![]() Traditionally, tools for ETL primarily were used to deliver data to enterprise data warehouses supporting business intelligence (BI) applications. Therefore, one of the immediate consequences of ELTs is that you lose the data preparation and cleansing functions that ETL tools provide to aid in the data transformation process. In contrast, with ELTs, the staging area is in the data warehouse, and the database engine that powers the DBMS does the transformations, as opposed to an ETL tool. They sit between the source system (for example, a CRM system) and the target system (the data warehouse). In ETL, these areas are found in the tool, whether it is proprietary or custom. ![]() Both ETL and ELT processes involve staging areas. However, as the underlying data storage and processing technologies that underpin data warehousing evolve, it has become possible to effect transformations within the target system. Traditionally, these transformations have been done before the data was loaded into the target system, typically a relational data warehouse. This means that the data must undergo a series of transformations. ![]() OLAP tools and SQL queries depend on standardizing the dimensions of datasets to obtain aggregated results. In a traditional data warehouse, data is first extracted from "source systems" (ERP systems, CRM systems, etc.). ETL and ELT, therefore, differ on two main points: The transformation step is by far the most complex in the ETL process.
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