When Your Favorite Tool Gets a Makeover

You know that feeling when your favorite app suddenly changes its interface? That mix of excitement and anxiety about whether the changes will actually improve your workflow or just mess with muscle memory you’ve spent years building.

That’s exactly what happened when dbt Labs dropped dbt Fusion on the analytics engineering community. The reactions were… let’s call them passionate. Some folks were celebrating like they’d just discovered fire, while others were questioning whether this marked the beginning of the end for open-source dbt.

I’ve been watching this unfold with genuine curiosity. Having worked with dbt since its early days, I’ve seen how the tool has evolved from a scrappy open-source project to the backbone of modern analytics engineering. But this latest release feels different—bigger, more consequential.



What Actually Is dbt Fusion?

Before we get into the drama, let’s talk about what dbt Fusion actually does. Think of dbt as having two main parts: the stuff you see and work with (your models, tests, documentation), and the engine that makes it all work behind the scenes.

dbt Fusion is essentially a complete rebuild of that engine—the part you never see but definitely feel when things are slow.

The old engine had some fundamental limitations. It wasn’t a true SQL compiler, which meant it couldn’t catch errors before you ran your models. It also had parsing issues that made larger projects painfully slow. You’d run dbt run and then… wait. And wait some more.

The new Fusion engine changes this completely. It’s built as a proper SQL compiler, which means it can spot issues in your code before you even try to run it. This architectural shift enables some pretty compelling features:

  • 30x faster development speeds (their claim, not mine—but the early reports are promising)
  • Real-time error detection in your IDE
  • State-aware orchestration that only builds what’s actually changed
  • Automatic field propagation when you rename columns or models

That VSCode extension everyone’s talking about? It’s only possible because of this engine rewrite. The extension can now understand your dbt project structure deeply enough to provide intelligent autocomplete, navigate between models, and catch errors as you type.



The Performance Promise: Real or Hype?

Let’s be honest about the performance claims. A 30x improvement sounds incredible, but context matters here.

If you’re working with a massive dbt project—think hundreds or thousands of models—this could be genuinely transformative. Those teams that currently wait 10-15 minutes for parsing to complete before anything happens? They’re the ones who’ll see the biggest impact.

But here’s the thing: most data teams aren’t operating at that scale. If your dbt project has 50 models and parsing takes 30 seconds, shaving that down to 1 second is nice but hardly revolutionary. It’s the difference between grabbing a coffee and checking your phone.

The real value might not be in the raw speed improvement but in the development experience. When your IDE can catch errors immediately, when you can navigate between models with a click, when field renames propagate automatically—that’s where the daily friction disappears.

I’ve been testing the VSCode extension, and while it’s not going to change my life, it does make the small annoyances disappear. No more typos in model names. No more hunting through files to find where a field is defined. It’s the kind of quality-of-life improvement that you don’t realize you needed until you have it.



The Open Source Anxiety

Here’s where things get interesting—and where the community got nervous.

When dbt Labs announced they were “combining Core and Cloud,” a lot of people interpreted this as the death of open-source dbt. The fear was understandable: we’ve seen this pattern before with other tools that started open-source and gradually moved toward proprietary models.

But I think this fear is misplaced, at least for now. Tristan Handy and the team at dbt Labs have been pretty clear that both Core and Fusion will remain open-source. You can run Fusion locally, just like Core. The VSCode extension is free for teams up to 15 users.

More importantly, dbt’s success is fundamentally tied to its open-source nature. The community, the ecosystem of packages, the widespread adoption—all of this exists because dbt Core was free and open. Killing that would be like burning down the foundation of their own business.

That said, I understand the concern. The messaging around “combining” Core and Cloud was confusing. It sounded like they were deprecating Core, even though that wasn’t the intention. Better communication could have prevented a lot of the anxiety.



The Analyst vs Engineer Divide

There’s another layer to this controversy that’s worth exploring: the shift in dbt’s target audience.

dbt started as a tool for analytics engineers—people with strong SQL skills who understood data modeling principles and could think architecturally about data transformation. But as the tool has grown, it’s attracted a broader audience, including analysts who might not have that same technical depth.

This isn’t necessarily bad, but it does create tension. Features like dbt Canvas (the drag-and-drop interface for building models) are clearly aimed at less technical users. While the output is still SQL, the abstraction layer can hide important details about what’s actually happening.

I’ve seen this pattern in other tools. The no-code/low-code approach can be powerful, but it can also create technical debt when users don’t understand the underlying principles. If you don’t have strong governance, style guides, and training, you can end up with a mess of auto-generated models that nobody really understands.

The key is balance. Making dbt more accessible is good for the ecosystem, but not at the expense of the engineering rigor that made it valuable in the first place.



What This Means for Your Team

So where does this leave you? Should you rush to adopt Fusion, or stick with Core for now?

If you’re running a large dbt project and performance is a real pain point, Fusion is worth exploring. The parsing improvements alone could save your team significant time. The VSCode extension is also genuinely useful if you’re doing a lot of dbt development.

For smaller teams or projects, the benefits are less compelling. Core isn’t going anywhere, and it still works great for most use cases. You can always migrate to Fusion later when the ecosystem is more mature.

The bigger question is strategic: how do you want to position your team as the analytics engineering landscape evolves? dbt Fusion represents a bet on a more integrated, IDE-centric development experience. If that aligns with where you want to go, it’s worth investing in.

But if you prefer the flexibility of the command-line workflow, or if you’re concerned about vendor lock-in, sticking with Core makes sense. The beauty of having both options is that you can choose the approach that fits your team’s needs and philosophy.



The Bigger Picture

Stepping back, dbt Fusion feels like a natural evolution rather than a revolution. dbt Labs needed to rebuild their engine to support the features they wanted to build. They packaged it as a major release, which created expectations and anxiety in equal measure.

The controversy isn’t really about the technology—most people agree that faster parsing and better IDE integration are good things. It’s about what this represents for the future of analytics engineering.

Are we moving toward a world where data transformation becomes more abstracted, more automated, more accessible to non-technical users? Or are we doubling down on the engineering rigor that made dbt successful in the first place?

The answer is probably both. The analytics engineering field is maturing, which means it needs to serve a broader range of users and use cases. The challenge is doing that without losing the technical excellence that got us here.



Looking Forward

I’m cautiously optimistic about dbt Fusion. The performance improvements are real, even if they’re not revolutionary for every team. The development experience enhancements are genuinely useful. And the commitment to keeping everything open-source addresses the biggest community concern.

But I’m also watching carefully. The true test of Fusion won’t be the initial release—it’ll be how dbt Labs balances the needs of different user segments over time. Can they make the tool more accessible without dumbing it down? Can they add enterprise features without neglecting the open-source community?

The analytics engineering community has always been good at holding vendors accountable. We’ve built our careers on tools that respect our intelligence and give us control over our work. As long as dbt continues to do that, the specific engine under the hood matters less than the principles it embodies.

What’s your take on dbt Fusion? Are you planning to make the switch, or sticking with Core for now? The beauty of having options is that we can all choose the path that makes sense for our teams and projects.

The conversation around dbt Fusion reflects something bigger: our field is growing up, and with that growth comes complexity. But if we stay focused on building reliable, understandable, and maintainable data systems, the specific tools we use become secondary to the principles we follow.