BigQuery Pipeline Optimization

Problem and constraints

A critical datamart query was consuming 96%+ of BigQuery slot time, bottlenecking every report downstream of it and driving cloud cost up.

Approach

Read EXPLAIN ANALYZE output to find full table scans. Redesigned partitioning on event_date, added clustering keys on the high-cardinality filter columns, and restructured CTEs to reduce data shuffled.

Architecture

Diagram of the datamart query path: full table scan on the original query, then partitioning on event_date, clustering keys on the filter columns, and restructured CTEs in the rewritten query. — Query path before and after repartitioning and clustering.

Measured result

The same model and data volume now runs 9x faster, with 96% less slot time and 58% less data shuffled, and overall pipeline efficiency up 30%.

What	Value	Scope and source
faster processing	9x	one production datamart model, same data volume, full-refresh run
less BigQuery slot time	96%	same model and query, measured before and after the rewrite
less data shuffled	58%	after repartitioning on event_date and adding clustering keys
overall pipeline efficiency gain	30%	across the reporting layer the model feeds

Stack

BigQuery SQL Partitioning Clustering Cost Optimization