Post by Avantika Penumarty

Senior Data Engineer (Former @Meta) | Scaled Data Infrastructure for 1B+ Users | Empowering 20k+ Engineers to think in Systems, not Tools | AI & Data Tech Creator | Open to Senior IC Roles

The Fivetran + dbt Labs merger is being sold as the future of open data infrastructure. Here's what it actually is: two VC-backed unicorns that burned through growth narratives deciding consolidation is cheaper than competing for the same 10,000 customers. Fivetran was last valued at $5.6 billion. dbt Labs at $4.2 billion. Combined ARR is approaching $600M. You do the math on what that means for the multiples. I'm not saying the product story is bad. It's not. Fivetran moves data. dbt transforms it. Together they cover most of the pipeline and 80-90% of Fivetran customers already run dbt anyway, so there's real integration logic here. What I'm watching is dbt Core. The promise is that it stays open source under Apache 2.0 and nothing changes. That's the official line. But every time an open source project gets absorbed by a consolidating commercial entity, the same thing quietly happens: the commercial product gets the engineering investment, the open source gets the bug fixes. Not saying that's the plan. But that's historically the pattern. The other thing nobody's really talking about: this merger is the loudest signal yet that the "modern data stack" era is ending. The age of buying 6 best-in-class point solutions and duct-taping them together with YAML files is giving way to consolidated platforms that want to own the whole pipeline. Snowflake is building transformation natively. Databricks has everything from ingestion to serving. Now Fivetran + dbt want to be the open alternative to those closed ecosystems. It's a reasonable bet. But "open" and "backed by investors who need an exit" have historically been in tension. Watch what happens to Core investment over the next 18 months. That'll tell you everything. - - - - - - - - ā™»ļø Repost if you're a dbt Core user watching this closely. šŸ“Œ Follow for weekly breakdowns of what's actually happening in data infrastructure.