How a Fortune 500 Retailer Modernized Data Infrastructure to Enable Real-Time Decisions and Enterprise Agility
A global retail corporation partnered with ShyftLabs to modernize its legacy data infrastructure, replacing outdated batch processing with real-time streaming and a scalable, secure architecture. The transformation streamlined data operations, reduced integration complexity, and positioned the organization to act on insights faster and with greater precision.
The Challenge
The client's data architecture was built on legacy systems that relied heavily on batch processing. These processes created delays in how business-critical data was transferred and consumed, often limiting the organization's ability to execute timely decisions across pricing, inventory, and customer experience initiatives.
Information lag and lack of visibility introduced compliance risks and created challenges in meeting the demands of real-time analytics platforms. Additionally, one-off integrations had accumulated over the years, forming a complex patchwork that was difficult to manage and costly to maintain.
The company needed a modern data platform that could centralize governance, reduce operational friction, and support the shift to real-time, event-driven systems without disrupting ongoing business operations.
The Solution
ShyftLabs delivered an enterprise-scale data modernization platform designed for resilience, speed, and extensibility. The solution replaced the client's fragmented data movement workflows with a unified architecture capable of supporting real-time, governed data access across the enterprise.
The platform was built around three core pillars:
A federated GraphQL architecture that standardized schema management and enabled secure, role-based data access while allowing backward-compatible integrations with legacy systems.
A centralized data pipeline orchestration layer that brought together version-controlled transformation logic, automated validation, and workflow visibility—helping teams maintain control while accelerating development.
A real-time Kafka streaming layer that supported high-throughput updates across domains like pricing and inventory, with full monitoring, fault tolerance, and observability.
The system was built to integrate gradually with existing environments and enabled phased transitions with no disruption to business operations.
The Results
The shift from batch processing to real-time streaming significantly improved data availability across key business functions such as pricing and inventory. By unifying architecture through GraphQL and centralized orchestration, the company reduced integration overhead and improved coordination across teams. Built-in governance and structured training enabled internal teams to maintain and extend the platform, laying a scalable foundation for future analytics and operational innovation.
Shift to Real-Time Data Access
Batch-based jobs were replaced with a real-time Kafka streaming architecture, improving data availability across pricing and other critical domains.
Unified Data Architecture
GraphQL and centralized orchestration tools simplified integrations, reduced maintenance complexity, and improved cross-team coordination.
Repeatable, Governed Workflows
Version-controlled pipelines, validation rules, and standard governance protocols helped ensure consistency, quality, and audit readiness.
Stronger Internal Capabilities
Training and enablement sessions prepared internal teams to maintain, expand, and innovate using the new infrastructure from day one.
Key insights
Standardization Unlocks Long-Term Agility
Centralizing architecture and schema management reduced integration effort and made it easier to support new use cases.
Real-Time Data Enables Faster Action
Moving away from batch processes allowed teams to respond to changing business conditions with greater speed and accuracy.
Governance and Enablement Are Critical
Technology alone is not enough. Training, documentation, and internal ownership ensured the system was sustainable and adopted
Modern Infrastructure Supports Business Growth
The new platform is positioned to support advanced analytics, personalization, and other strategic initiatives that were limited by legacy systems.