When creating analytical data marts for online stores, developers often encounter discrepancies in total sums and group distributions due to incorrect JOIN operations and data granularity mismatches. A common issue is 'fan-out', where joining multiple child tables, such as order items and payments, to a parent order table leads to row multiplication and inflated metrics.
To ensure accuracy, it is recommended to aggregate child tables by order ID before joining them to the main order dataset. This approach prevents the duplication of order totals and payment amounts. For historical customer segments, using SCD Type 2 dimensions requires matching the order date against the validity period of the customer attribute to avoid incorrect segment assignment.
Data validation should include checking for missing or duplicated order IDs using a count of copies. Analysts must define specific rules for each join, including expected match counts and handling of late-arriving dimensions. Simply obtaining a result without errors in SQL does not guarantee the correctness of the final report.
The source material also highlights the importance of managing late-arriving data and ensuring that category-level distributions are proportional. For instance, payment amounts should be distributed based on the weight of individual items within an order to maintain consistency across different analytical cuts.
