What are Full Refresh and Incremental Loads?

The Core Problem: Your transactional database has 50 million customer records. Every night, you need to update your analytics warehouse. Do you copy all 50 million rows, or just the 200,000 that changed today? Full refresh takes the simple approach: truncate the target table, then reload everything from scratch. If your source has 50 million rows and you run nightly, every single run reads and writes all 50 million rows, regardless of how many actually changed. You start fresh each time. Incremental loads are smarter about change detection. You identify what changed using mechanisms like updated_at timestamps, monotonically increasing IDs, or Change Data Capture (CDC) that reads transaction logs. Then you apply only those deltas: inserts for new rows, updates for modified rows, and sometimes deletes. Why This Matters: The choice affects cost, latency, and complexity. That 200,000 row delta is only 0.4 percent of total volume. Process 0.4 percent instead of 100 percent and your pipeline finishes in minutes instead of hours, costs dramatically less in compute and storage, and delivers fresher data to dashboards. But incremental loads require tracking state, handling late data, and ensuring you never miss or double count changes. Full refresh is conceptually simpler: no state, no watermarks, just reload everything. The target always matches the source at extraction time.