The Data Extraction tool is designed as the modern, high-performance successor to the Business Intelligence Cloud Connector (BICC). While both tools serve the same core purpose—moving bulk data out of Oracle Fusion for use in downstream systems like data warehouses—they differ significantly in their underlying architecture, performance, and user experience.
1. Data Source and Application Load
BICC: Queries the live transactional database of Oracle Fusion Applications. Because of this, large BICC extracts can compete for resources with the live application, creating high load and potentially impacting transactional performance.
Data Extraction Tool: Queries a Read-Optimized Data Store (RODS), which is a continuously refreshed replica of the transactional data. This isolation means the tool has near-zero load on the core application database, allowing for faster and more reliable extractions without affecting live users.
2. Performance and Scalability
Performance: BICC is rated as having "Medium" performance, whereas the Data Extraction tool is rated as "Very High".
Incremental Support: Both tools support incremental (delta) extracts. BICC has offered this for years against the live database. The Data Extraction Tool also supports full and incremental extracts, but runs them against the replicated RODS layer — meaning incremental extraction adds no additional load to the transactional database, which isn't guaranteed with BICC's live-database queries.
Execution Models: The new tool supports both synchronous processing for small, immediate requests and asynchronous/batch processing for high-volume scheduled pipelines.
3. Query Language and Schema
Query Language: BICC uses standard extract definitions often based on BI Publisher-style report definitions. The Data Extraction tool uses BQL (Business Object Query Language), a language purpose-built for this extraction layer.
Schema Type: BICC utilizes complicated views, while the Data Extraction tool leverages a business object data model with "clean" business and extraction views that simplify data organization.
The Data Extraction tool is being extended to support data extraction for integration use cases, where SQL queries can be modeled as business object queries against the Read-Optimized Data Store. These extract queries can be defined across multiple entities and include filter conditions to produce the required data shape for integration requirements.
An AI Agent, Data Extraction Query Transformer, 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 data extraction from Oracle Fusion Applications, use Read-Optimized Data Store based data extraction instead of Oracle Analytics Publisher.
4. User Experience and Interface
User Interface: BICC uses a legacy UI, while the Data Extraction tool features a modern, Redwood-based experience.
Ease of Use: The new tool is designed for self-service with a point-and-click interface for selecting pillars (e.g., Supply Chain & Manufacturing), functional areas, and specific attributes.
5. Data Delivery and Coverage
Output Delivery: BICC can be configured to push data directly to an external Storage Cloud (like an OCI bucket). Currently, the Data Extraction tool's native output lands in Universal Content Management (UCM); moving these files to an OCI bucket requires a custom automation layer.
Object Coverage: BICC is a proven tool with established coverage across Fusion. The Data Extraction tool is in its initial release phase with growing coverage—Oracle is actively expanding support module-by-module.
