Your company needs to run interactive SQL analytics on several petabytes of data kept in Google Cloud Storage and Amazon S3, but it does not want to provision or manage any database servers or clusters. Which BigQuery advantage directly satisfies this requirement?
It supplies built-in scripts for you to create and scale dedicated VM clusters whenever you need to run large queries.
It offers licenses for third-party Hadoop distributions so you can manage cross-cloud queries on your own infrastructure.
It provides a serverless, managed analytics engine that can query data across multiple clouds through BigQuery Omni, removing all cluster provisioning tasks.
It requires you to load all external data into Cloud SQL first, simplifying operational maintenance.
BigQuery is a serverless, fully managed data warehouse. Because compute resources are allocated and scaled automatically, administrators do not need to set up or operate infrastructure. In addition, BigQuery Omni lets the same SQL engine query data where it sits in Google Cloud, AWS, or Azure, eliminating the need to move or copy datasets. The other options describe capabilities BigQuery does not require, such as manual cluster management, staging data in Cloud SQL, or licensing Hadoop distributions.
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What does serverless mean in the context of BigQuery?
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What is BigQuery Omni and how does it enable cross-cloud queries?
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Why is BigQuery better suited for interactive SQL analytics on petabyte-scale data compared to traditional database clusters?
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What is BigQuery Omni?
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Why is serverless architecture important in BigQuery?
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How does BigQuery differ from Hadoop for analytics on large datasets?
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