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Answer: Define a SQL view on the marketing table that selects only approved fields and uses aliases to rename them according to sales conventions.
Creating a **View** is the most efficient and simple solution because: 1. **Zero Data Duplication**: A view is a logical construct that does not copy physical data, avoiding extra storage costs and keeping the architecture clean. 2. **Automatic Synchronization**: Since the view reads directly from the source marketing table, any updates to the source data are immediately visible to the sales team without needing a synchronization job or pipeline refresh. 3. **Schema Customization**: SQL aliases (`AS`) allow the data engineer to rename fields on the fly to match the sales team's expectations, and the `SELECT` clause effectively hides unapproved columns. 4. **Low Maintenance**: Only a single `CREATE OR REPLACE VIEW` statement needs to be maintained, unlike `DEEP CLONE` (Option A), `CTAS` (Option D), or parallel writes (Option B), which all involve data duplication, increased compute costs, and complex synchronization logic.
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A data engineer needs to provide the sales organization with access to data stored in a marketing table. However, there are two issues: specific field names do not match the sales team's naming conventions, and the table contains several unapproved marketing-specific columns that should not be visible to the sales team. Which solution addresses these requirements while prioritizing architectural simplicity and minimizing maintenance overhead?
A
Create a new table using Delta Lake's DEEP CLONE functionality to replicate data into a sales-specific schema, then manually rename columns.
B
Introduce a parallel write operation in the current production pipeline to update a second table specifically formatted for the sales team.
C
Define a SQL view on the marketing table that selects only approved fields and uses aliases to rename them according to sales conventions.
D
Use a CREATE TABLE AS SELECT (CTAS) statement to generate a derivative table and configure a scheduled production job to propagate updates.
E
Export the filtered and renamed data as a CSV file and automate an email delivery system to send it to the sales organization.