Incremental Loading in Databricks from SQL Server

Published 2026-08-19 in Data Engineering

Are you still ingesting data from RDBMS systems using full loads? If your source database supports CDC (Change Data Capture) or CT (Change Tracking) , you can build a much more efficient incremental ingestion pipeline by processing only the records that have changed . Here’s the typical approach: 🔹 CDC captures inserts, updates, and deletes 🔹 CT identifies which rows have changed 🔹 Databricks reads only the changed records instead of reloading the entire table 🔹 Delta Lake MERGE INTO applies those changes efficiently to the target tables Why use incremental loading? Reduce load on source RDBMS systems Lower Databricks compute and processing costs Improve pipeline execution time Reduce unnecessary data movement Scale ingestion more efficiently Build more reliable incremental pipelines For SQL Server → Databricks pipelines, CDC or CT can be a much smarter alternative to full-table loads—especially when dealing with large and frequently changing tables . The principle is simple: Don’t move everything. Move only what changed.

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