Testing Data Pipelines With Realistic Synthetic Fixtures
Synthetic fixtures beat mocks by testing actual transform logic against realistic data.
Priya Nanthakumar
Data Trust Report — Page 2
SQL Assertions for Monotonic and Sequence Columns
Distinguish monotonic ordering from gap-free continuity in SQL assertions.
Tomasz Wielecki
Regex-Based Format Validation for String Columns
Structurally malformed strings hide in plain sight and wreak havoc at scale across your pipeline.
Denise Okafor
Running Quality Score Columns With CTEs
Build data quality signals into your SQL pipelines using CTEs.
Priya Nanthakumar
Date Range and Freshness Validation in SQL
Catch stale data and malformed timestamps that standard quality checks miss.
Denise Okafor
Cross-Table Consistency Checks With Reconciliation Queries
Matching row counts masks data mismatches that reconciliation queries can catch.
Tomasz Wielecki
Conditional Aggregates for Data Quality Metrics
Segment-level metrics reveal data quality problems hidden by table-wide averages.
Marcus Seligman
Referential Integrity Spot-Checks Without Foreign Key Constraints
Spot missing references with a simple LEFT JOIN query pattern.
Tomasz Wielecki
Distribution Shift Detection Between Pipeline Runs
Detecting when data silently changes between pipeline runs.
Denise Okafor
Detecting Statistical Outliers in Numeric Columns
The right outlier detection method depends on your data's shape and what you're willing to miss.
Priya Nanthakumar
Null Rate Analysis Across Warehouse Tables
Continuous null rate monitoring catches pipeline breaks before downstream users do.
Marcus Seligman
Duplicate Record Detection Before Aggregation
Catching duplicates before aggregation prevents cascading errors across metrics.
Priya Nanthakumar