A data scientist is tasked with analyzing customer order data from an XML file. The file has the following structure: a root <orders> element containing multiple <order> elements. Each <order> element has an order_id and a customer_id as attributes. Nested within each <order> is an <items> element that contains a variable number of <item> child elements, each detailing a product with its product_id, quantity, and unit_price.
To perform a quantitative analysis of sales per product, the data must be flattened into a single, two-dimensional table. Which of the following describes the primary challenge in this process and the most appropriate method to resolve it?
The most significant challenge is handling orders that may not contain any <item> elements. This requires filtering out these empty orders or creating a row with null values for all item-related fields.
The main challenge is the implicit string typing of all data within the XML. The solution involves casting quantity and unit_price to numeric types after the structure has been fully parsed into an in-memory object.
The one-to-many relationship between an <order> and its nested <item> elements is the main challenge. The solution is to create a separate row for each item, duplicating the parent <order> data (order_id, customer_id) in each new row.
The primary difficulty is parsing XML attributes (order_id) separately from element text (product_id). This requires a custom parser to iterate through attributes first before processing child elements.
The correct answer identifies the core structural challenge of flattening a one-to-many relationship. To convert the hierarchical XML into a flat table for analysis, each <item> element must become its own row. To maintain the relationship to the original order, the parent data (order_id and customer_id) must be replicated for each item row. This process is also known as denormalization.
The option regarding XML attributes versus element text is incorrect because standard XML parsing libraries handle this distinction automatically; it is a fundamental feature of any parser and not the primary structural challenge. The option about data type conversion is a necessary data cleaning step, but it is separate from and typically follows the structural transformation of flattening. The primary challenge is reshaping the data's structure, not cleaning its values. The option concerning empty orders is a valid edge case to consider in data cleaning, but it is not the principal challenge of the flattening process itself. The fundamental problem is how to represent the one-to-many relationship when items are present.
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What does a one-to-many relationship mean in the context of XML data?
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What is the process of denormalization, and why is it used for flattening XML data?
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How can you handle empty or missing `<item>` elements during the flattening process?