Question: How is BigQuery ideal for organizations that run a data warehouse?
- BigQuery isolates data for machine learning.
- BigQuery improves analytics, lowers warehousing costs, and includes connectivity to other GCP products.
- BigQuery lets data analysts run data processsing pipelines to do transforms on incoming streaming data.
- BigQuery connects globally distributed industrial devices into a single network that can be managed efficiently.
Explanation
BigQuery is a fully managed serverless data warehouse for analytics at scale. It improves analytics by enabling fast SQL analysis over very large datasets without managing warehouse infrastructure. Its managed model can lower warehousing costs by reducing infrastructure administration and supporting flexible pricing. BigQuery also connects with other Google Cloud products for data loading, querying, sharing, and downstream analytics.
Why the other options are incorrect
Machine learning isolation does not describe the main data warehouse value of BigQuery.
Data processing pipelines describes Cloud Dataflow, not BigQuery.
Industrial device networks describes connected-device architectures, not BigQuery data warehousing.
Source for verification
https://cloud.google.com/bigquery/docs/introduction
https://cloud.google.com/bigquery/docs/external-data-sources
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