A store receives thousands of small updates each hour for credit card purchases. The team wants to keep data accurate after every new purchase. Which approach addresses these needs?
A streaming engine that writes aggregated metrics at the end of the day
A data lake that stores unstructured sale logs from multiple sources
A star schema that aggregates purchases across a data warehouse
A transaction-based design that uses row-level operations for each purchase record
A design centered on individual transactions is suited for frequent data changes. It updates records for each purchase, preserving data accuracy. Star schemas handle analytics with fewer updates, while streaming engines with end-of-day metrics and data lakes for unstructured data are not as effective for continuous row-level operations.
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