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Answer: Use managed export to export data from Cloud Datastore to a Cloud Storage bucket with Nearline or Coldline storage class., Use managed export to export data from Cloud Datastore and import it to a separate project under a unique namespace reserved for the export.
The correct methods for ingesting and archiving vehicle telemetry data in real-time using Cloud Datastore, while ensuring cost-effectiveness and the ability to create periodic snapshots for recovery or cloning, are options B and E. Option B involves exporting data to a cost-effective Cloud Storage bucket with Nearline or Coldline storage, and option E involves importing the exported data into a separate project under a unique namespace, both of which meet the specified requirements.
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You are tasked with designing a storage system for real-time ingestion of vehicle telemetry data using Cloud Datastore. The system must be scalable for long-term data growth, cost-effective, and capable of periodic snapshots for point-in-time recovery or environment cloning. These snapshots need to be archived for an extended period. Which two methods would you choose to meet these requirements?
A
Develop an application that uses Cloud Datastore client libraries to read all entities, format them into JSON, compress, and store in Cloud Source Repositories.
B
Use managed export to export data from Cloud Datastore to a Cloud Storage bucket with Nearline or Coldline storage class.
C
Develop an application leveraging Cloud Datastore client libraries to read entities, treat each as a BigQuery table row via streaming insert, and partition the table by export timestamp.
D
Use managed export to export data from Cloud Datastore and import it into a BigQuery table created for the export, then delete temporary files.
E
Use managed export to export data from Cloud Datastore and import it to a separate project under a unique namespace reserved for the export.
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