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Answer: Develop an application using Cloud Datastore client libraries to read entities, treating each as a BigQuery table row via BigQuery streaming insert. Add an export timestamp as an extra column and partition the BigQuery table by this timestamp., Utilize managed export to transfer data from Cloud Datastore into a dedicated BigQuery table for the export, then remove the temporary export files.
The correct methods for ingesting and archiving vehicle telemetry data in real-time with Cloud Datastore, ensuring cost-effectiveness and enabling periodic snapshots for recovery or cloning, are options A and B. Option A involves streaming data into BigQuery with timestamp partitioning for efficient querying, while option B uses managed export to BigQuery for temporary data handling before deletion.
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You are tasked with designing a storage system for real-time vehicle telemetry data ingestion using Cloud Datastore. The system must accommodate long-term data growth cost-effectively and support 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 achieve this?
A
Develop an application using Cloud Datastore client libraries to read entities, treating each as a BigQuery table row via BigQuery streaming insert. Add an export timestamp as an extra column and partition the BigQuery table by this timestamp.
B
Utilize managed export to transfer data from Cloud Datastore into a dedicated BigQuery table for the export, then remove the temporary export files.
C
Create an application with Cloud Datastore client libraries to read entities, format the data into JSON, compress it, and store in Cloud Source Repositories.
D
Employ managed export to move data from Cloud Datastore into a Cloud Storage bucket using Nearline or Coldline storage class.
E
Use managed export to export data from Cloud Datastore and import it into a separate project's Cloud Datastore under a unique namespace for the export.
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