Data✓ Mathematical
◆ The PatternAutomated checks that stop bad data before it reaches your model
Garbage in, garbage out — but in production, garbage arrives silently. Data quality gates enforce schema validation, range checks, freshness constraints, and completeness thresholds. They sit in your pipeline before feature engineering and before inference.
// Interactive — data quality pipeline flow
# Python — Great Expectations data quality check import great_expectations as gx context = gx.get_context() ds = context.sources.add_pandas("prod_data") asset = ds.add_dataframe_asset("batch", dataframe=df) expectations = [ gx.expectations.ExpectColumnValuesToNotBeNull(column="user_id"), gx.expectations.ExpectColumnValuesToBeBetween( column="price", min_value=0, max_value=100000), ]
Pattern bridge: Data quality gates are the production equivalent of missing data strategies — catching the problem before it corrupts your analysis. In markets, data hygiene (adjusting for splits, dividends, survivorship) serves the same protective role.