Build an Automated Oracle Fusion to Databricks Data Pipeline

Build an Automated Oracle Fusion to Databricks Data Pipeline

Land Oracle Fusion ERP, HCM, SCM, PPM, and EPM data in governed Delta tables without recurring exports, fragile scripts, or repeated data reconstruction.

SplashBI provides a prebuilt Oracle Fusion to Databricks pipeline that supports incremental change capture, Bronze, Silver, and Gold lakehouse layers, schema evolution, and governed access for SQL, notebooks, MLflow, reporting, and AI.

• Delta tables (Bronze, Silver, Gold layers)
• Delta MERGE & Auto Loader
• Automatic schema evolution

See how a major government entity consolidated Oracle Fusion and Oracle EBS data across more than 55 entities using a secure Azure and Databricks architecture.

Download the 500M-Row Case Study

See how a major government entity unified Oracle Fusion and Oracle EBS data across 55+ entities using secure, governed data pipelines.

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The extraction layer

Oracle Fusion Data Was Not Built for Lakehouse Analytics

Oracle Fusion is designed to run business operations. It was not designed to feed a Databricks lakehouse every time Finance, HR, Supply Chain, or data science teams need current data. That creates a difficult extraction layer between Oracle and Databricks:

Today

• Large historical extracts can be slow and difficult to manage
• Late-posted financial transactions may be missed by basic date filters
• ERP and HCM relationships must be reconstructed downstream
• Multi-entity security becomes harder to preserve
• Source schema changes can break custom pipelines
• Repeated full loads increase processing and maintenance
• Data science teams wait for curated Oracle datasets
• Separate Oracle Fusion and EBS pipelines fragment enterprise reporting

Custom scripts can move data, but they also leave IT responsible for scheduling, monitoring, error handling, change detection, and recovery.

With SplashBI

SplashBI replaces that maintenance-heavy process with an Oracle data pipeline platform designed for enterprise application data. The Oracle Fusion Cloud to Databricks flow delivers:

• Automated, native extraction across all Oracle Fusion Cloud modules
• Incremental CDC replication that captures late-posted financial entries
• Lakehouse-ready data structured into Bronze, Silver, and Gold Delta layers
• Preserved ERP & HCM business logic and source relationship context
• Full alignment with Unity Catalog governance and multi-entity security
• Automated execution monitoring, failure detection, and self-healing recovery

Automated workflow

Table-to-Table Replication for Databricks Lakehouse with SplashBI Data Pipeline

Moving Oracle files into cloud storage is not the same as building a usable lakehouse. If source data arrives without its structures, relationships, and business context, teams still need to reconstruct it before they can build analytical models.

SplashBI automates Oracle data replication into structured Databricks Delta tables.

What Table-to-Table Databricks Replication Means

Select Oracle Source Objects

Choose the Oracle Fusion or Oracle EBS modules, objects, and tables required for the use case.

Extract Through Prebuilt Pipelines

Use configured Oracle extraction processes rather than rebuilding source logic for every pipeline.

Preserve Source Context

Retain relevant structures, formats, relationships, and source semantics.

Land Data in Delta Tables

Deliver Oracle data into a Databricks Bronze layer for governed downstream refinement.

Merge Incremental Changes

Apply new and changed records through Delta MERGE patterns instead of repeatedly reloading the full dataset.

Refine for Consumption

Transform Bronze data into Silver and Gold layers for reporting, analytics, machine learning, and AI.

This Oracle Fusion data pipeline reduces downstream reconstruction while allowing Databricks teams to retain control over lakehouse transformations and business models.

How it works

How the Fusion-to-Databricks Pipeline Works

SplashBI combines prebuilt Oracle extraction with the scheduling, monitoring, delivery, and recovery functions expected from enterprise data pipeline automation tools. Standard configurations reduce the need for custom pipeline code while still allowing Databricks teams to refine and model data inside the lakehouse.

Select

Choose Oracle Fusion Cloud or Oracle EBS modules, objects, and tables.

Capture

Identify new and changed data using incremental CDC and configurable lookback logic.

Land

Load Oracle data into governed Databricks Delta tables.

Refine

Transform Bronze data into Silver and Gold layers based on analytical requirements.

Govern & Use

Make the data available through Unity Catalog, SQL Warehouses, notebooks, MLflow, BI, and AI tools.

Change data capture

An Automated CDC Data Pipeline for Late and Back-Dated Transactions

Basic date filter

SplashBI CDC with lookback window

Databricks integration

SplashBI Data Pipeline, Built for Oracle Fusion to Databricks Workloads

An Oracle Fusion Cloud to Databricks pipeline data should deliver data in a form that Databricks teams can govern, refine, query, and use across analytical and AI workloads.

Delta tables

Land Oracle Fusion and EBS data in Delta tables that support reliable downstream processing and analytical access.

Bronze, Silver, and Gold architecture

Use the Bronze layer for replicated source data, Silver for validated and conformed datasets, and Gold for business-ready analytical models.

Delta MERGE

Apply new and changed records to destination tables without rebuilding the full dataset during every refresh.

Auto Loader

Support scalable file ingestion patterns for configured Databricks pipeline architectures.

Schema evolution

Accommodate eligible source structure changes without forcing teams to manually rebuild the entire pipeline.

SQL Warehouses

Make governed Oracle data available for SQL analytics, dashboards, and downstream BI applications.

Notebooks

Give data engineering and data science teams access to Oracle data for exploration, transformation, and model development.

MLflow

Use governed enterprise data in machine learning experimentation, evaluation, and lifecycle workflows.

Unity Catalog

Apply centralized governance, lineage, discovery, and access controls across lakehouse data assets.

Enterprise reliability

Enterprise Reliability Without Pipeline Babysitting

Data pipelines become operational liabilities when teams must watch every load, investigate every failure, and manually restart interrupted jobs.

SplashBI includes built-in capabilities that reduce ongoing pipeline maintenance.

Incremental and full refreshes

Move new and changed data incrementally or run complete scheduled refreshes where required.

Configurable scheduling

Set recurring pipeline cadences or trigger extraction according to reporting and operational needs.

Retry logic and auto-resume

Recover interrupted loads without manually restarting the complete process.

Smart error handling

Identify pipeline issues and reduce the time required to investigate failures.

Monitoring and notifications

Track pipeline activity and receive notifications when extraction or delivery completes or fails.

Timestamped audit history

Maintain traceable records of pipeline runs for operational monitoring and audit requirements.

Automatic new-column detection

Identify eligible source-column changes based on the configured pipeline.

Security controls

Use role-based access, system-aligned permissions, and encryption in transit and at rest.

Cloud and on-premises sources

Replicate from supported cloud or on-premises applications into the Databricks environment.

Multi-source replication

Bring Oracle and other enterprise sources into one governed lakehouse.

From Oracle Fusion Tables to Databricks Analytics

Once Oracle Fusion data reaches Databricks, teams can use the governed copy across business and technical workloads.

Business Analytics

Finance Analytics

Use GL balances, journals, AP, AR, subledger, fixed assets, and project data for close reporting, financial analysis, reconciliation, and historical trends.

Workforce Analytics

Use worker, payroll, recruitment, benefits, time, and performance data for workforce reporting and cross-domain analysis.

Supply Chain Analytics

Combine procurement, inventory, orders, purchasing, and production data for operational and cost analysis.

Technical & Advanced Workloads

Lakehouse Transformation

Feed governed Oracle data into Databricks notebooks, Delta Live Tables, and Gold-layer models for lakehouse transformation, testing, and analytics engineering.

BI and Reporting

Serve Oracle data to SplashBI, Tableau, Power BI, Databricks SQL dashboards, or other reporting and semantic layers.

Machine Learning and AI

Make governed enterprise data available to notebooks, MLflow experiments, machine learning models, and AI workloads without building those processes around manual Oracle exports.

Enterprise case study snapshot

Oracle to Databricks Pipeline, Proven Across 55+ Entities and More Than 500 Million Rows

A major government entity needed to consolidate Oracle Fusion ERP, HCM, and EPM data with Oracle EBS across more than 55 underlying entities. SplashBI delivered an enterprise data pipeline into a Databricks Medallion Architecture within a sovereign Microsoft Azure environment.

See how a major government entity unified Oracle Fusion and Oracle EBS data across 55+ entities using secure, governed Databricks data pipelines.

Turn Oracle Fusion Data Into a Governed Databricks Lakehouse with SplashBI Data Pipeline

Replace manual exports, fragile scripts, and disconnected Oracle datasets with a structured Fusion-to-Databricks pipeline.

See how SplashBI can replicate your Oracle Fusion and Oracle EBS data into Databricks using the modules, refresh cadence, security model, and lakehouse layers your teams require.

FAQ

Frequently Asked Questions