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As Daangn’s data volume grew, their traditional full-dump approach using Spark for MongoDB began causing significant CPU spikes and failing to meet the two-hour data delivery Service Level Objectives (SLOs). To resolve this, the team implemented a Change Data Capture (CDC) pipeline using Flink CDC to synchronize data efficiently without the need for resource-intensive full table scans. This transition successfully stabilized database performance and ensured timely data availability in BigQuery by focusing on incremental change logs rather than repeated bulk extracts.

Limitations of Traditional Dump Methods

• The previous Spark Connector method required full table scans, creating a direct conflict between service stability and data freshness.
• Attempts to lower DB load resulted in missing the 2-hour SLO, while meeting the SLO pushed CPU usage to dangerous levels.
• Standard incremental loading was ruled out because it relied on updated_at fields, which were not consistently updated across all business logic or schemas.
• The team targeted the top five largest and most frequently updated collections for the initial CDC transition to maximize performance gains.

Advantages of Flink CDC

• Flink CDC provides native support for MongoDB Change Streams, allowing the system to use resume tokens and Flink checkpoints for seamless recovery after failures.
• It guarantees "Exactly-Once" processing by periodically saving the pipeline state to distributed storage, ensuring data integrity during restarts.
• Unlike tools like Debezium that require separate systems for data processing, Flink handles the entire "Extract-Transform-Load" (ETL) lifecycle within a single job.
• The architecture is horizontally scalable; increasing the number of TaskManagers allows the pipeline to handle surges in event volume with linear performance improvements.

Pipeline Architecture and Implementation

• The system utilizes the MongoDB Oplog to capture real-time write operations (inserts, updates, and deletes) which are then processed by Flink.
• The backend pipeline operates on an hourly batch cycle to extract the latest change events, deduplicate them, and merge them into raw JSON tables in BigQuery.
• A "Schema Evolution" step automatically detects and adds missing fields to BigQuery tables, bridging the gap between NoSQL flexibility and SQL structure.
• While Flink captures data in real-time, the team opted for hourly materialization to maintain idempotency, simplify error recovery, and meet existing business requirements without unnecessary architectural complexity.

For organizations managing large-scale MongoDB instances, moving from bulk extracts to a CDC-based model is a critical step in balancing database health with analytical needs. Implementing a unified framework like Flink CDC not only reduces the load on operational databases but also simplifies the management of complex data transformations and schema changes.