ETL vs ELT: Choosing the Best Approach for Your Data Needs

By |2021-07-07T09:16:21+00:00June 18th, 2021|

The data needs of most organizations have evolved exponentially in the increasingly digital modern world. Businesses these days need to collect, process, and analyze billions of data inputs and events to allow them to make informed and meaningful decisions while preventing data breaches and protecting the intellectual assets of the organization. Effective data analysis and processing can increase the yearly profits of a business by as much as 8 to 10%. 

ETL (extract, transform, load) has been the traditional approach for data analytics and warehousing for the last couple of decades. ELT (extract, load, transform) is a more modern approach to data processing and has significantly changed the old paradigm. 

Since the inception of ELT, it has gained massive popularity among major businesses and organizations due to the plethora of benefits that distinguish it from the traditional data processing approach. However, ELT is not always the right choice for the big data needs of many organizations. Choosing the right approach requires a deeper understanding of both processes and careful evaluation of which process best suits the organization’s data needs.ETL vs ELT approach

Before we dig into the pros and cons of ETL vs. ELT, let’s first explore what happens when the “T” and “L” are switched.

ETL Definition: What is ETL?

Before choosing between ETL vs. ELT, it’s important to understand ETL’s meaning. 

What is ETL? 

To find the answer to this question, we need to explore the process step by step. 

When it comes to ETL definition, we can define it as a traditional data integration approach. ETL acronym (Extract, Transform, Load) works as an intermediary process. It helps transfer data from data sources into the target destination. 

The ETL process begins with extracting data from different sources into a staging space. This data may not always be uniform and can be in different formats. Transferring this data directly to the destination may cause errors. So, it’s best to cleanse it before transferring. This is where the transformation process begins. 

After transformation, the cleansed data is loaded into the specified destination(s) in batches. 

An ETL example is the aviation industry. Airlines keep track of airplanes, customers, and other useful information. They can load this data in a warehouse through the ETL approach. 

Now that you know what ETL is, it’s time to explore ELT’s meaning.

What is ELT? 

ELT’s meaning is quite different from the ETL process. The initial stage of ELT works the same way as the ETL stages. This means extracting raw data from varying data sources. Once data is in the ELT pipeline, you can load it into a data warehouse. 

Unlike an ETL data warehouse, there is no need to process and transform data before the loading stage. Data scientists can load raw data in the warehouse. As a result, they can access it for analysis as and when needed. 

It’s also important to note that BI tools can’t use big data without processing it. So, the next step is to cleanse and standardize data before it can be loaded into a target destination for further reporting and analysis. ETL warehouse normalizes the stored data for preparing customized dashboards and business reports.

ETL vs ELT Pros and Cons 

Let’s take a look at a few notable ETL vs ELT pros and cons: 

Benefits and Drawbacks of Cloud ETL 

What is ETL? What is ETL used for? 

ETL is a process that extracts data from different sources. After that, it processes and loads data into the target destination. 

With reference to ETL meaning, ETL tools work well with complex data. Since a myriad of ETL tools are available, it’s no more a challenge to transfer and store data in a warehouse. Moreover, it can be your go-to option for quick data analysis. 

Other prominent benefits of the ETL approach are data reliability and ease of compliance. Businesses must meet certain data protection rules. With ETL tools, you can fulfill HIPAA, CCPA, and GDPR standards. 

However, this traditional technology leads to certain challenges too. For instance, the process of loading data is time-consuming. Also, ETL involves moving data to a staging server for transformation. As a result, this may lead to potential bottlenecks. But ELT addresses the problem by leveraging the computing power of the underlying database. 

It’s also not easy to manage changing data needs. Furthermore, you can’t perform calculations on data as it’ll end up replacing the existing data. 

Benefits and Drawbacks of ELT Pipeline 

A major advantage of the ELT process is its flexibility. Unlike ETL, you can load the entire available raw data in a warehouse. Users can decide what kind of data they need when performing analysis. 

ELT method is a resource-efficient and time-saving mechanism. When discussing ELT meaning, we can’t ignore its negative aspects. Data present in ELT warehouses isn’t as reliable as an ETL data warehouse. The warehouse accommodates the entire available data without sorting. So, it’s not out of the question that data may be redundant. Also, data monitoring and governance are a hassle when it comes to the ELT approach. 

After discussing ETL vs ELT pros and cons, let’s compare some distinct characteristics of ETL and ELT methods.

ETL vs ELT Architecture: Key Differences 

This section focus on prominent ELT and ETL differences. 

  • Data Size 

A major difference between ETL and ELT is the data size. This refers to the amount of data it can manage. ETL warehouses work best with smaller datasets. ELT systems, however, are capable of handling a massive amount of data. 

  • Data Loading Time 

ETL vs ELT architecture also differs in terms of total waiting time. This means the total time to transfer raw data into the target warehouse. ETL is a time-consuming process. It’s because data teams need to load it first into an intermediary space for transformation. After that, the data team loads the processed data into the said warehouse. 

ELT architecture offers support for unstructured data. So, it eliminates the need for transformation before loading. Data, regardless of its nature, can be directly transferred to the warehouse. This approach reduces the loading time. 

  • Data Analysis Time 

Another ETL vs ELT difference is the time required for performing analysis. Since data in an ETL warehouse is in transformed form, data analysts can analyze it without delays. But data present in an ELT warehouse isn’t transformed. So, data analysts need to transform it when needed. This approach increases the waiting time for data analysis. 

  • Compliance 

Cyber attacks affected 155.8 million US individuals in 2020 alone. To reduce the risk of data theft, businesses now need to follow CCPA, GDPR, HIPAA, and other data privacy regulations. This is why compliance is a critical difference when it comes to the ETL vs ELT approach. 

ETL tools remove sensitive information before loading it into the warehouse. As a result, this prevents unauthorized access to data. On the other hand, ELT tools load the dataset into the warehouse without removing sensitive information. So, this data is more vulnerable to security breaches. 

  • Transformation Process 

The order of the transformation process is a leading ELT and ETL difference. ETL approach processes and transforms data before loading it. Alternatively, ELT tools don’t transform data right after extraction. They rather load data in the warehouse as it is. Data analysts can choose the data they need and transform it right before analysis. 

  • Unstructured Data Support 

Unstructured data support is another prominent difference between the ETL vs ELT approach. ETL integration is compatible with relational database management systems. Hence it doesn’t support unstructured data. In other words, you can’t integrate unstructured data without transforming it. 

ELT process is free of such limitations. It can transfer structured and unstructured data into the warehouse without hassles. 

  • Complexity of Transformations 

Another difference between ETL vs ELT architecture is transformation complexity. ELT approach enables moving a huge volume of data to the target destination. However, you can’t push down certain advanced transformations such as specific types of names or address parsing to the underlying database. So, they must be performed in the staging server.  At times, this can result in a “data swamp”.  It’s a challenge to manually sort and cleanse this bulk data stored in one place. 

The traditional ETL approach makes the process much simpler. It’s because you can cleanse data in batches before loading it. 

  • Availability of Tools and Experts 

From Astera Centerprise to SSIS and Informatica PowerCenter, a myriad of different types of ETL tools are available in the market. Since this technology has existed for decades, businesses can make the most of these effective tools. But we can’t say this for ELT which is a relatively new technology. As a result, limited ELT resources and tools are available to meet customer needs. 

Furthermore, plenty of ETL experts are available whereas the ELT expert workforce is scarce.

ETL vs ELT use cases

ETL vs ELT: Use Cases 

It’s a common question, why ETL is used in some cases and why ELT is a preferred choice in other scenarios. In this section, we intend to determine when to use ETL or ELT methodologies. 

When ETL is the Right Choice 

When to use ETL? The following scenarios provide the answer to this common concern. 

1. When Data Requires Complex Transformations 

As per ETL definition, its design supports complex data transformations. Also, it’s suitable when data is smaller in amount. 

2. When There are Privacy Concerns 

The ETL process removes sensitive information before loading data to a destination. This procedure reduces the risk of confidential information leaks. Moreover, it also ensures that your organization doesn’t violate compliance standards. 

3. When the Organization is Data-Driven 

Historical data provides a holistic view of business processes. From customers to suppliers, it offers detailed insights into stakeholder relationships. ETL meaning suggests this process is the ultimate choice for this purpose. It can help in preparing custom dashboards and precise reports. 

4. When Data is in Structured Format 

If you’re unsure when to use ETL, then determine the nature of the data. ETL use cases are applicable when structured data is available. It doesn’t support unstructured data. The process ensures that data transferred to the target warehouse is in a structured form. 

When ELT is the Right Choice 

1. When Availability of Data is a Priority 

ELT meaning suggests that the technology can handle a substantial amount of data. It can load data into the target warehouse whether it is structured or unstructured. Thus it’s your best option when your organization needs quick access to available data. 

2. When Data Analysts are ELT Experts 

It’s not always easy to find ELT experts since the technology is still evolving. But if an organization has access to experts, then adopting the ELT process can be your best bet. 

3. When Budget isn’t a Problem 

ELT process enables you to load information without transformation. So, it saves you from the high initial costs of data processing. However, remember that finding and onboarding ELT experts can be expensive. If an organization has a sufficient budget, it can go for this approach. 

4. When Debugging and Fixing Errors is Vital 

In comparison with ETL use cases, ELT is a suitable choice for fixing errors. In the ETL pipeline, you need to perform the entire Extract, Transform, Load process again to locate and fix errors. But with the ELT approach, the process is quite simple. You can do so by transforming selected data that is already transferred to the destination. 


ETL and ELT approaches prepare data for detailed analysis. No matter which method you opt for, Astera Centerprise can meet your needs. 

Its feature-rich GUI works well with most operating systems including Windows and Linux. This data integration solution is easy to use for proficient developers as well as newbie data analysts. You need not write a complex block of code to perform the desired task. Instead, you can perform advanced operations with the help of drag-and-drop functions. 

The software speeds up the data integration process through the optimal use of resources. It can seamlessly extract and transform data from disparate sources. Moreover, it comes with a built-in job scheduler to automate workflows.