explore-data

Dataset profiling automatically checks null rates and duplicates by analyzing a table or uploaded file. It generates a comprehensive profile covering row and column counts, distinct counts, and top values to reveal underlying data quality issues.

Understanding a new table's shape is achieved by generating a comprehensive data profile. This process identifies column types, row and column counts, and cardinality, providing immediate clarity on the dataset's overall composition.

Yes, you can get suggestions on which dimensions and metrics to analyze from a raw file. Beyond identifying quality signals, the profiling process provides decision guidance to help you determine your next analytical steps and relevant metrics.

Yes, data profiling automatically detects column types and cardinality. When you run the profile on a new dataset, it produces a detailed summary that includes distinct counts and column types, allowing you to assess the structure without manual inspection.

To identify suspicious values and data quality issues in a dataset, run a profile exploration to surface null rates and duplicates. This highlights potential anomalies and cleaning requirements, helping you quickly assess the reliability of your data.