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Answer: Utilize schema versioning with backward-compatible changes only, redirecting write operations to the latest schema version while maintaining read operations across all versions.
1. Utilizing schema versioning with backward-compatible changes ensures that changes do not break existing applications or queries relying on the previous schema structure, allowing for a seamless transition. 2. Redirecting write operations to the latest schema version ensures new data is written to the updated schema while read operations can still access previous versions, enabling immediate availability of new schema versions for writes without downtime. 3. Supporting read operations across all schema versions ensures existing queries and applications can access data without interruptions, achieving zero downtime during schema evolution. 4. This approach offers flexibility in managing schema changes by supporting multiple versions concurrently, minimizing risks during the evolution process. Thus, schema versioning with backward-compatible changes and redirecting writes to the latest version is ideal for zero downtime schema evolution in a data lakehouse.
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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?
A
Stage schema changes in a parallel environment, performing atomic swaps once new schemas are fully validated.
B
Utilize schema versioning with backward-compatible changes only, redirecting write operations to the latest schema version while maintaining read operations across all versions.
C
Implement an in-place schema update mechanism, locking the dataset for the minimal required time to apply changes.
D
Rely on Delta Lake‘s schema merging features, allowing for seamless schema evolution without impacting ongoing operations.
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