What is ELT?

ELT, or Extract, Load, Transform, is a data integration process that extracts raw data, loads it into a target system first, and then applies the necessary transformations. Unlike the traditional ETL (Extract, Transform, Load) method, the ELT process allows raw data to be loaded and then transformed, offering advantages in terms of flexibility, scalability, and improved performance.

Functionality and Features

ELT takes advantage of increased computational power and storage capacity of modern data systems, allowing data transformations to occur within the target database. Key features of ELT include:

  • Direct data handling: ELT processes raw data directly, allowing for a more flexible approach to data transformation and manipulation.
  • Scalability: The load-first approach allows ELT to handle large volumes of data more efficiently.
  • Real-time processing: ELT can handle both batch and real-time data processing.

Benefits and Use Cases

ELT brings several advantages to data management:

  • Flexibility: With ELT, raw data is available in the target system, enabling data scientists to perform ad-hoc queries.
  • Efficiency: ELT leverages the full computational power of the target system, allowing for more efficient data handling.
  • Scalability: ELT can handle large volumes of data, making it suitable for big data applications.

Integration with Data Lakehouse

In a data lakehouse environment, ELT proves to be particularly beneficial. The ELT process allows raw data to be loaded directly into the data lakehouse, where it can then be transformed as needed for data analytics purposes. This allows for a more flexible, scalable data infrastructure, making it easier to perform complex data analytics tasks.


The performance of an ELT process largely depends on the computational capabilities of the target system. In a data lakehouse environment, this means that performance can be significantly improved by using a system designed to handle large volumes of data, such as Dremio's self-service data platform.


What's the difference between ETL and ELT? While ETL processes transform data before loading it into the target database, ELT loads raw data first and then performs transformations within the target system.Why might you choose ELT over ETL? If you're handling large volumes of data and need greater flexibility and scalability, ELT may be a better choice as it leverages the full computational power of the target system.


Data Lakehouse: An architecture that combines the best elements of data lakes and data warehouses, facilitating unified data analytics.

Data Transformation: The process of converting data from one format or structure into another.

Dremio and ELT

Dremio's self-service data platform is designed to optimize ELT performance in a data lakehouse environment. With its powerful query engine, Dremio can handle the transformations of large volumes of data loaded into the system. This surpasses traditional ELT methods, offering improved scalability and flexibility for big data applications.

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