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Question 4 of 27
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I assume
The best option is A. Cloud Data Fusion pipeline (Cloud Storage to BigQuery). Option A is best because Cloud Data Fusion is visual and fast for pipeline building, scalable, handles transformations visually, and provides data quality insights within the pipeline. Option B (BigQuery load + SQL) is incorrect because scheduled queries are less of a pipeline and offer fewer built-in data quality features. Option C (BigQuery load + Data Fusion BQ to BQ) is incorrect because it's inefficient and redundant to load to BigQuery before Data Fusion. Option D (Dataflow template) is incorrect because while scalable, Data Fusion is often quicker to build visually for simpler pipelines. Therefore, Option A, Cloud Data Fusion, is the best balance of speed, scalability, and data quality for this task.
There should be more detail in the question. Though both Dataflow and Datafusion can be used. Datafusion is more suitable if you dont want to code and let google do the work. In case there is more complexity in daily analysis of the CSV, Dataflow is the best approach as it provide in built templates and custom template creation both.