How do you prevent data duplication in movement workflows?

To prevent data duplication in movement workflows, you need to implement strategies that ensure each piece of data is processed exactly once, even in scenarios like retries, system failures, or parallel processing. The core approach involves combining unique identifiers, idempotent operations, and transactional checks. By designing workflows to recognize and handle duplicate data at each step, you minimize redundancy and maintain consistency.

First, use unique identifiers to track data across systems. Assign a UUID, hash, or composite key to every record or message before moving it. For example, when transferring records from a database to a message queue, generate a unique ID for each record. Systems consuming the data can check this identifier against a log or database to confirm whether the data has already been processed. This is especially useful in event-driven architectures where the same event might be retried due to network issues. Tools like Kafka use offsets to track message consumption, while databases can leverage timestamps or version numbers to identify new or updated records.

Second, design operations to be idempotent. Idempotency ensures that repeating the same operation multiple times doesn’t change the result. For instance, an API endpoint that updates a user’s profile should use a PUT request with a unique identifier instead of a POST to avoid creating duplicate entries. In batch processing, a workflow might check if a record exists before inserting it. Message queue consumers can also deduplicate by storing processed message IDs in a cache (e.g., Redis) and skipping duplicates. This approach handles retries gracefully without requiring complex state management.

Finally, enforce transactional integrity between data movement steps. Use transactions to atomically mark data as “processed” in the source system while moving it to the destination. For example, when exporting records from a database, update a last_processed timestamp within the same transaction as the data read. If the movement fails, the transaction rolls back, ensuring the data isn’t marked as processed. In distributed systems, combine this with idempotent writes and acknowledgment mechanisms (e.g., message queue ACKs) to confirm successful processing before removing data from the source. This prevents duplicates caused by partial failures or system restarts.