Synchronizing data between relational and NoSQL databases involves establishing a reliable method for data transfer and consistency across these distinct systems. This process often includes identifying which data needs synchronization, determining the direction of data flow (one-way or two-way), and choosing the right tools or techniques for the task. Common scenarios include using middleware or ETL (Extract, Transform, Load) processes to move data from a relational database, like MySQL or PostgreSQL, to a NoSQL database such as MongoDB or Cassandra, and vice versa.
One effective way to synchronize data is through the use of change data capture (CDC). CDC allows you to monitor changes in the source relational database and then replicate those changes to the NoSQL database in real-time or at scheduled intervals. For instance, using tools like Debezium or Apache Kafka, you can capture insertions, updates, and deletions from your SQL database, then format that data appropriately to store it in your NoSQL database. This maintains data integrity, ensuring that the NoSQL database reflects the most up-to-date information from the relational source.
Another method involves scheduled batch processes. In this approach, data is periodically exported from the relational database and imported into the NoSQL database. This can be done using scripts or dedicated ETL tools like Apache NiFi or Talend, which can handle data transformation as needed. Although this method does not provide real-time synchronization, it may be sufficient for applications where slight delays in data accuracy are acceptable. Developers should choose the method that aligns best with their application requirements, data criticality, and system architecture.