Enterprise Data Engineering: CDC and Kafka for SQL-to-Mongo Sync

In many enterprise environments, the "Source of Truth" often resides in a legacy SQL database, while the "Read Layer" demands the speed and flexibility of MongoDB. Keeping these two systems in sync through manual application code can be error-prone and inefficient. A robust solution to this challenge is Change Data Capture (CDC), which treats database logs as a continuous stream of events.

Leveraging Debezium and the Kafka Ecosystem

Instead of writing custom synchronization scripts, consider utilizing Debezium. This powerful tool "tails" the SQL transaction logs—such as the binlog for MySQL and the Write-Ahead Log (WAL) for Postgres—and streams every insert, update, or delete operation into an Apache Kafka topic. This method is non-invasive, as it imposes zero load on your production SQL database by reading from the logs rather than querying the tables directly.

Transforming Data on the Fly

Data stored in SQL databases is typically normalized, leading to a flat structure. In contrast, MongoDB thrives on denormalized, nested documents. To address this difference, you can use Kafka Streams or a simple Node.js consumer to "shape" the data as it flows through the pipeline. For instance, when an "Order" is updated in SQL, the consumer can fetch the corresponding "Customer" details and save a single, rich document into MongoDB. This approach ensures that your read-heavy applications have all the necessary data conveniently located in one place.

Guaranteeing Data Consistency

In a distributed synchronization pipeline, achieving "Exactly-Once" processing is crucial. By implementing Idempotent Consumers, you can guarantee that even if a network glitch causes a message to be sent multiple times, the state in MongoDB remains accurate. This level of data integrity is essential for financial or compliance platforms, where a single missing update can result in incorrect reporting and significant repercussions.