Understanding The Difference: ETL Vs. ELT

ELT is Extract, Load, and Transform and performs sophisticated data transformations. They look for a data warehouse to perform the basic transformations. ETL is Extract, Transform, and Load, and data is moved from source to the warehouse. It maintains compliance and data privacy by cleaning sensitive information. Also, they secure the data after being uploaded into the data warehouse. ETL vs. ELT is easy to understand, and here are the differences.

Adoption of technology 

When it comes to the adoption of technology, and you look for various tools, ETL is the best. It has a well-developed process, and the ETL experts are available and working for the past twenty years. However, ELT is new, so it is a challenge to find experts. Also, it is said that developing an ELT pipeline is tougher compared to ETL.

Add calculations

ELT can add calculated columns to the present dataset. In the other one, calculation either replaces the existing columns. With that, you can add the dataset by pushing the calculation outcome to the target data system.

Availability of data

When it comes to the availability of data, ETL loads, and transforms data that you think is required. Accordingly, the information will be available, but ELT can load the information instantly. With that, users can decide afterward which data to analyze and transform.

Compatibility with data lakes

About data lakes, ETL is not a solution. Data is transformed  to be integrated with a structured relational data warehouse system. ELT provides a path for ingesting unstructured data while transforming data as per the analysis.

Complexity in transformation and data size

ETL is perfect with smaller data sets requiring complex transformations. ELT is prominent while dealing with a huge amount of unstructured and structured data.


ETL can eliminate and edit sensitive data before making it available in the data warehouse. All these things make it easier to satisfy HIPAA, GDPR, CCPA compliance standards. It safeguards information from inadvertent and hacker exposure. ELT allows in uploading the data before editing and removing any sensitive detail. In that way, it can violate GDPR, CCPA, and HIPAA standards. With that, it will be easier for hackers to access the data. If the cloud-server is in a different country, you can violate some compliance standards.

Data warehousing support 

ETL is working with on site and cloud-based warehouses. For that, it needs a structural and relational data format. ELT provides a cloud-based data warehousing solution supporting unstructured, structured, and semi-structured data types.

The difference in the aggregations

In ETL, with the increase in the size of the dataset, aggregations become more complicated. However, with ELT, a massive amount of data can be quickly processed with a powerful and versatile cloud-based data system.

Hardware requirements

Cloud-based ETL platforms will not require any specific hardware. Online ETL procedures have costly hardware requirements. But in ELT, they are cloud-based, eliminating the need for any hardware.

Maintenance requirement 

If you use a physical server solution, you will know that it requires frequent maintenance. But with ELT, as it is cloud-based, so there will not be much maintenance required.

Complexity in implementing 

ETL pipelines are easy to create as many professionals are there. They can highly incorporate ETL tools, which help in facilitating the process. As it is a new technology, so the process is still developing. With that, there are not many experts with requisite ELT knowledge and skills.

Transformation process

The transformation happens inside the system, and no staging area is required in ELT. But in ETL, it is exactly the opposite. Here, transformation happens outside the data warehouse within a staging area.

Unstructured data support 

Unstructured and structured data can be used by ETL. But when it is about transferring the unstructured data, it is not possible. But ELT allows uploading unstructured data and making it available for intelligence systems.

In the end

There has been a rise of both the platforms, and it completely depends upon your team, which one will be a perfect fit. ETL maintains data compliance and privacy by eliminating sensitive data. ELT lets the transformation while reducing the requirement for data staging. Both offer exciting perks, so you need to carefully access and determine which one to use.


etl vs elt
etl vs elt

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