For this question, refer to the TerramEarth case study. Considering the technical requirements, how should you reduce the unplanned vehicle downtime in GCP?
A.
Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.
B.
Use BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a Multi-Regional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.
C.
Use Cloud Dataproc Hive as the data warehouse. Upload gzip files to a Multi-Regional Cloud Storage bucket. Upload this data into BigQuery using gcloud. Use Google Data Studio for analysis and reporting.
D.
Use Cloud Dataproc Hive as the data warehouse. Directly stream data into partitioned Hive tables. Use Pig scripts to analyze data.
A. Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.
This approach leverages the real-time data streaming capabilities of Cloud Pub/Sub and Cloud Dataflow, the scalability and efficiency of BigQuery for data analysis, and the powerful visualization and reporting features of Google Data Studio. This combination ensures timely insights and quick response to issues, thereby reducing unplanned vehicle downtime.
A. Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.
Technical requirement : Create a backup strategy
Is bigquery a suitable system for data backup . Wouldn't a better system for backup be cloud storage.
Only B has that option
A is correct, using dataflow to clean and/or convert the data for analysis makes more sense.
B does not show any sign of how data will be loaded to bigquery (as gzip) or after conversion, it seems broken process to me.
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