How can schema evolution be managed in a data lakehouse architecture to ensure zero downtime and immediate availability of new schema versions for both read and write operations?

Real Exam

Stage schema changes in a parallel environment, performing atomic swaps once new schemas are fully validated.

5.6%

Utilize schema versioning with backward-compatible changes only, redirecting write operations to the latest schema version while maintaining read operations across all versions.

60.7%

Implement an in-place schema update mechanism, locking the dataset for the minimal required time to apply changes.

5.6%

Rely on Delta Lake‘s schema merging features, allowing for seamless schema evolution without impacting ongoing operations.

28.1%

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How can schema evolution be managed in a data lakehouse architecture to ensure zero downtime and immediate availability of new schema versions for both read and write operations?