What is ELT?

ELT is a modern process of extracting, loading, and transforming data, more agile and cost-effective than traditional ETL.

Innovative and flexible, ELT reduces the time between data extraction and transformation and accelerates your company's positioning more competitively in the market.

In this article, you will understand what ELT is and how this revolution in the world of data science came about.

Happy reading!

Understand what ELT is

ELT is a modern process of extracting, loading, and transforming data, more agile and cost-effective than traditional ETL.

In this data pipeline model, there are then three steps that go like this:

1) extraction of data from various sources;

2) loading the collected data into a data warehouse;

3) transformation of raw data into modeled data.

ELT greatly reduces data load times by allowing data transformation to be done by analytics engineers or data analysts, without the reliance on programming-focused professionals such as developers and data engineers.

Facilitating Data Transformation with dbt

In the ELT, data transformation is done seamlessly, as the speed is independent of the size or complexity of the data, thanks to cloud infrastructure technologies.

To transform data, it's good to take advantage of the modern technologies we have at our disposal, such as dbt, you know?

Indicium is an expert in dbt! And we have professionals with expertise in this tool because of the official partnership with dbt Labs.

One of the advantages of this tool is that it allows analytics professionals to operate information using SQL, a relatively simple query language.

In addition, dbt favors the organization of the data warehouse construction environment because its use requires a low learning curve even for those who are not data science professionals.

As a result, the data team's work process becomes more agile, faster, and more efficient.

How did ELT come about?

Let's take a step back. Conventionally used in the data transformation process, the most widely used method is still ETL.

Notice the inversion of the letters in the acronym (ELT X ETL)? We've talked a lot about ETL on our blog...

And... With the big data revolution and the improvement of cloud infrastructure technologies, ELT has emerged as an innovative option and has been gaining appreciation by modern data science teams.

ETL, on the other hand, is still successful, of course, but it is more inflexible, requiring you to make a selection that excludes raw data, for example.

Now, by making an important reversal of steps in data transformation, we gain greater agility.

The use of several systems implies a lot of time both in loading and in the transformation of large volumes of data. As a result, more costs and more demand for specific technical professionals are generated.

The reversal of the steps was the solution in the search for a more modern approach towards flexible, secure and agile scalability.

In short: what appears to be a simple change of acronym results in the optimization of data projects and causes positive impacts on the final product.

What is the ELT used for?

The ELT is a pipeline model that serves your company to manage the immense volume of data for its analysis effectively.

It serves small businesses and today's largest businesses due to its ability to generate millions of pieces of information daily.

And for the successful implementation of business intelligence (BI), it will be necessary to collect, clean, process and transform everything into a final material that allows its use and analysis to generate value for your company.

And since we can always do everything better, that's where we chose ELT.

With the transformation done directly in DW, professionals such as analytics engineers can work directly connected to it to visualize data, identify trends, optimize processes, and act quickly with the help of business intelligence tools.

Discover 5 benefits of ELT

A phenomenon among the most modern data science teams, ELT has several benefits, in addition to its agility. Below, we have listed five very interesting ones for you to know.

1. Cost: ELT is an adaptable, affordable, and scalable process for diverse types and sizes of companies and, unlike its precursor ETL, requires a lower maintenance fee, making your final investment an advantageous and even profitable alternative.
2. Charging time: With an integrated system, data is only once uploaded into the storage device and its fast process is one of the main differentiators.
3. Support for data warehouses: ELT is scalable across cloud infrastructures, and supports large volumes and diverse data sources, structured or unstructured.
4. Usability: Its approach is collaborative and the ELT can be used by more technical professionals, data engineers and even the end user.
5. Transformation time: because it is done in an integrated way with the help of cloud infrastructure technologies, because, even though the volume of data increases over time, the speed of transformation in ELT is much higher compared to that of ETL.

Does your company need ELT?

In fact, every business needs ELT because it generates information, regardless of its size or business segment.

To generate the revolution you want your company to succeed, in your data project, evaluate having a strategy that adopts an ELT.

Here at Indicium, we are protagonists in business intelligence, big data and analytics and we apply ELT in the methodologies we create and teach.

Count on our specialized team and we will revolutionize your company.

Get in touch by clicking here.

See you later!