Writing BigQuery SQL that answers a reporting question

BigQuery uses standard SQL to query large datasets stored in tables and views, letting analysts generate reports and extract insights directly from the console, bq CLI, client libraries, or connected BI tools. Understanding query structure, functions, and cost/performance behavior is essential for the Associate Data Practitioner exam.

1 · Learn the must-know

• 1BigQuery uses GoogleSQL (standard SQL) by default; legacy SQL must be explicitly enabled and is largely deprecated.
• 2Queries can reference tables using fully qualified names in the form project.dataset.table, which is required when querying across projects.
• 3Common aggregation and analysis functions include SUM, COUNT, AVG, GROUP BY, ORDER BY, and window functions (e.g., RANK, ROW_NUMBER) for ranking and running calculations.
• 4BigQuery charges on-demand queries based on bytes scanned, so using SELECT with specific columns instead of SELECT * and filtering with WHERE clauses on partitioned/clustered columns reduces cost.
• 5Saved queries, scheduled queries, and views (including materialized views) allow recurring reports to be automated and reused without rewriting SQL.
• 6Query results can be exported to Google Sheets, Looker Studio, or other BI tools for visualization, or saved as new tables/views for downstream use.

2 · Check your understanding