Challenges
Overcoming complex data engineering challenges.
PVH faced significant challenges with their data engineering processes, crucial for areas like demand forecasting and supply chain optimization. Issues included excessive duplicate code that hindered performance, readability, and future development.
Client
PVH Corp.
Project
AI, Big-data optimization
Partners
Amazinum
About the client
The foundation for sustainable and profitable growth.
PVH aims to make Calvin Klein and TOMMY HILFIGER the world's most desirable lifestyle brands and become a top-performing fashion group. The PVH+ Plan drives growth by excelling in product and consumer engagement, leading in the digital marketplace, developing a data-driven operating model, and improving efficiencies while investing in growth.
Solution
Strategic solutions for optimized data management.
A team of expert engineers engaged with PVH's existing codebase, utilizing advanced tools like Google Cloud Platform, BigQuery, and Apache Spark to enhance data processing and analysis. They focused on ensuring code uniqueness and reusability through design patterns and functional programming, conducting thorough testing to meet project requirements and quality standards.
Result
Performance and scalability for long-term business goals.
The team reduced the code size by over ten times and improved execution speed by more than 80%. They provided PVH with a robust and future-ready data engineering framework, significantly enhancing data processing and scalability. This comprehensive solution not only exceeded PVH's expectations but also empowered them to achieve sustained growth and efficiency. A successful collaboration that demonstrates our capability to deliver high-impact data engineering solutions tailored to meet complex business needs.
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