Natural Language to BigQuery SQL
Turns a plain-English question into BigQuery SQL.
Prompt template
2 variablesWrite a BigQuery Standard SQL query that answers the question below, using only the tables and columns listed. Query the gold layer of the lakehouse. Never select customer identifiers or names; aggregate to at least 10 customers per group. Question: {question} Available tables: {schema} Return the SQL, then one sentence explaining what it does.
Playground
Simulated sandboxOwnership and sensitivity
Who approves access
No approval needed. Self-service: subscribe and use, logged for chargeback.
Open to all staff. Aggregates, policy text, public series and synthetic data.
Self-service: subscribe and use, logged for chargeback.
- GenAI
- Generates text; prompt screening for customer data and output review apply.
Example input & output
Example input
- {question}
- How many ETB customers in each segment converted to a second product in the last 90 days?
- {schema}
- `abmb-lakehouse.gold.customer_360` (customer_token, segment, product_count, first_product_date, etb_flag); `abmb-lakehouse.gold.product_openings` (customer_token, product_code, opened_date)
Example output
SELECT c.segment, COUNT(DISTINCT c.customer_token) AS converted_customers FROM `abmb-lakehouse.gold.customer_360` AS c JOIN `abmb-lakehouse.gold.product_openings` AS o ON o.customer_token = c.customer_token WHERE c.etb_flag = TRUE AND o.opened_date >= DATE_SUB(CURRENT_DATE('Asia/Kuala_Lumpur'), INTERVAL 90 DAY) AND o.opened_date > c.first_product_date GROUP BY c.segment HAVING converted_customers >= 10 ORDER BY converted_customers DESC; Counts existing-to-bank customers per segment who opened another product in the last 90 days, suppressing groups under 10.
Details
- Author
- Arjun Nair
- Version
- v1.9
- Rating
- ★ 4.6
- Runs
- 2.4K
- Licence
- Group Reuse
- Updated
- 2026-07-28
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