Whether ELT (Extract, Load, Transform) or ETL (Extract, Transform, Load) is better depends on the specific needs of your organization and the nature of your data.
Traditionally, ETL has been the more popular approach because it allows data to be transformed and cleansed before it is loaded into a data warehouse or other target system. This can help ensure that the data is accurate and consistent, which is important for business intelligence and analytics.
However, ELT has become increasingly popular in recent years because it allows data to be loaded into a target system first, and then transformed and cleansed as needed. This approach can be faster and more scalable, since it avoids the need to move large volumes of data across the network during the transformation process.
ELT can also be more flexible, since it allows data to be transformed and cleansed in the target system using SQL or other tools, rather than requiring a separate transformation engine. This can make it easier to adapt to changing business needs and data sources.
Ultimately, the choice between ELT and ETL will depend on factors such as the size and complexity of your data, the processing power and storage capacity of your target system, and the specific requirements of your organization. In some cases, a hybrid approach that combines elements of both ELT and ETL may be the most effective solution.