What is ETL (Extract, Transform, Load)?
ETL is a data pipeline pattern that extracts data from sources, transforms it into a target structure, and loads it into a destination such as a data warehouse.
Definition
ETL stands for Extract, Transform, Load, a three-stage process for moving data between systems. In the extract stage, data is pulled from one or more source systems; in the transform stage, it is cleansed, reformatted, deduplicated, and reshaped to match the target schema; in the load stage, it is written into the destination. ETL is widely used to populate data warehouses and to consolidate data from multiple operational systems for analysis. A related variant, ELT, loads raw data first and transforms it inside the destination, which is common with modern cloud data platforms.
How ETL Works in ERP
ETL processes regularly pull data out of an ERP, reshape it, and load it into a data warehouse or business intelligence platform for reporting across the business. Transformations might combine ERP data with figures from a CRM or e-commerce system to give a unified view. ETL is also used during data migration to convert legacy data into the structure a new ERP expects. Scheduled ETL jobs keep analytical systems refreshed without burdening the live ERP with heavy reporting queries.
ERP Vendors with Strong ETL
Oracle ERP Cloud
Enterprise cloud ERP with deep financials and analytics
SAP S/4HANA Public Cloud
Standardised cloud ERP with quarterly auto-upgrades and low TCO
Oracle NetSuite
The original cloud ERP — built for fast-growing companies
Workday
Cloud HCM + financials for services and people-centric orgs
Frequently Asked Questions
What is the difference between ETL and ELT?
In ETL data is transformed before it is loaded into the destination, while in ELT raw data is loaded first and transformed inside a powerful destination such as a cloud data warehouse, which suits large modern data volumes.
Is ETL only used for data warehouses?
No; while warehousing is a primary use, the same extract-transform-load pattern is also used for system migrations, data consolidation, and feeding analytics or machine-learning pipelines.