A retail company keeps its transactional data in BigQuery and wants store managers to view always-current sales dashboards and trigger automated Slack notifications when targets are missed. Which characteristic of connecting Looker directly to BigQuery best enables both requirements?
Looker pushes all query processing down to BigQuery, giving dashboards real-time data without moving or duplicating it.
Looker requires exporting pre-aggregated files from BigQuery to Cloud Storage before visualizations can be built.
Looker limits concurrent users to reduce BigQuery cost, so dashboards update only after batch refreshes.
Looker copies BigQuery tables into its own in-memory engine every hour, providing near-real-time snapshots.
When Looker is connected live to BigQuery, every dashboard tile or scheduled alert sends SQL directly to BigQuery. BigQuery performs the computation on the latest tables, so results are returned without copying or caching data. Looker's modeling layer can then deliver the fresh numbers to dashboards or push them to workflows such as Slack. The other choices describe methods that involve data extracts, periodic copies, or limitations that would prevent real-time insight and automated delivery.
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What advantages does connecting Looker to BigQuery provide compared to periodic data extracts?
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How does Looker integrate with BigQuery for real-time data visualization?
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