Fivetran’s dbt Labs Acquisition and What It Means

Exciting news in the data world as two of our partners, Fivetran and dbt Labs, recently announced they’re merging forces, combining Fivetran’s ingestion and loading with dbt’s powerful transformation framework.

This partnership marks a major milestone in the evolution of the modern data stack, with the potential for even greater efficiency and flexibility for data teams everywhere.

We’re particularly glad to see both companies reaffirm their commitment to maintaining dbt Core’s open-source licensing, ensuring accessibility and continued innovation for the broader data community. Tristan Handy taking ownership of the open-source strategy and community engagement is also an encouraging sign of the joint commitment to keeping dbt Core evolving and open.

With this announcement, organizations are asking whether this means faster insights or tighter lock-in.

Our view is that there are gains to be had when ingestion and modeling merge, but flexibility matters more than ever. Now is a great time to think about where your organization is in its data maturity journey and the ongoing calculus of build versus buy. If you’re concerned about vendor lock in, you need a neutral control tower which provides a way to coordinate tools, catch issues early, and keep your options open.

The answer: Orchestrated, composable architecture.

Ingestion + Transformation: What Changes, What Doesn’t

When Fivetran and dbt Labs fully integrate, data ingestion and data transformation, the two most critical layers of the modern data stack, become part of a single vendor ecosystem. Fivetran and dbt are saying that product offerings won’t change and that this will bring tighter integration, fewer moving parts, and less wiring.

For teams that value agility, composability, and customization, it raises new questions about visibility, flexibility, and control.

That’s where Brooklyn Data comes in. We can help your team design a data stack with a neutral orchestration layer, i.e. your control tower, to manage the flow from ingestion to modeling to activation. With independent orchestration (using Dagster, Airflow, or similar tools), you get the best of both worlds where you can leverage dbt’s testing and modeling framework as part of your stack without an overreliance on managed tools. This lets you leverage a mature and supported tool suite while maintaining more control and ownership and keeping your stack portable, reliable, and future-proof.

Orchestrators act as air traffic control for your data: scheduling, validating, and monitoring each job so your stack stays fast, stable, and independent—no matter who owns which piece below the surface.

Let’s take a marketing team as an example of what this could look like. When marketing teams depend on data, every delay costs momentum. Campaigns stall waiting for clean numbers, analysts spend cycles chasing inconsistencies, and changing a single connector can disrupt workflows.

By layering a neutral control tower above your ingestion and modeling tools, marketers regain visibility, flexibility, and control. You can see how data moves, catch issues early, and keep using the tools you already know.

Here’s what that means in practice:

• Speed to insight: Go from “Which campaign worked?” to “Here’s why”—in minutes, not cycles.
• Trust & transparency: Know what ran, what changed, and why—all in one place.
• Freedom to choose: Keep your stack open and composable as tools evolve.
• Cost control: Schedule, scale, and pause workloads to prevent runaway spend.

Building the Data Stack that Works for You

We believe that the modern data stack is all about keeping your options open. Consolidation can simplify your stack if you maintain clear contracts between layers and you have a clear value proposition for build versus buy. An increasingly appealing avenue is to use an independent orchestrator to manage your ELT. That’s how you future-proof your data strategy, ensuring you never get stuck on someone else’s roadmap.

At Brooklyn Data, we’re here to help you design and implement the stack that solves your business’s needs so you can move faster, stay flexible, and build data systems that evolve with you.