ETL and ELT

ETL

Extract, Transform and Load is the technique of extracting the record from sources  to a staging area, then transforming or reformatting with business manipulation performed on it in order to fit the operational needs or data analysis, and later loading into the goal or destination databases or data warehouse. 

ELT

ELT stands for Extract, Load and Transform is the various sights while looking at data migration or movement. ELT involves the extraction of aggregate information from the source system and loading to the target method instead of transformation between the extraction and loading phase. Once the data is copied or loaded into the target method, then change takes place. 

Comparing ETL and ELT

	ETL	ELT
Technology Adoption	ETL has been in the market for over two decades now and is relatively easier to find developers who have vast experience in designing ETL systems.	On the other hand, ELT is a new technology that is more focused on cloud-based warehouses. Searching suitable engineers to develop ELT pipelines are as easy as for ETL.
Data Availability	In an ETL workload, the data which is required only for analytics or reporting is being loaded into the warehouse, leaving other unnecessary data in the source systems as is.	Whereas in an ELT system, we tend to load anything and everything into a warehouse or a data lake from where it can be analyzed at a later point of time.
Calculated Fields and Transformations	Yes, in ETL, we can add or remove specific columns while transforming the data in the ETL tool. We can also add calculated columns and load them to the warehouse.	In ELT, additional columns are directly added to the existing dataset in the warehouse. Usually, there is no modification of the source columns.
Transformation Complexities	In an ETL workload, we can implement much complex data transformations as and when required. All these transformations occur in-memory.	In an ELT workload, the more focus is given towards analyzing highly variable structured and unstructured data that is arriving at a high pace rather than complexities.
Infrastructure	Most of the traditional ETL tools need to be installed on-premises which incur a lot of cost to the analytics workloads.	ELT, on the other hand, is mostly cloud-based and doesn’t require to be installed on the premises.
Postproduction Maintenance	In an ETL pipeline, that is installed on-premises, maintenance is frequently required.	ELT, since it is cloud-based or server less, no or very little maintenance is required.
Transformation Area	In an ETL pipeline, the transformations are applied in memory in a staging layer before the data is being loaded into the data warehouse.	In ELT, the transformations are applied once the data has been loaded into the warehouse or a data lake. In this case, usually, there is no requirement for a staging layer unlike in the ETL.
Support for semi-structured and unstructured data	Although an ETL tool can read data from semi-structured or unstructured data sources, it is usually transformed in the staging layer and only stored as a proper structure in the warehouse.	ELT is designed to handle all types of data structures from semi-structured to unstructured data in the data lakes which can be further analyzed.