Sub-Millisecond Order Visibility Across Retail and Manufacturing
Near Real-Time Orders (NRTO)
Enabling sub-millisecond order visibility across retail and manufacturing operations
Challenge
Retail and manufacturing organizations struggle to access near real-time order data due to:
- Siloed systems across business units
- Batch-oriented data architectures
- High latency when querying centralized platforms
Even with modern Lakehouse platforms in place, order data is often delayed by minutes or hours, making real-time operational use cases impractical.
Solution
C2S designed and implemented a Near Real-Time Orders (NRTO) framework using a modern Lakehouse-plus-cache architecture.
We centralized order data into a Lakehouse platform (Databricks, Microsoft Fabric, or Snowflake) using a Medallion Architecture, and paired it with a high-performance in-memory caching layer powered by Azure Managed Redis.
This approach enabled near real-time data access without compromising governance, scalability, or cost efficiency.
Architecture Highlights
- Centralized order data modeled in the Lakehouse
- Gold-layer datasets optimized for real-time serving
- Azure Managed Redis used as a low-latency access layer
- Multi-region support for globally distributed operations
Business Impact
- Sub-millisecond access to near real-time order data
- Near real-time visibility across retail and manufacturing divisions
- Reduced Lakehouse compute load for operational queries
- Scalable, repeatable architecture for high-volume orderffic
The Outcome
C2S helped customers achieve near real-time order visibility that was previously considered extremely difficult due to siloed systems and legacy designs.
The NRTO framework is now a repeatable pattern for enabling real-time operational use cases on top of governed Lakehouse platforms.
