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ETL vs ELT: All You Need to Know

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Our world becomes more and more data-driven, as different industries and institutions embrace big data and leverage it to enhance their decision-making and performance. However, to get the most value from data, companies need to approach data management complexly, otherwise, any positive intention will drown among unstructured data scattered within dozens of storage. 

In light of this, data integration is extremely important for businesses. There are a lot of reasons that might push you to encounter data integration. 

For example, you decided to break data silos and implement one big system for the whole company. To launch the new solution, you need to extract data from old sources and transfer them to a new solution. Nowadays, no one would even consider doing it manually, as it would be too cumbersome, wasteful, and error-prone. Data science grants you suitable instruments on how to go data transfer and integration efficiently, but to really benefit from them, you first need to choose the strategy that would be suitable for your particular case.

There are two types of data integration process: ETL (extract, transform and load) and ELT (extract, load and transform). Both are in use today, but their range of implementation varies.

Though this might look like purely technical information, you need to understand the pros and cons of each. The process of data integration usually takes a significant amount of time and resources, hence, you need to understand which would bring the most business value and won’t stall the operations and further development of your business. 

So, how do ETL and ELT differ? Should your company choose the one or could they be interchangeable? Read our article to learn these details.

What Does ETL vs ELT Mean?

ETL and ELT are two different approaches to data integration. They both still lead to the same result: integrating data from various data sources into one unified, however, they imply different ways how to reach this outcome.

ETL: Extract, Load, Transform

The abbreviation explains the ETL processes: firstly, the raw data is extracted from different sources (SQL and NoSQL databases, XML, JSON, CSV files, or cloud storage), then, it is moved to the data staging area, where the data goes through necessary transformations, and finally, it is loaded into the target system (data warehouse). 

ETL is a traditional approach that appeared on the scene in the 1970s. As you can see, it requires an additional link between initial data sources and the target system, where complex data transformations can occur and where extracted data will turn into structured data prepared for further manipulations, e.g. data analytics.   

ELT: Extract, Transform, Load

ELT includes the same stages as ETL: data extraction, data transformation, and data loading. However, the order of these operations differs. In ELT, extraction is followed by loading and only then by transformation.

ELT process doesn’t require data staging, as the unstructured data gets into the target system immediately. ELT works with cloud data warehouses, as well as data lakes, with the latest allowing to upload of structured or unstructured data. Hence, you can just upload any raw data and store it in this way until the moment when you need to transform a particular piece of data.

The Key Differences Between ETL and ELT

The differences in stage order lead to other distinctions that make ELT and ETL suitable for different projects. Let’s contemplate them.

Pros and Cons Of ETL and ELT

So, let’s dive deeper into the nature of ETL and ELT. What are the benefits of each of these processes, and what are the disadvantages that might neutralize any positive sides? 

ETL: Extract, Transform, Load

So, what about this old-school approach?


Enhanced data governance and compliance
A lot of regulations and compliance like HIPAA or GDPR demand that you encrypt or remove sensitive data. With the ETL process, it would be much easier to conduct necessary manipulations with such types of data before loading it into the data storage, and hence, remove the possibility of exposing something that you should not.
Mature process
ETL was introduced a long time ago, hence, there are a lot of specialists that have tried everything numerous times and know how to work with the ELT process with their eyes opened. Also, you can find multiple convenient tools on the market to easily build any data pipeline you might need.
Faster data analysis
With the ELT process, you have processed data in your data warehouse. Hence, further manipulations like data analysis should be conducted much faster, as your data is already prepared for them, and you don’t need to spend any time at this point on that.
Cost-efficient data storage
Basically, ELT filters your data before loading it to the target system. This means that you get only the data you need, and you don’t need to store those pieces that aren’t important to you. So, in this case, you will probably need less storage, which in turn means lower expenses on this item in your budget.


Prolonged loading time
While you save time on data analysis that might come after the ETL process, you will slow down on transformation and loading steps. As the data transformation process occurs on a separate server, you need to wait until it ends, and only then it will get to the data warehouse.
High initial & maintenance costs
You will need to invest a certain amount of money to define and set up the process. But it won’t be the end: things tend to change, especially in today’s realities, and every change will be an additional expense for you.
Lack of flexibility
ETL is not famous for being flexible, so if you have any changes in data formats or data sources that will change your structure, you need to stop and redefine the process from the start. Of course, you can try and predict changes beforehand, but then, you need to cover all possible options.

ELT: Extract, Load, Transform

And what about the second approach?


High speed of implementation and data loading
It is much easier to start with the ELT process, as there is no middle stage between data extraction and data loading. The data is loaded almost immediately to your data lake or another type of storage you have chosen.
As a data lake is the storage type mostly used with ELT, you can upload data of any format to the system. There is no need to define anything beforehand, and hence, you don’t need to tailor the process to new data formats or sources. The further transformation here is the last step and this gives the team much more flexibility to do it.
Easier error-fixing
With ETL, to fix the errors, you need to start the whole process from the beginning which isn’t very convenient or resource-saving. On the other hand, with ELT, you can start from the last successful step and start from the point where the error was detected.
Lower initial cost
As you don’t need to define all your process in advance and set them up, you don’t need to allocate any expenses there, which makes ELT cheaper in this regard.


Weaker data security and low compliance
As you load big volumes of raw data into your target system, you may add compromised or corrupted data there as well, which brings certain security risks. It might also be harder to stay compliant with regulations without any preliminary actions. Hence, you should pay additional attention to these risks and implement the necessary step to protect yourself.
Lower analysis speed
As the data transformation happens after it is loaded, it would take more time to prepare the data for analysis. The system needs to inspect a large amount of data before transforming it, and it might not be a quick process.
Young approach
ELT has come to us with the rise of cloud infrastructure not so long ago (compared to ETL), hence, the knowledge is still developing and the community is smaller than ETL one. However, as the popularity and spread of ELT processes are growing, the number of ELT specialists expands as well.

Use Cases: What Data Integration Method Would Be Suitable for Your Company?

The information we have described should give you a general impression about the ELT and ELT, the way they work, and their benefits and downsides. Still, how to choose the right approach for your business? 

While every case is unique and should be evaluated separately, we collected a few common situations when one or the other approach would work the best.

When to Use ETL

ELT works the best with small data sizes, for applications where data security and anonymization are a priority, and for data migration from legacy systems.

So, use the ETL process in the cases we describe here.

When to Use ELT

ELT offers a range of benefits that would be great for big volumes of data or when it is crucial for businesses to have constant access to this data. 

Let’s closely contemplate cases when you should choose ELT for your data integration process.

The Comparison Table Between ETL and ELT


ETL (Extract, Transform, Load)

ELT (Extract, Load, Transform)

Stores Data

Only structured

Structured, unstructured, semi-structured

Data Volume



Type of storage

Both on-premise and cloud





Load time




Easy to implement

Harder to implement, need to use additional tools


Continuous, if data formats are being changed



High startup and maintenance, low storage cost

Low startup and maintenance, storage cost will grow as the data volumes grow.  

Use cases

  • small volumes of data
  • sensitive data integration
  • data migration from legacy system
  • high volumes of data
  • need to have constant access to data
  • need for cost-efficient solution

In Conclusion

It is impossible to say what type of integration is better than the other. Being different in nature, they have their own value which would be useful for different types of cases. Hence, to choose the one, you need to carefully assess your case. 

Altamira’s data governance specialists would help you with that. If you need to conduct data migration or data integration for your new solutions, we can provide you with a strategic assessment and governance strategy that will help you to maximize the value your business might get from the data you have been generating. 


Both ELT (extract, load, transform) and ETL (extract, transform, load) have their pros and cons, which make them suitable for one type of case and inefficient for others. Hence, it is hard to say whether one is better, we can only say that the other is better than the other for your specific case.

Some businesses might choose ELT over ETL as the first is much more flexible, faster, and cheaper in case of data transfer and storage.

ETL stands for extract, transform, load, while ELT is for extract, load, and transform. The main difference lies in the order of operations: ETL transforms data on a separate server before uploading it to the target data warehouse, while ELT loads data to a target system first and transforms it by request.

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