Aftermath of the DBT + Fivetran Merger: What It Really Means for Data Teams
Fivetran’s model has always been about automation and ease of pipelines that just work. DBT, on the other hand, gave analysts the power to build and own transformation logic with transparency and control. Together, they’ve created something powerful, but potentially risky for the rest of the ecosystem.
For data teams, the aftermath is about choice.
Will integration become simpler, or will vendor lock-in deepen?
Will innovation accelerate, or will consolidation slow it down?
At Matatika, we see this as a reminder that openness still matters. The best data stacks are not built on dependency; they’re built on flexibility. When your ETL, ELT, and analytics layers can evolve independently, you can keep scaling without rewriting your entire architecture every time the market shifts.
Because when one vendor controls ingestion and transformation, efficiency might come at the cost of agility.