ETL vs ELT: The Difference and Why ELT Is Better

ETL vs ELT: The Difference and Why ELT Is Better
Table of Contents
In: Data Engineering

When it comes to data processing, there are two main methods: ETL and ELT. ETL, or Extract-Transform-Load, is the older of the two processes and is still used in many businesses today. However, ELT, or Extract-Load-Transform, is becoming more and more popular due to its many benefits. In this blog post, we will define both processes and explain the differences between them. We will also discuss why ELT is a better option for most businesses and offer some use cases to illustrate this point.

ETL Definition: Getting Down to the Basics

ETL is a data processing method that involves extracting data from outside sources, transforming it to fit the needs of the business, and then loading it into the company’s data warehouse.

The transformation step is often the most time-consuming and difficult part of the ETL process. It generally includes cleansing the data, which can be a tedious and manual task. Once the data has been transformed, it is ready to be loaded into the company’s data warehouse where it can be used for reporting and analysis.

ELT — the next generation of ETL

ELT is a newer data processing method that has gained popularity in recent years. Unlike ETL, ELT does not require the data to be transformed before it is loaded into the database. This means that the data can be loaded into the database first and then transformed later, which is a much simpler and more efficient process.

One of the biggest benefits of ELT is that it eliminates the need for a separate data transformation step. This makes the entire process much faster and easier to manage. In addition, ELT offers greater flexibility when it comes to data processing. Businesses can easily add new data sources and quickly adapt to changing needs.

Use Cases: when should you use ETL vs ELT?

ETL is best suited for businesses that have a large amount of data that needs to be processed regularly. ETL can also be used for data that requires extensive transformation before it can be loaded into the database.

ELT, on the other hand, is ideal for businesses with less data or those that need to load data into the database more quickly. ELT is also a good option for businesses that want more flexibility when it comes to data processing.

Use Case: Business Intelligence

Business Intelligence is the process of turning data into insights that can be used to make better business decisions.

To do this, businesses need to have a data warehouse—a centralized repository of all their data.

Often we find that businesses have data scattered across different systems, making it difficult to get a complete picture of what’s going on. This is where ELT comes in.

ETL can be used to extract data from multiple sources and load it into a data warehouse. Once the data is in the warehouse, it can be transformed and analyzed to provide insights

Delivering a single point-of-view

After the first two steps within the ELT process is complete, the source data is consolidated within an analytics-ready environment for analytical workloads.

This allows businesses to start building transformations and data models on the source data - all from a single point.

Improving efficiency and productivity

It is worth considering that if all the source data is extracted and loaded into the warehouse, transformations can be easily applied (and changed) when business rules invariably change. This allows for a single version of the truth and therefore improved data quality.

It is an often overlooked benefit of the ELT pattern that it allows you to treat your transformation layer in your data warehouse as you would treat software within a product and engineering team. We now have the technology to apply transformations that are plain SQL queries against your source data.

Providing historical context

When you are dealing with data that changes over time, it is essential to be able to track these changes.

In an ELT approach, the entire history of the data is retained every time a load is run. This allows for easier auditing and provides more context when analyzing the data.

It also means that if something goes wrong, it is easy to roll back to a previous version of the data.

In an ETL approach, on the other hand, you would need to maintain separate copies of the data for each point in time, which quickly becomes impractical.


In the ELT approach, the Transforming step is where things get interesting. We briefly mentioned that we are supporters of keeping transformations as standard SQL queries. dbt (data build tool) is an open-source tool that allows for the orchestration of these SQL transformations.

The power of dbt lies in its ability to handle dependencies. For example, if you have a table that needs to be transformed, and that transformation depends on another table that also needs to be transformed, dbt will take care of that for you.

This is a huge benefit because it allows you to focus on the transformation itself, without worrying about the order in which they need to be applied. We have only just scratched the surface here in terms of what dbt is capable of; it can also handle documentation, testing and much, much more.

Everyone at Horizon Data (including the Project Management Function) has completed the dbt Courses. We are that sold on the framework.

What to look for when you choose an ELT tool

When you are choosing an ELT tool, there are a few things you should keep in mind.

First, the tool should be able to handle the volume of data you need to process.

Second, it should be able to connect to all the different data sources you need to use.

And finally, it should be easy to use and maintain by having a user-friendly interface and good documentation.


It is an important consideration, as your data volume is likely to grow over time as your business grows.

A good ELT tool will be able to handle this growth without needing to be replaced.


Your ELT tool needs to be able to connect to all the different data sources you need to use.

This includes databases, CRMs, ERPs, and so on. Additionally, it should be able to connect to data in the cloud as well as on-premise datasets.

Fivetran and Stitch Data both can connect to a wide range of data sources.

There are many different ELT tools on the market, so choosing the right one can be a challenge. It is quite common to make use of more than one ELT tool at the same time working off of the strengths of each.

At the end of the day, it is important to choose a tool that caters for both the immediate and long-term needs of the business.

The Bottom Line

ELT provides several advantages over ETL, the most important being its ability to provide a single point of view. The improved efficiency and productivity can be seen in everything from data preparation to audits and debugging. If you’re looking for an easier, more streamlined way to work with your data, ELT is the approach for you.

We are huge supporters of the ELT approach here at Horizon Data. If you have any questions or would like to learn more about ETL vs ELT, please contact us. We would be more than happy to chat with you about your specific needs and how we can help.

Drop us a line

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