ETL vs ELT: The Difference

ETL (Extract, Transform, Load) is a traditional data integration process that has been used for decades. In this approach, data is first extracted from various source systems, then transformed into the desired format, and finally loaded into the target data warehouse or database. The transformation happens in a separate processing server between the source and target systems. ETL is particularly useful when you need to apply complex transformations to your data before loading it into your data warehouse.

ELT (Extract, Load, Transform) is a modern approach to data integration where raw data is first extracted from source systems and immediately loaded into the target data warehouse. The transformation of data happens within the target system itself, leveraging the computing power of modern data warehouses. This approach has gained popularity with the advent of cloud data warehouses that can handle large-scale data transformations efficiently.

The main difference between ETL and ELT lies in where and when the transformation occurs. In ETL, transformation happens in a middle tier before loading data into the target system. This requires additional processing infrastructure and can create a bottleneck in the data pipeline. In ELT, data is loaded in its raw form first, and transformations occur within the target system. This approach is more flexible as you can transform data as needed and can always access the original raw data.

Choose ETL when:

• You have limited storage in your data warehouse
• You need to comply with specific data privacy regulations
• Your transformations are complex and resource-intensive
• You’re working with legacy systems

Choose ELT when:

• You have a modern cloud data warehouse
• You need maximum flexibility with your data
• You want to perform ad-hoc analysis on raw data
• You have large volumes of data and need faster loading times