1Overview of the Data Extraction Tool

The Data Extraction tool enables you to extract application data for reporting and integration purposes.

You can extract application data from a read-optimized data store. Extraction runs on a replicated Oracle Autonomous AI Lakehouse instance that’s synchronized with transactional data. This approach reduces the load on the transactional instance by abstracting business objects from the physical Fusion Applications data model.

The tool supports extracts based on standard business views and user-defined queries. Use a user-defined query when you have complex requirements for extracting data from multiple tables.

How to Use This Playbook

This playbook provides an end-to-end workflow for extracting data using the Data Extraction tool.

Follow these steps in sequence:

1. Create and Configure Data Extracts

   Define the data that you want to extract by creating extract definitions that use standard views or user-defined queries, and group them based on your requirements.

2. Create and Manage Extract Schedules

   Create extract schedules, configure the extract output destination, and manage scheduled extracts.

3. Run and Monitor Extract Jobs

   Schedule and run extract jobs, monitor their execution, and verify that the output meets your requirements.

4. Configure Customer-Managed OCI Object Storage (Optional)

   If you use Customer-managed OCI Object Storage, configure storage destinations and the required Identity and Access Management (IAM) policies before using them in extract schedules.

5. Access and Download Extract Output

   Retrieve the generated output files and download the extracted data.

6. Automate Data Extraction Using REST APIs

   After validating your extract configurations, use REST APIs to automate scheduling, monitoring, and data retrieval as part of integration workflows.

Learn About Data Extraction

Watch this video to get an overview of the Data Extraction tool.