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Risks & governance
Data sensitivity
Pandas best practices
Reporting & connections
The HTML report
Fleet recording
Connections
Cross-run governance
Extending table descriptions
Governance in dbt models
OpenMetadata bridge
Static governance scan
Examples
Customer pipeline (Narwhals)
Streaming analytics (Narwhals)
Native pandas tracking
ML scoring by region (pandas + scikit-learn)
Governance in docstrings
Great Expectations checkpoints
Streaming analytics (PySpark)
Feature engineering (pyspark.ml)
ML scoring by region (PySpark + spark.ml)
Cross-engine: Spark -> pandas -> Spark
Comprehensive: Spark -> pandas loop -> Spark
Experimental: tracking Series & in-place assignment
Making a function opaque
Fleet: recording runs
Fleet: risks across pipelines (streaming CLV)
Fleet: risks on untracked sources
Governance from table comments (experimental)
Great Expectations on PySpark
Bootstrapped (unmodified script)
Tracking installed (wheel) code
Formal risk checklist (Markdown)
Changelog
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