Know your data better!Datavines is Next-gen Data Observability Platform, support metadata manage and data quality.
A deterministic 0–100 hygiene score — README, license, CI, tests, docs, and freshness.
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.
Grounded in datavines's README, structure, and recent commits — answers won't invent code they haven't seen.
A Monday email with what shipped, in plain English — no account needed.
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Fix PostgreSQL custom SQL errors
Corrected how table names with schema prefixes are parsed when executing custom SQL queries. The system now properly recognizes when a table name already includes a schema prefix (e.g., "schema.table") and handles it correctly across Flink, Local, and Spark execution engines.
Custom SQL metric error export
Added support for custom SQL metrics to export error data, including a new CustomCountSql plugin and enhanced table discovery functionality across Flink, Spark, and Local execution engines. Users can now define custom SQL queries for metrics with better error tracking and data export capabilities.
Add PostgreSQL server support
The server now supports PostgreSQL as a metadata database backend in addition to MySQL. Users can initialize PostgreSQL using the new datavines-postgresql.sql script and select the database by passing the appropriate profile flag when starting the server.
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.