![]() ![]() ![]() You will be able to outline some of the multiple methods for loading data into the destination system, verifying data quality, monitoring load failures, and the use of recovery mechanisms in case of failure.įinally, you will complete a shareable final project that enables you to demonstrate the skills you acquired in each module. ![]() You will also define transformations to apply to source data to make the data credible, contextual, and accessible to data users. You will identify methods and tools used for extracting the data, merging extracted data either logically or physically, and for importing data into data repositories. They write new content and verify and edit content received from contributors. During this course, you will experience how ELT and ETL processing differ and identify use cases for both. Encyclopaedia Britannica's editors oversee subject areas in which they have extensive knowledge, whether from years of experience gained by working on that content or via study for an advanced degree. ![]() ELT processes apply to data lakes, where the data is transformed on demand by the requesting/calling application.īoth ETL and ELT extract data from source systems, move the data through the data pipeline, and store the data in destination systems. ETL processes apply to data warehouses and data marts. The other contrasting approach is the Extract, Load, and Transform (ELT) process. One approach is the Extract, Transform, Load (ETL) process. After taking this course, you will be able to describe two different approaches to converting raw data into analytics-ready data. ![]()
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