Your analytics team maintains a 5-TB orders fact table that grows by about 50 million rows each day. Looker Studio dashboards repeatedly aggregate monthly revenue by region and product category for the last 24 months. Results must load in seconds and show new data within 30 minutes while keeping BigQuery costs low. Which solution best satisfies these requirements?
Purchase a BI Engine reservation on the orders table; BI Engine will automatically serve the monthly revenue without making any other changes.
Replace the dashboard SQL with a standard view; BigQuery will automatically reuse cached results as long as the SQL text is unchanged.
Schedule a nightly batch job that writes the monthly aggregates to a separate BigQuery table and point the dashboards at that table.
Create a BigQuery materialized view that performs the monthly revenue aggregation and rely on its automatic incremental refresh and query-rewrite capabilities so dashboards read the precomputed results.
A BigQuery materialized view can pre-aggregate the required metrics so dashboard queries read from a small, precomputed table instead of scanning the entire fact table. BigQuery refreshes materialized views automatically and incrementally-most updates appear within a few minutes and never later than 30 minutes-so the freshness objective is met. The query optimizer can transparently rewrite dashboard queries to use the materialized view, delivering sub-second response times and lower query costs. A nightly batch table violates the 30-minute freshness target, a standard view re-scans the base table whenever data changes and the result-cache is invalidated, and simply purchasing BI Engine without restructuring still leaves the engine to process large volumes of raw data on each run and may exceed the in-memory quota.
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