How do you validate that data has been successfully loaded?

Validating successful data loading involves verifying that all expected data has been transferred accurately and completely from the source to the destination. The process typically starts by comparing record counts between the source system and the target database or file. For example, if you load a CSV file with 10,000 rows into a PostgreSQL table, you’d run a query like SELECT COUNT(*) FROM table_name to confirm the target matches the source. Checksums or hashes (e.g., MD5 or SHA-256) can also validate file integrity during transfers. For structured data, schema checks ensure columns, data types, and constraints (like NOT NULL fields) are correctly applied. Automated tests in ETL pipelines often include these validations to flag discrepancies early.

Beyond basic counts and schemas, deeper validation involves sampling records and checking data quality. For instance, you might verify that date formats align with expectations, numeric values fall within valid ranges, or mandatory fields contain no nulls. Suppose you’re loading customer data—you could run a query to check for invalid emails (LIKE '%@%.%') or implausible birthdates (e.g., dates in the future). Logging and error handling during the load process are critical: tools like AWS Glue or custom scripts often capture rejected records, which you’d review to identify patterns (e.g., recurring formatting issues). This step ensures not just completeness but also consistency and accuracy.

Finally, automated testing frameworks and monitoring tools help scale validation. Libraries like Python’s pandas or Great Expectations enable programmatic checks, such as asserting statistical distributions or uniqueness of keys. For example, a test might confirm that a product inventory table’s stock_quantity column has no negative values. Integrating these checks into CI/CD pipelines ensures validation occurs with every deployment. Post-load, dashboards or alerts (using tools like Grafana) can track metrics like row counts over time or error rates. Combining automated tests with manual spot-checks provides a robust safety net, ensuring data meets both technical and business requirements before it’s used downstream.