In this video tutorial, you'll learn how to use parameters in Oracle AI Data Platform workflows to create more dynamic, reusable, and automated data pipelines. By passing values between notebooks and workflow tasks, you can control execution paths, configure runtime behavior, and streamline multi-step data processing workflows.
In this guide, you'll learn how to:
- Retrieve and use job-level parameters in notebooks to dynamically define catalog, schema, and table names at runtime.
- Apply the getParameter function with default values to create flexible and reusable notebook logic.
- Create and update task-level parameters using setTaskValue to share information between workflow tasks.
- Build data ingestion processes that generate, validate, and load data while capturing execution results as parameters.
- Reference task-level parameters across notebooks to control downstream processing and analysis.
- Configure workflow if-else tasks that use parameter values to determine whether subsequent tasks should run.
- Monitor workflow execution results and verify parameter values, task outputs, and created catalog objects.
Using parameters in notebooks and workflows helps you build adaptable data pipelines that can respond to runtime conditions, pass information between tasks, and reduce the need for hard-coded values. Watch the full tutorial to see how parameters work together to automate workflow execution and support more scalable data engineering processes in Oracle AI Data Platform. For more details on parameterization, check out Oracle’s Help Center documentation.
To expand on these workflow automation concepts, explore the Automation and Data Sharing with Oracle AI Data Platform Workbench course, which shows how to automate data operations and share data assets securely across your organization using Oracle AI Data Platform.
