ETL vs ELT: Key Differences
ETL transforms before loading; ELT loads and then transforms in the destination. Compare the two models and why ELT dominates the Modern Data Stack.
You’ve probably already come across these two terms while researching techniques and processes related to kickstarting your data journey, but do you already know the differences between ETL vs ELT in data integration?
In this blog post, 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 core topic of this article, we need to reinforce that data integration is the starting point for any successful data operation. It is the process of acquiring your data from all distributed sources (CRMs, ERPs, spreadsheets, emails, etc.) and centralizing it in a single location so it is available and easy to access, accelerating its use for insight generation within your company.
Integrating data is vital to your data-driven strategy because it prevents the creation of data silos and other bottlenecks that make life harder for your data team and get in the way of improving your data maturity. The well-known ETL and ELT are nothing more than data integration processes, but make no mistake: the order of the letters makes all the difference!
ETL vs ELT: Understand These Concepts
Erathos has already explained what ETL is here on the blog, but in this article we’ll 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". In other words: first, 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 stage is performed before data loading, allowing data to be stored in an analysis-optimized format. This model 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 stage is executed outside the data storage system, avoiding overload and allowing it to remain efficient even with large data volumes.
What is ELT?
ELT means "Extract, Load, Transform". The order of these stages is reversed compared to ETL: first, data is extracted from its sources and then loaded into a storage system. Later, it is transformed so it is in the desired format and accessible as needed. This approach has several advantages over ETL.
For example: since data is stored in its original format, you can perform more flexible analyses without needing to convert it back if that need arises later. In addition, because the transformation stage is carried out in the same data storage environment, it is easier to maintain and access files and build historical series. ELT is commonly used in more modern analytics systems, where data is stored in analytical databases.
However, the ELT approach also has some disadvantages. For example: because data is stored in its original format, querying it may be slower and less efficient than if it were stored in an analysis-optimized format. Also, the transformation stage is performed within the data storage system itself, which can overload it and reduce performance in some cases.
What’s the Difference Between the Two?
In both acronyms, the letters have the same meaning, but don’t be mistaken: the order of the letters is very important because it indicates different data integration methods. Each letter refers to the order in which these processes happen. 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. Each approach has its pros and cons, and the choice depends on your business’s specific needs.
Conclusion
Both ETL and ELT approaches have advantages and disadvantages. The choice of which one to use depends on the specific needs of each project and on what type of database is most suitable for your company.
ETL is commonly used in systems where data is stored in relational databases. In ETL, the transformation stage happens before data loading, which allows data to be stored in a format optimized for later analysis. This helps speed up data usage, bringing efficiency and agility to the process since the data is already prepared. On the other hand, this can introduce some inefficiencies, especially when dealing with large data volumes.
In that case, using ELT can be a good option because, since data transformation is done inside the storage environment, it provides greater flexibility for using information in business decision-making. To decide which option is best for your business needs, you should keep in mind complexity, data volume, and your data usage requirements.
You’ve probably already come across these two terms while researching techniques and processes related to kickstarting your data journey, but do you already know the differences between ETL vs ELT in data integration?
In this blog post, 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 core topic of this article, we need to reinforce that data integration is the starting point for any successful data operation. It is the process of acquiring your data from all distributed sources (CRMs, ERPs, spreadsheets, emails, etc.) and centralizing it in a single location so it is available and easy to access, accelerating its use for insight generation within your company.
Integrating data is vital to your data-driven strategy because it prevents the creation of data silos and other bottlenecks that make life harder for your data team and get in the way of improving your data maturity. The well-known ETL and ELT are nothing more than data integration processes, but make no mistake: the order of the letters makes all the difference!
ETL vs ELT: Understand These Concepts
Erathos has already explained what ETL is here on the blog, but in this article we’ll 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". In other words: first, 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 stage is performed before data loading, allowing data to be stored in an analysis-optimized format. This model 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 stage is executed outside the data storage system, avoiding overload and allowing it to remain efficient even with large data volumes.
What is ELT?
ELT means "Extract, Load, Transform". The order of these stages is reversed compared to ETL: first, data is extracted from its sources and then loaded into a storage system. Later, it is transformed so it is in the desired format and accessible as needed. This approach has several advantages over ETL.
For example: since data is stored in its original format, you can perform more flexible analyses without needing to convert it back if that need arises later. In addition, because the transformation stage is carried out in the same data storage environment, it is easier to maintain and access files and build historical series. ELT is commonly used in more modern analytics systems, where data is stored in analytical databases.
However, the ELT approach also has some disadvantages. For example: because data is stored in its original format, querying it may be slower and less efficient than if it were stored in an analysis-optimized format. Also, the transformation stage is performed within the data storage system itself, which can overload it and reduce performance in some cases.
What’s the Difference Between the Two?
In both acronyms, the letters have the same meaning, but don’t be mistaken: the order of the letters is very important because it indicates different data integration methods. Each letter refers to the order in which these processes happen. 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. Each approach has its pros and cons, and the choice depends on your business’s specific needs.
Conclusion
Both ETL and ELT approaches have advantages and disadvantages. The choice of which one to use depends on the specific needs of each project and on what type of database is most suitable for your company.
ETL is commonly used in systems where data is stored in relational databases. In ETL, the transformation stage happens before data loading, which allows data to be stored in a format optimized for later analysis. This helps speed up data usage, bringing efficiency and agility to the process since the data is already prepared. On the other hand, this can introduce some inefficiencies, especially when dealing with large data volumes.
In that case, using ELT can be a good option because, since data transformation is done inside the storage environment, it provides greater flexibility for using information in business decision-making. To decide which option is best for your business needs, you should keep in mind complexity, data volume, and your data usage requirements.