BigQuery Query Generator - AI-Powered Google Cloud Analytics

Google BigQuery is the leading cloud data warehouse for analytics at scale. AI2sql BigQuery Query Generator helps data engineers and analysts write efficient queries that process petabytes of data while minimizing costs.

BigQuery-Specific Features

Standard SQL Support

• Arrays and STRUCTs

• Nested and repeated fields

• Window functions

• User-defined functions (UDFs)

• GEOGRAPHY types for geospatial

• ML integration (BQML)

BigQuery Query Examples

Working with Nested Data

Input: "Flatten nested order items and calculate totals"

SELECT 
  order_id,
  customer.name as customer_name,
  customer.email as customer_email,
  item.product_name,
  item.quantity,
  item.price,
  item.quantity * item.price as line_total
 FROM orders,
 UNNEST(items) as item
 WHERE order_date >= '2024-01-01';

Partitioned Table Query

Input: "Query events from the last 7 days efficiently"

SELECT 
  event_name,
  COUNT(*) as event_count,
  COUNT(DISTINCT user_id) as unique_users
 FROM analytics.events
 WHERE _PARTITIONDATE >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)
 GROUP BY event_name
 ORDER BY event_count DESC;

BigQuery ML

Input: "Create a model to predict customer churn"

CREATE OR REPLACE MODEL customer_analytics.churn_model
 OPTIONS(
  model_type='LOGISTIC_REG',
  input_label_cols=['churned']
 ) AS
 SELECT 
  days_since_last_order,
  total_orders,
  total_spent,
  support_tickets,
  churned
 FROM customer_analytics.customer_features;

Cost-Optimized Query

Input: "Sample 1% of large table for exploration"

SELECT *
 FROM large_table
 TABLESAMPLE SYSTEM (1 PERCENT);

Cost Optimization Tips

Our generator produces cost-efficient queries:

• Selects only needed columns (avoid SELECT *)

• Uses partition filters

• Leverages clustering

• Appropriate use of LIMIT for exploration

• Materialized view suggestions

BigQuery Best Practices

• Partition pruning for date-based tables

• Clustering for high-cardinality filters

• Denormalization for performance

• Appropriate data types

• Avoiding expensive operations

Start Building BigQuery Queries

Query petabytes efficiently. Describe your analytics needs and get cost-optimized BigQuery SQL.