DataData ManagementTechnology

ETL Versus ELT—Does It Matter?

Data managers and business analysts are often familiar with the term ETL, or or at least broadly aware that it stands for “Extract, Transform, Load.” A Google search of the three-letter initialism brings back nearly 44 million results, nearly all of which are related to data management.

In recent years, the phrase “ELT” has cropped up more and more in discussions surrounding data management and data transfer. While Google returns 74 million results, most of them are about the “Extremely Large Telescope” under construction in the deserts of Chile.

In short, the phrase “ELT” as it relates to data management is somewhat poorly understood, at least compared to ETL. Yet understanding how they differ and where they overlap, and how the terms relate to k3 data integration, is crucial to deciding the future of your enterprise data management.

What Is ELT?

ELT stands for “Extract, Load, Transform.” It’s the same process as ETL, only the last two steps are reversed.

The traditional ETL process is made up of three steps:

  • Extract the data from one location.
  • Transform the data from one format to another, based on its assigned new location.
  • Load the data into its new location.


This is the process K3 data prep tools use to stream data in real-time using low-code solutions.

ELT stands for “Extract, Load, Transform.” It’s the same process as ETL, only the last two steps are reversed.

WIth that in mind, what are the advantages and disadvantages of both?

ETL Vs. ELT In Practice

The term “ETL” was first coined in the 1970s, as data warehousing took off in large-scale businesses. “ELT” is a more recent definition, one that has risen in use alongside the expansion of data lakes.

Thanks to cloud computing innovations such as Amazon Redshift, Snowflake, and Microsoft Azure, users can now store any type of data in one location, in contrast to data warehouses, which only accept structured and semi-structured data.

Thanks to cloud computing innovations such as Amazon Redshift, Snowflake, and Azure, users can now store any type of data in one location, in contrast to data warehouses, which only accept structured and semi-structured data

ELT: Pros & Cons

Pro: Faster processing times, on average

Because the data isn’t transformed prior to being loaded into the data lake, processing times in ELT are faster on average compared to ETL, particularly with large amounts of data. The power requirements will scale with the size of the upload, however by saving the transformation until the end, teams can potentially save time.

Con: ELT can be a walled garden

Companies like Matillion that offer ELT services work exclusively with Amazon Redshift and Snowflake. After all, ELT specifically relies on the cloud-based nature of these data lakes to function.  The challenge here is portability.  What happens when either one of these data stores radically changes their business model?   We love both Snowflake and Redshift, but getting locked in a walled garden is always expensive.

Pro: Cloud scalability

Many businesses generate varying amounts of data depending on time of year. Historically, this reality meant buying extra server space and allocating more IT resources to accommodate these shifts. With ELT, overhead costs come down due to the easy scalability of cloud data lakes.

Con: ELT is nearly impossible to run on premise

ELT is purely a cloud creature.  While we love everything about the cloud, there are certain real enterprise instances where cloud is just not an option.  Some data will alway live on premise because it is simply too sensitive, or because that is how an enterprise has decided to do things.

PRO TIP:

Be sure to ask your ELT vendor this question: “Do you have access to any of my data?” If so, this could be a disastrous situation for companies with important data. There are some vendors using customer data to train AI, ML, and other functions.

SUPER PRO TIP:

Get a written guarantee that your vendor does not maintain any backdoor, control layer, or other mechanism that enables them to read your confidential data.

K3 ETL For Data Transformation

K3’s ETL tools are platform agnostic. They work with Redshift and Snowflake as effectively and smoothly as they do with Oracle, SQL, MongoDB, or virtually any data storage choice. Better yet, K3 ETL is low-code, allowing non-developers and IT teams alike to get more data transfer done faster than ever.

To see it in action for yourself, try out a free demo below:

Request a Demo

K3 Guide

K3 Guide

Navigating the pathway to surfacing and making useful data from a myriad of sources can be daunting. Our K3 Guide is here to share best practices, objective insights and modern approaches to solving modern data prep and integration challenges.