data-exploration

To profile a dataset, you generate column-level summaries, null counts, data types, and basic statistics to assess dataset health and structure. This systematic profiling identifies schema issues and potential anomalies before analysis.

Data profiling is the process of exploring datasets to understand their shape, quality, and patterns before analysis. You need it when onboarding new data sources to assess structure, column distributions, and potential anomalies.

You detect nulls and outliers by running a data profiling workflow that identifies anomalies, missing values, and potential quality issues. This quality assessment guides necessary data cleaning and downstream transformations.

Yes, the data profiling workflow applies to new data sources to assess structure, data quality, and potential anomalies across common data types. It evaluates column distributions and schema understanding without requiring specific dependencies.

The best way to explore a new dataset is running a systematic profiling workflow to produce a summary report. This identifies schema understanding, null and outlier detection, and actionable recommendations to inform modeling decisions.

After identifying data quality issues during profiling, you should follow the recommended next steps to guide data cleaning and transformations. The profiling report informs modeling decisions and downstream processing actions.