WorkMarch 1, 2024

Data Quality Monitoring: Automated Testing and Time-to-Market Alerting

Data Engineering

Octave Olivetti

Project Notes (full article to be written)

Source: Luxury beauty brand - Data Consistency monitoring Context:

Implementation of automated data testing
Optimization of time-to-market for developers
Automated verification of data engineer work

Technologies:

Data Warehouse: BigQuery
Testing Framework: Great Expectations or custom Python
Monitoring: Cloud Monitoring, custom dashboards
Alerting: Slack, email notifications
Orchestration: Airflow (test execution)

Implemented Tests:

Row count validation (expected vs actual)
Schema validation (columns match expected schema)
Freshness checks (data updated within expected time window)
Null value checks (critical fields not null)
Range validation (values within expected ranges)
Referential integrity checks

Results:

Real-time alerting on data quality anomalies
Automated verification of data engineer work
Reduced time-to-market for new features
Data quality incidents detected before impacting business
90% reduction in production data quality issues

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38M+

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€5B+

Managed Revenue

32+

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7+

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