Run a Query executes SQL queries against your BigQuery datasets to retrieve, analyze, and transform data for your marketing workflows.

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When to Use It

  • Execute custom SQL queries to retrieve specific data
  • Generate reports and analytics for marketing campaigns
  • Use it as AI Tool for AI Agents to run dynamic SQL queries

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Inputs

FieldTypeRequiredDescription
ProjectSelectYesGoogle BigQuery project to execute the query in
QueryText AreaYesSQL query to execute against BigQuery tables

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Outputs

OutputDescription
Query ResultsArray of rows returned by your query with execution metadata

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Credit Cost

1 credit per operation.

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Real-World Examples

BigQuery AI Agent: 
AI Agent → List Datasets → List Tables → Get Table Schema → Run Query
"Allow AI to fully explore BigQuery and run intelligent SQL queries"
Monthly Campaign Analysis: 
Run Query → AI Analyze Data → Send Email
"Execute custom SQL to analyze campaign performance and email insights"

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Understanding Query Results

The returned data includes: Query Output:
  • Array of rows with your query results
  • Column names matching your SELECT statement
  • Data types preserved from BigQuery tables
  • Null values handled appropriately

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SQL Query Examples

Campaign Performance Analysis: 
SELECT 
  campaign_name,
  SUM(spend) as total_spend,
  SUM(conversions) as total_conversions,
  ROUND(SUM(conversions) / SUM(spend), 2) as cost_per_conversion
FROM marketing_campaigns 
WHERE DATE(created_at) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY campaign_name
ORDER BY total_conversions DESC

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Best Practices

Query Optimization:
  • Use WHERE clauses to limit data scanning and reduce costs
  • Select only the columns you need rather than using SELECT *
  • Filter by date ranges to avoid processing unnecessary historical data
  • Use LIMIT for testing queries before running on full datasets
Cost Management:
  • Preview query costs in BigQuery console before execution
  • Use partitioned tables and filter by partition keys
  • Monitor bytes processed to manage BigQuery costs
  • Consider creating views for frequently used complex queries
Performance:
  • Use proper data types and avoid unnecessary conversions
  • Leverage clustering and partitioning for large tables
  • Test complex queries with LIMIT before full execution
  • Monitor execution times and optimize as needed

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Tips

SQL Development:
  • Test your queries in BigQuery console first
  • Use query validation to check syntax before execution
  • Comment your SQL for team documentation
  • Save frequently used queries as templates
Dynamic Queries:
  • Use parameterized queries for flexible filtering
  • Combine with other workflow nodes for dynamic table/column names
  • Consider using variables from previous workflow steps
  • Plan for different data types and null value handling
Integration:
  • Results integrate seamlessly with other Markifact nodes
  • Use with AI Analyze Data for insights on query results
  • Combine with Rename Fields to standardize output column names
  • Perfect for feeding data into Google Sheets or other destinations

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FAQ