ETL e ELT: entenda a ordem que altera o produto do pipeline de dados

ETL and ELT are data pipelines used for extracting, transforming, and loading data into repositories. Simply reversing one of these processes can completely change the final product.

Modern analytics operations process huge volumes and varieties of data, which happens more slowly with ETL compared to ELT .

Therefore, the ELT process is the new sensation in the data world . It is a scalable, modern and flexible approach that allows modern companies to position themselves competitively in their market.

Understand what really changes for the data pipeline with this change in the order of the acronym from ETL to ELT .

Extract, transform and load: what is ETL?

ETL is a data pipeline (or set of steps) that compiles data transformation processes into three different stages:

1. (E) extraction of data from different and diverse sources
2. (T) data transformation for use
3. (L) loading data into a data warehouse (DW) in the cloud or on-premises

Already quite traditional and known by those who work in the area, ETL requires the right tools for its proper functioning, and the entire process can be harmed.

During the extraction stage , data is collected from various sources, such as spreadsheets and CRMs, for example. After being extracted, the data is transformed into accessible formats for analysis. Finally, they are loaded into a DW to be stored and made available for quick consultation.

The main purpose of ETL is to acquire the right data, prepare it for use in reports, and save it for easy access and analysis later. This process helps business professionals and developers have more time to focus on other activities.

Extract, load and transform: what is ELT?

Unlike ETL, which is more traditional and popular, ELT appears as a modernization of this pipeline, making the process more agile just by reversing the data transformation steps .

In this data pipeline model , the stages are divided into the following order:

1. Extracting raw data from multiple sources
2. loading the extracted data to a DW
3. transformation of raw data into modeled data within DW

This simple inversion is responsible for reducing data loading time , for example, in addition to allowing business professionals to work with this information directly in a data warehouse , without depending on highly technical professionals, such as developers and data engineers.

This way, the work is better divided, with data engineering professionals being responsible for the extraction (E) and loading (L/load) stages . The other stages are the responsibility of professionals who better understand business rules, such as analysts , data scientists and analytics engineers .

In addition to this division of tasks, reversing processes from ETL to ELT also causes other impacts on the final product.

As? Check out the following topic. 😉

ETL and ELT: 8 order differences that change the data pipeline product

ETL and ELT are data pipelines used for extracting, transforming, and loading data into repositories. Simply reversing one of these processes can completely change the final product.

Let's see, then, eight characteristics that change with the inversion of these processes. Check out!

1- Charging time

In ETL, data loading only happens after the transformation, requiring different tools for each step and increasing execution time due to the need to repeat the loading process with each data transformation.

In ELT , data is loaded only once into a storage device, such as a data warehouse , where the data will be transformed for use.

2- Transformation time

In ETL , as data volumes grow, transformation time increases considerably . On the other hand, in ELT, the transformation stage is faster as it is carried out with the help of cloud infrastructure technologies . Here, speed is independent of the size or complexity of the data.

3- Maintenance time

Maintenance time rates are high within ETL , this is because updating the data repository demands recurring work from professionals who are expensive and scarce in the market, such as engineers and data engineers , in addition to developers.

In ELT, the scenario changes, since the data will always be ready (transformed) and available for use within DW .

4- Implementation complexity

The ETL pipeline requires less storage space during the initial steps.

ELT demands in-depth knowledge of advanced tools used in the modern analytics approach , as well as a well - structured data repository architecture.

5- Data limitation

Before loading, within the ETL, the data must be transformed, always taking care, as anything not selected will be lost .

Within ELT , raw data is available in the data warehouse , depending only on the data retention policy that is part of the operation.

6- Support for data warehouses

ETL is developed to support relational databases , on-premises and legacy systems.

ELT is built to support large volumes and diverse data sources, structured or unstructured, in a scalable way on cloud infrastructures .

7- Usability

Because it uses fixed tables and schedules, ETL is predominantly used by technical IT professionals , developers, engineers and data engineers .

ELT , in turn, has a scalable, flexible and collaborative approach, and can be used by both technical professionals and the end user of the business .

8- Cost/benefit

For small and medium-sized businesses, ETL may not be a cost-effective approach due to factors mentioned above, such as the high maintenance fee .

ELT , as it is scalable, adaptable and accessible for businesses of all sizes, is a much more viable, economical and modern solution .

Does your company need ETL or ELT?

Regardless of the size of your company, if it generates value from data , an adequate pipeline is essential to the success of your business.

Count on Indicium ’s specialized team to implement ETL or ELT processes in your company, according to your needs.

We are one of the largest B2B companies in all of Latin America , and we have cutting-edge data professionals at your disposal.
Get in touch now by clicking here .