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Answer: Implement clustering in BigQuery on the package-tracking ID column to organize data efficiently.
Clustering in BigQuery enhances query performance by organizing data according to specified column values. For a table with ingest-date partitioning, clustering on the package-tracking ID column (Option A) groups related data, thereby minimizing the data scanned during queries. While Option B might offer some benefits, it's less effective for queries targeting specific package IDs. Option C, though useful for cost-saving by tiering older data, doesn't directly boost query performance. Option D suggests a viable but potentially impractical approach due to the need for data re-ingestion.
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What strategies can enhance query performance in BigQuery for analyzing geospatial trends within a package-tracking data table that utilizes ingest-date partitioning and originates from an Apache Kafka stream?
A
Implement clustering in BigQuery on the package-tracking ID column to organize data efficiently.
B
Opt for clustering in BigQuery based on the ingest date column to streamline data retrieval.
C
Leverage an external data source by archiving older data to Cloud Storage files and establishing a new table.
D
Reconstruct the table with data partitioning focused on the package delivery date for optimized query execution.
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