You’ve built a medallion architecture, applied role-based permissions, and extracted and enriched the data. Now it’s time to automate the process and connect your curated data to analytics tools.

The third e-learning module in the Oracle AI Data Platform Workbench Essentials series, Automation and Data Sharing with Oracle AI Data Platform Workbench, shows you how to turn individual notebooks and tasks into repeatable, scheduled workflows and how to securely share enriched datasets with downstream consumers, without unnecessary copying.

In this course, you’ll learn how to:

• Build, schedule, and manage workflows that orchestrate one or more tasks and run on-demand or on a schedule
• Create multi-step jobs using common task types (Notebook, Python, If/Else branching, Nested Job) and understand how job runs, dependencies, and parameters work together
• Monitor and troubleshoot workflow runs, using run details and logs to diagnose failures and keep pipelines reliable
• Configure secure data sharing using Delta Sharing to share governed data assets while maintaining access control through the platform’s permissions model
• Connect curated data to Oracle Analytics Cloud using supported connection options

Why this matters

Automation helps reduce manual steps and errors, keeps data current, and standardizes execution. This enables teams to deliver trusted data to analytics and AI consumers faster and more consistently.

Who should take this course?

This module is ideal for data professionals who are new to AIDP Workbench and have either completed the first two courses in the series or have equivalent experience, including data organization and environment setup, and basic familiarity with OCI and data engineering.

Select this link to access the course: Automation and Data Sharing with Oracle AI Data Platform Workbench.