses.

dbt had an ecosystem advantage and they were open source and this helped really dramatically for lots of people to have low barrier friction to just try it out and spread organically. They first got started with very horizontal. People could just get started without ever even talking to sales and think that was a competitive advantage.