Uploaded May 2026 | Updated September 2026, 2 weeks ago
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
00:37 Job-Level Parameters
01:05 Task-Level Parameters
01:25 Generate and Validate Data
01:54 Reuse Parameters Across Tasks
02:20 Configure Workflow Logic
03:18 Run and Review the Workflow
Find training paths for Oracle Analytics and AI from beginner to advanced and expert: https://social.ora.cl/6007B8XvIB
Explore self-paced short courses in the Oracle Analytics and AI Learning Hub: https://social.ora.cl/6006B8XvLr
See how parameters are used in jobs and tasks: https://social.ora.cl/6003B8Xv03
Learn how to set up a robust and scalable data foundation using Oracle AI Data Platform Workbench in our self-paced Analytics and AI Learning Hub course: https://social.ora.cl/6007B8Xv0f
Like what you learned about Oracle AI Data Platform? Subscribe now https://social.ora.cl/60025k87j
Join us at Oracle AI World: https://social.ora.cl/60035eVvJ
Contact AIDP sales: https://social.ora.cl/6003FLUMT
Learn more about Oracle AIDP: https://social.ora.cl/60047h76Q
Join the Oracle AIDP Community: https://social.ora.cl/60087h7Bj
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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
00:37 Job-Level Parameters
01:05 Task-Level Parameters
01:25 Generate and Validate Data
01:54 Reuse Parameters Across Tasks
02:20 Configure Workflow Logic
03:18 Run and Review the Workflow
Find training paths for Oracle Analytics and AI from beginner to advanced and expert: https://social.ora.cl/6007B8XvIB
Explore self-paced short courses in the Oracle Analytics and AI Learning Hub: https://social.ora.cl/6006B8XvLr
See how parameters are used in jobs and tasks: https://social.ora.cl/6003B8Xv03
Learn how to set up a robust and scalable data foundation using Oracle AI Data Platform Workbench in our self-paced Analytics and AI Learning Hub course: https://social.ora.cl/6007B8Xv0f
Like what you learned about Oracle AI Data Platform? Subscribe now https://social.ora.cl/60025k87j
Join us at Oracle AI World: https://social.ora.cl/60035eVvJ
Contact AIDP sales: https://social.ora.cl/6003FLUMT
Learn more about Oracle AIDP: https://social.ora.cl/60047h76Q
Join the Oracle AIDP Community: https://social.ora.cl/60087h7Bj
Follow us here:
LinkedIn: https://social.ora.cl/60035eXuF
X: https://social.ora.cl/6008abVgG
Instagram: https://social.ora.cl/60095eXDN
Facebook: https://social.ora.cl/60065enQp
