ELT vs ETL: What's the Difference?

ETL (extract, transform, load) and ELT (extract, load, transform) are two common data-processing methods. ETL refers to extracting data from one source, transforming it into a format for another system, and loading it into the target system. In contrast, ELT loads the data into the target system first and then transforms it.

Key terms

Extract is the process of acquiring data from various sources. This data can be stored in multiple formats, such as text files, databases, or social media feeds.

Transform refers to the process of converting the data into a format that the target system can use. This may involve cleaning up the data, such as removing duplicate records or correcting errors. The transformed data should be in a format that can be easily and quickly loaded into the target system.

Load is the process of loading the data into the target system.

What is ETL?

ETL is a process that involves extracting data from a source, transforming it into a format that can be used by a destination system, and loading it into that system. The transformation step may involve cleaning or normalizing the data, as well as transforming it into the desired format. ETL is commonly used when data needs to be moved from one database or system to another.

What is ELT?

ELT is a newer data processing architecture that has emerged in recent years. ELT is similar to ETL, but the transformation step is performed after the data has been loaded into the target system. This approach can be more efficient as it can use the processing power of the target system to perform the transformation and also allows for the parallelization of data processing tasks, significantly speeding up processing times.

What are the differences between ELT and ETL?

ELT can be more fault-tolerant than ETL. If there is an error in the transformation process, it can often be rectified without re-extracting and re-loading the data. This can save a considerable amount of time and effort.

Another advantage of ELT is that it can sometimes be cheaper to implement, as it may not require specialized hardware or software. ELT can allow for greater data exploration, as the transformation step can be performed after the data has been loaded into the target system. This provides flexibility that may not be possible with ETL.

However, ELT can also have some disadvantages. For example, it can be more complex than ETL, making it difficult to debug and troubleshoot.

How do ETL and ELT work?

The rise of social media and other online sources has meant an explosion of data — and an urgent need for business to find new ways to manage this data more efficiently. ETL and ELT are two approaches that can be used to help organizations leverage massive quantities of data in the modern digital landscape.

Companies must first extract data from its source to use ETL effectively with data lake implementations. This data can come from various sources, such as databases, API calls, or flat files. Once the data is extracted, it needs to be transformed into a format that can be loaded into the target system. This may involve cleaning up the data, converting it to a different file format, or adding new columns or fields.

The transformed data gets loaded into the target system. This could be a data warehouse, an analytics platform, or another type of database. Once the information is in the target system, it can be used for various purposes, such as reporting, analysis, and decision-making.

Both ETL and ELT employ similar goals, yet they are approached differently. Using ETL, the extracted data moves from the processing server to the data warehouse only after it has been successfully transformed. This is a two-step process that involves extracting the data from its source and then transforming it into a format that can be loaded into the warehouse. ELT, on the other hand, does not involve a transformation step. The data is extracted from its source and then loaded directly into the data warehouse.

The benefits of ELT

While ELT and ETL both have their advantages, ELT is the winner when it comes to overall speed, flexibility, and scalability.


ELT beats ETL hands-down when it comes to speed. In an ETL process, data must be extracted from multiple sources, transformed into a single format, and then loaded into the target system. With ELT, data is extracted and then loaded into the target system first. Transformation can then be performed on the data while it’s in the target system, which is often more efficient.


ELT allows businesses to query their data directly without going through a transformation step. This can be exceedingly helpful when organizations need to explore their data or when they need to make ad-hoc changes to their data pipeline.


ETL tools are typically designed to work with a specific volume and type of data. When datasets grow beyond this, processes break down and result in lost data or inaccurate results. ELT solves this problem by performing transformations in a target system that is designed to handle large volumes of data.

When is ELT the right choice?

ELT is a good option for organizations for which one or more of the following apply:

  • Have large amounts of data
  • Use data that is constantly changing
  • Do complex transformations
  • Perform real-time analytics
  • Need to move data quickly from one system to another
  • Want to keep costs low
  • Want to avoid the special hardware and software requirements of ETL

Common ETL/ELT challenges

While ETL and ELT are common methods for processing data, each comes with its own set of challenges. Data quality, volume, and velocity each may pose problems for ETL, while data cleansing, transformation, and enrichment can be tough for ELT. Data quality can be compromised at any stage of ELT/ETL, from extraction to transformation to loading. Likewise, data loading and integration can be complex during either process. As a result, it’s vital to have robust quality control measures in place at each stage of ELT/ETL.

ETL and ELT can require specialized hardware or software, adding to the cost of implementation. They can be time-consuming, resource-intensive, and complex, making them difficult to debug and troubleshoot. The cost of data transport, processing, and storage can also challenge some companies. Another challenge is performance: large data sets can be time-consuming to process, and ELT/ETL can be CPU-intensive.

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