ETL (extract, transform, load) and ELT (extract, load, transform) are both data integration processes that transfer raw data from a source system to a target database. However, ETL  and ELT are two different approaches to manipulate data and get information into a data warehouse. Each of them has unique characteristics and is suitable for different data needs. 

The primary difference between ETL and ELT is that ETL transforms data before loading it on the server, while ELT transforms it afterward. ETL follows a traditional method ideal for complex transformations of smaller data sets, but ELT is a modern technology that provides more flexibility to analysts as it processes both structured and unstructured data.  

Before  choosing the right data integration method for your business, you need to consider a few factors such as data storage, analysis, and processing.      

What is ETL?

ETL stands for extract, transform, and load. In this process, the data flows from the source to the target. ETL tools, which are typically server-based data integration solutions, first extract the data from different RDBMS source systems. Then, the data is transformed such as applying calculations, etc., and finally the data is loaded into the Data Warehouse system.

When ETL tools first emerged four decades ago, the servers that databases ran on did not have the computing power of today. So, ETL solutions were developed to alleviate the data processing workload. They typically provided additional database and application connectivity and data manipulation functions that were previously limited in database engines.

What is ELT?

ELT is a different method where data is first written before transforming. It is the target system that does the transformation in this system.  The data is first copied to the target and then transformed in another place.

ELT supports massively parallel processing (MPP) and  is usually used with databases like Hadoop cluster, data appliances or cloud-based data platforms such as Snowflake, Amazon Redshift, and Microsoft Azure SQL Data Warehouse.   

ETL vs. ELT: Key Difference 





Data is transformed at the staging server and then transferred to data warehouse DB

Data remains in the DB of the data warehouse

Code Usage 

Used for small amount of data

Used for high volumes of data

Time Load

Time intensive 


Time Transformation

Transformation time increases as data size grows

In ELT process, speed is never dependent on the size of the data


High maintenance

Low maintenance

Use Case

ETL model used for on-premises, relational and structured data

Used in scalable cloud infrastructure which supports structured, unstructured data sources

Data Lake Support

Does not support data lake

Allows use of data lake with unstructured data


High costs

Low entry costs using online software 

Support For Unstructured Data

Mostly supports relational data

Support for unstructured data available


Most tools have unique hardware requirements that are expensive

Uses the hardware dedicated to the database server

Benefits of using ELT

With more companies making the transition to cloud-based data warehouses, ELT is gaining relative popularity. With ELT, data professionals work directly inside the warehouse for faster productivity, increased scalability, and fewer errors. The infrastructure and architecture are far simpler than on-premises data warehouses and can be scaled up and down as needed. The ELT process reduces waste, improves speed, and removes annoying bottlenecks. ELT is better suited to performing more sophisticated data transformations, as it relies on the MPP of the underlying database to do the work. 

Data Warehouse Automation and ELT

ELT can be further improved with data warehouse automation software such as WhereScape. WhereScape automates the full data warehouse lifecycle and can save months or years of development time compared to manual coding. All work done with WhereScape is automatically documented, which decreases human error and improves efficiency.