A typical extraction schedule would be daily incremental extracts followed by either weekly or monthly full extracts to bring the warehouse in sync with the source on a weekly or monthly basis. This adds significantly to the cost of maintenance of the ETL process.Ī key challenge in defining the CDC strategy is it has the potential to disrupt the transaction processing during extraction. However, commercially available ETL tools work efficiently with static data structures and designing processes that recognize source data structure changes and repair extraction processes dynamically is a complex programming problem which is not supported out-of-the-box by them. html pages, textual data involves custom programming as most commercially available ETL tools do not have these plug ins. ![]() If compatible and standard drivers are not available, it needs to be coded and maintained in the Extraction repository.Įxtraction of data from unstructured data sources, e.g. This implies availability of efficient APIs which allows the ETL processes to interface with the data sources and extract the required data fields efficiently and accurately. Any data extraction tool, program or script needs to be able to parse the source data. One of the challenges in integrating data across heterogeneous sources is the availability of compatible drivers across diverse data sources. As ETL process involves various stages of transformation, homogenization, and cleansing, it is a routine programming problem for all business applications. ![]() ![]() The name ETL came into existence in the early years of the 21st century, when formalization of data science as a discipline came into existence. Extract, Transform and Load is a commonly used acronym.
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