ETL vs ELT: Key Differences and When to Use Each Method

Today’s organizations have access to vast amounts of data, which they can mine to uncover patterns and insights. The difficult part is bringing all that data together into a unified view. Data is often housed in multiple disparate locations and across formats. How do organizations combine it for greater access, visibility, and analysis? 

Many look to ETL and ELT to do just that. These two data processing methods are the most common means of gathering, cleansing, and storing disparate data. At first glance, it may look like the only difference between the two methods is the order of the acronyms. However, there are larger distinctions between the two, including common use cases, advantages, and disadvantages. Understanding the key difference between ETL and ELT is the first step to knowing when to employ each method. 

ETL vs ELT: A summary

ETL and ELT are both methods of data processing that can be used to manage and analyze data. Understanding the difference between the two methods is essential to determining which is most suitable for your data strategy, management, and organizational goals. 

ETL stands for “Extract, Transform, Load.” Data is pulled from a variety of sources, processed and transformed into a suitable format, and then loaded into its final destination (e.g., data warehouse). This method cleans, enhances, and organizes data before it is stored. It generates structured, reliable data for business intelligence (BI) purposes. 

ELT, on the other hand, means “Extract, Load, Transform.” As with ETL, the data is first extracted. However, it is then loaded into the final destination for storage, such as a data lake. Finally, the data is transformed within the storage system. ELT also results in data that can be leveraged for BI analysis. 

So, how do you know which data processing method is best for your needs? It depends on your data volume, infrastructure, and objectives. ETL is generally preferred for complex data transformation projects, legacy systems, and extensive data cleansing. ELT works well for processing large volumes of data and real-time processing requirements.

History of ETL and ELT

ETL began in the 1970s to integrate data from disparate sources into a centralized location. Organizations dealing with data across multiple locations needed an efficient way to consolidate all of this information in one place, so ETL became the go-to method. It was originally mostly manual but evolved to include automation in the late 1980s. 

ELT emerged as cloud computing advanced. By the 2010s, it had grown in popularity as this method better leveraged the scalability and processing capabilities of cloud-based tools. 

What is ETL?

ELT or Extract, Transform, and Load can be used for data integration. Here’s how it works:

1. Extract: Data pulls from source systems such as databases, files, APIs, and other data repositories.
2. Transform: The data is then transformed into the target format that aligns with the target data warehouse schema. It may also be cleaned and enhanced at this stage. 
3. Load: In the last step, the now-transformed data is loaded into its final system, where it can be queried and analyzed. 

Several tools are available on the market designed to support ETL processes, including enterprise software, open-source, cloud-based, and cloud options. The following are some of the most popular picks:

• IBM InfoSphere DataStage: A powerful ETL tool for enterprise data integration and transformation.
• Integrate.io: A low-code data integration platform offering hundreds of connectors. 
• Magic ETL: A drag-and-drop ETL solution that requires no coding in SQL. 
• Informatica: A data integration tool providing advanced ETL functionalities and data governance.
• Microsoft’s SQL Server Integration Services (SSIS): A component of the Microsoft SQL Server database software that can be used for data migration tasks. 

Pros and cons of ETL

Wondering the pros and cons of ETL? The following are some of the most significant advantages and disadvantages of the data processing method:

ETL pros:

• Prioritizes data quality before the loading phase
• Works with on-prem systems
• Flexible regarding environment
• Mature process
• Delivers a structured, cleaned dataset

ETL cons:

• Preprocessing stage can slow operations 
• Heavy use of computational resources for transformation
• Not as useful for changing data requirements
• Does not work well for handling large volumes of data

Overall, ETL works well in environments with strict data quality standards, such as finance or healthcare. It’s also suitable for scenarios involving extensive data transformation and the use of legacy systems. 

What is ELT?

ELT or Extract, Load, and Transform is a newer data processing method. Unlike with ETL, data transformation occurs last and is done on an as-needed basis. Here’s how it works:

1. Extract: The data is first extracted from disparate sources.  
2. Load: The raw data is then loaded into a storage system such as a data lake or cloud-based data warehouse.
3. Transform: Finally, the data is transformed within the storage system.

When might you choose to leverage ELT over ETL? If you’re managing large volumes of data, the scalability of the cloud can work in your favor. This method is also more flexible as data can be transformed as needed, which works well for evolving data requirements. Organizations can also enjoy real-time data processing with the use of ELT.

As with ETL, ELT has its pros and cons. Review the following advantages and disadvantages carefully to determine the best method for your needs. 

ELT pros:

• Useful for flexible data formats
• Transformation only occurs as needed, safeguarding resources
• High speed of loading in a cloud-based environment 
• Faster data availability

ELT cons:

• Works well in a cloud environment (a pro and a con)
• Concerns over storing data and meeting compliance requirements
• Newer method, so some stakeholders may question its value

As for ELT use cases, one of the most common scenarios where the data processing method makes sense is with big data analytics. ELT has the needed processing power if you’re working with massive volumes of disparate data. The use of cloud environments also allows for scalable, flexible storage. It is also preferred in operations where real-time analytics are paramount. 

The key differences between ETL and ELT at a glance

Below, we break down some of the most important factors when choosing between ETL and ELT. 

Choosing the right approach: ETL vs ELT

When selecting ETL or ELT, start by considering your data volume and complexity. If you’re working with well-defined data structures and integration needs, ETL is likely a wiser choice. The same idea applies to any strict requirements regarding data quality and transformation. On the other hand, if flexibility and scalability are your top requirements, ELT is a better choice. In large-scale data environments, the ELT method allows you to lean on the processing power of cloud platforms to avoid latency issues. It is also a good choice for scenarios where you need access to data across multiple sources quickly. 

Next, consider your infrastructure. ETL works best in traditional on-prem systems and with legacy systems. Conversely, ELT functions best in cloud-based environments with advanced computing capabilities. Finally, factor in requirements for processing. ETL is generally slower due to preprocessing requirements. If you need real-time (or close to it) processing, you may prefer to implement ELT.

Real-world examples of ETL and ELT

It can be helpful to consider how these two data processing methods play out in the real world. In the case of an online streaming service, data is collected across multiple sources including:

• Website activity logs
• Subscription data from the mobile app
• Customer feedback surveys
• Server logs

How can the streaming service bring all of this information into a unified view? With ETL, the raw data will be gathered regularly at a scheduled time. The raw data is processed, cleansed, and enhanced in a separate database. As it is processed, it is formatted to suit the final destination. Finally, the data can be moved into a data warehouse. There, it is available to be analyzed for BI purposes. The benefit of this method is that the data quality is prioritized. The downside is that there isn’t much room for flexibility in data requirements, and the preprocessing aspect of the method will take time. 

The streaming service could also choose to try the ELT method. This approach extracts the raw data detailed above and moves directly into a data lake. The data lake stores the raw data and then transforms it as necessary. The company can transform the data on demand, which provides enhanced scalability and flexibility. This method requires cloud infrastructure, so it wouldn’t fit an on-prem environment. 

Best practices for implementation

Whether you choose ETL or ELT, there are some best practices for implementation that can help you get the most from either method. 

ETL implementation best practices:

• Know your data requirements. Set yourself up for success by defining data sources, quality standards, desired target format, and any system requirements. 
• Leverage data validation and cleaning rules during the transformation process. Doing so will help to generate accurate, consistent data. 
• Document your ETL process and related transformations. 
• Leverage incremental loading techniques to update only new or changed data to reduce processing speed. 
• Predefine scheduling to extract and transform data. 
• Automate ETL workflows as much as possible. 

ELT implementation best practices:

• Use cloud scalability to manage large volumes of raw data and perform complex transformations.
• Only transform data on-demand for extra flexibility and reduced burden on resources. 
• Leverage bulk loading to manage massive datasets. 
• Carefully manage raw data storage to meet compliance and security requirements.
• Carefully document ELT processes and transformations for reference. 
• Speed up data processing by optimizing transformations in the target system. 

These best practices help drive efficient and effective implementations of ETL and ELT. If you’re interested in making data transformation a success in your organization, connect with Domo. Our drag-and-drop ETL tool makes it easy to extract data from multiple sources, transform it, and load it into Domo- no coding required. Here’s how it works.