EL, ELT & ETL: What to Choose & When?

Image by Freepik

Extraction, Storage and Transformation are the initial steps of data processing and management. Depending on the order of the steps and situation, organizations use three major techniques. These techniques have their own advantages and disadvantages. In this article, let’s explore the differences between each of these techniques and discuss when each technique is most appropriate.

1 — EL: Extract & Load

Image Courtesy — Flat Icon

Extract and Load or EL is used when the data is stored in the system with the same format that we extracted the data. In this technique, there will not be any transformation involved.

This can be used in the below scenarios:

• When the API which has the data, has the same schema that we use in our storage
• When the extracted data are already preprocessed to match our requirement

2 — ELT: Extract, Load & Transform

Image Courtesy — Flat Icon

ELT or Extract-Load-Transform is used when raw data will be loaded directly into the Data Lake and then transformation techniques will be applied.

This can be used in below scenarios:

• When the consumers don’t need to see the imported data in real time, you can batch-process raw data at a defined time.
• When there is a time concern in importing data
• When there is limited computational power or performance

3 — ETL: Extract, Transform & Load

Image Courtesy — Flat Icon

ETL or Extract-Transform-Load is used when the transformation techniques applied to the imported data occur before it gets stored in the destination.

This can be used in below scenarios:

• When migrating data from one data source such as a No-SQL Database to another such as a MySQL database
• When consumer needs real-time data in their dashboards
• When you need to connect data from other sources with the incoming dataset
• When you need to apply Machine learning techniques before storing the data

Apart from the above-mentioned scenarios and definitions, the technique that you choose will depend on a variety of factors such as the size and complexity of the data set, the resources available for processing and storage, and the specific needs and goals of the organization and stakeholders. By getting a better understanding of the differences between these techniques, organizations can make better decisions on what technique needs to be used.