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I was trying to figure out key differences between using GCP Vertex AI feature store and Saving preprocessed features to BigQuery and loading whenever it gets necessary.

I still cannot understand why to choose the first option, rather than the second option, which seems to be easier and more accessible.

Is there any good reason to use feature store in Vertex AI, rather than storing features in BigQuery tables formats?

Applicable Math
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  • I guess it's if you have training that doesn't use BigQuery. For instance: train a model with images and feature store data. I also guess that the feature are optimized to be used by Tensorflow. But I agreee, it's so simple to use BigQuery for that!! – guillaume blaquiere Dec 15 '22 at 15:46
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    Hi @Applicable Math, If my answer addressed your question, please consider accepting and upvoting it. If not, let me know so that I can improve my answer.Accepting an answer will help the community members with their research as well. – Shipra Sarkar Dec 19 '22 at 07:38

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Vertex AI Feature Store and BigQuery, both can be used to store the features as mentioned by you. But Vertex AI Feature Store has several advantages over BigQuery that makes it favorable for storing features.

Advantages of Vertex AI Feature Store over BigQuery :

  • Vertex AI Feature Store is designed to create and manage featurestores, entity types, and features whereas BigQuery is a data warehouse where you can perform analysis on data.
  • Vertex AI Feature Store can be used for batch and online storage but BigQuery is not a solution for storage.
  • Vertex AI Feature Store can be used for sharing the features across the organization from the central repository which BigQuery does not provide.
  • Vertex AI Feature Store is a managed solution for online feature serving which is not supported by BigQuery. For more information, you can check this link.
Shipra Sarkar
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