How do you integrate data quality checks into ETL processes?

Integrating data quality checks into ETL processes involves embedding validation rules at each stage—extraction, transformation, and loading—to identify and handle issues early. This ensures reliable data flows and minimizes downstream errors. Checks can range from basic schema validation to complex business logic enforcement, depending on the data’s use case. The goal is to catch problems like missing values, incorrect formats, or invalid relationships before they propagate.

During the extraction phase, validate raw data against expected schemas and formats. For example, check that a CSV file’s date columns match YYYY-MM-DD or ensure required fields like customer_id are not null. Tools like JSON Schema or Python’s pandas can automate schema validation. You might also profile data to detect anomalies, such as unexpected spikes in row counts or outliers in numeric columns. If a source API returns malformed JSON, the extraction process should log the error and halt or route problematic data for review. Implementing row-level checks here prevents invalid data from entering the pipeline.

In the transformation phase, enforce business rules and data consistency. For instance, ensure sales totals are non-negative or that product categories align with predefined values. Use SQL constraints (e.g., CHECK clauses) or framework-specific tests (like dbt’s built-in assertions) to validate transformed data. If aggregating customer orders, verify that sums match source system totals. Additionally, deduplicate records using window functions or libraries like PySpark’s dropDuplicates(). For complex logic, such as validating address formats, integrate external APIs or regex patterns. Failed checks here might trigger data correction workflows or alerts to stakeholders.

Finally, during the loading phase, confirm data integrity before writing to the target system. Check referential integrity (e.g., foreign keys in a relational database) or ensure unique primary keys. For time-series data in a data warehouse, validate partition alignment. Tools like Great Expectations or custom scripts can compare pre- and post-load row counts to detect ingestion gaps. If loading to a cloud database, use transactional writes to avoid partial updates. Log all quality issues—such as rejected rows—to a monitoring system (e.g., Grafana) and notify teams via Slack or email. This phase ensures only clean, audited data reaches end users, while maintaining traceability for debugging.