How A Technology Leader Built High-Volume Oracle Data Pipeline with Seamless Cloud Integration Achieving 99.9% Reliability

Venkata Kishore

Senior Software Engineer

5 mins

Published : October 14, 2025

Oracle data pipeline automation, BICC OCI ATP integration

About the Client

The client is a global technology enterprise specializing in networking and IT infrastructure solutions. With operations spread across continents, its systems generate massive data volumes daily across functions like finance, supply chain, and project management.

To keep business-critical analytics timely and accurate, the client needed a robust, automated, and scalable way to extract and synchronize Oracle Fusion Cloud data with downstream systems and analytical platforms eliminating manual effort and enabling faster decision-making.

This initiative reflects similar digital transformation goals as seen in our Oracle Cloud Manufacturing - Wood Optimization project, where automation and scalability redefined data accuracy and operational efficiency.

The Challenges

The client faced growing challenges in managing large-scale data extraction and synchronization from Oracle Fusion Cloud to their internal analytics systems.

Key Challenges:

High Data Volume and Frequency
- With over 150 key tables across multiple Fusion modules (Procurement, Finance, and SCM), manual extractions were time-consuming and error-prone.
- Business functions required daily refreshes of data for analytics and compliance, but the existing manual jobs couldn't keep up.
Lack of Automation and Standardization
- Data extractions from Oracle Fusion were handled manually, often through ad hoc scripts.
- No centralized orchestration meant each module team ran separate extraction processes, creating inconsistencies in timing and structure.

A similar challenge was resolved in Oracle ERP & SCM Finance/Procurement Integration, where standardized frameworks improved accuracy and speed.

Integration Bottlenecks
- Without a unified data pipeline, downstream systems faced delays of several hours before fresh data became available.
- This affected key processes like supply chain tracking, project billing, and financial reporting.
Operational Inefficiency and Resource Drain
- Technical teams spent significant time maintaining scripts, managing file transfers, and validating data loads.
- The process required multiple checkpoints and frequent manual intervention to fix data mismatches or reloads.
Scalability and Data Security Concerns
- The legacy method could not scale to future data growth or new business modules.
- Sensitive financial data transfers lacked standardized encryption and monitoring mechanisms.

The absence of a unified, automated integration framework meant that the organization risked delayed insights, inconsistent data quality, and increased operational costs - a problem reminiscent of the multi-system dependencies tackled in Fusion-EBS Coexistence Supply Chain

The Solution

We designed a modular, automated, and high-performance data integration framework using Oracle's native cloud technologies - including BICC, BIACM, OCI, and ATP - to extract, stage, and synchronize Fusion data with downstream analytics platforms.

Key Solution Elements:

End-to-End Data Pipeline Architecture
- Built an end-to-end ETL-style data flow connecting Oracle Fusion → BICC → OCI Buckets → ATP Database.
- The pipeline could handle large, concurrent data jobs while ensuring fault tolerance and recovery.

This mirrored our Oracle Data Recovery Automation, where resilience and recoverability were key architectural principles.

Automated Extraction Framework
- Leveraged BI Cloud Connector (BICC) for bulk data extraction from over 150 Fusion standard tables.
- Mapped each table to corresponding Physical View Objects (PVOs) via BI Applications Configuration Manager (BIACM), ensuring clean and standardized data access.
- Automated the scheduling and execution of extractions through custom job sets, removing all manual steps.
Scalable Cloud Staging on OCI
- Used Oracle Cloud Infrastructure (OCI) Object Storage Buckets as a secure and scalable staging layer for data files.
- Implemented file versioning and error-handling mechanisms to ensure data integrity during transfer.
- Introduced decoupled data movement - separating extraction from load - to improve system performance and manage retry cycles seamlessly.

Comparable scalability was achieved in our SmartMPS Master Production Scheduling, where modular cloud design supported high-volume planning workloads.

High-Performance Data Loading with ATP
- Developed custom DBMS packages to efficiently load staged files from OCI into Autonomous Transaction Processing (ATP) tables.
- Created dynamic mappings to automatically handle new table additions without requiring redevelopment.
- Optimized loading using parallel processing and bulk insert operations, achieving faster load cycles.

This high-throughput design philosophy is consistent with our Oracle Cloud Mining UI Automation, emphasizing reliability and performance at scale.

Monitoring and Error Handling
- Built a real-time monitoring dashboard using SQL Developer and ATP views for job-level traceability.
- Configured automated alerts and logs to flag failed or delayed jobs instantly.
- Incorporated retry logic, error queues, and reconciliation checks to ensure consistent data delivery.
Data Security and Governance
- Enforced strict encryption for file transfers via OCI policies.
- Applied role-based access controls to safeguard sensitive financial data.
Scalability and Reusability
- The framework was designed as plug-and-play, capable of supporting additional Oracle modules and third-party data sources without redesign.

The transformation resulted in a fully automated, error-resilient data ecosystem, giving the client complete control and visibility over their cloud data operations.

The outcome resembles the data integrity and automation success delivered in Oracle ERP & SCM Finance Transformation, reinforcing Elfonze's ability to deliver measurable efficiency through Oracle Cloud.

Explore more enterprise cloud automation successes in the Elfonze Success Stories hub

Business Impact

Data Availability : Reduced latency from several hours to near real-time data synchronization across systems

Operational Efficiency : Eliminated 80% of manual intervention previously required for file handling and validation

High Throughput : Achieved 3x higher data extraction and loading speed

Reliability : Attained 99.9% success rate across all scheduled jobs

Scalability : Seamlessly expanded to support new modules and datasets without additional rework

Decision-Making : Enabled timely insights for business-critical functions like supply chain, procurement, and financial planning

Venkata Kishore

Senior Software Engineer

I have been involved in Extreme Networks projects focused on synchronizing data from all Oracle Fusion tables to ATP (Autonomous Transaction Processing) tables using BIACM. This initiative handled high-volume data transfers from Fusion to ATP. As part of the implementation, I designed Offers and Job Sets, leveraging OIC (Oracle Integration Cloud) to trigger and automate the data synchronization process to ATP.