dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
This week brought targeted fixes across dbt's adapter ecosystem: Snowflake now honors transient seed configuration and properly handles PEM key formatting, Databricks correctly gates view replacements, BigQuery matches snapshot columns case-insensitively, and DuckDB handles timezone-aware fixtures.…
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Who ships this repo — author concentration and the bus factor across the last 300 mainline commits.
How welcoming this repo is to contributors — issue throughput, close time, responsiveness, and good-first-issue count.
What this project is built on — dependency count by ecosystem, the license mix, and anything worth a legal look before you adopt it.
Whether this project's CI can be trusted — pass rate, run times, flaky runs, and which workflow is the weak link.
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fix(databricks): wait for cluster start in Python models, surface Command API errors
fix(dbt-agate): #15412 convert Arrow FixedSizeList instead of panicking on Snowflake VECTOR
Start making profile setup generic instead of exposing one setup function per adapter type
A floor, not a guess: counts only commits whose author, co-author trailer, or message explicitly credits an AI tool (Claude, Copilot, Cursor, aider, Codex…). Based on 30 mainline commits. Unattributed AI code isn't counted here — the full audit estimates that separately.
