A retailer keeps click-stream data in BigQuery. Marketing staff want to build their own dashboards and ask ad-hoc questions without writing SQL, while central IT must guarantee that metrics such as conversion rate remain consistent across reports. Which Google Cloud solution best meets both needs, and why?
Looker, because its LookML-based semantic model lets non-technical users create dashboards while enforcing centrally defined metrics.
Cloud Bigtable, because its NoSQL architecture automatically generates dashboards for business users.
Pub/Sub, because streaming query results to subscribers removes the need for users to know SQL.
Cloud Storage, because giving analysts direct access to CSV exports ensures consistent calculations in offline spreadsheets.
Looker is designed to democratize data access. IT teams define business logic and metrics once in LookML, creating a governed semantic layer. Business users can then explore data through a drag-and-drop web interface and build dashboards without writing SQL, while every analysis still relies on the centrally managed definitions. The other options either do not offer a self-service analytics interface (Cloud Bigtable, Cloud Storage) or are messaging tools not intended for interactive reporting (Pub/Sub).
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What is LookML in Google Looker?
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How does Looker enable non-technical users to create dashboards?
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Why is Looker preferred over Cloud Bigtable or Pub/Sub for interactive reporting?
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What is LookML in Looker?
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How does Looker ensure consistent metrics in dashboards?
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Why is Looker better for ad-hoc analysis compared to Cloud Bigtable?
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