During the transformation phase, data is sorted, organized, and cleansed.
Transformation: Once the data has been successfully extracted, it is ready to be refined. Extraction allows many different kinds of data to be combined and ultimately mined for business intelligence. The extraction locates and identifies relevant data, then prepares it for processing or transformation. Extraction: Data is taken from one or more sources or systems. There are three steps in the ETL process: In essence, ETL allows companies and organizations to 1) consolidate data from different sources into a centralized location and 2) assimilate different types of data into a common format. To put the importance of data extraction in context, it’s helpful to briefly consider the ETL process as a whole. ETL/ELT are themselves part of a complete data integration strategy. These locations may be on-site, cloud-based, or a hybrid of the two.ĭata extraction is the first step in both ETL (extract, transform, load) and ELT (extract, load, transform) processes. Data extraction makes it possible to consolidate, process, and refine data so that it can be stored in a centralized location in order to be transformed. What is Data Extraction?ĭata extraction is the process of collecting or retrieving disparate types of data from a variety of sources, many of which may be poorly organized or completely unstructured. In this article, we define the meaning of the term “data extraction” and examine the ETL process in detail to understand the critical role that extraction plays in the data integration process. 
But before that data can be analyzed or used, it must first be extracted. The question is: how do we make the most of it? For many, the biggest challenge lies in finding a data integration tool that can manage and analyze many types of data from an ever-evolving array of sources. We have access today to more data than ever before.
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