What Is the Difference Between ELT and ETL?

The data integration techniques ETL (extract, transform, load) and ELT (extract, load, transform) move unprocessed data from a source system to a target database, like a data warehouse or data lake. These data sources may be spread across several distinct repositories or within older systems, but thanks to ELT and ETL, they may be moved to any preferred target data location.

What Does ELT (Extract, Load, Transform) Mean?

ELT allows for the extraction of unstructured data from a source system and loading it onto a destination system for later transformation. There is no requirement for data staging because the extracted, unstructured data is readily available to business intelligence systems. ELT uses data warehousing to perform fundamental data transformations including data validation and duplicate data removal. These procedures are utilized for huge volumes of unprocessed data and are updated in real-time. Compared to its more established sister, ETL, ELT is a more recent process that hasn’t yet realized all of its potentials. Originally, the ELT procedure was built upon SQL scripts that were hard-coded. Compared to the more sophisticated ETL techniques, such SQL scripts are more likely to contain potential code flaws.

What Does ETL (Extract, Transform, Load) Mean?

Prior to loading data into the target systems, unstructured data from a source system is extracted, and specific data points and potential “keys” are discovered. The source data is extracted into a staging area and sent to the target system in a conventional ETL scenario. All data kinds are organized and cleaned during the data transformation process in the staging area. Owing to this transformation procedure, the newly organized data can now be used with the intended data storage systems . Relational databases, which had previously dominated the market, were how ETL was initially intended to operate. Since the 1970s, when data engineers began working on ETL, these procedures have undergone tremendous improvement.

Benefits of ELT and ETL


Although the data is disorganized when it is relocated, the ELT approach offers faster implementation than the ETL process. The transformation prevents a migration slowness by occurring after the load function. ELT decouples the transformation and load phases to avert risks of the migration process from being halted by a coding error (or other flaws in the transformation step). ELT also avoids issues with server scalability by utilizing the data warehouse's processing power and size to facilitate transformation on a large scale. Working in conjunction with cloud data warehouse solutions allows ETL to support unstructured, structured, semi-structured, and raw data types.


ETL requires more time to implement but produces cleaner data. Thi technique ideally suits smaller target data sources that require less regular updates. ETL utilizes on-site data warehouses and cloud-based SaaS systems to work with cloud data warehouses.

There are a lot of open-source and paid ETL tools available. Some of their features and advantages include:

Comprehensive automation and user-friendly features can automate the entire data flow and suggest rules for extracting, transforming, and loading data.
A visual drag-and-drop tool for defining data flows and rules.
Support for complicated data management helps with string manipulation, data integration, and complex calculations.
Security and compliance that encrypt sensitive data, whether or not in use, and are approved as adhering to rules set forth by the government as well as the private sector, such as HIPAA and GDPR. Foe enhanced customer privacy, ETL offers a more secure approach to encrypting, deleting, or disguising individual data fields.

ELT and ETL Use Cases


High-volume data sets or real-time data use scenarios are the optimal cases for application of an ELT procedure.

Examples include:

Organizations with huge data volumes: Meteorological systems, like weather services, regularly collect, assemble, and use massive amounts of data. This category also includes companies with a lot of transactions. Data transfers from the source can be made more quickly using the extract, load, and transform methods.
Organizations that want immediate access: Stock exchanges produce and consume enormous amounts of data in real-time, where delays can be detrimental. Large-scale suppliers of components and materials also require immediate access to the most recent data for business information.


ETL is most effective when used to migrate data from legacy systems and synchronize various data use scenarios. Here are some specific instances:

Businesses that must synchronize data from many sources: Businesses that are merging their ventures may share a variety of partners, suppliers, and customers. These data can be formatted differently and kept in multiple data repositories. ETL works to transform the data into a uniform format before loading it onto the final data location.
Companies with outdated legacy systems that need to update their data: The ETL procedure is necessary to convert the data from the old systems into a format that is compatible with the updated database structure.

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