Big Data Glossary
What is ELT?
Definition of ELT
What is ELT?
ELT stands for Extract, Load, Transform. It is a variation of the traditional ETL process, where the data is first loaded into the destination system, and then transformed and cleaned.
The main difference between ETL and ELT is the order of operations. In ETL, data is extracted, transformed and then loaded into the destination system. In ELT, data is extracted, loaded into the destination system and then transformed. This approach is made possible with the advancements in technology that allows for faster and more powerful data processing capabilities in the destination system, such as data warehouses or cloud data lakes.
The benefits of using ELT are that it allows for more flexible and efficient data processing. The data can be loaded quickly into the destination system, and then transformed and cleaned using the powerful processing capabilities of that system. This can reduce the time and resources needed for data processing, and also allows for more advanced and sophisticated transformations to be performed, such as data modeling and machine learning.
ELT is particularly useful in big data and real-time analytics use cases where it is important to have the data available as soon as possible for analysis and decision-making. It also allows for more efficient use of resources, as the data transformation and cleaning can be done in parallel with the loading process.
It is important to note that ELT is not always the best approach, and it depends on the use case, the complexity of the data and the available resources. In some cases, ETL might be more appropriate, for example, if the data needs to be transformed before it is loaded to meet certain compliance or security requirements.
The All-in-One Platform for Modern Integration
Crosser is a hybrid-first platform that in one Low-code platform has all the capabilities that you traditionally would need several systems for.
In one easy-to-use platform:
Crosser Solution for Data Mining
Want to learn more about how Crosser could help you and your team to:
- Build and deploy data pipelines faster
- Save cloud cost
- Reduce use of critical resources
- Simplify your data stack