Cloud-native data lake + Power BI reporting with RLS for 2,000+ users
A leading US‑based agri enterprise needed to modernize its legacy data landscape to support data‑driven decision‑making. With 20+ years of historical data locked in Oracle EBS and fragmented across silos, analytics performance and visibility were limited. CES migrated the data to a scalable Azure‑based architecture, delivering role‑based insights, predictive analytics, and self‑service reporting for over 2,000 users – eliminating silos and improving productivity, visibility, and ROI.
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The Challenge
Multiple Data Silos Slowing Analytics
High Data Latency Across Systems
Limited Predictive Analytics for Role-Based Decisions
the client
Precision Agriculture | Agri Retail
Technology Stack
- Oracle E-Business Suite (Source)
- Azure Data Lake
- Azure Data Factory
- Azure Synapse (SQL / DW)
- Power BI
- SQL
- Azure Active Directory (RLS & Access Control)
Solution Area
- Data Engineering & Analytics | Data & Analytics
the impact
Enterprise-Wide Data Visibility
2,000+ Users Role-Based Insights
Predictive Analytics Enablement
Scalable Azure Architecture for Growth
The shift was cloud-data-led.
The result: scalable, role-based insights.
The Need
Leadership wanted data-driven decision-making across the organization with data insights tailored by role for ~2,000 users. The environment also needed to support long-term analytics over 20+ years of historical data while modernizing the platform for scalability, security, and advanced analytics.
Challenges
- Fragmented data and slow analytics throughput: Multiple silos and latency issues made data transfer and reporting slow, limiting visibility and decision speed.
- Legacy analytics constraints and end-of-support risk: The existing reporting layer faced end-of-support challenges (Oracle Discoverer), creating risk around compatibility, supportability, and feature continuity.
- Role-based access and future-ready analytics requirements: The platform required row-level security, scalable cloud-native architecture, multi-device access, and a self-service BI model while enabling predictive analytics.
- Azure cloud-native architecture redesign: Revised the platform architecture and implemented a scalable Azure-based data ecosystem designed for long-term analytics and growth.
- Historical data migration to Azure Data Lake: Integrated and migrated 20+ years of historical data from Oracle EBS into Azure Data Lake to support deeper analytics and consistent data access.
- Modern reporting and exploration with Power BI: Improved data visualization and reporting using Power BI, enabling drill-down insights and multi-device access for business users.
- Role-based security + predictive analytics enablement: Implemented row-level security (RLS) to control visibility by role and enabled predictive analytics on top of the curated data foundation.
- Delivered curated, consolidated data with silos removed for broader visibility and exploration.
- Migrated 20+ years of historical data from Oracle EBS into Azure Data Lake to support long-horizon analytics.
- Introduced drill‑down dashboards and visual reporting in Power BI for faster insight consumption across roles.
- Implemented predictive analytics with row-level security, supporting secure adoption across ~2,000 users.