Databricks Certified Data Engineer - Professional
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A data engineering team manages a suite of aggregate tables used by business intelligence dashboards, production machine learning models, and customer-facing applications. New requirements for a specific application necessitate renaming several existing fields and adding new columns to a heavily shared aggregate table.
Which strategy addresses these requirements while ensuring zero disruption to existing downstream consumers and minimizing administrative overhead?
A
Establish a new Delta table with the updated schema (renamed and additional fields) to serve the application. Simultaneously, create a view under the original table name that aliases the new fields back to their original names, ensuring legacy queries continue to function without modification.
B
Apply the schema modifications directly to the existing table and distribute a global notification to all stakeholders providing instructions on how to use Spark SQL aliases or Delta Time Travel to map the new schema back to legacy requirements.
C
Implement the updated schema in a new table and utilize Delta Lake's DEEP CLONE functionality to keep the original and updated tables synchronized, ensuring both sets of requirements are physically persisted.
D
Replace the existing physical table with a logical view containing the original query logic. Concurrently, create a separate physical table with the new schema specifically for the customer-facing application.
E
Perform an in-place OVERWRITE of the table to match the new specifications and update the table's metadata comments to warn users of the breaking schema changes and field renames.