DEA-C01 Question 255
Single answerYour team is designing a data pipeline to process product sales data in Amazon Redshift. The schema for the sales data is expected to evolve over time as new fields are added to accommodate business requirements. How can you design the schema to handle these changes while minimizing operational overhead?
- A
Use the
CREATE TABLEcommand with a fixed schema and manually alter the table whenever new fields are added. - B
Implement a late-binding view in Redshift to abstract schema changes from downstream users.
- C
Use a schema-on-read approach by storing the data in Amazon S3 and querying it with Amazon Athena.
- D
Enable automatic schema evolution in Redshift by using JSON or SUPER data types for flexible data storage.
Show answer and explanation
Correct answer: D
Explanation
When designing for schema evolution in Amazon Redshift, using the JSON or SUPER data types provides flexibility to store and query semi-structured or evolving data without frequent schema changes. These data types allow new fields to be added dynamically, reducing operational complexity and maximizing adaptability to business requirements.
- A. Incorrect.
This option would require manual intervention every time the schema changes, increasing operational overhead. It is not an optimal solution for handling frequent schema evolution.
- B. Incorrect.
While late-binding views can abstract schema changes from downstream users, they do not inherently handle schema evolution. You would still need to manage changes manually at the table level.
- C. Incorrect.
A schema-on-read approach with Amazon Athena is a flexible option, but it does not leverage Amazon Redshift's capabilities for schema evolution within a relational database.
- D. Correct.
Using JSON or SUPER data types in Amazon Redshift allows for semi-structured data storage, enabling automatic schema evolution. This minimizes operational overhead and accommodates changing requirements.