ETL and ELT are data integration pipelines that transfer data from multiple sources to a single centralized source and perform some transformation and processing steps to it. The difference between these two is ETL transforms the data before loading, and ELT transforms the data after loading. But before diving deeply into them, let's first understand the meaning of E, L, and T. T for Transform - Transforming the data is a process of cleaning and modifying the data in a format so that it can be used for business analysis. L for Loading - It involves loading data to a target system, which may be a data warehouse or a database. ETL is the first standardized data integration method that emerged in the 1970s due to the evolution of disk storage.

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Ferdinand Magellan was a Portuguese explorer who launched a Spanish expedition that completed the first circumnavigation of the Earth. It is in this spirit that Magellan was used as the name of the end-to-end entity matching system that is developed at the University of Wisconsin. Entity matching (also known as entity resolution or reference reconciliation or deduplication) is a major task in the larger problem of data integration, a problem that is pervasive in many organizations. Despite being a subject of extensive research for many years, the entity matching problem is surprisingly simple to describe and understand. It is to determine whether two different representations refer to the same real-world entity. Doe, UWisc) and (John Doe, Univ. of Wisconsin)--refer to the same person.