Skip to main content

​
What it does

The Snowflake SQL tool executes SQL queries directly on your Snowflake data warehouse. Perfect for data analysis, reporting, business intelligence, and accessing large-scale datasets with Snowflake’s cloud performance.
Requires Snowflake Integration: You need to set up a Snowflake integration before agents can use this tool.

​
Key features

  • Execute any SQL query on Snowflake warehouses
  • Automatic warehouse management and suspension
  • Enterprise-grade security and performance
  • Support for complex analytical queries
  • Clean, structured result formatting

​
Parameters

ParameterTypeRequiredDescription
querystringYesThe SQL query to execute on Snowflake

​
Common use cases

​
Data analysis and reporting

SELECT 
  DATE_TRUNC('month', order_date) as month,
  SUM(revenue) as total_revenue,
  COUNT(DISTINCT customer_id) as unique_customers
FROM sales_data 
WHERE order_date >= '2024-01-01'
GROUP BY month
ORDER BY month DESC;
Analyze sales trends and customer metrics.

​
Customer segmentation

SELECT 
  customer_segment,
  AVG(lifetime_value) as avg_ltv,
  COUNT(*) as customer_count
FROM customer_analytics
WHERE last_purchase_date >= DATEADD(month, -6, CURRENT_DATE())
GROUP BY customer_segment
ORDER BY avg_ltv DESC;
Segment customers based on behavior and value.

​
Performance monitoring

SELECT 
  warehouse_name,
  AVG(execution_time) as avg_execution_time,
  SUM(credits_used) as total_credits
FROM query_history
WHERE start_time >= DATEADD(day, -7, CURRENT_DATE())
GROUP BY warehouse_name
ORDER BY total_credits DESC;
Monitor warehouse performance and costs.

​
Data quality checks

SELECT 
  table_name,
  COUNT(*) as row_count,
  COUNT(DISTINCT primary_key) as unique_keys,
  SUM(CASE WHEN primary_key IS NULL THEN 1 ELSE 0 END) as null_keys
FROM information_schema.tables t
JOIN your_database.your_schema.your_table data ON 1=1
GROUP BY table_name;
Validate data quality and integrity.

​
Complex analytics

WITH monthly_cohorts AS (
  SELECT 
    customer_id,
    DATE_TRUNC('month', first_purchase_date) as cohort_month,
    DATE_TRUNC('month', order_date) as order_month
  FROM customer_orders
)
SELECT 
  cohort_month,
  COUNT(DISTINCT customer_id) as cohort_size,
  AVG(DATEDIFF(month, cohort_month, order_month)) as avg_months_active
FROM monthly_cohorts
GROUP BY cohort_month
ORDER BY cohort_month;
Perform advanced cohort analysis.

​
Snowflake-specific features

  • Automatic scaling: Warehouses scale based on query complexity
  • Zero-copy cloning: Access cloned data without additional storage costs
  • Time travel: Query historical data states
  • Secure data sharing: Access shared datasets across organizations
  • Semi-structured data: Query JSON, Parquet, and other formats natively

​
Best practices

  • Use appropriate warehouse sizes for your query complexity
  • Leverage Snowflake’s columnar storage with SELECT specific columns
  • Use clustering keys for large tables to improve performance
  • Take advantage of result caching for repeated queries
  • Monitor credit usage and optimize expensive queries
  • Use LIMIT clauses for exploratory queries

​
Troubleshooting

“Warehouse not found” or “Access denied”
  • Verify your Snowflake integration is properly configured
  • Check that the specified warehouse exists and is accessible
  • Ensure your user has the necessary permissions
“SQL compilation error”
  • Validate your SQL syntax for Snowflake
  • Check that table and column names exist
  • Verify data types in your queries
“Insufficient privileges”
  • Ensure your user has SELECT permissions on the tables
  • Check that you have USAGE permissions on the database and schema
  • Verify warehouse usage permissions
“Query timeout”
  • Consider using a larger warehouse for complex queries
  • Optimize your query with proper filtering and indexing
  • Break large queries into smaller, more focused ones
⌘I