During the monthly refresh of a sales dashboard, an ETL pipeline ingests a third-party CSV file into a staging table and then loads it into the data warehouse. After the most recent run, thousands of rows now display NULL in the Customer_ID column, so lookup joins in downstream reports fail. A quick inspection shows the vendor changed the file delimiter from a comma to a pipe (|), shifting every field by one position. Which report-validation technique, if it had been executed immediately after the staging load, would most likely have detected this corrupt data before it reached the dashboard?
Add a calculated field in the dashboard that substitutes "Unknown" for any NULL Customer_ID value.
Increase the ETL job frequency from monthly to hourly so future files are processed sooner.
Apply corporate branding and color-scheme guidelines to the dashboard template before publication.
Perform a source-to-target reconciliation that checks row counts and column-level hash totals between the CSV and the staging table.
A source-to-target reconciliation compares record counts, column mappings, and aggregate or hash totals between the raw source file and the staging/warehouse tables. Because the delimiter change mis-aligned columns, the reconciliation would have shown unexpected NULL values or mismatched totals for Customer_ID, alerting the team to corruption before the data fed any reports. The other options either attempt to mask the problem (replacing NULLs), simply run the pipeline more frequently, or adjust visual design elements; none of them validate that the loaded data is structurally and semantically correct, so they would not have identified the underlying issue.
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