ETL vs ELT

What is ETL?

Extract — Transform — Load

In this method, data is extracted from source systems, converted into the desired format in an intermediate staging area and then usually into a Data warehouse (DWH) loaded. In the past, ETL was the predominant method, particularly when DWH did not have the processing power to perform complex transformations.

What is ELT?

Extract — load — transform

In this process, the data is extracted at the beginning and then loaded directly into the target system (usually DWH). The transformation only takes place afterwards.

This approach uses the robust computing power of modern data platforms (e.g. cloud-based platforms) to handle extensive transformations.

ETL Process

extracting

(Raw) data is collected from various source systems. These can be structured, semi-structured, or unstructured. Examples include databases, files, software-as-a-service (SaaS) applications, Internet of Things (IoT) sensors, or application events. This step does not yet differentiate between ELT and ETL.

laden

From this step, the main difference between the two data management processes begins:

At ETL approach Once the data is sent to a server for processing (e.g. Data warehouse) delivered.

At ELT approach On the other hand, the transfer takes place directly to the destination (e.g. Data Lake). With ELT, there is less time between extraction and deployment, but the effort on the local server structure is significantly higher.

transformation

The transformation involves structuring and standardizing the raw data in a database or DWH. Although the cost of data storage is significantly higher in this case, it offers more options for further processing and evaluation, such as business intelligence, data analysis or reporting.

The difference between ELT and ETL

Both methods play a central role in data processing. However, due to their different approaches, they present different benefits and challenges, depending on business requirements. In the table below, we'll look at the key differences to help you find the best path for your data strategy.

Transition from ETL to ELT

For a long time, ETL was considered the standard for data integration, as the target repository was traditionally considered a data warehouse. However, high hardware costs, growing IT requirements and long waiting times for ad hoc analyses are increasingly putting the ETL process in the background.

The move from ETL to ELT is a growing trend as companies are better off with increasing business requirements and existing cloud integrations with ELT.

ETL or ELT? Which is better?

Both ETL and ELT are a method that brings data together in order to obtain information from it. Which of the two processes is right for your company depends entirely on your requirements. Factors such as the existing network architecture, the use of cloud technologies, the amount of data and data source systems, and the available budget influence the decision.

Do you have specific questions or do you need support with integration into your company? We look forward to hearing from you.