What is the difference between ELT and ETL?
Data integration combines data from different sources to provide a unified and complete dataset for analytical purposes. Data integration is an important step for information management and analytics. It helps to make more informed decisions and provides a more unified view of the data. There are different types of techniques for data integration, from which commonly used are:
Extract, Load, Transform (ELT)
Extract, Transform, Load (ETL)
ETL is a data integration technique used in data warehousing and business analytical purposes to transform and feed data to the system. The data is extracted from multiple sources and then transformed into the desired structure or format. The transformation includes data cleansing, validation, and aggregation. Finally, the transformed data is loaded into the system. ETL transforms the whole data once and then feeds it to the system.
Extract, Load, Transform (ELT)
ELT is a data integration technique in which the data extracted from multiple sources is loaded and then transformed as per requirement. This enables faster integration of data into the system. ELT utilizes the parallel processing capabilities to the maximum as the data required is transformed continuously and then forwarded to the system to perform different queries.
ETL vs. ELT
The difference between ETL and ELT is:
Extract, Transform, Load (ETL) | Extract, Load, Transform (ELT) |
Data is transformed before loading into the system. | Data is transformed after loading the data into the system. |
The transformation process occurs outside the main system. | The transformation process occurs inside the main system. |
ETL is preferred when transformation logic is complex, and data requires cleansing. | ELT leverages the main system’s power and uses parallel processing to transform data. |
ETL does not add a computational load of transforming the data to the system. | ELT offers a scalable data loading option but adds additional processing cost of transforming the data in the system. |
ETL ensures that quality data is loaded into the system after pre-processing and cleansing. | As data is being transformed in the system, ELT allows it to cater to changing business requirements. |
Test your knowledge
What is the primary purpose of data integration?
Enhancing data security
Creating a unified and complete dataset for analytical purposes
Improving system performance
Minimizing data extraction time
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