Databricks framework to validate Data Quality of pySpark DataFrames and Tables
C-Rank Grade: A (Welcoming) - 60/100
External PR Merge Rate: 36%
Response Time: 3h
First Timer Success: 20%
databrickslabs/dqx has a recorded first-timer success rate of 20.0%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in databrickslabs/dqx respond to incoming external pull requests in approximately 2.8 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 60.4 places databrickslabs/dqx in the A tier.
The external contributor pull request merge rate for databrickslabs/dqx is 35.7%, based on public PR activity from non-core contributors.
databrickslabs/dqx currently has 4 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Collaborative maintainer environment in databrickslabs/dqx. Reviews community pull requests with focus on project quality.
Discussions in databrickslabs/dqx focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.
Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.
Average Response Latency
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External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
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Historical health & responsiveness evolution
External PR acceptance
First PR success rate
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Maintainer review activity heatmap & prime window prediction
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Maintainers are most responsive around 03:00 UTC (03:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 03:00 to 07:00 UTC (03:00 - 07:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~4 hours (45% approval). Large PRs (>500 lines) take ~13 hours (16% approval). Keep your diff compact for rapid review.
Is there an existing issue for this? I have searched the existing issues Problem statement Validation is_unique can potentially evaluate 123 and 123 even though they are from functional perspective the same value. Proposed Solution Add optional trim_strings parameter. Additional Context No response
Is there an existing issue for this? I have searched the existing issues Problem statement No simple way to collect end-user feedback apart from GitHub issues. Proposed Solution General Feedback - Google Form? Embedded in the DQX Website. Report Bug --> Github Issues Feature Request --> Github Feature Request Additional Context No response
Raising the mlflow[databricks] floor to >=3.13.0 (in #1489, to pick up mlflow/mlflow#20599) left two pieces of scaffolding behind that no supported version needs any more. Both are small and self-contained. 1. The artifact_path= fallback in log_sklearn_model_compatible is dead src/databricks/labs/dqx/anomaly/mlflow_registry.py inspects mlflow.sklearn.log_model and prefers name= when the signature offers it, falling back to artifact_path= when it does not. MLflow 3.x accepts both, so the name= branch is always taken and the fallback is unreachable on any version the floor admits. Worth keeping the helper's shape if it is doing other work, but the dead branch and its comment can go. 2. The dummy Databricks config profile fixture is probably removable tests/integration_anomaly/conftest.py::_ensure_databricks_config_file writes a config profile with token = dummy purely to work around mlflow/mlflow#20599, which the new floor contains. This one cannot be settled from CI: CI always supplies
Is there an existing issue for this? I have searched the existing issues Problem statement The currently list of metrics is limited to basics such as min, max, stdev etc. Proposed Solution Include additional statistics beyond minimum and maximum values, such as the 10th percentile, along with the percentage of occurrences within each bucket or range. Additional Context No response
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When evaluating whether to contribute to databrickslabs/dqx, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of databrickslabs/dqx acknowledge new external contributions in approximately 2 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 35.7% were successfully merged into the primary branch.
databrickslabs/dqx has a recorded first-timer success rate of 20.0%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~2 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 60.4 places databrickslabs/dqx in the Welcoming tier.
The external pull request merge rate is 35.7%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, databrickslabs/dqx currently has 4 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for databrickslabs/dqx are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.