The most obvious difference between ETL and ELT is the difference in order of operations. 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.
Difference between ELT and ETL
| ELT | ETL |
|---|---|
| ELT tools do not require additional hardware | ETL tools require specific hardware with their own engines to perform transformations. |
| Mostly Hadoop or NoSQL database to store data. Rarely RDBMS is used | RDBMS is used exclusively to store data. |
| As all components are in one system, loading is done only once. | As ETL uses staging area, extra time is required to load the data. |
| Time to transform data is independent of the size of data | The system has to wait for large sizes of data. As the size of data increases, transformation time also increases. |
| It is cost effective and available to all business using SaaS solution | Not cost effective for small and medium business. |
| The data transformed is used by data scientists and advanced analysts. | The data transformed is used by users reading report and SQL coders. |
| Creates ad hoc views. Low cost for building and maintaining | Views are created based on multiple scripts. Deleting view means deleting data |
| Best for unstructured and non-relational data. Ideal for data lakes. Suited for very large amounts of data | Best for relational and structured data. Better for small to medium amounts of data |
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