explore-data

Dataset profiling evaluates an unfamiliar table or file to surface its schema, grain, date coverage, and column-type breakdown. It flags high null rates, cardinality surprises, duplicates, and format inconsistencies to guide your next analytical steps.

Data quality assessment flags high null rates, suspicious placeholder values, duplicates, skew, and encoding inconsistencies. By evaluating distributions and cardinality surprises, it reveals underlying patterns and quality problems before you run deeper analysis.

Pattern and relationship discovery identifies candidate join keys and suggests dimensions and metrics worth analyzing. It evaluates schema and distributions to recommend follow-up analyses tailored to your dataset's structure and analytical angles.

Yes, dataset profiling applies when encountering unfamiliar tables or uploaded files. It validates schema, evaluates nulls and distributions, and generates a comprehensive profile including data quality flags and recommended metrics regardless of the source format.

After generating a data profile, review the recommended dimensions, metrics, and suggested follow-up analyses. The profile provides analytical recommendations and identifies relationship candidates to help you decide what to analyze next.

Schema inference evaluates column-type classification and flags high null rates alongside suspicious placeholder values. Incorrect types or high null rates often indicate format and encoding inconsistencies, data skew, or cardinality surprises within the dataset.