Cloud Digital Leader Practice Question: Google Cloud products, services, and solutions
A company currently uses Hadoop and Spark for batch data processing on a large on-premises cluster. They want to migrate these workloads to Google Cloud with minimal rewriting of existing Spark and Hadoop jobs. Which Google Cloud service is the best fit?
⚠ Common exam trap
The GCDL exam often tests the misconception that Cloud Dataflow can run Spark/Hadoop jobs natively, when in fact it requires rewriting into Apache Beam, while Dataproc is the direct managed equivalent for these frameworks.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Cloud Dataproc — managed Apache Spark and Hadoop with minimal code changes.
Cloud Dataproc is a managed service for running Apache Spark and Hadoop clusters on Google Cloud. It supports the same open-source versions of Spark and Hadoop that the company currently uses, allowing them to migrate their existing batch processing workloads with minimal code changes. This makes it the best fit for the stated requirement of minimal rewriting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Cloud Dataflow — it runs all Spark and Hadoop jobs natively.
Why it's wrong here
Cloud Dataflow is a unified stream and batch data processing service that runs Apache Beam pipelines, not native Spark or Hadoop jobs. Dataflow does not execute Spark RDD transformations or Hadoop MapReduce jobs; migrating to Dataflow would require rewriting all existing jobs in the Beam programming model. Since the requirement explicitly emphasizes minimal rewriting, Dataflow is not suitable for this migration scenario.
- ✓
Cloud Dataproc — managed Apache Spark and Hadoop with minimal code changes.
Why this is correct
Cloud Dataproc is Google Cloud's managed service for Apache Hadoop and Spark, providing native support for Spark, Hadoop MapReduce, Hive, and Pig. Because it runs the same open-source distributions and supports standard job submission interfaces, existing jobs can be migrated with minimal changes by simply pointing them at Dataproc clusters and Cloud Storage (via the gs:// filesystem) instead of HDFS. This makes it the ideal choice for migrating on-premises Hadoop/Spark workloads without rewriting application code.
- ✗
BigQuery — it runs Spark SQL queries via BigQuery Spark stored procedures.
Why it's wrong here
BigQuery does support Spark stored procedures for running Spark SQL within its serverless environment, but it is fundamentally a SQL data warehouse, not a general-purpose execution engine for arbitrary Hadoop or Spark batch jobs. Existing Hadoop and Spark workloads often rely on RDDs, DataFrames, and custom JARs that do not map cleanly to BigQuery's SQL-centric model. Therefore, choosing BigQuery would require significant re-architecting of the jobs, contradicting the requirement for minimal code changes.
- ✗
Google Kubernetes Engine — deploy Spark clusters on Kubernetes.
Why it's wrong here
Deploying Spark on GKE introduces significant operational overhead for cluster management and containerisation, which directly contradicts the "minimal rewriting" requirement for existing Hadoop and Spark jobs. While GKE offers excellent flexibility and fine-grained control for custom Spark environments, it necessitates adapting existing operational practices and potentially job submission methods to a Kubernetes-native approach. It would be a strong choice for building new, highly customised, containerised data platforms or when modernising existing ones with a focus on Kubernetes orchestration.
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Cloud Digital Transformation
Key term
Google Cloud
Google Cloud is a suite of cloud computing services offered by Google that provides infrastructure, platform, and software solutions over the internet.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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