Get Table Schema retrieves the structure and column details of a specific BigQuery table to understand its data format before inserting or querying data.

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When to Use It

  • Review table structure before importing data from other platforms
  • Understand available fields before building reports or queries
  • Use it as AI Tool for AI Agents to understand table structures
  • Validate table structure after schema changes

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Inputs

FieldTypeRequiredDescription
ProjectSelectYesGoogle BigQuery project to search in
DatasetSelectYesBigQuery dataset containing your target table
TableSelectYesTarget BigQuery table to get schema from

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Outputs

OutputDescription
Table SchemaComplete schema information with column details and metadata

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Credit Cost

1 credit per operation.

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Real-World Examples

BigQuery AI Agent:

AI Agent → List Tables → Get Table Schema → Run Query
"Allow AI to discover tables and understand their structure before querying"

Data Migration Planning:

Get Table Schema → Rename Fields → Insert Rows
"Review table structure before importing data from other platforms"

Analytics Setup:

Get Table Schema → AI Analyze Data → Write to Sheets
"Understand available fields before building reports"

Schema Comparison:

Get Table Schema (Prod) → Get Table Schema (Dev) → AI Analyze Data
"Compare schemas across environments for consistency"

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Understanding Schema Information

The returned data includes:

Table Metadata:

  • Table ID, dataset ID, and project ID
  • Creation time and last modified date
  • Total number of rows in the table
  • Table description and labels

Column Details:

  • Column names and data types (STRING, INTEGER, FLOAT, etc.)
  • Field modes (NULLABLE, REQUIRED, REPEATED)
  • Column descriptions and documentation
  • Nested field structures for complex data types

Useful for:

  • Planning data imports and exports
  • Understanding data structure before building queries
  • Documenting table specifications for teams
  • Validating data quality and consistency

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Best Practices

Schema Documentation:

  • Save schema information for reference when building reports
  • Export schemas to spreadsheets for team documentation
  • Track schema changes over time for data governance

Data Validation:

  • Always check schema before inserting new data
  • Verify field modes (nullable/required) for data quality
  • Use schema info to validate data types before insertion

Query Optimization:

  • Understand field types to write efficient queries
  • Use schema info to avoid scanning unnecessary columns
  • Plan proper data type conversions and casting

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Tips

Field Information:

  • Schema includes field modes (NULLABLE, REQUIRED, REPEATED) - crucial for data validation
  • Column descriptions provide business context about field usage
  • Nested structures show complex data relationships

Integration Planning:

  • Use schema data to auto-generate documentation or data dictionaries
  • Compare schemas between different environments or versions
  • Plan column mapping for data migration projects

Performance:

  • Schema info helps optimize query performance by understanding field types
  • Use schema to identify partitioned and clustered columns
  • Plan data loading strategies based on table structure

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FAQ