1. Integration Playbooks for SCM
  2. Data Extraction Tool

Data Extraction Tool

The Data Extraction tool leverages a read-optimized data store to ensure faster and more reliable data extraction.

By seamlessly integrating with Oracle Autonomous AI Lakehouse, the tool supports near real-time replication of data from Oracle Fusion Cloud Applications. Rather than extracting data directly from the transactional database of Oracle Fusion Applications, the tool performs data extracts against the replicated data. This approach significantly reduces the load on core applications by offloading data extraction to a read-optimized data store, improving overall performance and efficiency for both transactional activities and data extraction needs.

This modern, Redwood-based solution is the long-term replacement for Business Intelligence Cloud Connector (BICC) and Oracle Analytics Publisher for data extraction. It provides a streamlined, efficient experience that helps you access and extract data using a more unified and modern interface.

Key Features

  • Select one or more business views or extraction views or register extraction queries to re-use in different extracts.
  • Perform full and incremental extracts.
  • Schedule extractions or run them on demand.

  • Submit asynchronous and synchronous extraction queries.

  • Enable agent-driven callbacks on extract completion.

  • Monitor and view extract statuses.
  • Apply BICC column headers to the selected views and attributes to support migration from BICC for existing customers.
  • Export extract data to Oracle WebCenter Content, Oracle Managed Storage, or Customer Managed Storage.

Best Practices

  • Use only for bulk extracts.
  • Don't extract all columns. Extract only what's required.
  • Don't schedule all extracts together. Create multiple jobs instead.

    For example, decouple heavy view-object (VO) extracts into separate jobs. Including them in common jobs might cause them to run late in the cycle and extend the extract window. Multiple jobs can run in parallel.

  • Apply filters to your extraction queries to ensure that only relevant data is retrieved. Doing this will speed up the extraction process, reduces the volume of unwanted data, and makes the next processing and analysis more efficient.

Constraints

  • Custom objects and analytic views aren't supported.
  • Not suitable for real-time data extraction.
Note:

The Data Extraction Query Transformer AI agent is available to convert SQL statements into extract queries. These extract queries can be executed on demand in synchronous mode within integrations or included as part of the existing asynchronous data extracts.

For detailed implementation steps and guidance, refer to My Oracle Support: Data Extraction Agent Workflow for RODS-Based Integrations Use cases (Doc ID KB930290).

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