How Altimetrik Helped a Premium Apparel Company Cut Retail Data Processing Time by 76%

A leading premium apparel company partnered with Altimetrik to modernize its enterprise master data ecosystem and improve operational efficiency across merchandising and planning operations. The organization needed a scalable, reliable framework to manage product, vendor, and location data while reducing delays in downstream data availability.

Altimetrik implemented a modernized data engineering architecture with advanced validation, parallel processing, near real-time data streaming, and enhanced telemetry. The transformation significantly accelerated data delivery, improved data quality, optimized infrastructure efficiency, and enhanced the overall retail customer experience.

Frequent rule changes and evolving business attributes let flawed data slip through the existing architecture, creating downstream issues for end users. Onboarding new data took roughly 25 hours, and routine updates required 3 hours, largely because a single monolithic pipeline generated massive files, sometimes running into gigabytes.

Debugging was slow and labor-intensive, and sequential processing pushed server memory and CPU usage as high as 90%, further delaying publication. The team was not short on effort; the architecture simply could not keep pace with the business.

Altimetrik partnered with the client to re-architect the master data pipeline end to end, moving it from a slow, monolithic process to a fast, transparent, near real-time system. The solution delivered five core capabilities:

• Validation Layer: Added an additional data validation step just before the enterprise data exchange to catch flawed data and instantly notify the support team for fast resolution.
• Parallel Processing: Broke the monolithic file into smaller files that could be processed in parallel, cutting outbound file processing time from roughly 10 hours to about 1 hour.
• Real-Time Data Availability: Replaced batch-generated JSON files with direct queries from the MDM system streamed into Kafka every hour, removing file creation and retrieval delays entirely.
• Enhanced Data Tracking: Introduced a correlation ID framework to trace every data batch from inbound to outbound, including third-party systems, for full pipeline visibility.
• Optimized Queries: Refined query logic to run a single consolidated query across all IDs instead of many smaller ones, sharply reducing CPU load.

This engagement reflects how Altimetrik approaches data engineering: not as a one-time fix, but as the foundation for what a business builds next. By replacing a rigid, monolithic pipeline with a fast, transparent, scalable architecture, Altimetrik gave the client’s team the speed and confidence to support downstream systems and customers with accurate, near real-time information.

The work continues. The client and Altimetrik are extending this foundation toward a fully scalable, enterprise-wide data-as-a-service model, one built to flex as new business needs, channels, and data sources emerge.