Orbit Datajump acts as a data orchestration and modeling layer purpose-built for Oracle Fusion—and it’s fully compatible with Databricks as the underlying compute engine. 

Together, they form a robust, scalable, and intelligent pipeline from Oracle Fusion Cloud to analytics-ready data models. 

How Orbit Datajump Helps Databricks Handle Oracle Fusion 

1. Smart Data Ingestion from Fusion Sources 

• Orbit connects to OTBI, BI Publisher, BICC, and supported APIs to extract data efficiently and reliably. 

• Supports incremental loading, change tracking (where available), and automated scheduling into Databricks. 

• No manual scripting needed for extractions or job orchestration. 

2. Fusion-Aware Data Modeling in Databricks 

• Orbit builds predefined, modular, and normalized data models directly on top of Databricks’ Delta Lake. 

• These models translate Fusion’s flattened reports into clean star schemas—with fact and dimension tables covering: 

• General Ledger 

• Payables & Receivables 

• Procurement 

• Workforce & Payroll 

• Projects and more 

• Your Databricks Lakehouse instantly understands Oracle Fusion’s business logic with Orbit DataJump and prebuilt data models. 

3. Built-in Handling for Schema Drift & Custom Fields 

• Orbit detects schema changes from Oracle Fusion (like added custom fields, new flexfields, or updated LOVs). 

• These changes are version-controlled and propagated through the pipeline to keep your models in sync—no manual rebuilds required. 

• This makes Databricks a resilient destination for dynamic SaaS data. 

4. Optimized ELT and Delta Architecture 

• Orbit leverages Databricks’ Delta Lake format, with support for: 

• Time-travel 

• Partitioned and compacted data storage 

• Scalable transformations 

• Ingest once, and transform as needed—at scale. 

5. Accelerated Time-to-Insight 

• Orbit’s prebuilt semantic layer and business logic dramatically reduce the time needed to deliver Fusion-based dashboards and KPIs. 

• Output is ready for Power BI, Tableau, Looker, or even ML workloads in Databricks. 

• Go from raw Fusion data to dashboards in weeks, not quarters. 

Orbit Architecture Snapshot: Orbit + Databricks + Oracle Fusion 

Oracle Fusion Cloud 

    └── OTBI / BI Publisher / BICC and other Fusion APIs 

        └── Orbit Datajump (Extraction + Transformation + Modeling) 

            └── Databricks Lakehouse (Delta Lake Tables) 

                └── BI Tools / ML Models / Data Apps 

Key Benefits of Orbit + Databricks Integration 

Challenge with Fusion	How Orbit + Databricks Solves It
Inconsistent access methods	Unified ingestion pipeline (OTBI, BI, BICC and other Fusion APIs)
Flattened schemas	Normalized, analytics-ready models on Delta Lake
Schema drift and custom fields	Auto-detection and dynamic pipeline adaptation
Long development cycles	Prebuilt models reduce dev time by 70–80%
Scaling with data volumes	Leverages Databricks’ distributed compute

Real-World Use Case: MARTA – Fusion data migration to Databricks 

Goal: Migrate Fusion Financial, SCM and HCM data to Databricks for analytical needs of MARTA. 

Without Orbit: 

• Data engineers manually extract BI Publisher reports. 

• Build custom staging logic in Databricks notebooks. 

• Spend months reconstructing GL journal models and reconciling differences. 

With Orbit: 

• Setup and migrate Fusion Cloud data in weeks 

• Used prebuilt GL and AP models deployed in Databricks. 

• Orbit handles schema drift and DFFs automatically. 

• Built dashboards in Powerbi in weeks. 

Databricks is a best-in-class platform for data transformation, AI, and large-scale processing. But without the right Fusion-aware modeling and orchestration layer, it can struggle with SaaS data like Oracle Fusion Cloud. 

Orbit Datajump bridges that gap and empowers your team to treat Oracle Fusion like any other analytics-ready data source for Databricks — reliably, scalably, and fast.