MySQL CDC to BigQuery: what periodic syncs miss, and how binlog avoids it
This article explains the technical limitations of SELECT-based data synchronization between MySQL and BigQuery, advocating for binlog-based Change Data Capture (CDC). It details why CDC is more reliable for capturing deletes and intermediate row states compared to periodic batch jobs.
Why it matters
Data integrity is critical for modern data engineering pipelines; understanding CDC helps developers avoid data loss in analytics warehouses.
MySQL CDC syncs miss deletes and intermediate updates. Learn how binlog-based Change Data Capture works, what MySQL settings it requires, and how to land it reliably in BigQuery.
Most MySQL-to-warehouse pipelines run on the same pattern: a scheduled job selects rows, compares them to what was there before, and writes the difference. It works, until it doesn't.
A SELECT -based sync only sees what exists right now. It has no way to know a row existed and was deleted between two runs, no way to see intermediate states of a row that changed more than once, and it puts real load on your production database every time it scans a large table just to find a handful of changed rows.
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