ETL vs ELT: when to reverse the data transformation process?

Although ETL is still a widely used process for data transformation , ELT adoption is becoming more frequent.

The question that remains is: when to perform this reversal of steps in the data transformation process?

In this post, we will answer this and other questions on the subject. In the end, you will be able to discover which option makes the most sense for your company and your projects. Let's go?

What is ETL?

ETL (extract, transform, load) is the traditional means of data transformation, in which the steps are conducted in the following order: data extraction, transformation and loading . In short, it is the process of moving transformed data from multiple repositories into a unified data warehouse .

It is a complex and time-consuming method, usually assigned to data engineering teams . Therefore, business analysts and data scientists are limited to consuming this data at the end of the process, which leads to delays in delivery and data obsolescence.

Considering these and other limitations, it was necessary to reformulate the ETL process to meet technological demands and solve the challenges of today's big data .

What is ELT?

ELT (extract, load, transform) is the modernization of the ETL process . It is a more agile process created to overcome the challenges of increasingly complex data operations in a world dominated by large volumes of data.

But what does this mean?

In the ELT approach, unlike the ETL approach, data transformation occurs right after the information is collected and loaded into a centralized data repository, not before.

Why did this happen?

With the emergence of tools such as Amazon Redshift and Google Big Query, which are data warehouses in the cloud, there are practically no more scalability limitations in terms of data.

From this, it became possible to extract and load raw data into data lakes or data warehouses to then be transformed, through SQL (the universal language of data), into structured data.

For this reason, and for greater project efficiency and agility , the ETL process was inverted in its last stages, guaranteeing several benefits .

The main differences between extracting , loading and then transforming are:

• be less complex than ETL.
• work with more innovative tools .
• need fewer IT professionals .
• be more accessible to business professionals.
• focus on a transformation language : SQL .
• be done in a modular way .
• facilitate greater governance .
• facilitate versioning.
• facilitate the separation of environments .

Now, with these modernizations, professionals from other areas can work on the data transformation process more easily .

In other words, teams from the business areas, who already know first-hand what they need to carry out their work, have the autonomy to streamline their function. Thus, they can increase the company's revenue , reduce various costs, among other optimizations .

And the benefits do not stop there!

These are just some of the advantages of adopting modern data approaches like ELT.

ETL vs ELT: when is it necessary to invert?

The answer to this question largely depends on you and the business environment in which you are located.

ETL may be a good option for you, but it may limit the scale of your data infrastructure due to its complexity.

To deal with large volumes of data, it requires specialized data engineering teams, knowledge of several programming languages ​​and, generally, does not follow a market standard.

In other words, even though it performs large-scale data processing, it is more expensive and complex.

Therefore, with today's growing big data , companies need the ability to deal with it faster and spend less. This is when the need to reverse the process for ELT comes in .

With this, companies can analyze large sets of data without having to carry out constant and costly maintenance. Using ELT, infrastructure challenges remain within the data warehouse, taking the focus away from the processing system and allowing teams to focus on the data.

For these and other reasons, ELT is consolidating itself as the future of data storage.

Is ELT better than ETL? Why?

Whether through the ETL or ELT process, this data processing step is an extremely important operation for modern companies.

However, although there are similarities between the two methodologies, for most data driven businesses , which have a large analytical workload , ELT has become the most suitable option .

Compared to the traditional ETL process, it features:

• saving time in the process as a whole.
• reducing various data ingestion costs
• reduction of technical debt .

Furthermore, with ELT, decision-making occurs faster and new analytical capabilities can be adopted at any time, increasingly increasing the efficiency of data processing .

Do you want to apply ELT in your company?

Now that you know everything about the ELT process, it's time to put it into practice.

And if you need help implementing ELT in your business, our team specializes in this methodology and knows the best tools on the market .

Count on us to help you.

Get in touch to talk about your project today by clicking here .