data processing time reduced from 10+ hours to seconds or minutes with the new ELT pipeline
clients can now experiment with pricing configurations in real time via a Streamlit interface
architecture supports seamless onboarding of additional customers with strict data isolation per client

A strategic pricing consultancy faced challenges in processing large-scale customer pricing data. Their existing solution relied on Python scripts and Excel, which struggled to handle the increased data volume from a new customer. Processing times ballooned to over 10 hours, making iterative analysis impractical and hindering their ability to provide timely pricing insights.
The consultancy needed a flexible, scalable, cloud-based solution that could process data rapidly, support iterative experimentation, and lay the groundwork for a pricing platform that could accommodate additional customers while maintaining data security and isolation.
OneSix designed and implemented a modern ELT (Extract, Load, Transform) data processing pipeline leveraging Azure Data Factory and Snowflake. The architecture streamlined data ingestion, transformation, and analytics, delivering rapid performance and scalability. Key elements of the solution included:
The solution drastically improved data processing efficiency, reducing execution time from over 10 hours to just minutes or seconds. This rapid performance enabled:
The consultancy praised the performance and adaptability of the new solution, highlighting how the architecture enabled rapid, iterative development and delivered consistent, high-quality pricing insights.
Testimonial
"OneSix’s solution transformed our data processing capabilities. What used to take hours now takes seconds, allowing us to experiment and deliver pricing insights with unprecedented speed and accuracy."
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