Handling schema changes in source systems during extraction requires a combination of detection, adaptation, and versioning strategies. The primary goal is to ensure data pipelines remain functional and accurate when source schemas evolve. Common approaches include schema validation during extraction, maintaining backward compatibility, and using versioned schemas. For example, if a source system adds a new column or renames a field, the extraction process must either accommodate the change gracefully or flag it for review to avoid broken pipelines or data loss. This often involves automated checks, logging discrepancies, and defining rules for handling unexpected schema variations.
One practical method is to implement schema validation at the start of the extraction process. Tools like Apache Avro or JSON Schema can validate incoming data against a predefined schema, ensuring compatibility. If a mismatch is detected—such as a missing column or altered data type—the pipeline can either apply predefined transformations (e.g., default values for new fields) or pause and alert developers. For instance, if a source system renames a column from user_id to customer_id, the extraction layer could use a lookup table to map the new name to the old one, maintaining consistency downstream. Versioned schemas stored in a registry (e.g., Confluent Schema Registry) allow pipelines to reference specific schema versions during extraction, reducing ambiguity.
Proactive measures also play a key role. Contract testing between source and destination systems can enforce compatibility guarantees, such as avoiding breaking changes like column removals. Change data capture (CDC) tools like Debezium can track schema changes in databases and propagate them through the pipeline. Additionally, fostering communication between teams ensures developers are notified of upcoming schema changes in advance. For example, if a source team plans to deprecate a field, the extraction process can be updated incrementally rather than reacting to a sudden break. Automated rollback mechanisms and staging environments for testing schema changes further mitigate risks, ensuring stability in production pipelines.