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Your data engineering team is analyzing BigQuery query plans and Cloud Billing reports. They notice that analytics queries against a highly normalized snowflake schema are incurring high costs and slow performance due to massive join operations and full table scans on specific date and region columns.
You need to minimize query costs and optimize performance for these analytical workloads.
Which optimization strategy should you implement?
Denormalization in BigQuery often involves using nested and repeated fields (represented as STRUCT and ARRAY data types) to store related records within a single table, rather than splitting them across multiple relational tables. Clustering is a technique where BigQuery automatically sorts the underlying data blocks based on the values of up to four specified columns.
BigQuery is a massively parallel processing (MPP) system that thrives on wide, denormalized datasets. While it supports normalized schemas, the overhead of shuffling data during massive joins degrades performance and increases costs. Combining denormalization with clustering directly addresses both the join overhead and the full table scan inefficiencies identified in the query plans.
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