Examples

Each page below shows a runnable example’s source code next to the interactive report it produces. The reports are real conformare output: explore the process diagram, the per-step profilers, the column highlighter and the governance register. Spark and Great Expectations demos appear only when those optional dependencies were available at build time.

Browse the 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)

---

Table of contents

• 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)