As in any industry there is jargon but in the world of data searches for what is ETL and what is ELT leads to confusion. ETLs and ELTs are two data integration processes. Both are used to extract, transform, and load data from one or more sources into a target system. The target could include a database, data warehouse or data lake. ETL stands for Extract, Transform, Load, while ELT stands for Extract, Load, Transform.
ETL vs ELT
The primary difference between ETL and ELT is the order in which the transformation step occurs. In ETL, the transformation step occurs before the data is loaded into the target system, while in ELT, the transformation step occurs after the data is loaded into the target system.
ETL is well-suited for smaller target data repositories that require less frequent updating. It is a batch-oriented process that is best suited for structured data, traditionally this is used in data warehousing. ETL tools include Talend, ADF (Azure Data Factory) and SSIS (Microsoft SQL Server Integration Services).
ELT is ideal for large amounts of raw data that require real-time updates. It is a newer process and is better suited to unstructured data. Loading data into a data lake and then processes is a key example of ELT in action. The big data systems using Apache either through spark or in the form of Databricks are examples of ELT. A typical example would be the medallion architecture of importing data in as a Bronze layer and then transforming to silver and gold layers.
In conclusion, both ETL and ELT have their own advantages and use cases. ETL is well-suited for smaller target data repositories that require less frequent updating, while ELT is ideal for large amounts of raw data that require real-time updates.
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Blog Posted by David Laws