When seeding data for development, what is a key requirement?

When seeding data for development, what is a key requirement?

• A. Ensure idempotence of seed scripts and provide resets and sample data.
• B. Seed only once and never reset.
• C. Seed data should be avoided in development.
• D. Use production data in development.

Answer: (A)

When seeding data for development, you want the process to be repeatable and safe, with a reliable baseline and realistic sample records. Idempotence means running the seed process multiple times won’t create duplicates or leave the database in an inconsistent state; seed scripts should either upsert existing records or check for presence before inserting. A reset mechanism is important so you can drop data or reinitialize the environment to a known starting point, which is crucial for reproducible tests and clean onboarding. Having sample data provides meaningful records to work with, helps verify relationships and behavior in the app, and makes development and testing more efficient. The other approaches fall short because seeding only once and never resetting leads to drift and flaky tests; avoiding seed data removes a useful baseline for development and testing; and using production data in development raises privacy, security, and compliance concerns and can contaminate test results.

When seeding data for development, you want the process to be repeatable and safe, with a reliable baseline and realistic sample records. Idempotence means running the seed process multiple times won’t create duplicates or leave the database in an inconsistent state; seed scripts should either upsert existing records or check for presence before inserting. A reset mechanism is important so you can drop data or reinitialize the environment to a known starting point, which is crucial for reproducible tests and clean onboarding. Having sample data provides meaningful records to work with, helps verify relationships and behavior in the app, and makes development and testing more efficient.

The other approaches fall short because seeding only once and never resetting leads to drift and flaky tests; avoiding seed data removes a useful baseline for development and testing; and using production data in development raises privacy, security, and compliance concerns and can contaminate test results.