How do you design clustering systems that handle schema evolution in real-world data pipelines?

Schema evolution is handled using flexible feature schemas, backward-compatible transformations, and versioned pipelines.

In real systems, input data schemas change frequently. Clustering pipelines must tolerate missing, new, or modified features. This is handled by schema registries, feature defaults, and transformation layers that normalize data into a stable feature space. Without this, clustering results become inconsistent or invalid.