ETL vs ELT: Key Differences Fully Explained

You've probably heard of these two terms when researching techniques and processes associated with launching your data journey, but do you know the differences between ETL vs ELT in data integration?

In this blog, Erathos will help you deepen your knowledge of data ingestion and understand why these two terms are different.

Data Integration

Before we dive into the main topic of this post, we need to reinforce that data integration is the starting point for every successful data operation. It is the process of acquiring your data from all scattered sources (CRMs, ERPs, Spreadsheets, emails, etc.) and centralizing it in a single location so that it is available and easily accessible to speed up its use for generating insights within your company.

Integrating data is vital for your data-driven strategy because it avoids data silos and other bottlenecks that make life harder for your data team and hinder your path to improving your data maturity. The famous ETL and ELT are nothing more than data integration processes, but make no mistake: the order of the letters makes a total difference!

ETL vs ELT: Understand these concepts

Erathos has already explained what ETL is here on the blog, but in this article, we will revisit this concept so you can understand the main differences between it and ELT. First of all, it's important to define what each of these terms means:

What is ETL?

ETL stands for "Extract, Transform, Load". That is: first, the data is extracted from its source, then transformed into the ideal format for analysis and use within the company, and finally loaded into a data storage system.

This approach is generally used in more traditional data analytics systems, especially where storage is done in relational databases.

In this type of system, the transformation step is performed before loading the data, allowing the data to be stored in a format optimized for analysis. This system brings some advantages. For example: since the data is already in the desired format when loaded into the system, querying is much faster and more efficient. On the other hand, the transformation step is performed outside the data storage system, avoiding overheads and allowing it to remain efficient even with large volumes of data.

What is ELT?

ELT stands for "Extract, Load, Transform". The order of these steps is reversed compared to ETL: first, the data is extracted from its sources and then loaded into a storage system. Subsequently, it is transformed so that it is in the desired format and accessible as needed. This approach has several advantages over ETL.

For example: because the data is stored in its raw format, it is possible to perform more flexible analyses without the need to transform it back to that format if the need arises at some point. In addition, since the transformation step is performed within the same data storage environment, it is easier to maintain and access files and build historical series. ELT is typically used in more modern data analytics systems, where data is stored in analytical databases.

However, the ELT approach also has some drawbacks. For example: because the data is stored in its raw format, querying it can be slower and less efficient than if it were stored in a format optimized for analysis. In addition, the transformation step is performed in the storage system itself, which can overload it and decrease performance in some cases.

What is the difference between the two?

In both acronyms, the letters have the same meaning, but make no mistake: the order of the letters is highly important, as it indicates distinct data integration methods.  Each letter refers to the sequence in which these processes occur. As we mentioned earlier, in ETL, data is extracted, transformed, and then loaded into a data storage structure. In ELT, data is extracted, loaded, and transformed as needed. But is one approach better than the other?  

Well, the truth is there is no single answer to this. Each approach has its pros and cons, and the choice of which to use will depend on your business's specific needs.

Conclusion

Both the ETL and ELT approaches have their advantages and disadvantages. The choice of which to use will depend on the specific needs of each project and which type of database is best suited for your company.

ETL is usually more common in systems where data is stored in relational databases. Here, the transformation step is performed before loading, which allows it to be stored in an optimized format for subsequent analysis. This speeds up data usage, bringing efficiency and agility to the process once the data is cleaned. On the other hand, this can introduce some inefficiencies, especially when dealing with high data volumes. 

In this case, ELT is highly valuable, because since the data transformation is done inside the storage environment, it provides greater flexibility for using the information in business decision-making. To decide on the best option for your business needs, you must keep in mind the complexity, volume, and your specific requirements for using the data.