data-exploration

Data profiling reveals table structure, column types, and grain while assessing completeness, consistency, and freshness. You get a concise profile including keys, null rates, and notable anomalies for any unfamiliar dataset.

Schema documentation records table keys, data types, and structural relationships during data profiling. It outputs a reusable template capturing quality findings and schema details for analytics preparation and QA checks.

Data quality validation assesses completeness, consistency, freshness, and accuracy during the profiling pass. It applies structural checks and column-level profiling to identify null rates and notable anomalies.

Data profiling discovers potential relationships by enforcing structural checks and documenting schema relationships. It reveals table grain, keys, and column-level patterns to map connections across analytics workflows.

Run data profiling when preparing unfamiliar data for analytics, performing QA checks, or modeling. It reveals structure, data quality, and potential relationships before you commit the dataset to downstream processes.

The profiling output includes row and column counts, grain, keys, data types, null rates, and notable anomalies. It provides a reusable schema documentation template recording all quality and structural findings.