Question 1

What's the difference between Dataproc and BigQuery?

Accepted Answer

Dataproc runs open-source engines like Apache Spark and Hadoop on managed clusters, which is great when you need Spark libraries, custom code, or Hadoop ecosystem tools. BigQuery is a serverless data warehouse optimized for SQL analytics. Use BigQuery when SQL is enough and you want minimal operations; use Dataproc when you need Spark/Hadoop processing, custom dependencies, or specific frameworks.

Question 2

When should I use Dataproc?

Accepted Answer

Use Dataproc when you want managed Spark/Hadoop with fast cluster creation, and you need control over the runtime (Spark configs, libraries, initialization actions) or compatibility with existing Hadoop/Spark jobs. It’s a good fit for ETL, log processing, feature engineering for ML, and migrating on-prem Hadoop/Spark workloads to the cloud. If you don’t want to manage clusters at all, consider serverless options like Dataproc Serverless (for Spark) or other serverless data processing services.

Question 3

How much does Dataproc cost?

Accepted Answer

Dataproc costs are mainly driven by the underlying compute (VMs), storage, and network you use, plus Dataproc-specific charges depending on the Dataproc mode and features. Key pricing factors include: number and type of VM instances (master/worker), how long clusters run, autoscaling settings, use of preemptible/spot VMs for workers, persistent disk size/type, and whether you use ephemeral clusters per job. To reduce cost, many teams run short-lived clusters, enable autoscaling, and use preemptible/spot workers for fault-tolerant Spark jobs.

Dataproc

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