The best place to learn data engineering. Built and maintained by the data engineering community.
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 data-engineering-wiki'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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docs(concepts): add Data Security, Ethics, and Compliance overview note (closes #100) (#177)
docs(concepts): add Data Contract concept documentation (closes #104) (#176)
Auto-publish vault on merge
The site now automatically publishes to Obsidian Publish whenever changes are merged to the main branch, streamlining the deployment process.
Remove Data Oculus documentation
Deleted the Data Oculus tool page since the platform is no longer operational, and added DataKitchen links to the link checker's ignore list since they're valid but can't be automatically verified.
Removed unused images and config
Deleted 5 unreferenced image assets that were no longer linked from any pages, and removed a redundant VS Code editor configuration file whose line-ending setting is already enforced by the repository's git attributes.
Add alt text and description link to DataDriven sponsor placement (#153)
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.