Google Professional Data Engineer
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You're planning to use Cloud Datastore for handling real-time vehicle telemetry data and need a storage solution that efficiently manages continuous data growth without high costs. Additionally, you want to create periodic snapshots for point-in-time recovery and data cloning across different environments, archiving these snapshots for a long duration. Which two strategies effectively meet these requirements for managing Cloud Datastore data snapshots? (Choose Two)
A
Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
B
Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
C
Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
D
Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
E
Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.