How to Migrate On-Prem Spark Jobs to Google Cloud with Minimal Changes?

PDE By islever Editorial Last updated

Short answer

D Move your data to Cloud Storage. Run your jobs on Dataproc.

Question

You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum. What should you do?
  1. Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.
  2. Rewrite your jobs in Apache Beam. Run your jobs in Dataflow.
  3. Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances.
  4. Move your data to Cloud Storage. Run your jobs on Dataproc. Correct Answer

Key Takeaways

Tests knowledge of Google Cloud's managed big data services and the requirement to minimize refactoring; candidates often mistakenly choose full rewrites like Beam/Dataflow or manual VM management instead of leveraging native compatibility.

Migrating existing Apache Spark workloads to Google Cloud requires a managed service that maintains ecosystem compatibility. The community unanimously agrees that moving data to Cloud Storage and running jobs on Dataproc is the optimal solution for minimizing code changes and meeting tight deadlines.

Common mistake: Candidates frequently select Dataflow (Option B) due to its popularity, but overlook the explicit constraint to avoid rewriting Spark jobs into Beam pipelines.

Expert Analysis

Why the Answer Is Correct

Dataproc is explicitly designed as a fully managed, scalable environment for Hadoop and Spark ecosystems. It natively supports existing Spark scripts, allowing direct migration without rewriting logic. Pairing it with Cloud Storage provides a durable, cost-effective data lake that integrates seamlessly with Dataproc's architecture. This approach directly satisfies the managed service requirement while preserving codebase integrity.

Why the Other Options Are Wrong

Option A forces a complete rewrite to SQL, violating the minimum code change constraint. Option B requires converting Spark RDDs/DataFrames to Apache Beam pipelines, which is time-consuming and contradicts the tight timeline. Option C involves manually provisioning and patching Compute Engine instances, failing the managed service requirement and increasing operational overhead.

Community Comment Notes

Community feedback overwhelmingly validates Option D as the industry-standard migration path. Multiple users highlight Dataproc’s native Spark support and zero-code-refactor advantage (Comment 1, 2, 5). Contributors consistently emphasize that "minimal code changes" and "tight timeline" are key decision drivers pointing directly to Dataproc over alternative architectures.

Official Reference

https://cloud.google.com/dataproc/docs/concepts/overview

This link opens the official vendor documentation in a new tab.

Exam Strategy

When exam questions emphasize managed services, Spark/Hadoop compatibility, and minimal code changes, immediately prioritize Dataproc over custom VM setups or pipeline rewrites. Always cross-reference architectural constraints against service capabilities before selecting alternatives like Dataflow or BigQuery.

Community Votes

D
100%

100% of anonymous learners picked answer D. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 8 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 8

  1. ML6 👍 3 Answered D
    D) That is what Dataproc is made for. It is a fully managed and highly scalable service for running Apache Hadoop, Apache Spark, etc.
  2. GCP001 👍 3
    D. Move your data to Cloud Storage. Run your jobs on Dataproc. Dataproc is managed service and not needed much code changes.
  3. scaenruy 👍 3 Answered D
    D. Move your data to Cloud Storage. Run your jobs on Dataproc.
  4. hussain.sain 👍 1 Answered D
    D is correct. Dataproc is the most suitable choice for migrating your existing Apache Spark jobs to Google Cloud because it is a fully managed service that supports Apache Spark and Hadoop workloads with minimal changes to your existing code. Moving your data to Cloud Storage and running jobs on Dataproc offers a fast, efficient, and scalable solution for your needs.
  5. meh_33 👍 1 Answered D
    option D, minimum code changes
Show 3 more comments
  1. Sofiia98 👍 2 Answered D
    of course D
  2. Matt_108 👍 2 Answered D
    Clearly D
  3. JyoGCP 👍 2 Answered D
    Option D

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