Your retail company streams point-of-sale transactions into BigQuery every few seconds. Business analysts need to explore the latest data and build their own dashboards without writing SQL or managing infrastructure. Which Google Cloud approach best satisfies these requirements?
Load the data into Cloud SQL and visualize it with Google Sheets add-ons.
Stream the data into Cloud Pub/Sub and create dashboards in Cloud Monitoring.
Connect Looker directly to BigQuery and let analysts use Looker's semantic layer for self-service exploration.
Export the data to Cloud Storage and analyze it with Cloud Dataprep.
Looker offers a governed semantic model that lets non-technical users explore data and create dashboards through a drag-and-drop interface, while BigQuery's streaming ingest makes the newest records immediately queryable. Connecting Looker directly to BigQuery therefore delivers real-time, self-service analytics with no infrastructure for the analysts to manage. The other options either move data away from BigQuery, rely on tools that do not provide governed self-service BI, or use services that are not intended for interactive dashboard creation.
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What is Looker's semantic layer?
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How does BigQuery support real-time data analytics?
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Why is connecting Looker directly to BigQuery better than other approaches for analysts?
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What is BigQuery's streaming ingest?
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What is Looker's semantic layer?
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Why is Looker better suited for dashboards than Cloud Monitoring?
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