Top CDC (Change Data Capture) tools are typically compared based on how efficiently and accurately they capture, process, and synchronize data changes across distributed systems in real time. One of the primary evaluation factors is data synchronization capability, including support for database replication, event streaming, schema evolution, and consistency across multiple data sources and targets. Latency is another critical metric, measuring how quickly the tool detects and transfers changes with minimal delay, which is especially important for real-time analytics, cloud migration, and streaming architectures. Integrations are also heavily assessed, focusing on compatibility with databases, data warehouses, Kafka, ETL pipelines, cloud platforms, and analytics tools. Pros and cons are usually evaluated around scalability, fault tolerance, ease of setup, monitoring features, operational complexity, and pricing. In real-world performance scenarios, the most effective CDC tools are those that provide reliable low-latency data movement, maintain high throughput under heavy workloads, minimize data loss risks, and support continuous real-time synchronization for modern data-driven applications and enterprise systems.