BigQuery for Data Engineers: Partitioning, Clustering, and Query Optimization

<p>Follow Priya Raman, a data engineer at Fleetwise Logistics, as she migrates a struggling delivery-analytics pipeline onto BigQuery. Across five hands-on lessons you'll learn why separating storage from compute changes BigQuery's cost and performance tradeoffs, how table partitioning and clustering keep queries fast and bills small, how to read dry-run cost estimates and enforce spending limits with bytes-billed caps and quotas, and which BigQuery-specific SQL patterns, like avoiding SELECT *, using nested and repeated fields, and reaching for approximate aggregation functions, keep production queries efficient.</p>

Tools: BigQuery, SQL, Google Cloud Platform, bq CLI, Data Warehousing