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Answer: Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
The correct answer is **B**: Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage. This approach creates a partitioned structure that isolates errors, allowing for easier recovery without affecting other months' data. Exporting and compressing the data into Cloud Storage offers a cost-effective backup solution, optimizing storage costs by reducing space requirements. - **Why option A is incorrect**: While snapshot decorators can aid in point-in-time recovery, they're not optimized for long-term backup storage. Cloud Storage provides a more cost-effective and accessible solution. - **Why option C is incorrect**: A single table structure complicates error isolation and recovery, making it harder to manage without impacting the entire dataset. - **Why option D is incorrect**: Duplicating data within BigQuery increases storage costs and complexity without offering significant recovery benefits over Cloud Storage backups.
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Scenario: You rely on BigQuery as your primary analytics platform, with new data added daily and processed by an ETL pipeline for end users. The ETL pipeline undergoes frequent updates, and errors might remain undetected for up to two weeks. How should you structure your data in BigQuery and optimize your backups to efficiently recover from potential errors while minimizing storage costs?
A
Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.
B
Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
C
Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
D
Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
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