Eliminated $15M+ in annual licensing costs by migrating to a Spark-MapR architecture, enabling faster, parallel data processing while preserving SLA commitments and reducing compute overhead.
A leading retail analytics provider ran a stock alert system that notified retailers and vendors of SKU-level changes using predictive analytics. The system depended on hundreds of siloed data marts hosted on a commercial data warehouse, incurring multimillion-dollar license costs annually.
They wanted to move out of the commercial data warehouse to eliminate higher license fee, without having any impact to the alert notification system SLA time.
On-Premise MapR + Spark Architecture: Selected Spark for high-performance processing, natively supported by MapR FS.
Centralized Data Storage: Migrated siloed data marts into a unified storage system for easier access and scalability.
Multi-Level Partitioning: Partitioned data across multiple layers to avoid redundant reads and improve job isolation.
Parallel Algorithm Execution: Enabled multiple ML models to run simultaneously by isolating reads per retailer.
Computation Cost Optimization: Reduced system memory and CPU load through efficient data slicing and targeting.
License-Free Operations: Eliminated $15M+ in annual licensing fees by moving off commercial data warehouses.
Faster Parallel Processing: Spark-powered execution and data partitioning enabled simultaneous job execution across retailers.
Improved Performance Efficiency: Reduced memory and CPU consumption through optimized partition-based data access.
Scalable, Centralized Storage: Replaced siloed data marts with a unified MapR FS for on-premise scalability.
SLA Compliance Maintained: Maintained system performance to ensure timely stock alerts with no disruption.
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