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A media company is migrating its batch processing workflows, which involve large datasets (terabytes), from on-premises Hadoop to Google Cloud. The existing jobs are written in Apache Spark. The company needs a fully managed, scalable, and cost-effective solution that can execute these Spark jobs without requiring extensive infrastructure management. Which Google Cloud service should you recommend?
- ACompute Engine with custom Spark clusters.
- BBigQuery for serverless data warehousing and analytics.
- CDataflow for stream and batch data processing.
- DDataproc for managed Spark and Hadoop clusters.
Show answer & explanationAnswer & explanation
Correct answer: D. Dataproc for managed Spark and Hadoop clusters.
Dataproc is a fully managed, highly scalable service for running Apache Spark and Hadoop clusters. It allows easy migration of existing Spark jobs without operational overhead, making it ideal for the scenario described.
Why the other options are wrong
- A. Compute Engine provides VMs, but managing custom Spark clusters on it requires significant operational effort, contradicting the 'fully managed' requirement.
- B. BigQuery is a data warehouse for analytics, not a platform for running Apache Spark batch processing jobs directly.
- C. Dataflow is excellent for stream and batch processing but uses Apache Beam, requiring rewriting existing Spark jobs, which is not ideal for direct migration.
Google Cloud Dataproc
Dataproc is a fully managed service for running Apache Spark, Hadoop, Presto, and other open-source data tools on Google Cloud, simplifying cluster management.
- Provides ephemeral or long-running clusters.
- Compatible with existing open-source tools and APIs.
- Offers auto-scaling and cost-effective resource management.
Memory trick: When the elephant of Hadoop needs to sparkle in the cloud, Dataproc is the shepherd.