Change Data Capture is the foundation that makes everything else work
Change Data Capture sounds unsexy. It monitors database transaction logs and streams row-level changes to downstream systems. No machine learning. No neural networks. Just watching for changes and propagating them.
It's also the foundation that makes everything else work.
Convoy, the freight company, used CDC with Debezium and Kafka to stream data from production databases to their data warehouse. Before: 1-2 hours latency between a change and analytics availability. After: seconds to milliseconds. Same underlying databases. Same warehouse. Different infrastructure connecting them.
Architecture comparison scoring where higher is better (0-100)
Source: Convoy Engineering Blog, Forrester 2025 Data Streaming ROI Analysis
Maximum latency values (ms) by operation from NOEIN range model
Source: StreamNative — Latency Numbers Every Data Streaming Engineer Should Know
The ROI is real. Confluent's 2025 report: 44% of organizations report 5x ROI on data streaming investments. Forrester's Total Economic Impact study documents $2.5 million in savings over 3 years with 257% ROI from Confluent Cloud.
“Global data pipeline tools market: $12B in 2024, projected $48B by 2030. 26.8% CAGR.”
— Market Analysis, 2024
Projected market size (USD Billions)
Source: Market Analysis, 2024
For manufacturing, CDC enables the real-time ERP-to-MES connection that everyone wants but few have. Changes in the ERP (new orders, inventory adjustments) stream to production systems immediately. Changes on the shop floor (completions, quality holds) stream back. No batch jobs. No overnight syncs. No CSV exports.
The technology stack is mature: Debezium for capture, Kafka for streaming, time-series databases for storage. Open source. Battle-tested. The hard part isn't the technology. It's knowing which data to capture and how to model the relationships between systems.