analyze-data-quality

You can assess data quality by applying compact profiling to check grain, keys, nulls, duplicates, and schema shape, ensuring your dataframe or table is trustworthy enough for analysis, modeling, or dashboards.

Schema drift happens when the data structure changes unexpectedly. Identifying schema drift is a key data quality profiling step that prevents broken downstream pipelines, corrupted dashboards, and invalid model training.

Yes, you can evaluate query results for broken joins, referential integrity, and distribution shifts. These risk-focused checks identify analytical impacts like leakage, late backfills, or stale partitions that can bias reports and models.

Yes, this data profiling approach works directly with tables, query results, files, and dataframes. It assesses completeness, uniqueness, validity, and timeliness against the dataset's intended analytical use.

You test for dataset freshness and duplicates by running quality profiling checks. This evaluates timeliness and uniqueness to catch stale partitions or duplicate rows before they corrupt downstream pipelines or modeling.

The best way to find data leakage and late backfill issues is to run temporal and segment analysis on your dataset. This identifies distribution shifts and analytical risks that can quietly bias experiments and models.