FAQ

BI Connector extracts Oracle Fusion data and works with Microsoft Fabric or Azure Data Factory to orchestrate scheduled delivery into Databricks. Pre-built pipeline templates support full and incremental refreshes while eliminating the need to develop custom Oracle Fusion extraction logic.

No. Microsoft Fabric or Azure Data Factory continues to orchestrate and schedule your data pipelines. BI Connector replaces the Oracle Fusion extraction layer, handling Oracle authentication, extraction, incremental refreshes, retries, and Oracle-specific complexities.

BI Connector supports Oracle Fusion base tables, BI Publisher data models, Fusion SQL queries, OTBI analyses, BICC PVOs, and Data Extraction Tool business objects. Support for the Data Extraction Tool business objects is in the immediate roadmap.

Data engineers can select the extraction approach that best fits their reporting, analytics, and data warehouse requirements while delivering the resulting data into Databricks.

The Oracle Fusion Query Vault is a library of ready-to-use SQL queries for common Oracle Fusion reporting and data requirements that can be used as data sources in BI Connector. It includes queries across areas such as Finance, HCM, and SCM that can be used as a starting point for reporting, customized for specific requirements, and run through BI Connector to access Fusion data.

Custom ETLs rely on file-based workflows that require additional processing before data reaches Databricks. BI Connector extracts Oracle Fusion data using Oracle-supported reporting assets and automates delivery through Fabric or Azure Data Factory, reducing integration complexity.

Yes. BI Connector supports initial full loads followed by automated incremental refreshes that process only new and changed Oracle Fusion data, helping improve pipeline efficiency and reduce processing time.

Yes. BI Connector delivers Oracle Fusion data into Databricks, allowing your team to create Delta tables, transformations, star schemas, feature engineering pipelines, and AI-ready datasets using your existing Databricks workflows.