How to Manage Schema Changes in AI Pipelines Without Breaking Consumers

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AI document pipelines typically involve three distinct schemas — source, internal, and index — that evolve independently, and conflating them is the root cause of most pipeline failures. The source schema is controlled by upstream systems and can change without notice, while the internal schema should be stable and source-agnostic, and the index schema should change conservatively to avoid costly rebuilds. Using a dedicated adapter per source as a 'shock absorber' between source and internal schemas ensures the rest of the pipeline remains insulated from upstream changes. Schema compatibility modes borrowed from streaming systems — BACKWARD, FORWARD, FULL, and their TRANSITIVE variants — provide a structured framework for safely deploying schema changes. AI pipelines in particular require FULL_TRANSITIVE compatibility because reprocessing historical data is a routine operation, meaning every schema version must remain mutually readable across all prior versions.

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ProgrammingDEV Community ·

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