ETL vs. ELT

At first glance, it may be difficult to discern the differences between ETL and ELT. While similar in appearance, the acronyms refer to different approaches to moving and processing data, revealing the evolution and growth of data over the years.

ETL and ELT are processes used by data integration tools. Through each process, data is pulled from different sources and transformed into useful information.

ETL stands for Extract, Transform, and Load. Conversely, ELT stands for Extract, Load, and Transform. This variation in the order of operations between the transformation and loading steps is most evident when looking at the role of the data warehouse, the amount of data sources and volumes, and processing times. 

ETL: The Original Method

The more traditional approach, ETL, extracts data from various sources and transforms it in the data warehouse before loading. This method gained popularity in the early 1990s when companies began integrating data from legacy systems into a data warehouse.

With ETL, data is transformed en route to the data warehouse, arriving in its finished state. But with greater complexity and an increased volume of data, the data loading process is slowed down. Alternatively, ELT loads data first, leveraging the data warehouse to make changes to the data, increasing the data footprint and transformation capacity.

Since ETL requires data transformation prior to loading it into the data warehouse, additional tools, resources, and servers may be required outside of the data warehouse.

ELT: The New Approach

As a more modern approach to data processing, ELT switches the order of operations, loading data directly into the data warehouse before transforming it. The cloud has played a large role in the need for ELT. 

It is estimated that 328.77 million terabytes of data are created every day, and ELT is a response to the complexities surrounding large quantities of data. From social media to mobile devices, websites, videos, images, and the Internet of Things (IoT), the sheer volume of data now is far greater than when ETL gained popularity over 30 years ago.

One major difference between ETL and ELT is that ELT allows data to remain In its own environment, avoiding the intermediate step of using an external resource for data processing.

As a result, data does not need to be unloaded, producing a solution that is more robust and able to handle increased volumes of data. Native communication within the ELT approach also allows for optimizing existing technologies, improving performance, deployment speeds, and scalability.

With its in-house approach, the data warehouse takes on a more active role in data processing with ELT. This makes ELT an adaptable, cost-effective option. However, because the data processing occurs in the same environment, processing capabilities might be strained. 

Which Is the Best Approach?

Where ETL is the traditional approach, and ELT is the modern alternative, both methods have advantages and disadvantages. Deciding between the two depends on factors such as data volume, level of complexity, performance requirements, and the capabilities of the data warehouse platform. 

However, created to cater to on-premise systems, ETL’s manual loading process is slower, requires more resources, creates a bottleneck effect that slows data flow, and leads to increased costs. ETL adds an additional layer of work and more tools to manage data, especially as companies continue to rely on big data. 

With the adoption of the cloud and emerging technologies, ELT has become the preferred order of operations, better equipped to process complex data in larger volumes across multiple platforms with fewer resources. This is especially true for enterprises. Through automation and the use of existing information systems, ELT can transform data directly in the data warehouse, evading the inefficiencies of ETL. 

The sequential differences of each data processing method are responses to the needs of the moment. As cloud usage and multi-cloud architectures like Amazon and Google continue to gain popularity, ELT bypasses the intermediate layer of removing data from its environment for processing. As a result, ELT emerges as a more practical solution able to perform well across a variety of use cases.