ETL: extract, transform, load, extracts data from legacy systems, loads data to a staging area for transformation, requires more definition at the onset, specific data points need to be identified for extraction along with any potential “keys” to integrate across disparate source systems, the source of input data is often tracked by using metadata, the business rules for data transformations need to be constructed, ELT: extract, load, transform, copies or exports the data from the source locations, loads the raw data directly into the target data store to be transformed as needed, useful for ingesting high-volume, unstructured data sets as loading can occur directly from the source, can be more ideal for big data management since it doesn’t need much upfront planning for data extraction and storage, this technology is still a developing process, meaning that best practices are still being established, increasingly popular with the adoption of cloud databases,

ETL vs ELT

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