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A large e-commerce company uses BigQuery as its primary data warehouse. They have a daily batch job that loads millions of new customer orders into a `raw_orders` table. Analysts frequently query this table, filtering by `order_date` and `customer_id`. The table currently has over a billion rows and queries are becoming slow and expensive. The team needs to optimize query performance and reduce costs for these common analytical queries. Which BigQuery feature should be implemented to address this issue?
- AClustering
- BMaterialized Views
- CExternal Tables
- DPartitioning
Show answer & explanationAnswer & explanation
Correct answer: A. Clustering
BigQuery clustering organizes data in storage based on the contents of specified columns. When queries filter by clustered columns, BigQuery uses the clustered metadata to quickly prune irrelevant data, significantly reducing the amount of data scanned and thus improving performance and reducing costs. Partitioning on `order_date` would also be beneficial but clustering on `customer_id` *in addition* would further optimize queries filtering on both.
Why the other options are wrong
- B. Materialized views pre-compute results, which can help, but they add maintenance overhead and might not be optimal for highly dynamic filtering combinations.
- C. External tables allow querying data stored outside BigQuery, which doesn't address the performance and cost issues of querying a large internal BigQuery table.
- D. Partitioning by `order_date` would improve queries filtering by date, but clustering on `customer_id` would provide additional benefits for queries filtering on both `order_date` and `customer_id` by co-locating similar customer data within each partition.
BigQuery Clustering
A BigQuery feature that organizes data within a table (or table partition) based on the values of one or more specified columns.
- Improves query performance for filtered columns
- Reduces data scanned, lowering costs
- Works best with high-cardinality columns
Memory trick: Cluster your data for faster, cheaper insights.