Synchronizing data between relational and NoSQL databases involves balancing consistency, performance, and data model differences. The most common approaches include using change data capture (CDC), application-level dual writes, or event-driven architectures. Each method has trade-offs depending on latency tolerance, data volume, and system complexity. The goal is to ensure both databases reflect accurate, up-to-date information while minimizing disruptions to applications.
One effective method is leveraging CDC tools to track changes in the relational database and propagate them to the NoSQL system. For example, tools like Debezium can monitor a MySQL binlog or PostgreSQL write-ahead log (WAL) to capture inserts, updates, and deletes. These changes are then transformed into a format suitable for the NoSQL database—like JSON documents for MongoDB—and applied asynchronously. This approach minimizes application code changes but requires careful handling of schema differences. For instance, a relational table with foreign keys might need denormalization to fit a document model, which can complicate transformation logic.
Another strategy involves application-level synchronization, where the service writes to both databases simultaneously (dual writes). For example, an e-commerce app might write order data to PostgreSQL and also persist a denormalized version in Cassandra for faster reads. However, this introduces risks: if one write fails, the databases become inconsistent. To mitigate this, use transactional outbox patterns—store events in an outbox table within the relational database transaction, then relay them to the NoSQL store via a message broker like Kafka. This ensures at-least-once delivery and decouples the systems. Developers must also handle edge cases, such as conflicting updates or data type mismatches (e.g., SQL datetime vs. Unix timestamps in NoSQL). Testing with realistic data models and monitoring synchronization latency are critical for reliability.