data-exploration

Dataset profiling reveals structure, quality, and potential relationships by measuring grain, primary keys, nulls, uniqueness, and common value patterns. This process generates schema notes and actionable insights for confidently planning analyses.

Data profiling is the process of examining unfamiliar datasets to understand structure, data quality, and potential relationships. You need it during dataset discovery to identify grain, nulls, and foreign-key candidates before planning analyses.

Finding potential foreign-key candidates and hierarchies across multiple tables requires systematic relationship discovery. This involves analyzing distinct counts, distributions, and documented schema issues to reveal structural connections within the dataset.

You can generate schema documentation and data quality checks by applying structured profiling requirements to raw datasets. This measures nulls, uniqueness, and distributions, outputting actionable insights and suggested next steps for analysts.

The best way to assess data quality and identify patterns in multiple tables is applying structured profiling requirements. This measures nulls, distinct counts, and distributions, revealing documented issues and common value patterns across the dataset.