Different Models for Incremental ETL

What is Incremental ETL?

Incremental ETL is a regular process that updates a data warehouse by adding, modifying, or marking records to keep data current.

How does Incremental ETL differ from Initial ETL?

Initial ETL is a one-time process to populate a data warehouse, while Incremental ETL continuously updates the warehouse with new, modified, and historical data.

What types of data does Incremental ETL handle?

It processes new data (e.g., new employees or customers), modified data (e.g., price changes or promotions), and deleted data (e.g., inactive customers or students).

Why is Incremental ETL necessary?

Since data warehouses are non-volatile, updates must be scheduled to ensure the most current data is available for business intelligence and analytics.

What are the four major Incremental ETL patterns?

1. Append Pattern – Adds new data without modifying existing records.

2. In-Place Update – Updates specific rows without changing the total number of records.

3. Complete Replacement – Overwrites entire data sets, replacing old records.

4. Rolling Append – Maintains a fixed historical window by adding new data and removing the oldest records.

Which Incremental ETL patterns are most commonly used today?

Append and In-Place Update are the most commonly used, while Complete Replacement and Rolling Append are less frequent, often found in older data warehouses.

What is the purpose of the Rolling Append pattern?

It ensures that only a fixed time window of historical data (e.g., 36 months of sales) is maintained by replacing the oldest data with new data.