You query a specific BigQuery table multiple times per day. The base table is petabytes but you apply filters and simple aggregations and need faster, up-to-date results. What should you do?

Creating a materialized view is the most effective solution. Materialized views pre-compute and store the results of a query, including filters and aggregations, allowing for significantly faster query execution, especially on large datasets. They are automatically refreshed, ensuring up-to-date results without manual intervention. Running a scheduled query to pull data daily would create a new table, but it wouldn't inherently accelerate the original query and would require managing a separate dataset. Using a cached query might provide some speedup for identical, recent queries, but BigQuery's cache is ephemeral and not guaranteed for repeated, slightly varied queries or long-term performance. Limiting columns is a good practice for efficiency but won't provide the same magnitude of performance improvement as pre-computing the aggregated results with a materialized view for petabyte-scale data.