This is the Electee Review. Today, why copying entire databases overnight is no longer good enough and what change data capture actually does about it. The article lays out its core argument plainly. Businesses that still rely on nightly batch jobs to move data from one system to another are working with numbers that are already stale before anyone opens a dashboard. Change data capture, or CDC, is presented as the fix, a method that tracks and moves only the rows that actually changed the moment they change instead of hauling an entire table across the wire on a fixed schedule. The guide breaks CDC into two mechanisms. Trigger-based CDC attaches triggers directly to database tables, firing an event on every insert, update, or delete. It's straightforward to set up, but it adds overhead to the source database and can slow down the very system it's monitoring. Log-based CDC instead reads the database's own transaction log, the internal record every database already keeps, to detect changes without touching the live tables at all. That makes it lighter on production systems and the preferred approach for anyone running at scale. Where this gets practical, according to the piece, is for small and mid-sized businesses. CDC isn't framed as an enterprise-only concern. The argument is that SMEs can plug it into platforms like Electy to get near real-time analytics without building custom data pipelines from scratch. That's the pitch. Real-time visibility that used to require a dedicated data engineering team is now accessible with far less infrastructure. What the article doesn't dwell on is the complexity CDC introduces, schema changes, log retention limits, and the operational discipline needed to keep a pipeline healthy. A guide selling the benefits of a method is not the same as a warning label. Still, the underlying claim holds up. Batch data movement is a poor match for decisions that need to happen now, not tomorrow morning. That's the review.