Oracle AI Data Platform workflows can use job-level and task-level parameters to control execution, pass values between notebooks, and build reusable data pipelines. See how parameterized tasks support ingestion checks, conditional analysis, and runtime validation in Oracle AI Data Platform at the resources linked below.
This tutorial shows how Oracle AI Data Platform uses parameters in notebooks and workflows to create reusable, dynamic data pipelines. It begins with job-level parameters that define catalog, schema, and table names at runtime, then shows how notebooks can retrieve those values, apply defaults when values are missing, and build fully qualified object names without hardcoding. The walkthrough then demonstrates task-level parameters for sharing ingestion status, row counts, and target table details between workflow steps so downstream tasks can respond to earlier results. It also covers data generation, write operations, ingestion validation, and conditional if-else logic that controls whether analysis runs after loading. The workflow review highlights how task results, parameter values, and data previews appear in the graph tab, while the Master Catalog confirms the created objects. By combining parameterized notebooks with workflow control, Oracle AI Data Platform supports flexible automation for analytics and data engineering teams.
00:00 Introduction to Parameters in Workflows
00:33 Understand Job-Level Parameters
01:22 Retrieve Parameters in Notebooks
02:13 Build Dynamic Table Names
02:48 Pass Task-Level Parameters
03:42 Generate and Validate Data
04:40 Reuse Parameters Across Tasks
05:39 Configure Workflow Logic
06:42 Run and Review the Workflow
07:37 Verify Results in the Master Catalog
08:05 Final Review and Next Steps
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