Why is extract, transform, load (ETL) a useful design pattern for USD data exchange? Choose two.
A.
It guarantees that all data converted to OpenUSD is lossless and identical to the original format.
B.
It separates concerns, making it easier to maintain and adapt data exchange pipelines for different needs.
C.
It ensures that all OpenUSD workflows use a standardized data structure, regardless of the original format.
D.
It helps preserve the integrity of the original data format while allowing tailoring for different use cases.
The Answer Is:
B, D
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Explanation:
ETL is useful for USD data exchange because it separates the problem into disciplined phases instead of forcing extraction, interpretation, optimization, and client-specific restructuring into one converter step. NVIDIA’s Learn OpenUSD data exchange guidance describes a two-phase approach, extract and transform, inspired by ETL. The extract phase should translate source data to OpenUSD as directly as possible, mapping source concepts to USD concepts to preserve the integrity and structure of the original data. The transform phase then applies optional changes such as user export options, content-structure changes, and optimizations for workflow or client performance.
Option B is correct because this separation of concerns makes converters easier to maintain, test, and adapt. Option D is correct because the pattern preserves source fidelity first, then permits controlled tailoring for specific downstream needs. Option A is incorrect because NVIDIA explicitly notes that data exchange is typically lossy and not every data model maps directly. Option C is incorrect because the guide states there is no single content structure suitable for every organization or workflow. This aligns with Data Exchange → Two-Phase Data Exchange, Data Extraction, Data Transformation, Export Options, and Pipeline Adaptability .
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