ETL VS. ELT: Stream or Batch Your Warehouse Data

In this article, we will discuss two of the most important data integration processes when dealing with large amounts of data from many different sources. We are talking about ETL and ELT. 

We will go through what ELT and ETL are, how they are used, and which one is better for your organization. We will also discuss the main differences between them and some of the benefits of the two processes. 

This article will help you understand the difference between these two options and help you choose the optimal process for your business based on your data size.

The ETL and ELT Concepts

Data plays an important role in nearly every business operation. For your data to be valuable, you need a way to gather data from an unlimited number of sources, organize it, and centralize it into a single repository. This is why we’ll be reviewing ETL and ELT processes.

ETL stands for extract, transform, and load. These are the three steps that ETL is based on to blend data from multiple sources and load it to a new warehouse.

The first step in ETL is to extract data from an array of sources and place it in a destination, then, in the transformation step, we apply any business rule to achieve the required tasks on this data.

The last step in ETL is the loading process. This is where we load the transformed data to a new warehouse so it can be used directly.

The same goes for ELT, but instead of transforming the data after extraction, we load the data to the new warehouse and then do the transformation. We will discuss the differences between the two processes later on.

It is important to note that ETL is now evolved to support the requirement of integration for data via stream processing.

Organizations now need both ETL and ELT in order to gather data together, maintain accuracy, and provide analytics and reporting for data in real-time to the data warehouse.

The Difference between ETL and ELT

In essence, ETL and ELT are two different approaches to data integration. The main distinction between them is the order of events of transformation and loading of the data. 

In ETL we apply a transformation to the data while it’s being loaded, but in ELT we transform the data after it’s been loaded to the warehouse.

ETL has the advantage of landing data in its finished transformed state which can handle real-time scenarios. As the complexity of data and volume increases, the ability of the ETL tool to load the data in a timely fashion is compromised thus leading us to ELT.

With ELT we load the data without any transformation. Then after it’s been loaded we make changes to our data in the transformation step. This can help maximize the data capacity without worrying about the time it takes to apply transformation rules.

One of the use cases that makes you choose ELT over ETL is when the organization needs its data to be loaded as soon as possible without having to wait for the transformation to end.

ETL and ELT have a lot in common since each successful integration can transfer data from a source to a warehouse or a lake. The difference is when the data will be transformed or loaded. So we’ll discuss the differences between them in detail and help you decide which one to choose.

Benefits of Using ELT

  • Leveraging Data Warehouse

           ELT is the best solution in open source cloud platforms, which improves security and eliminates data challenges. It also leverages the capabilities of cloud data warehouses.

  • Scalability

          This allows organizations to expand their resources due to the scalability of  their cloud services.

  • Complexity:

          ELT has a great GUI which is easy-to-use and simplifies the process.

  • Massive data

          ELT is chosen when dealing with huge amounts of structured data.

  • Low maintenance

           Since ELT is in the cloud, users don’t have to worry about maintenance. It is updated on its own without the user manually having to make any updates.

  • High Speed

          Due to the high data availability, users can load all the data with high speed and start to analyze and transform it. 

Benefits of Using ETL

  • Handling Big Data

           Using ETL tools allows you to use large data sets together in different forms such as structured and unstructured from different sources using Cloud object store..

  • Performance

           ETL ensures that users have access to a huge amount of data. Due to the transformation before loading the data, the data is ready to use after it’s loaded.

  • Easy to implement

           With the use of ETL tools, it is easy to implement and facilitate the process of ETL.

  • Lower cost

          Data integration in ETL requires a lower cost than ELT. Also, the cost of data warehousing is lower since you decide what data has to be imported.

  • Better data freshness

           Data is always updated and ready to be queried, which means that it’s good for real-time scenarios.

  • More secured

           In ETL you can detect and remove sensitive information before loading it to the data warehouse which ensures compliance with GDPR, HIPAA, and CCPA standards and protects data from being                         hacked.

  • Flexible

           ELT is flexible compared to ETL in terms of storing new unstructured data. With ELT, you can store different types of data without having to structure the data first. This saves time for developers                       when dealing with new data and not having to restructure these data.


After learning the difference between ETL and ELT, you can now choose which one to utilize based on your business needs. 

To sum up, ETL is considered the preferred approach, as there are well-developed ETL tools and platforms that help with data extraction, transformation, and charging. Also, it’s less locked into the data warehouse, as the process of data extraction and transformation is done in the lake. You can prepare the data through stream processing and deliver the analytics data in real-time to the data warehouse.

ELT adds more delay to Its process, and lack of performance and flexibility as it’s a batch process. So It’s preferable to use ETL architecture instead.

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Published in: Blog , Cloud Architecture
Upsolver Team
Upsolver Team

Upsolver enables any data engineer to build continuous SQL data pipelines for cloud data lake. Our team of expert solution architects is always available to chat about your next data project. Get in touch

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