A data analyst at an e-commerce firm has two separate sources: (1) a relational database table that stores millions of sales transactions and (2) a comma-separated file exported from the customer-service platform that contains post-purchase satisfaction scores for each order ID. Before she can calculate metrics that compare revenue with average satisfaction, the analyst needs to create a single view that joins the two sources on the common order ID. Which data manipulation technique should she apply first to combine the datasets for further analysis?
Data Blending combines records from different data sources-such as a SQL table and a CSV file-into one logical dataset, enabling analysts to run calculations and visualizations across all the integrated fields. Techniques like data reorienting (pivoting), normalization, or recoding transform values or structure within a single source but do not, by themselves, merge separate sources.
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What is data blending?
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How is data blending different from data normalization?
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What role does a common key, like 'Order ID,' play in data blending?