For organizations leveraging both Oracle Fusion Cloud and Microsoft Azure Fabric, the efficient migration of large datasets is a crucial integration challenge. While the idea of a direct HTTP connector might initially seem straightforward, it’s often an impractical and inefficient method for transferring substantial volumes of data. This guide outlines a robust, scalable, and recommended approach that utilizes Oracle’s specialized tools alongside Azure’s powerful data integration services, ensuring a smooth and reliable data migration process.

Why Direct HTTP Connectors Fall Short:

Relying on direct HTTP connectors, typically through REST APIs, for migrating entire tables from Oracle Fusion Cloud presents several significant drawbacks:

• Data Volume Constraints: Oracle Fusion Cloud’s REST APIs are primarily designed for transactional interactions, not mass data extraction. They often impose strict limits on the number of records per request (e.g., 500 rows), making them highly inefficient for large tables.
• Performance and Throttling Risks: Frequent and numerous API calls required to fetch extensive datasets can lead to performance bottlenecks and trigger throttling mechanisms within the Oracle Fusion Cloud service, hindering the migration speed.
• Increased Complexity: Implementing a custom solution would necessitate handling intricate aspects such as pagination, error recovery, data transformations, and scheduling. This bespoke development adds considerable time, effort, and ongoing maintenance overhead.

The Optimal Solution: A Phased Approach

A far more dependable and scalable methodology involves a two-stage process:

1. Staging Data: Extracting the necessary data from Oracle Fusion Cloud into a temporary storage location.
2. Ingestion into Azure: Loading that staged data into Azure Fabric.

Phase 1: Extracting Data from Oracle Fusion Cloud via BICC

Oracle’s preferred tool for high-volume data extraction from Fusion Cloud applications is the Oracle BI Cloud Connector (BICC). BICC is purpose-built to pull significant data quantities from Oracle Fusion Cloud and prepare them for external consumption.

• BICC Configuration: Within your Oracle Fusion Cloud environment, configure BICC to retrieve data from Public View Objects (PVOs). PVOs are pre-defined, simplified views of application data, streamlining the extraction process.
• Selecting a Storage Destination: BICC delivers the extracted data in CSV format. The most common and recommended destination for these files is Oracle Cloud Infrastructure (OCI) Object Storage.
• Scheduling Extracts: Establish recurring data extracts (e.g., daily or weekly) to ensure Azure Fabric remains synchronized. BICC also supports incremental extracts, which efficiently capture only new or modified data following an initial full data load.

Phase 2: Ingesting Data into Azure Fabric Using Azure Data Factory

Once the data resides in OCI Object Storage, Azure Data Factory (ADF), a core component of Azure Fabric, becomes instrumental for its ingestion.

• Creating an ADF Linked Service: Establish a connection in ADF to OCI Object Storage by providing the necessary credentials and connection details.
• Defining an ADF Dataset: Create a dataset within ADF that points to the CSV files in OCI. Wildcard characters can be effectively used to process multiple files simultaneously.
• Building an ADF Pipeline: Construct a data pipeline using the Copy Data activity.
  • Source: Configure the source to be the dataset pointing to your CSV files in OCI.
  • Sink: Specify your desired Azure Fabric destination, such as a Lakehouse or Data Warehouse.
• Pipeline Scheduling: Automate the execution of this pipeline to run immediately after the BICC extraction is complete, creating a fully automated and continuous data migration workflow.

An Alternative: Oracle BI Publisher

For specific scenarios, Oracle BI Publisher can serve as an alternative to BICC, particularly when:

• Granular control over the output format is required.
• Complex data transformations are needed before extraction.

In such a workflow, a data model defines the extraction scope, and a report generates the CSV files. Azure Data Factory then ingests these files, similar to the BICC method. However, for straightforward bulk table extraction, BICC generally offers superior efficiency.

Conclusion:

By adopting the recommended two-step process involving Oracle BICC for extraction to OCI Object Storage and Azure Data Factory for ingestion into Azure Fabric, organizations can achieve a highly efficient, scalable, and reliable solution for migrating Oracle Fusion Cloud data. This approach bypasses the limitations of direct HTTP connectors, ensuring accurate and timely data availability within your Azure Fabric environment.