ETL vs ELT - maximnl/mais GitHub Wiki

Companies turn data into useful information using two approaches: ETL or ELT. These are the two options. As ETL way is broadly supported, MAIS mainly ELT type but of course it will benefit from having a good quality data warehouse. Even if a company has a data warehouse, tt is common to have less structured user data, or data that is available via reports only. In many cases a combination of ETL and ELT can be applied. MAIS gives companies an edge with ELT as most operations can be done by the business users.

As stated above, ETL = Extract, Transform, Load. ELT, on the other hand = Extract, Load, Transform.

With ELT , the transformation is moved to the end of the process. By doing so we simplify the extraction process for the ICT. We enable business users to take care of the data transformations by using MAIS configuration and MAISED interface (or any other). Many transformations are changing due to business process changes. By applying the business knowledge directly and by enabling self service we gain process efficiency.

According to IBM, ​“the most obvious difference between ETL and ELT is the difference in order of operations. ELT copies or exports the data from the source locations, but instead of loading it to a staging area for transformation, it loads the raw data directly to the target data store to be transformed as needed.

While both processes leverage a variety of data repositories, such as databases, data warehouses, and data lakes, each process has its advantages and disadvantages. ELT is particularly useful for high-volume, unstructured datasets as loading can occur directly from the source. ELT can be more ideal for big data management since it doesn’t need much upfront planning for data extraction and storage. The ETL process, on the other hand, requires more definition at the onset. Specific data points need to be identified for extraction along with any potential ​“keys” to integrate across disparate source systems. Even after that work is completed, the business rules for data transformations need to be constructed. While ELT has become increasingly more popular with the adoption of cloud databases, it has its own disadvantages for being the newer process, meaning that best practices are still being established.”