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Friday, July 14, 2023

What are the differences between ETL and ELT

 In Informatica, ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two approaches used for data integration and processing. Here are the key differences between ETL and ELT in Informatica:

1. Data Processing Order:



ETL: In the ETL approach, data is extracted from various sources, then transformed or manipulated using an ETL tool (such as Informatica PowerCenter), and finally loaded into the target data warehouse or system. Transformation occurs before loading the data.

ELT: In the ELT approach, data is extracted from sources and loaded into the target system first, typically a data lake or a data warehouse. Transformation occurs after loading the data, using the processing power of the target system.

 

2. Transformation:

ETL: ETL focuses on performing complex transformations and manipulations on the data during the extraction and staging process, often utilizing a dedicated ETL server or infrastructure.

ELT: ELT leverages the processing capabilities of the target system, such as a data warehouse or a big data platform, to perform transformations and manipulations on the loaded data using its built-in processing power. This approach takes advantage of the scalability and processing capabilities of modern data platforms.


3. Scalability and Performance:

ETL: ETL processes typically require dedicated ETL servers or infrastructure to handle the transformation workload, which may limit scalability and performance based on the available resources.

ELT: ELT leverages the scalability and processing power of the target system, allowing for parallel processing and distributed computing. This approach can handle large volumes of data and scale more effectively based on the capabilities of the target system.


4. Data Storage:

ETL: ETL processes often involve extracting data from source systems, transforming it, and then loading it into a separate target data warehouse or system.

ELT: ELT processes commonly involve extracting data from source systems and loading it directly into a target system, such as a data lake or a data warehouse. The data is stored in its raw form, and transformations are applied afterward when needed.






5. Flexibility:

ETL: ETL provides more flexibility in terms of data transformations and business logic as they can be defined and executed within the ETL tool. It allows for a controlled and centralized approach to data integration.

ELT: ELT provides more flexibility and agility as it leverages the processing power and capabilities of the target system. The transformations can be performed using the native features, tools, or programming languages available in the target system.


Here is the summary:



Ultimately, the choice between ETL and ELT in Informatica depends on factors such as the volume and complexity of data, the target system's capabilities, performance requirements, and the specific needs of the data integration project.

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