ETL vs. ELT: What's the Difference?

• ETL (Extract, Transform, and Load) is a data integration method that involves extracting data from various sources, transforming it, and loading it into a destination.
• ELT (Extract, Load, Transform) is a method where the transformation happens after the data is loaded. Most commonly, it leverages the processing power of your data warehouse itself.
• ETL (Extract, Transform, Load) is like a store that sells ready-made products. It first orders all raw materials (extract) and uses them to create products (transform). It then ships the products to consumers (load).
• ELT (Extract, Load, Transform) is like a DIY warehouse. It buys raw materials (extract) and sells them to customers (load). Customers then use those materials to build products in their homes (transform).

There are two primary ways that ETL and ELT differ:

• ETL transforms data in a separate server, whereas ELT transforms data in the destination warehouse.
• ETL does not transfer raw data into the destination warehouse, whereas ELT loads raw data directly into it.

Is ELT Replacing ETL?

• ETL tools are great at moving data from different sources into a relational data warehouse. So, if that works for you, there's no need to replace it.
• However, if you have a large volume and less-common data sources, then using ELT can improve accessibility to the data.
• If you've already invested in big data and cloud storage that needs to scale as you grow, then using ELT would be preferable to using ETL.

ETL vs. ELT: Which Should You Use?

Which is better, ETL or ELT?

Whether ETL or ELT is better depends on the specific needs and goals of the organization.

You might prefer ETL if:

• You have small data sets that require less complex transformations.
• You aren't using real-time data.
• You want to ensure the data is sanitized to comply with various regulations.
• You don't want your target system to have unstructured data.
• Your target system has limited processing capabilities.

You might prefer an ELT solution if:

• You want to capture data in real-time.
• You have structured and unstructured data and want more flexibility in data transformations.
• You have the resources to manage data lakes.
• You can hire ELT experts.
• You want to load data faster.