data-explore-data

To profile a CSV dataset, provide the file to generate a structured data profile containing per-column metrics, types, null counts, and quality signals. It reads tabular inputs and outputs actionable profiling results revealing data shape and anomalies.

The best way to explore Parquet file metadata is to submit the file for automated schema discovery and per-column profiling. This process reveals distinct values, null counts, and underlying patterns to help you understand dataset quality and structure quickly.

Yes, you can use data profiling to find anomalies and nulls in Excel or JSON datasets. The profiling mechanism supports these formats to output structured quality signals, helping you detect missing values and irregularities across columns.

Dataset exploration works with both file-based inputs and database tables. You can provide a table name or upload a supported file format like CSV, Parquet, or JSON to generate a comprehensive data profile with quality checks.

After generating a data profile, you should review the per-column metrics and anomaly hints to guide follow-up analyses. The profiling results provide actionable guidance for data cleaning, quality remediation, and downstream analytical decisions.

You need to perform data profiling before starting your analysis when you are unfamiliar with a dataset's shape, schema, or quality. Profiling reveals per-column types, nulls, and distinct values, ensuring your downstream analysis avoids unexpected data errors.