Extractĭuring data extraction, raw data is copied or exported from source locations to a staging area. The easiest way to understand how ETL works is to understand what happens in each step of the process.
While ELT has become increasingly more popular with the adoption of cloud databases, it has its own disadvantages for being the newer process, meaning that best practices are still being established. This work can usually have dependencies on the data requirements for a given type of data analysis, which will determine the level of summarization that the data needs to have. Even after that work is completed, the business rules for data transformations need to be constructed. Specific data points need to be identified for extraction along with any potential “keys” to integrate across disparate source systems. The ETL process, on the other hand, requires more definition at the onset.
ELT can be more ideal for big data management since it doesn’t need much upfront planning for data extraction and storage. ELT is particularly useful for high-volume, unstructured datasets as loading can occur directly from the source. While both processes leverage a variety of data repositories, such as databases, data warehouses, and data lakes, each process has its advantages and disadvantages. ELT copies or exports the data from the source locations, but instead of loading it to a staging area for transformation, it loads the raw data directly to the target data store to be transformed as needed. The most obvious difference between ETL and ELT is the difference in order of operations.
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