Databricks AI/BI Parameters Demystified

Key insights

• Parameters vs filters
  Parameters inject values into the SQL before it runs, while filters usually apply after data is returned or at the field level. This makes parameters better for shaping queries and filters better for quick client-side slicing.
• Performance benefits
  Using parameters can save compute by reducing the data returned and avoiding unnecessary processing; changing a parameter causes the dataset to re-run with the new predicate, so queries return only the needed rows.
• How to create and configure
  Create parameters directly in a SQL dataset using the colon syntax, then set type, choose single vs multi selection, and provide a default value to control behavior and avoid errors.
• Date parameters and limits
  Date parameters can be wired into queries with start/end values and a BETWEEN clause, but the date range picker limitation means it cannot directly bind to parameters; authors must use workarounds in the UI.
• Workarounds and patterns
  Common patterns include using an “All” sentinel with OR logic to allow all-inclusive views and enabling Allow multiple selections for multi-value filtering; these balance flexibility with predictable SQL behavior.
• Governance and cost control
  Exposing parameters to end users can increase query frequency and compute cost, so decide which parameters are user-facing versus author-controlled and use global or per-widget filters where appropriate to limit re-runs.

Keywords

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