Real-time database streaming: when 15-minute batches beat CDC pipelines

Change Data Capture has become the default answer for database activity monitoring. Debezium connectors stream PostgreSQL or MySQL transaction logs to Kafka, feeding analytics stores like Redshift or Snowflake. The architecture sounds clean: CDC reads write-ahead logs, Kafka partitions handle throughput, Flink or Spark processes windows and deduplication.

The reality is messier. Enterprise teams report that full streaming pipelines require schema registries to avoid mismatches, constant monitoring via Grafana for lag tracking, and careful tuning of Debezium offset management for large tables. AWS RDS Postgres CDC adds another layer of configuration complexity.

Here's the trade-off that vendors don't lead with: incremental batching every 15-30 minutes handles most reporting, analytics, and compliance requirements. The operational overhead drops significantly. No real-time consumer coordination. No schema registry synchronization. Simpler rollback procedures when things break.

The streaming approach makes sense for specific use cases. Fraud detection needs sub-second latency. Real-time recommendation engines benefit from instant user behavior updates. But most enterprise analytics workloads don't require it.

What changed: The global real-time analytics market is projected to hit $25 billion by 2026, driven by vendor messaging more than actual operational needs. AWS Kinesis can handle millions of records per second. Flink processes NYC taxi data in seconds. The technology works. The question is whether your organization needs it to work.

Before choosing between Debezium Postgres-to-Kafka pipelines and scheduled batch jobs, map your actual latency requirements. If 15 minutes suffices, the simpler architecture probably wins. If you need snapshot mode configurations, incremental snapshots for large tables, and heartbeat tuning, make sure the use case justifies it.

The pattern holds across PostgreSQL versus MySQL CDC performance comparisons. The technology differences matter less than understanding your actual requirements. History suggests that overengineering for theoretical real-time needs creates more problems than it solves.