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Executes a custom SQL query against your connected data warehouse and uses the results as a data source in your workflow. Use it when you need to pull data using complex SQL that goes beyond what Get Data provides.

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Configuration

SettingDescription
QuerySQL SELECT statement to execute against the data warehouse
DWH Auth IDData warehouse authentication ID (default: 0)
Result FieldsExpected result columns and types (auto-detected if omitted)
Virtual Object NameNamespace prefix for output fields (default: query)

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Result Field Types

TypeDescription
strString/text
intInteger
floatDecimal number
boolBoolean
dateDate
datetimeDate and time

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How It Works

1

Write your SQL query

Write a SELECT statement using your DWH’s SQL syntax. Reference tables with bare names, schema-qualified names ("schema"."table"), or fully qualified names.
2

Define result fields (optional)

Specify expected output columns and their types. If omitted, BonData auto-detects them from the query results.
3

Use as a data source

The query results become the starting point for your workflow, available as Mentions in downstream nodes.

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Output

A dataset containing the query results. Each column becomes available as a Mention in downstream nodes, named as {virtual_object_name}.{column_name}.
The Query node acts as a workflow starting point — it does not have an input. Only SELECT statements are supported.

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Example

Pull a custom report from Snowflake: 
SELECT
  account_id,
  account_name,
  SUM(revenue) as total_revenue,
  COUNT(DISTINCT contact_id) as contact_count
FROM accounts
JOIN contacts ON accounts.id = contacts.account_id
WHERE created_at > '2024-01-01'
GROUP BY account_id, account_name
HAVING SUM(revenue) > 10000

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Best Practices

  • Use Query when you need JOINs, aggregations, or complex WHERE clauses that Get Data doesn’t support
  • Specify Result Fields explicitly for predictable downstream behavior
  • Keep queries efficient — add WHERE clauses to limit the dataset size
  • Test your query directly in your DWH console before using it in a workflow
  • Get Data — simpler data fetching without writing SQL
  • Code Execution — run Python code for transformations that SQL can’t express
  • Transform — apply SQL expressions to data already in the workflow