ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are data integration methods used to transform & transfer data from one place to another. However, each has unique characteristics and is suitable for different data needs.
Whether it’s ETL or ELT, both processes involve the following steps:
- Extract: Source data is extracted from the original data source in an unstructured format.
- Transform: Additional transformation must be done to clean and model data before it can be practically useful for other analytics applications.
- Load: In the ELT model, data is copied directly into the data warehouse without significant modification. In an ETL model, data would be transformed into a suitable format before loading it into the warehouse
ETL
Extract, transform, and load (ETL) extracts raw data from sources, transforms the data on a secondary processing server (staging), and then loads the data into a target database.
ETL is used when data must be transformed to conform to the data regime of a target database.
ETL requires the transformations to happen before the loading process. This often means that data teams are left waiting as they can’t access any information until the whole process is completed.
Because Engineering teams typically own the extract and transform process, analysts have little visibility into the logic that has been used for the transformation process. This makes it hard for analysts to understand exactly what the data represents or help the engineering team fix any bugs with the transformation code, often leading to incorrectly drawn conclusions.
ELT
Unlike ETL, extract, load, and transform (ELT) does not require data transformations to take place before the loading process.
ELT loads raw data directly into a target data warehouse.
With ELT data pipeline, data cleansing, enrichment, and data transformation all occur inside the data warehouse itself.
Raw data is stored indefinitely in the data warehouse, allowing for multiple transformations.
This is enabled by the fact that modern cloud data warehouses are extremely scalable and separate storage from compute resources.
With the transformation happening in the warehouse and as it’s typically defined using SQL, analysts can contribute to or entirely own the transformation logic.
Differences
1. For ETL, the process of data ingestion is made slower by transforming data on a separate server before the loading process. ELT in contrast, delivers faster data ingestion, because data is not sent to a secondary server for restructuring and data can be loaded & transformed simultaneously.
2. In ETL process needs time for transformation completion. As the data size grows, transformation time increases. In ELT process, speed never depends on the size of the data.
3. The raw data retention of ELT creates a rich historical archive for generating business intelligence. As goals and strategies change, BI teams can re-query raw data to develop new transformations using comprehensive datasets. ETL on the other hand, does not generate complete raw data sets that are endlessly quarriable.
4. ELT can process data sets that contain both structured and unstructured data while ETL is typically used for structured data.
5. ETL does not have data lake compatibility like ELT.
6. ETL process may require continuous maintenance to ensure it’s up to date with the changing input sources. In contrast ELT will have less maintenance as the process is simpler and more automated. Since transforming is the last step in the process, it’s easier to fix bugs in your transformation pipeline. Unlike in ETL, you can re-run only the updated transformation to get the correct output.
7. Most companies are required to encrypt, remove, or mask data to protect their client’s privacy. If they do not try their best to do so, they may violate compliance standards and risk the client’s sensitive data. Because ETL completes transformation before loading data into the server, it is better for meeting compliance standards and transferring sensitive data than ELT which loads the data directly to the target database
8. Modern ETL has existed for 20+ years; its practices & protocols are well-known and documented. ELT is a newer form of data integration; less documentation & experience. There are limited tools available that offer complete support for ELT processes.
9. The initial cost in setting up your ETL process can be high, as you may need to define the processes and transformations you’ll need for your project. ELT tools can easily automate the process of onboarding data. As you don’t need to define transformations, the initial cost is lower in comparison to ETL.
Conclusion
- ETL and ELT are the two different processes that are used to fulfill the same requirement, i.e., preparing data so that it can be analyzed and used for superior business decision making.
- ETL transforms data on a separate processing server, while ELT transforms data within the data warehouse itself.
- ETL does not transfer raw data into the data warehouse, while ELT sends raw data directly to the data warehouse.
- Implementing an ELT process is more intricate as compared ETL, however, it is now being favored. The design and execution of ELT may necessitate some more exertions but it offers more benefits in the long run.
- Overall, ELT is an economical process as it requires fewer resources and takes a smaller amount of time. However, if the target system is not robust enough for ELT, ETL might be a more suited choice
References
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