Design Oracle Fusion SCM to Azure Databricks

Datalight — Fri, 14 Nov 2025 06:18:58 GMT

Hello Techie,

I am planning to migrate All module of Oracle fusion scm data to Azure Databricks.

Do we have only option of BICC (Business Intelligence Cloud Connector), OR any other option avaialble.

Can anyone please help me with reference architecture.

Thanks a lot.

Re: Design Oracle Fusion SCM to Azure Databricks

mark_ott — Sun, 16 Nov 2025 17:14:24 GMT

BICC (Business Intelligence Cloud Connector) is not the only option for migrating Oracle Fusion SCM data to Azure Databricks, though it is the most common and recommended tool for high-volume, scheduled extracts according to Oracle’s official guidance.

Alternative Extraction Options

Oracle BI Publisher (BIP): Suited for extracting smaller, customized datasets where control over output format or embedded logic is required. Not optimal for large-scale or incremental extractions, and generally considered a reporting tool rather than a bulk data extraction tool.
Oracle Fusion REST APIs: For low-volume or real-time data extraction needs, Fusion exposes rich REST and SOAP interfaces. These APIs are practical for incremental or event-driven pipelines but require custom development and may have API call limits.
Data Integration/ETL Tools: Third-party tools and services (like Azure Data Factory, BryteFlow, or RiteSync) can orchestrate extraction, often leveraging BICC, BIP, or direct API access—sometimes with additional features like change data capture (CDC), scheduling, and monitoring.
Direct JDBC/ODBC Connections: Primarily for on-premise Oracle environments, but not typically an option for Oracle Fusion Cloud data; access is usually limited to the SaaS application layer.

Reference Architecture Overview

A typical reference architecture for Oracle Fusion SCM to Azure Databricks includes:

Extraction Layer:
- Use BICC (scheduled bulk extract), BI Publisher (for special cases), or Fusion APIs to extract raw data or CSVs.
Data Ingestion/Storage:
- Store extracted files or API payloads in Azure Data Lake Storage Gen2 (ADLS Gen2).
- Optionally use Azure Data Factory for orchestrating the extract and load operations for both bulk and incremental data.
Processing and Analytics:
- Use Azure Databricks to transform, cleanse, and enrich the ingested data.
- Apply the medallion architecture (Bronze, Silver, Gold layers using Delta tables).
Consumption:
- Curated data sets feed downstream tools such as Power BI, reporting solutions, or machine learning workloads.

Example Reference Pipeline

Extract: BICC/BI Publisher scheduled extracts to ADLS Gen2 (via SFTP, REST, or native ADLS sinks).
Orchestrate: Azure Data Factory manages and monitors extracts, error handling, and incremental loads.
Transform: Databricks notebooks/scripts convert raw data to Delta Lake formats and maintain SCD/CDC logic.
Consume: Final data available for analytics and reporting.

This architecture ensures scalability, modularity, and compliance with both Oracle and Azure best practices.

Summary Table

Extraction Method	Bulk Loading	Real-Time	Complexity	Usage Scenario
BICC	Yes	No	Medium	All modules, large volumes
BI Publisher (BIP)	Limited	No	High	Custom formats, reports
REST/SOAP APIs	No	Yes	High	Real-time, low-volume
ETL Tools/ADF	Yes	Yes	Medium	Orchestration
3rd Party (RiteSync, Orbit)	Yes	Yes	Medium	Enhanced scheduling, CDC

In summary, while BICC remains the main enterprise-grade method, additional strategies are possible for specific needs or custom orchestration—especially if lighter extracts, more control over formatting, or real-time data is needed.

Re: Design Oracle Fusion SCM to Azure Databricks

Datalight — Thu, 26 Feb 2026 11:22:19 GMT

@mark_ott : Thanks a ton. sorry for late reply, as Client was not sure on the approach. your solution helps a lot. Thanks Again.

topic Design Oracle Fusion SCM to Azure Databricks in Data Engineering

Design Oracle Fusion SCM to Azure Databricks

Re: Design Oracle Fusion SCM to Azure Databricks

Alternative Extraction Options

Reference Architecture Overview

Example Reference Pipeline

Summary Table

Re: Design Oracle Fusion SCM to Azure Databricks