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Answer: Implement clustering in BigQuery on the package-tracking ID column.
The correct answer is B. Clustering in BigQuery can help improve query performance by physically organizing the data in the table based on the values of one or more columns. In this case, implementing clustering on the package-tracking ID column would allow queries focusing on individual packages to be more efficient, as the data would be more organized according to the unique tracking IDs. The problem described indicates increasing query processing time, which can be mitigated by efficient data organization using clustering.
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A shipping company operates with real-time live package-tracking data, which is streamed into Apache Kafka and subsequently loaded into BigQuery. The company's analysts aim to query this tracking data within BigQuery to examine geospatial trends throughout a package's lifecycle. The original table was created using ingest-date partitioning. However, as time has progressed, the query processing time has significantly increased. Your task is to migrate all data to a new table that utilizes clustering. What steps should you take?
A
Re-create the table using data partitioning on the package delivery date.
B
Implement clustering in BigQuery on the package-tracking ID column.
C
Implement clustering in BigQuery on the ingest date column.
D
Tier older data onto Cloud Storage files and create a BigQuery table using Cloud Storage as an external data source.
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