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Answer: Use Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
The most suitable option for a rapid lift-and-shift migration in this scenario is to use Dataproc for training existing Spark ML models while reading data directly from BigQuery. This approach allows you to continue using your existing Spark ML models without significant code changes, enabling a quick migration to Google Cloud. This solution leverages the managed Spark and Hadoop service provided by Dataproc and the native integration with BigQuery, efficiently handling large datasets.
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You work for an advertising company and have developed a Spark ML model to predict click-through rates for advertisement blocks. This development has taken place in your on-premises data center, but now your company is shifting operations to Google Cloud. Given that the data center is closing soon, a rapid lift-and-shift migration is essential. The data used for your model will be migrated to BigQuery. Since you regularly retrain your Spark ML models, it is necessary to migrate the existing training pipelines to Google Cloud. What steps should you take?
A
Use Vertex AI for training existing Spark ML models
B
Rewrite your models on TensorFlow, and start using Vertex AI
C
Use Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
D
Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
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