OLORIN
A solution that transforms data validation from a bottleneck into a strategic enabler—making your data pipelines clean, reliable, and future-ready.
Challenge
Modern data pipelines are increasingly complex, integrating diverse sources and evolving rapidly with changing schemas, user inputs, and business logic. This complexity often leads to undetected data quality issues that disrupt operations, delay insights, and erode trust. Traditional solutions either discard invalid data— losing valuable context—or require deep technical intervention, leaving business users unable to act independently.
Solution
This solution is a powerful, PySpark-native validation and observability framework built for Palantir Foundry. It empowers users to define flexible, reusable validation rules (“taggers”) that automatically flag invalid records without removing them. This preserves full data context while enabling real-time rootcause analysis. With intuitive dashboards and post-processing modules that enrich invalid records with actionable insights, the library bridges the gap between technical complexity and business usability—ensuring data quality is visible, traceable, and manageable by all stakeholders.
Impact
Empowerment
Business users and product owners can independently identify and resolve data issues through intuitive dashboards—reducing reliance on technical teams.
Efficiency
Accelerates detection and resolution of data quality issues at any pipeline stage, minimizing downtime and rework.
Resilience
Keeps pipelines running smoothly by isolating, not discarding, invalid records—ensuring continuity and trust in downstream processes.
Transparency
Every validation step is logged and traceable, supporting auditability, compliance, and continuous improvement.
Scalability
Designed for distributed processing and easy customization, the library adapts to evolving data landscapes and business needs.