Comparisons anchored on dbt, the SQL transformation framework from dbt Labs, against other open-source transformation tools and its own hosted platform.
The two open-source SQL transformation frameworks compared: dbt's models and huge ecosystem against SQLMesh's virtual data environments, plan and apply deployments, incremental state tracking and dialect transpiling, with SQLMesh's dbt project compatibility and what switching involves.
Whether to stay on the Python dbt Core v1 line or move to dbt v2.0, the Rust rewrite now on the main branch: single-binary install, stricter parsing, Parquet artifacts, platform support and the split between Apache 2.0 source code and the dbt product license distribution.
Running dbt yourself with an orchestrator such as Airflow or Dagster against paying for the dbt platform: published Developer, Starter and Enterprise limits and prices, what the platform adds for scheduling and collaboration, and the CI, scheduling and documentation work a self-run setup needs.
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