Do you handle incremental data loading instead of full data loads?

Incremental data loading, also known as delta or change data capture (CDC), involves loading only the data that has changed since the last load rather than reloading the entire dataset. This approach reduces load time, network traffic, and storage consumption. Common techniques include using timestamps or version columns to identify new or updated rows, leveraging database triggers, or using log-based CDC tools that read transaction logs. In a typical ETL pipeline, the ingestion layer captures changes, the transformation layer processes only the delta records, and the target warehouse applies upserts or merges to keep data consistent. Incremental loading also improves data freshness and allows near real‑time analytics. It is especially valuable for large tables, high‑volume environments, or when the source system cannot afford downtime for full reloads.