ETL vs ELT – Which is Better for a Modern-Day Business?

ETL vs ELT -main

Data analysis, capturing, and interpretation has become key factor in business success. ETL and ELT are two types of data management practices that help organizations make data-driven decisions. To deliver these decisions and ensure that they are interpreted into business intelligence solutions, ETL and ELT are practiced, depending on the organizational requirements.  

Basically, with the increasing volume of data and its extraction from a progressively increasing number of sources, making business-appropriate decisions has become complex. With ETL and ELT, we can make this entire process a lot more efficient and rewarding.  

What is ELT and ETL? 

ETL and ELT are data integration processes. With these, we can shift or move raw data to a database. These databases are usually called data lakes or data warehouses. To send the data to the desired location, either ETL or ELT is implemented. ETL and ELT processes work with cloud-managed services, which helps provide universal access to interpreted data based on access control settings.  

1. Extract, Transform, Load (ETL) 

One of the ways organizations can use to store and manage data includes collecting, reformatting, and storing it on the desired server. After extraction, the data is formatted based on predefined parameters.  

This is the staging area, where data is transformed into understandable bits, visualizations, patterns, and trends. In the load phase or stage, the formatted data is moved to a data warehouse or data lake. From here, anyone with access to the storage server can access the data and make business decisions.  

ETL origins go back to the decade of 1970s when companies were starting to collect large amounts of data from multiple sources. To process, they started to arrange this data into different datasets.  

This led to a significant issue of disjointed and cluttered databases. And as these complex databases began to increase, collecting data was quickly becoming a redundant exercise with no beneficial outcome.  

Then ETL arrived and provided businesses with an effective way to manage large datasets with ease. For the next three decades, ETL was the mainstay for organizations to convert raw data into business intelligence.  

For Whom ETL is an Ideal Data Integration Method? 

Disperse Data Sources 

Businesses that have diverse and spread-out data sources will benefit the most from ETL. These are companies that have customers, suppliers, partners, and stakeholders in different regions addressed via multiple ventures. ETL helps these businesses collect data from different repositories and formats in unison, then load everything onto a target location.  

Shift from Legacy Systems 

A particular use case of the ETL system is when organizations working with legacy systems want to implement a collective data shift to a modern system. In this case, as well, the ETL process can extract, transform the data into an understandable format, and load it to the target location. So, we can say that ETL is an important part of digital transformation solutions 

So, ETL is best suited for situations where you have multiple environments and have to process data collectively before viewing it on a separate medium.  

2. Extract, Load, Transform (ELT) 

The second and relatively modern data integration process, ELT, has a different approach. Here, the data is extracted, loaded onto the target location, and transformed. The major benefit of loading before transforming is to faster processing.  

The speed of data transfer increases because there won’t be any coding-based errors, which may occur in the migration process. However, the transfer or migration comes before transformation, which eventually leads to a faster implementation system.  

Essentially, ELT is decoupling the loading and transformation process. Here, they are independent of each other, which leads to better performance. Because the transformation is done after data is loaded, it also helps decrease the computing power required to transform data before loading.  

The transformation is now the responsibility of the service provided you have chosen to implement the ELT integration process. Since the ELT system generally works with cloud-managed services, businesses can process structured, unstructured, raw, and semi-structured data with the same efficiency.  

For Whom ELT is an Ideal Data Integration Method? 

1. Large Volume of Data 

A key area where ELT implementation bears fruit is when the datasets are huge. We are talking about terabytes of data extracted or collected from different sources. For instance, in a weather forecasting system that has multiple types of data coming from a wide array of locations. In this case, the ELT system allows businesses to cover more ground with huge volumes of data and process it with speed.  

2. Real-Time Data Requirement 

Organizations that rely on correct data analysis and that too, in real-time will certainly want to use the ELT implementation system. A trading company requires access to accurate data insights and that too, in real-time. Similarly, large-scale distributors and suppliers also require real-time access to accurate data insights. These types of businesses will find access to the insights they need with ETL helping them improve business performance with intelligence.  

Today we have smart solutions that help further improve the ELT data implementation. These solutions help complete the data transformation cycle irrespective of the data type and extension.  

Going Through Each Stage Separately 

Both ETL vs. ETL has three core stages. While their order has been rearranged, the functionality and purpose remain the same. Let’s discuss them separately. 

1. Extract 

In both types of processing, extract comes first. The first extraction stage means collecting and copying data from a pool of sources. The data can come from simple and dynamic ERP solutions, CRM systems, SQL, NoSQL databases, SaaS systems, emails, mobile applications, websites, web pages, excel databases, and so on.  

Because there are multiple and disparate sources to collect data, the extraction is time-consuming and complicated. This is intricate work and must be done carefully.  

There are three types of Extraction processes; 

Full Extraction

Full extraction means when systems cannot differentiate between new and old records. Due to this, the only way is to pull out all the data irrespective of the time, type, and extension.  

Partial Extraction

This extraction is the most convenient and easy method for pulling out data from different source systems. Partial extraction has notifications that will send alerts whenever records change.  

Incremental Extraction

This one is similar to partial extraction but with a difference. No notifications are sent in this process. With this, we can extract only that data that has been modified.  

However, with the changing order of Transformation and Load, we will have to work on the extraction part differently. In ETL, you must plan ahead about the type of data that has to be extracted because it will go for transformation in the next stage.  

In ELT, we can extract data without any filters because the data will be sent to the data warehouse immediately. Once loaded, we can decide on the transformation.  

2. Transform 

Transform is the second stage in ETL and the third stage in ELT. This is the stage where extracted data is transformed into meaningful insights. With specially built solutions and technologies, we can give the command to sort, filter, duplicate, cleanse, convert, translate, remove, encrypt, join, and split data.  

Transformation makes data readable and makes room for an effective analysis. Here again, the shift of Transform and Load can determine the speed and efficiency of the entire process.  

In ETL, the transformation occurs outside the data warehouse in a separate and independent staging area. Dedicated engineers and specialists work together to implement the transformation processes. Any type of conversions and processing happening here can only be done once. It is a complicated procedure.  

To change the type of analysis midway means modifying the entire pipeline and working on it from scratch.  

On the other hand, the ELT transformation process is a bit more flexible and business-friendly. This is because the data for transformation is taken from the data warehouse. Here it can be changed, transformed, or modified, any number of times.  

3. Load 

Loading is the second stage in ELT and the third stage in ETL. The task here is to load or add data to the data warehouse. From the warehouse, any user can access and view it depending on the access control settings and permissions.  

In ETL, the data is prepared and then sent to the warehouse. Mostly, the engineers do this with SQL and by arranging it in a tabular form. With ELT, the entire data set is first loaded into the warehouse. This reduces the time required to process the raw data by a huge margin.  

Head-to-Head Comparison of ELT vs. ETL 

Until now, you must have understood that the ELT vs. ETL is not only about changing the position of two stages, replacing Load with Transform. There are several fundamental differences, and they arise due to the swapping of the stages.  

Head-to-Head Comparison


ETL and ELT processing help businesses with precision and high-value data integration to improve business performance and analysis. Both the methods lead to offering business intelligence solutions allowing organizations to leverage actionable insights.  

While choosing a service provider catering to ELT and ETL implementation, prefer one with automation and scheduling systems along with standardized query support. Moreover, a service provider with a faster query response will bring results with speed and scalability.  

It is important to identify the type of data you have, the storage capabilities of the service provider, and your business needs while choosing the best solution. With data analysis becoming an integral part of business growth and success, it is essential to ensure process simplification while focusing on the results.  

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Jayant Joshi is Data Engineer at DEV IT and has 24 years of experience in the IT Industry. He has expertise in Data Operations, ETL, Advanced Excel, and Excel VBA. He has excellent problem-solving capabilities. He always tries his best to suggest/deliver a permanent solution to the problem.

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