Whether ELT replaces ETL depends on the use case. While ELT is adopted by businesses that work with big data, ETL is still the method of choice for businesses that process data from on-premises to the cloud.
Globally, 97 zettabytes of data will be produced, stored, exchanged, and used in 2022, and that number will rise to 181 zettabytes in 2025. This expansion is anticipated to last well into the foreseeable future after 2025. Source — Statistica
It is obvious that data is expanding and pervasive. As a result, the demand for innovative processes to accurately gather, organize, and interpret data will also increase. Data-driven enterprises will have the opportunity to further data engineering practices. Under data engineering, data pipelines are formed by utilizing data integration strategies on disparate data from the source.
On the same line, there are two data integration strategies — ETL (Extract, Transform, and Load) and ELT (Extract, Load and Transform).
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Differences in data integration procedures
The fundamental difference between the two data pipelines is ETL transforms data on a separate processing server, while ELT transforms data within the data warehouse itself.
In the area of data integration, the transform stage is a game-changer for both ELT and ETL. Let’s assume that the questions that data consumers seek clarification are on: How many people have visited my website’s contact page in the past three months?
In an ETL data integration process, data engineers often take website visitor data (source data), extract it, and transform the data in accordance with a set of specifications, such as what we can accomplish with this data, where we can store it, and how it can help other departments.
However, ETL’s high initial cost is one of its most obvious disadvantages. In the case of an on-site data storage, the cost will be in the hundreds of thousands of dollars range. The initial cost will be quite significant because data engineers must start by designing a transformation algorithm, even if they use cloud-based storage.
When using ETL, the process of transforming data before loading it into the storage system can be somewhat time-consuming. Fortunately, it does have certain benefits. The data is already pre-structured as it is loaded, making analysis quick and simple.
On the other hand, data engineers store a lot of raw data, therefore ELT is perfect for broad-based analytics. They can both perform extensive analyses of historical data or run very small adjustments to obtain reports about particular topics. This is prohibited by ETL because it doesn’t keep raw data.
Apart from the differences in the data integration processes, companies choose ELT or ETL on the basis of their preference. Let’s examine this in more depth.
Read the full blog to know if ELT is replacing ETL.
