Understanding ELT: Extract, Load, Transform

ELT, a method of processing data, is becoming one of the most valuable strategies companies can use. Not only does it help companies get more insights from their data, but companies also get those insights faster and at a lower cost. Its predecessor process, ETL (don’t confuse the order of the letters), still has its place. However, Deloitte summed up the difference between the two processes: “The future role of ETL… is ELT.” In this article, we’ll go over the basics of ELT, its benefits, use cases, and what to look for in an ELT tool. 

Basics of ELT

What is ELT exactly? ELT stands for “extract, load, transform.” It’s an alternative process to the traditional ETL, or “extract, transform, load.” As companies process data, the data generally needs to go through some changes to remove duplicates, clean errors, reformat, and otherwise prepare the data for storage. Both ELT and ETL achieve this goal, but they do it in different ways. 

In traditional ETL, the data is extracted, cleaned and transformed into the proper format, and then loaded into a data warehouse. However, in ELT, the data is extracted, loaded directly into a central storage repository, and then cleaned in the repository. 

Key components of ELT process

The ELT process has three main components. They’re the same components of ETL, and although there are a few differences in the process, here are the core steps:

1. Extract. The “E” in ELT stands for extract. The extraction component involves extracting, or gathering, data from multiple sources across your organization. 
2. Load. The raw—and usually unstructured—data is loaded into a central repository. This can be a data warehouse, but for ELT, it’s usually a data lake. 
3. Transform. The data is transformed to fit the target system. This may involve cleaning the data, reformatting it, eliminating irrelevant data, aggregating and joining data, and otherwise organizing it to fit your goals. 

A brief history of ELT

Back in the 1970s—ancient times in computer technology—data started becoming more available and shareable. Computer analysts would extract data from simple spreadsheets, manually transform and clean the data, and then upload it somewhere else. Most of the data was stored in simple relational databases.

As memory capacity increased in computers, companies started using a more formal ETL process. The advent of data warehouses in the ‘90s led to more storage options drawing different types of data from multiple sources. Some of the earliest ETL tools, such as Pentaho Kettle and Talend Open Studio for Data Integration, used a single program with one SQL dialect with only one server operating in a local network. Data engineers developed scripts to help automate data transformation. Data was no longer just for computer companies with geeky engineers; all companies started realizing the importance of using data, making ELT in business a crucial part of understanding a company’s information. Data types became more complex, too, and started coming in more formats from more sources. 

Although data and ETL tools continued to develop over the next several years, the next major change was cloud computing. The cloud completely changed the way data was stored, transferred, and processed. Suddenly, companies could store much larger amounts of data in the cloud, which eliminated much of the need for on-premises hardware. In some cases, cloud computing allowed companies to transform data in the cloud without any need for a temporary server. 

What are the main differences between ETL and ELT? 

While ELT and ETL have many of the same basic processes, the small differences end up having big implications. Each process has its own advantages and disadvantages. Depending on what your goals are and what your computing infrastructure is like, you may lean toward one or the other. Here are some of the main differences between ETL and ELT:

Where the data is transformed

The biggest difference between ETL and ELT—and the one that’s most obvious from the acronyms—is when the data transformation takes place. In ETL, data is loaded into a staging area where it’s transformed before being loaded into a warehouse. However, in ELT, data is loaded directly into the warehouse or other repository and is transformed there; there’s no staging area. This makes ELT faster. 

If you work in an industry that demands near-instant updates (like the stock market), you’ll likely opt for ELT. ELT also allows you to only transform the data you need at the specific time. However, if your data needs extensive cleaning, it might be more efficient to use ETL so you have a chance to make sure your data is accurate. You’ll probably use ETL if you use relational databases or work with mostly structured data. 

The importance of security

Of course, data security and safety is important (or, at least, should be important) to every process at every company. However, if you’re working with sensitive data, this might be a key difference for you to consider. 

In traditional ETL, the transformation phase is more systematic and can include more compliance and data privacy procedures. Because the transformation phase occurs on a staging platform, companies have more granular control over the details of the transformation process to make sure the transformations meet certain regulatory standards. 

With ELT, on the other hand, raw data is uploaded directly to the repository without any changes. This means that data could potentially sit for an indeterminate amount of time in the repository without being encrypted, masked, or anonymized. That’s why many companies that deal with sensitive data, such as healthcare companies, tend to use ETL. 

Where data is stored

Traditional ETL was designed with data warehouses. ELT data can still be stored in a data warehouse, but it’s more common for ELT processes to store data in a data lakehouse or something similar that can hold both structured and unstructured data in the cloud. ELT takes a wider variety of raw data from more APIs and other sources, 

What your infrastructure is like

Because ELT offers faster, real-time transformations and can handle heavier data loads, it has to be done on the cloud. Traditional ETL does transformation outside the repository and can run in small batches, so it almost always needs specific hardware and is likely to have on-prem components. 

How does the ELT process work?

The first step in both ELT and ETL is extracting data. Usually, ETL is best suited for structured data that can be represented in tables and is the better choice when working with complex transformations of smaller data sets. However, when working with larger data sets, a major advantage of ELT is that you can extract and directly upload raw data from a wider variety of sources. You can work with a wider variety of raw data, including unstructured data like videos and images. 

The second step in ELT is loading. You can upload your freshly extracted data directly into your data lake or other cloud repository. Modern cloud systems are more powerful than on-prem systems, so ELT processes enable you to upload vast amounts of data quickly. Multiple nodes can work in parallel to achieve this in a single step, rather than having to upload data in batches. The data does not need to be cleaned or otherwise organized at this stage; you can simply load it into your repository to make sure the data is safe and in a centralized place. 

The third and final step of ELT is transformation. Transformation is done within the repository. You can choose to transform all of the data at once, or you can only transform the small amount of data you need for a specific project. You can perform many different kinds of transformations on the data at this point, including these transformation techniques:

• Masking or anonymizing data if you’re working with sensitive information
• Deduplicating and getting rid of errors
• Aggregating numbers (like adding and subtracting certain fields together)
• Modifying text strings
• Reformatting and denesting data and making values such as dates and names consistent as well as make sure the data is in a format appropriate for its end destination
• Converting numbers (such as miles to kilometers or converting different types of currencies)

Benefits of ELT

While both ETL and ELT have their place, ELT has several distinct advantages. If your organization is ready to grow, here are some of the benefits you’ll get from investing in ELT.

Scalability

Scalability is a major benefit of ELT over ETL. Traditional ETL, because it has to be loaded onto a staging area and may need to use on-prem equipment, tends to be slower; larger volumes of data may need to be uploaded in batches. In contrast, ELT is designed with cloud environments in mind, so this process can handle larger volumes of data much faster. For organizations ready to grow quickly and start making the most of your data, ELT is where you want to focus your energy. 

Faster insights

Transformation in ELT data warehouses or datalakes can happen much faster than in ETL processes. Transformation can run continuously, offering companies real-time data to make more accurate predictions and smarter business decisions. Multiple cloud servers can run nodes in parallel to process data simultaneously, rather than having to slowly upload data in batches like in ETL. 

Flexibility

In ELT, you can upload raw data directly in the ELT data warehouse, then run a variety of transformations later. You have the flexibility to adapt to different data transformation logic later. In addition to helping you be more nimble in your business, this flexibility has another benefit: a coding error in transformation in one area doesn’t have to ripple the mistake through later transformations. 

Top use cases for using ELT

ELT has many practical applications in the real world. There are many use cases where ELT is a strategic option. You’ll know ELT is right for you if…

…You need real-time data

Because updates happen so quickly and each change affects other values, any company that deals with the stock market needs to opt for ELT. If you need real-time or near-real-time data to make crucial business decisions, ELT is for you. 

…You’re ready to let go of legacy systems

We get it: legacy systems have a lot of stability and advantages. It can be a big investment to change entirely to the cloud and put a lot of new systems in place. But if you’re prepared to make the jump and ditch the hardware, ELT offers faster, more flexible, more scalable processing. 

…You want to cut costs

ELT systems cost less to maintain than traditional, rigid ETL systems. They also tend to be more energy efficient. The up-front cost is worth it! 

…You’ve recently added more business tools

Customer relationship management (CRM), enterprise resource planning (ERP), supply chain management (SCM), and more—most businesses are constantly adding more tools. Each one is useful, but if you’re not looking at the data holistically, you’re missing out on a lot of value. ELTs can help you process data from all these different sources, even if the data is in a variety of different formats. 

How to choose the right ELT tool

There are many ELT tools out there—after all, the global data pipeline SaaS market was valued at more than USD $6 billion in 2022 and is expected to hit $17.6 billion by 2027. As you’re choosing the right ELT tool for you and your company, here are some things to look for. 

Ease of use is one of the most prominent characteristics you should consider. Many ETL platforms have low-code or no-code options. Many tools have drag-and-drop functionality that allows users with no SQL experience to join in without a problem. 

You’ll also want to ask about connectors. ELTs connect with many APIs and work with more kinds of data than ETL tools do, but you’ll still want to check and make sure the tool you’re looking at can handle the types of data you’ll need it to process. 

Another factor is what you’ll be using the tool for. You’ll need to assess the data literacy of your employees and what kinds of use cases you have for the software, as those will help you determine how well the ELT tool will meet your company’s needs. 

Getting the most from your ELT data

Scalability, efficiency, and visibility are all advantages of ELT. It’s not a coincidence they’re all advantages of using Domo. With Domo’s data transformation tools, you can choose your own indexing and system tables, refine scripts directly in Domo, and orchestrate data pipelines easily. Whether you’re a drag-and-drop user or a write-your-own-custom-R-script user, Domo has options for you to get the most from your ELT data. Try the tools for free today!