ETL and ELT

ETL (Extract, Transform, and Load) and ELT (Extract, Load, and Transform) are acronyms that describe the three stages of a data pipeline.

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ETL (Extract, Transform, and Load) and ELT (Extract, Load, and Transform) are acronyms that describe the three stages of a data pipeline. Both the ETL and ELT process involve cleaning, enriching, and transforming data, with the difference being when you transform the data. 

Extract

In the extract step, you are pulling data from a source. The data can be structured or unstructured. 

Transform

The transform step cleans the data and formats it into a structure that matches the format of the target data storage system. 

Load

This is the process of moving the data into the storage destination and analyzing the information with business intelligence tools. 

ETL is the most commonly used approach to gather and reformat data. With ETL, data is taken from a variety of sources, and transformed before it’s loaded into a system of defined formats and styles. Afterwards, the transformed data is loaded into a data warehouse. This process is best for small data sets that require complex transformations. Analysis can happen immediately once the process is complete, since the data is transformed before being loaded into the data warehouse.  

The ELT Process

In the ELT process, data is extracted from one or more sources and is loaded into the data warehouse without any formatting. It’s a lower-cost process that’s suitable for structured and unstructured data. The ELT process has lower maintenance overhead since data is loaded without being transformed.

Since all the data is loaded into the target database, transformation can happen at any time on an as-needed basis. While there is lower maintenance in loading data using ELT, the costs for storing this non-transformed data can be higher, since the non-transformed data consumes more space. The benefits, however, is that you can conveniently mine this data for relevant business intelligence. This process of mining for business intelligence can be slow if there isn’t sufficient processing power. 

The difference between ETL and ELT is when the data is transformed and how much data is retained. There are challenges inherent in both processes. While ETL has been the standard process for data integration, it requires more hardware and IT support and takes longer to complete. 

If your priority is speed, ELT is going to be the best choice since the process to deliver raw data is faster. More businesses are shifting to an ELT process thanks to the availability of cloud-based services. However, with ELT you may face challenges with compliance of privacy regulations, such as GDPR and CCPA since you are loading data all at once. The benefit of ETL is that you can eliminate certain data before it’s loaded to avoid privacy violations. 

Whether you choose ETL or ELT, it’s essential to use data tools, especially automation, when managing your data pipeline. Your solution will depend on a variety of factors, such as the long-term needs of your business, the type of data you have, and your data storage.

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