Moving Data from MySQL to BigQuery: Manual Export, DTS, and CDC

The article explains why MySQL alone can become a bottleneck when dashboards and reports start lagging, and introduces BigQuery as a solution for heavy‑lifting. It walks through three practical approaches to move data from MySQL to BigQuery: a manual mysqldump export, Google’s managed BigQuery Data Transfer Service (DTS), and a real‑time change‑data‑capture (CDC) pipeline. It compares the trade‑offs of each method, noting that manual export is fine for one‑off jobs but becomes fragile when schemas change, DTS offers quick, scheduled batch imports with built‑in monitoring but lacks near‑real‑time freshness, and CDC reads MySQL’s binary log to stream only new rows, updates, and deletes, reducing load and latency. The article also highlights common pitfalls such as connection issues, data mismatches, slow MySQL scans, and data gaps, and stresses the importance of early validation, continuous monitoring, and cost control through partitioning and clustering. It concludes that choosing the right approach depends on data size, update frequency, and the need for freshness, and that the surrounding infrastructure—schema handling, monitoring, and maintenance effort—ultimately determines success.