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Answer: Create two separate BigQuery external tables on Cloud Storage and Cloud Bigtable. Use the BigQuery console to join these tables through user fields, and apply appropriate filters.
The correct answer is D. Creating two separate BigQuery external tables on Cloud Storage and Cloud Bigtable, and then using the BigQuery console to join these tables through user fields, is the most efficient approach. This method leverages BigQuery's powerful querying capabilities and minimizes the need for data duplication. Options A and C are less efficient because they involve additional data copying and processing steps. Option B, while feasible, might not be as efficient as directly querying external tables in BigQuery.
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You manage an application that leverages Cloud Spanner for storing the current state information of users, and Cloud Bigtable to log all user-triggered events. Additionally, Cloud Spanner data is exported daily to Cloud Storage for backups. One of the analysts on your team has requested to join data from Cloud Spanner and Cloud Bigtable for a subset of specific users. How would you fulfill this ad hoc request in the most efficient manner?
A
Create a dataflow job that copies data from Cloud Bigtable and Cloud Storage for specific users.
B
Create a dataflow job that copies data from Cloud Bigtable and Cloud Spanner for specific users.
C
Create a Cloud Dataproc cluster that runs a Spark job to extract data from Cloud Bigtable and Cloud Storage for specific users.
D
Create two separate BigQuery external tables on Cloud Storage and Cloud Bigtable. Use the BigQuery console to join these tables through user fields, and apply appropriate filters.
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