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Answer: Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
The correct answer is B because storing and updating the data in a regional Google Cloud Storage bucket and creating a federated data source in BigQuery is both cost-effective and efficient. Regional storage is cheaper than BigQuery storage, and using a federated data source allows BigQuery to directly access the updated data without the need to store redundant information in BigQuery itself. This approach ensures that the data remains up-to-date and can be combined with other BigQuery data without incurring unnecessary costs.
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You are employed at an economic consulting firm specializing in real-time economic trend analysis for companies. Your role involves utilizing Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, which are refreshed every 30 minutes. Your objective is to ensure that this pricing data remains current, enabling you to integrate it efficiently and cost-effectively with other datasets in BigQuery. What steps should you take to achieve this?
A
Load the data every 30 minutes into a new partitioned table in BigQuery.
B
Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
C
Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
D
Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.