DEA-C01 Question 284
Select 2Your team is working on a data pipeline that uses an Amazon S3 data lake to store customer data in Parquet format. Over time, the schema of the customer data is expected to evolve, such as adding new optional fields or modifying existing ones. Which techniques can you use to handle schema evolution in your pipeline while minimizing disruption?
- A
Use AWS Glue schema registry to manage schema versions and enforce compatibility rules.
- B
Enable versioning on the Amazon S3 bucket to store multiple versions of the schema.
- C
Use Apache Avro or Parquet formats, as they provide built-in support for schema evolution.
- D
Manually modify all downstream applications to accommodate schema changes.
- E
Leverage AWS Lake Formation to automatically reconcile schema changes in the data lake.
Show answer and explanation
Correct answers: A, C
Explanation
Handling schema evolution in a data pipeline requires robust solutions that minimize manual effort and ensure compatibility. AWS Glue schema registry provides a systematic way to manage schema versions, while formats like Avro and Parquet are inherently designed to support schema evolution. These techniques work together to ensure smooth schema transitions without disrupting downstream applications.
- A. Correct.
AWS Glue schema registry allows you to manage and enforce schema versions, ensuring backward or forward compatibility when schemas evolve. This is a best practice for handling schema evolution.
- B. Incorrect.
While enabling versioning on an S3 bucket helps retain multiple versions of files, it does not address schema evolution directly or provide mechanisms to handle schema changes.
- C. Correct.
Apache Avro and Parquet are designed to handle schema evolution, such as adding new fields or marking fields as optional, without breaking existing data. This makes them suitable for evolving schemas.
- D. Incorrect.
Manually modifying all downstream applications is error-prone and not scalable. Automated solutions like schema registries or schema-aware file formats should be preferred.
- E. Incorrect.
AWS Lake Formation does not automatically reconcile schema changes. It provides governance and access control for a data lake but does not address schema evolution directly.