ETL vs. ELT: 6 major differences to know

Understanding the differences between ETL and ELT is essential as a data manager because each has pros and cons.

Using the one that’s best for you will help validate data and improve its quality, allowing you to make better business decisions. So what are ETL and ELT tools, and which is best?

In this short guide, we’ll cover the six primary differences between ETL and ELT. We’ll also look at ETL vs. ELT examples while answering frequently asked questions.

What is ELT vs. ETL in a data warehouse?

ETL stands for “extract, transform, and load,” and ELT stands for “extract, load, and transform.” The primary difference is the sequence these events occur in.

With ETL, you transform data while moving it. But with ELT, you transform data after the moving process. Each integration method has advantages and disadvantages. Both are different from reverse ETL, which you can learn more about here.

The difference between ETL and ELT in data warehousing

ETL lands data in its finished form. This makes it easier to handle scenarios in real time. But there’s a balance, because when data volume increases, it takes longer to load.

This is where ELT can help. An ELT tool uses a database to create transformations. So instead of transforming and loading data at the same time, you load the data without changes. This makes it easy to increase the transformation capacity and data footprint.

Now let’s look at the ETL vs. ELT pros and cons to understand their main differences.

1. ETL offers faster analysis

You can analyze data much faster and more easily with ETL because it’s already structured and modified before you load it. This leads to quicker data-based marketing decisions.

When using ELT, you only transform the data after loading it, leading to a longer wait before analysis.

2. ETL helps you meet government compliance

ETL moves, masks, and encrypts sensitive customer information before loading it, which is vital to meeting regulations like the GDPR and CCPA.

With ELT, you’re loading raw, unencrypted customer data into a tool that might not meet specific privacy regulations. Many laws also prevent companies from storing data on a cloud outside a country’s borders, so ELT may present some regulation challenges.

3. ETL is widely used

ETL was developed first and has been the standard integration method for data engineers for over two decades. More data programs use ETL, and the countless experienced engineers in the marketplace make it easier to build data pipelines.

ELT is a newer approach to data integration, meaning there aren’t many applications and engineers behind it. However, ELT is still growing, and the number of engineers using it is increasing.

4. ELT loads data faster

The primary reason ELT is increasing in popularity is the speed at which it loads data.

Because you’re uploading data as soon as it’s available and in its raw state, the loading speed is faster than ETL and provides you with immediate access.

Although ETL provides faster analysis, the actual loading speed is far slower because data must first be transformed in a staging area before loading.

So if you’re transferring complex and large amounts of demographic data, consider ELT as it transforms data after moving it.

5. ELT is more flexible

ELT stands out as it’s flexible and can ingest data in any format when partnered with a data lake. Unlike ETL, you won’t have to worry about organizing your data since the data lake accepts anything.

A significant problem when using ETL is that it’s rigid compared to ELT.

If your data warehouse’s structure doesn’t support particular analyses or inquiries, you’ll have to modify your entire data warehouse.

6. ELT data is always available

Because ELT loads all data into a data lake, it’s always available, so you won’t have to structure new data to interact with existing data immediately.

Data integration made easy with Lytics

In short, ETL is the better option if you’re transferring small amounts of unorganized data because it transforms data while loading it, allowing you to make data-backed decisions faster.

However, large transfers can become a problem, and this is where ELT is better. It loads data before transforming it, so you can transfer more data at once. If you’re looking to implement ETL/ELT data pipelines, consider Lytics.