Exam Professional Data Engineer topic 1 question 209 discussion

Almost the same at #166:

You don't need to recreate a table to cluster it, contrary to partitioning, where you have to create a new table with the old data (migration) > If you alter an existing non-clustered table to be clustered, the existing data is not automatically clustered. Only new data that's stored using the clustered columns is subject to automatic reclustering. https://cloud.google.com/bigquery/docs/clustered-tables#limitations

B. Implement clustering in BigQuery on the package-tracking ID column.

B. Implement clustering in BigQuery on the package-tracking ID column.

Definitely B

This looks like Question #166 Option B, implementing clustering in BigQuery on the package-tracking ID column, seems the most appropriate. It directly addresses the query slowdown issue by reorganizing the data in a way that aligns with the analysts' query patterns, leading to more efficient and faster query execution.

Answer is B

Query Focus: Analysts are interested in geospatial trends within individual package lifecycles. Clustering by package-tracking ID physically co-locates related data, significantly improving query performance for these analyses. Addressing Slow Queries: Clustering addresses the query slowdown issue by optimizing data organization for the specific query patterns. Partitioning vs. Clustering: Partitioning: Divides data into segments based on a column's values, primarily for managing large datasets and optimizing query costs. Clustering: Organizes data within partitions for faster querying based on specific columns.