Architectural Blueprint: Bi-Temporal Data Versioning for Unified Backend and AI Ecosystems

This source provides a technical blueprint for a dual-purpose data versioning architecture that replaces traditional destructive write models with a system designed for both operational agility and AI reproducibility,. It implements Command Query Responsibility Segregation (CQRS) at the data layer by using bi-temporal validity periods (valid_from and valid_to) within a 3NF relational database, enabling backend applications to perform low-latency "time-travel" queries. To support high-throughput AI training without impacting production performance, the architecture utilizes Change Data Capture (CDC) to asynchronously stream data into a columnar lakehouse, which facilitates deterministic feature extraction and reproducible model evaluation,,. Although this approach leads to a linearly scaling storage footprint and increased schema complexity, it ensures a highly responsive backend while providing the immutable historical datasets required for modern intelligent software.