The news about dbt acquiring SDF Labs has me thinking about how data tools evolve over time. There's a fascinating pattern here that we've seen before, and understanding it helps predict where things are going. Twenty years ago, the data world was simple, at least architecturally. You had databases, and you had BI tools. That was pretty much it. The interesting thing about this era wasn't its simplicity – it was how much people could actually get done with just these tools. Afterall, companies built entire businesses on Excel and a SQL Server.

Then something changed. Around 2015, we started seeing an explosion of specialized data tools. This wasn't an accident. It coincided with two things: cloud data warehouses becoming real businesses, and companies suddenly having way more data than we knew what to do with. This created what people started calling the "modern data stack." The name itself is interesting. Calling something "modern" usually means you think what came before it was somehow deficient. And in some ways, it was. The old way of working with data – writing stored procedures, managing complex ETL jobs, dealing with limited storage – was holding companies back.

But, the modern data stack solved these old problems by creating a new one: complexity. Instead of one complex system, you now have many simple ones. This worked great at first. Each tool did one thing well. dbt did transformations, Fivetran did extraction and loading, Looker did visualization.

What we're seeing now with dbt's acquisition of SDF is the next phase in this evolution. It's what happens when our data tools grow.

There's a parallel here with programming languages. In the beginning, you had assembly language. It was hard to use but gave you complete control. Then came higher-level languages like C, which made things easier but sacrificed some control. Then came languages like Python, which made things even easier but sacrificed even more control. But here's the thing: as these higher-level languages matured, we needed to solve increasingly complex problems. Python started simple, but look at modern Python: with type hints, async/await, and complex dependency management. It grew up.

This is exactly what's happening with dbt. When it started, it was beautifully simple: just write SQL with some Jinja templates. But as data teams started building more complex systems with dbt, their needs adapted with it. We needed better error messages, faster compile times, better IDE integration.

The acquisition of SDF is dbt really growing up. And this isn't a bad thing – it's necessary. When tools become critical infrastructure, we need to grow more sophisticated to meet the needs of their users. But there's something subtle and important happening here that most people are missing. The really interesting part isn't that dbt is adding features – it's that we’re adding understanding.

SDF's technology doesn't just run SQL faster (which solves a core user problem in dbt), it also makes the process of developing with dbt much more pleasant and metadata rich. 

I think this points to where the entire data tooling space is heading. The next big leap won't be about processing more data faster – it will be about understanding data better. Tools that can understand not just the syntax of what you're doing, but the semantics. Think about what Cursor is doing for programming. It's not just making programming faster – it has changed how programmers think about writing code. The same thing is about to happen in data.

What dbt is doing with SDF is a first step in this direction. I would think leadership at dbt would tell you that it’s not fundamentally changing what dbt does – it’s about doing the things users do much easier. The exciting part is that we're just at the beginning of this trend. As data tools get better at understanding intent, we'll see new kinds of tools emerge that we can barely imagine now. Just like how understanding programming languages led to tools like Cursor, understanding data better will lead to tools that can suggest optimizations, spot potential issues, and maybe even write transformations for us.

The companies that win in this space won't be the ones that build the most features. They'll be the ones that build the deepest understanding. And that's what makes this acquisition so interesting – it's not just about dbt getting better. It's about data tools growing up.

Congrats dbt & SDF - looking forward to seeing what comes from this acquisition