Ralph Lauren is a global luxury fashion and lifestyle brand operating approximately 350 retail stores worldwide. The company leverages advanced retail technologies to optimize inventory, sales, and customer experiences across its global operations.
Ralph Lauren’s retail ecosystem integrated multiple high-volume data sources, including RFID systems, True View, and Deep North analytics platforms. These systems continuously generated streaming data related to inventory movement, sales activity, and in-store customer traffic.
However, the existing architecture struggled to process this data at scale.
High-frequency streaming events were being dropped before reaching Amazon Redshift, leading to incomplete reporting and inconsistencies in downstream analytics. The infrastructure was not optimized for sustained real-time ingestion, especially during peak store activity periods.
As a result, reporting delays impacted visibility into inventory accuracy, sales performance, and customer behavior across stores, limiting the ability to make timely operational decisions.
YCOTEK redesigned the streaming ingestion pipeline to ensure reliable, scalable, and lossless real-time data processing.
At the center of the solution was the implementation of Amazon Kinesis Firehose, which buffered streaming events for up to five minutes before writing them into Redshift. This buffering layer stabilized ingestion during spikes in data volume and eliminated the data loss occurring in the previous architecture.
By enabling controlled, high-frequency batch writes into Redshift, YCOTEK ensured that large volumes of streaming data could be processed consistently without overwhelming downstream systems.
The architecture was designed to guarantee complete and accurate data availability for reporting and analytics workloads, supporting real-time operational visibility across Ralph Lauren’s retail network.
YCOTEK built the solution using a cloud-native data architecture powered by:
The combination of streaming, buffering, orchestration, and analytics technologies created a scalable infrastructure capable of supporting real-time retail intelligence across a global store network.
The redesigned streaming infrastructure significantly improved the reliability and quality of retail analytics across the organization. By ensuring 100% valid streaming data was available in Redshift, the solution delivered more accurate inventory, sales, and store traffic insights while eliminating data gaps in real-time reporting. With reliable, high-frequency data processing at enterprise scale, Ralph Lauren strengthened operational visibility across its global store network, creating a more dependable foundation for inventory management, retail analytics, and faster business decision-making.
Future-proofed enterprise reporting through cloud modernization, performance optimization, and AI-ready architecture.
Reducing cloud waste and building a scalable governance framework for long-term operational efficiency.
Empowering business users to access enterprise intelligence through natural language instead of dashboards.
Creating a trusted data foundation that enables self-service analytics and future AI adoption at scale.
Transforming a legacy stock exchange into a scalable digital trading ecosystem serving millions of users.