incremental load to warehouse

An incremental load to a warehouse means loading only the new or changed data since the last successful load, instead of reloading everything.

Why use it

• Faster than full reloads
• Uses less compute and storage
• Reduces source-system impact
• Better for near-real-time or frequent updates

Common incremental patterns

1. Timestamp-based

   • Load rows where updated_at > last_load_time
   • Simple and common

2. High-water mark / ID-based

   • Load rows with an increasing key, like id > last_max_id

3. CDC (Change Data Capture)

   • Reads inserts, updates, deletes from source logs
   • More reliable for full change tracking

4. Hash compare

   • Compare hashes of source and target rows to detect changes

Basic incremental load flow

1. Read the last successful load checkpoint
2. Extract only new/changed records from the source
3. Transform the data if needed
4. Upsert into the warehouse:
   • insert new records
   • update changed records
   • optionally delete removed records
5. Update the checkpoint

Example

If your warehouse last loaded data up to 2026-10-01 10:00:00, then the next load might extract:

SELECT *
FROM orders
WHERE updated_at > '2026-10-01 10:00:00';

Important considerations

• Handle late-arriving data
• Avoid missing updates when timestamps are not reliable
• Make loads idempotent
• Track deletes if needed
• Use a watermark or audit table

If you want, I can also show:

• a SQL example
• an ETL/ELT design
• or incremental load in Airflow/dbt/Spark