Explanation:
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.