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Answer: Export the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
Given the sensitivity of patient records and the large size of the data (10 TB), using Google's Transfer Appliance is a secure and efficient method. Transfer Appliance is designed to handle large-scale data transfers securely without exposing the data over the internet. Although using gsutil might seem time-efficient assuming high bandwidth, it's often unreliable for such large amounts of data and sensitive information due to potential network issues and speed constraints. Therefore, option B is the correct answer.
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You have been tasked with migrating a substantial amount of sensitive patient data, totaling 10 TB, from a relational database to Google BigQuery. Given the considerable size of this data and its sensitivity, it is crucial to design a migration solution that ensures both security and efficiency. What steps should you take to accomplish this?
A
Export the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
B
Export the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
C
Export the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.
D
Export the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.