Multi-Modal Research Data Correlation Pipeline Architecture

Design a comprehensive data correlation framework for heterogeneous scientific research datasets across multiple experimental domains. Create a modular Python architecture that can handle time-series, spectroscopic, genomic, and environmental sensor data with automatic type inference, missing value strategies, and cross-modal statistical relationship detection. Include performance optimization for datasets larger than 10GB, with explicit error handling for incompatible data structures and a robust logging mechanism for tracking data transformation processes.