When it comes to data management strategies, particularly data warehousing, there are two major strategies used in ETL and ELT . The difference between the two lies in the way they handle data, which is later used for business intelligence and analytics.

ETL stands for: Extract, Transform, Load: E -Data is extracted from different sources; T -Data gets cleaned and transformed into a suitable format for further data analysis or processing. This step is done before loading data into the target database, or in this case, data warehouse; L -Cleaned data is loaded into the data warehouse.

ETL Use Case: ETL works great when it is required to have the transformation process under a high level of control, and the storage requires perfect data integrity and quality. ETL is often used in scenarios, where data transformation has to be done before storing it, which is the case with legacy systems.

ELT stands for: Extract, Load, Transform: E -Data is extracted from source systems; L -Data is loaded into the data warehouse as is; T -Data transformation happens after the data has been loaded into the data warehouse. This transformation process is possible with modern data warehouses that have strong processing capability.

ELT Use Case: ELT comes in handy when working with a vast amount of data that needs to have scalable compute resources of the modern data warehouse. ELT allows for the flexibility of working with large data sets, especially when the needs for transformation can change.

Performance: ELT is efficient when working with large data volumes because data is loaded into the data warehouse with processing capabilities. ETL has more overhead due to pre-processing data by intermediate servers.

Flexibility: ELT provides more flexibility when it comes to querying data and data transformation due to the needs that might change. ETL requires the design of the transformation, which limits the flexibility of transformative power.

Data Quality: ETL can provide better data cleansing and validation before data is being loaded, which allows for data quality. ELT can upload data instantly, but errors can be fixed later.

In summary, ETL is a better choice when dealing with the need for quality data stored in the data warehouse, while ELT is more advantageous when requiring processing power and flexible terms for data storage, which is necessary for big data scenarios.