Question: The Cloud Dataproc approach allows organizations to use Hadoop/Spark/Hive/Pig when needed. It takes on average only 90 seconds between the moment resources are requested and a job can be submitted. What makes this possible?
- The separation of storage and compute.
- The use of queries and containers.
- The configuration of jobs and workflows.
- The absence of management and maintenance.
Explanation
Dataproc can start Hadoop, Spark, Hive, and Pig clusters quickly because data can remain in Google Cloud storage while processing resources are created only when needed. This lets clusters be temporary rather than long-running. Keeping persistent data outside the cluster reduces setup time and avoids tying storage capacity to compute capacity. The business value is faster processing with less idle infrastructure.
Why the other options are incorrect
Queries and containers describes unrelated execution concepts, not the Dataproc operating model.
Jobs and workflows help organize processing, but they do not explain rapid cluster availability.
Absence of management and maintenance overstates the value because configuration and operational choices still exist.
Source for verification
https://cloud.google.com/dataproc/docs/concepts/overview
https://cloud.google.com/dataproc/docs/concepts/connectors/cloud-storage
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