During a data-engineering sprint you must combine weekly e-commerce transactions with the latest product-catalog table. You decide on a full outer join so that no rows are discarded. To allow auditors to later see, for every resulting record, whether it came from the transaction file only, the catalog file only, or from both, which observation-tracking mechanism should you enable while performing the merge to provide that information with the least additional code?
Create an indicator column that labels each row as left_only, right_only, or both during the join.
Run separate anti-joins on each input and union those results with the inner-join output.
Compute a cryptographic hash for every row in each input and compare the hashes after the merge.
Replace all null values produced by the join with domain-specific default constants.
Most data-processing libraries (for example, SQL engines and pandas) let you add an indicator column during an outer join. When the indicator is enabled the merge automatically produces a categorical flag-commonly named _merge-with the values left_only, right_only, or both. This single column immediately reveals where each observation originated, making downstream auditing or filtering trivial. Computing hashes, filling nulls, or stitching together several anti-joins can all achieve similar insight but require extra steps and do not record the provenance of every row as directly or compactly.
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