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An enterprise is modernizing its enterprise data warehouse by migrating workloads to Google Cloud BigQuery. During the migration lifecycle, the data engineering team needs to evolve several core dataset schemas, implement table optimizations such as partitioning and clustering, and restructure underlying tables to support emerging analytics use cases.
However, numerous downstream business intelligence (BI) dashboards, reporting applications, and automated query jobs are already actively reading from the existing table structures. The business requires that underlying schema refactoring and performance optimizations occur without causing operational disruption or requiring immediate query rewrites across downstream consumers.
Which architectural pattern should the data engineering team implement to satisfy these requirements?
Facade views are logical SQL views created on top of base storage tables in BigQuery that implement the classic facade design pattern. They present a stable, decoupled logical schema to downstream analytical tools while abstracting the underlying physical tables and storage complexity.
Implementing facade views establishes an abstraction boundary between physical storage and downstream consumption interfaces. This directly solves the challenge of evolving warehouse schemas in place while ensuring business continuity for analytics consumers.
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