To validate that data has been successfully loaded, you first confirm the completeness and accuracy of the data transfer. Start by checking basic metrics like record counts between the source and target systems. For example, if loading a CSV file into a database, compare the number of rows in the file with the number of rows inserted into the table. Use SQL queries like SELECT COUNT(*) FROM table or scripting tools (e.g., Python’s pandas) to verify totals. This step ensures no data loss during extraction or transformation. Additionally, validate timestamps or unique identifiers to confirm the latest data was loaded, especially in incremental loads. For instance, check that the maximum date in the target table matches the source after a daily refresh.
Next, perform data quality checks to ensure values align with expectations. Test for nulls in columns that require non-null values, validate data types (e.g., ensuring numeric fields don’t contain text), and check ranges (e.g., dates within a valid timeframe). Use assertions in code, such as CHECK constraints in databases or unit tests in pipelines. For example, a test might fail if a user_id column has duplicates, indicating a primary key violation. Tools like Great Expectations or dbt can automate these validations by defining rules (e.g., "email addresses must contain '@'"). You can also sample data manually—for instance, spot-checking 10 records to confirm addresses or names match the source.
Finally, validate relationships and business logic. Ensure foreign keys reference valid records (e.g., all order.customer_id values exist in the customer table) and that aggregated values (sums, averages) match source calculations. Run integration tests to verify joins between tables produce expected results. For example, compare revenue totals in a dashboard with the source database after a load. Log validation results and set up alerts for failures—tools like Airflow or custom scripts can notify teams via email or Slack. For recurring loads, automate these checks in CI/CD pipelines to block deployments if validations fail, ensuring issues are caught before reaching production.