Data Warehouse

ETL vs ELT: Difference you must Need to Know

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Extract, Transform, Load or ETL and Extract, Load, Transform, or ELT essentially involves the collection of data from a variety of resources in the data warehouse. Business firms, having a data warehouse primarily make use of ELT or ETL.

They are recognized to be common procedures for the movement of volume of data as well as integration of the data so that you will be capable of correlating information from a bunch of resources, storing it in one place securely and allow the members of the company from various departments to find the specific data.

What is ETL exactly?

ETL contributes to being a process in which the data is extracted from a wide variety of resources and transform the same. After this, different actions are performed which include the application of the calculation as well as changing of the data type.

After the transformation of the data, the loading of the same is accomplished in the specific target database, which is referred to as data warehouse solutions. As ETL is performed, it does not bulk the process of heavy lifting or the transformation.

This process is used in case the data volume is small or moderate. In addition to this, it is also performed if the target database varies from the source data base and various data types are used. ETL is also performed when the data is structured as well as they undergo compute-intensive transformation.

What is ELT exactly?

ELT contributes to being the process which involves the extraction of the data, loading of the same in the data warehouse and transformation of the same, once loading is done.

In the case, the target database completes the process of transformation of data. ETL is known to occur with the NoSQL databases such as installation of cloud or Hadoop clusters.

ETL is primarily used if there is a wide volume of data. It is also used if the target and source database are of similar type. ETL is also performed in the data is unstructured. Besides this, the target database engine should be well adapted to handle huge data volume.

Difference between ETL and ELT

Here is a list of few of the major differences between ETL and ELT:


 ETL is used on an extensive scale for almost twenty years. It is designed for working with unstructured and structured data, relational database, as well as data of larger volume.

There are a wide assortment of best practices and experts which provide guidance to use ETL. IN addition to this, you can select from a wide assortment of ETL tools, available in the market.

ELT is not suitable for well adaptability as it is not designed for working with relational databases which are prominent in the market for the last twenty years.


The ETL tools which were used in the past were well suited to different relational databases. However, they are less geared for the unstructured data. Besides this, with the aid of the ETL tools, it is possible to map the data out which will be moved to the targeted database.

TO introduce any chances in the plan, it should include the restructuring of mapping and the loading of the data.

The ELT tools are capable of handling the combination of unstructured and structured data. Besides this, ELT tools should be moving the data to the target and thus the resulting data set should be more flexible.

Hardware requirements

A wide assortment of traditional ETL tools is available in the market which needs certain hardware. They also possess their own engines for the performance of transactions in the data. However, modern ETL platforms are known to run in the cloud.

ELT tools reap the benefits of compute power of the already existing hardware for the performance of different transactions on the data.

Better for

ETL is suited for structured data. It is also ideal for smaller data volume as well as complicated computation. In addition to this, it facilitated on-premise relational database.

The ELT, on the other hand, is suitable for unstructured data. In addition to this, it is an excellent choice for data lake and cloud environment. Also, ELT is used on a wide scale for computations with fewer complications and data of large volume.

Support for the data

ETL renders support to relational data whereas ELT bestows support to the unstructured data, which is readily available.


ETL involves the overwriting of the existing column or you require appending the dataset as well as push it to the target platform. In ELT, the calculated columns can be added easily to the already existing table.


The complexity of ETL enhances with the presence of additional data in the dataset. In ELT, the target platform has the prerequisite power for processing a significant amount of data in no time.

Look ups

Speaking of the procedure of ETL, both the dimensions and facts should be available in the specific staging area. IN ELT process, the data should be available as Extract and load will take place in a singular action.


ETL involves a huge cut off from the pocket for the start-ups and medium scale businesses. ELT, on the other hand, involves lower entry costs with the aid of online software as the service platform

Data lake support

ETL bestows support to Data Lake. ELT, on the other hand, enables the use of Data Lake along with unstructured data.

Implementation complexity

It is easy to implement ETL at the early phase. ON the other hand, for the implementation of ELT procedure, the business firm needs to have an in-depth knowledge of expert skills and tools.

Time maintenance

For ETL, high maintenance is required as you require choosing data for loading and transformation. Speaking of ELT, it involves low maintenance as data is found to be available always.

It is essential to find out the difference between ETL and ELT prior to the conduction of both the processes. These methods can be well suited to a variety of situations.