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ETL vs. ELT: Which Data Pipeline is Right for Your Business?

ETL and ELT are methods of moving data from one place to another and transforming it along the way. But which one is right for your business?

This post compares ETL and ELT in speed, data retention, scalability, unstructured data management, regulatory compliance, maintenance, and costs. By the end, you should know when to use each method in your data pipeline and why.

Key Takeaways:

  • ETL has been the standard data pipeline for decades due to its accuracy, efficiency, and flexibility.
  • ELT is a variant of the ETL process that loads data into a target database first and then transforms it. 
  • ELT is more straightforward and faster than ETL in many cases because it does not require data transformation on a stand-alone server—the data is transformed within the destination instead. 
  • Some key benefits of an ELT pipeline include real-time analytics, ease of maintenance, scalability, unstructured data support, and lower costs overall.

What is Extract, Transform, Load (ETL)?

In the business world, data is a lot like water. It needs to be extracted from where it's found, transported to where it's needed, and then stored for later use. This process is known as ETL: extract, transform, and load.

Like a plumbing pipeline, ETL moves data from one place to another, cleans it up along the way, and stores it in a central location. The extract stage corresponds to finding water in a river or well. The transform stage is when the water is cleaned and transported through pipes. And the load stage is when the water is stored in a reservoir.

ETL is the process of pulling data from one or multiple sources, transforming it, and feeding it to the designated destination.
A diagram showing the data flow from source systems to transformation before loading to the destination.

Key Benefits of ETL Pipeline

There are many reasons why ETL has been the standard data pipeline for decades. At a high level, ETL ensures a company has a single point of truth for data fetched from disparate sources. Since data is transformed before loading to the final destination for analysis, ETL ensures the data is high quality and accurate. 

Practically speaking, ETL improves data accuracy, efficiency, and flexibility through automation and transformations. ETL is also crucial for data governance. A well-designed pipeline keeps a recorded history, which helps with compliance with internal policies and external regulations. For instance, Improvado's ETL tool is HIPAA and SOC-2-compliant, so it can handle sensitive data.

Thus, an ETL pipeline opens doors to omnichannel customer experiences, business intelligence, and data-driven decision-making.

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What is Extract, Load, Transform (ELT)?

Extract, load, transform (ELT) is a variant of the ETL process that loads data into designated storage first and then transforms it. 

Back to the water metaphor: ELT is like when you turn on the faucet in your house to get water. The water is already in the house, so you just have to turn on the tap, and it comes out. ELT is the same thing for data. The data is already at the destination, so you just have to turn on the faucet, and it comes out transformed.

ELT gained momentum with the introduction of column-oriented databases, like ClickHouse and jQuery. Previously, companies had to spend time and resources upfront on building the extract-transform logic to save database resources. The new generation of databases can process data and complete calculations much faster, and they generally cost less. Thus, the need to transform raw data upon loading it has been eliminated. 

This reversal of the traditional ETL process can simplify data pipeline management and save time since you can do transformation parallel with loading. It offers a simpler and faster approach to data transformation, as it does not require data transformation as a separate instance. Instead, the data is transformed within the destination, which is typically a data warehouse.

 ELT is a process in which data is pulled from one or multiple sources, loaded into a target destination, and then transformed
A diagram showing the data flow from source systems to the data warehouse for transformation.

Key benefits of an ELT pipeline

ELT has gained popularity due to its simplicity and flexibility. Data teams can aggregate raw data from a variety of sources, access it for further analysis at any time, and come up with a transformation logic when it's truly needed. 

ELT is a fantastic choice for real-time data analytics, as it can load and transform data faster than ETL. ELT is also a better choice if your company operates complex or ever-changing transformation processes. 

In addition, ELT is easier to maintain than ETL since there's no need to manage a separate transformation software. And it still offers many of the same benefits as ETL, such as data accuracy and efficiency.

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ETL and ELT Processes Compared

After looking at the benefits of ETL and ELT, let’s compare the two processes side by side.


ELT is faster than ETL due to the timing of the transformation step. 

Let's say you're loading a data set that is one terabyte in size. With ETL, the entire data set would need to be loaded onto the transformation server before transformation could begin. But with ELT, the data can be loaded and transformed in parallel, significantly reducing the overall time needed to complete the process.

There are, however, some cases where ETL may be faster than ELT. This is usually when the data set is small and can be easily transformed on a stand-alone instance.

Retention of raw data

The ELT process extracts all raw data and stores it indefinitely in your data warehouse. Transformations are only later applied as needed—meaning you always retain the original data set, which is helpful for historical analysis and debugging.

For ETL, before loading data into the target data warehouse or database of your choice, the data undergoes extensive transformations. So ETL might transform data into an aggregated form to save space, making it difficult to trace back the original values unless you load both original and transformed data to a destination. If you want to change the output data or if the raw data source changes, you need to re-write the extraction-transform scripts (as it comes as one).


ELT is more flexible because all three steps (extract, load, and transform) are performed separately. That makes it easier to scale and change anything you want in the process.

On the other hand, ETL is more rigid because the transformation layer has an inherent limitation. It's harder to evolve as your business grows— for example, if you want to add advanced features like scheduled extractions, parallel extractions, advanced transformation logic, etc. It also requires more resources than tweaking ELT, as you need to simultaneously change both ends of the process. After all, what one does affects the other.

The same is true for quality assurance processes. With ETL, since extract and transform come together, it takes more work to set up QA processes and test the product. Comparatively, the ELT logic, where you first extract and load your data and only then transform it, is much easier to test.

Unstructured data

ETL systems aren't well-suited for dealing with unstructured data, such as log files, social media data, and email messages—it’s designed to work with structured data organized into rows and columns. ETL can be adapted to handle unstructured data, but only with an advanced transformation engine.

On the other hand, ELT systems are readily available for dealing with unstructured data, as they can load and transform data more efficiently.

Regulatory compliance

Some industries are subject to regulations that require data processing in a specific way. For example, the healthcare industry is bound to HIPAA. This compliance legislation states how companies can collect, utilize, or share protected health information (PHI) and electronic protected health information (ePHI) to protect the privacy of patients.

A company can configure ETL to meet these regulatory requirements, as the data can be cleansed and transformed before being loaded to the destination database.

ELT, in turn, is more prone to compliance violations. The system loads all data, regardless of its sensitive nature, and only then gets transformed or removed. The workaround for these limitations is to ensure robust security and data governance measures. 


In ETL and ELT systems, maintenance costs can be high but occur at different stages.

With ETL, you need to update extract-transform scripts constantly as raw data sources change over time, which can lead to increased maintenance overhead.

With ELT, most maintenance happens during the initial loading of data to storage and when transforming data. The first-load data storage can quickly become unmanageable because it acts as a dumping ground for incoming raw data. Regular cleanups and documentation efforts are set in place to manage the load. 

Additionally, transformation pipelines must be re-engineered each time a raw data source changes. This requires maintenance work but gives engineers more flexibility, as no data is lost if a transformation script fails to adjust to the new incoming data structure.


As anyone who's been through a software development project knows, costs can quickly spiral out of control. And when it comes to data projects, the cost of developing a robust ETL solution can be prohibitive, which is why some companies choose to go with ELT instead.

With ELT, much of the transform step can be handled by existing tools like dbt or with the help of SQL, both of which tend to be less expensive than traditional ETL solutions. Of course, there's still a need for experienced developers who know how to use these tools effectively. But overall, the cost of developing an ELT solution is likely significantly lower than the cost of developing an ETL solution from scratch.

For perspective, the average base salary of a mid-level to senior backend engineer in the US is $124,397 per year. Meanwhile, the average salary of an SQL data engineer or BI developer is around $91,055 per year. So, if you need to hire multiple developers to work on your pipeline, then ELT is more cost-effective.

It's worth acknowledging that the cost of storage is lower in ETL since it doesn't store raw data, but this difference isn't significant if using cloud storage. 

How to Decide Between ETL and ELT

Deciding between ETL and ELT can be difficult, as each approach has pros and cons. We've compiled some questions that can help you make the decision.

What kind of data do you need to process?

Is your data structured or unstructured, or a mix of both? ETL is best suited for structured data, while ELT can handle both structured and unstructured data.

How much maintenance is required?

Do the benefits of ETL outweigh the costs of maintaining it? For instance, you may need access to raw data history, which ETL provides. In this case, the benefits of ETL may be worth the extra maintenance costs.

How complex is the data processing pipeline?

The sophistication of your data processing pipeline will determine whether ETL or ELT is the better solution. For instance, ETL can execute complex transformation logic but performs best with smaller data sets, while ELT is ideal for large datasets but can handle any data size.

Do you need real-time data?

ETL processes data in batches, causing a delay between when the data is collected and when it's available in the destination database. ELT can also process data in batches, but it can also do it in real time, which is helpful if you need up-to-the-minute data. 

How experienced are your developers?

There's no one-size-fits-all answer to this question, as it depends on your engineering team's specific skills and experience. Generally speaking, more engineers are skilled in ETL approaches than in ELT. Once you have a data pipeline in place, BI/SQL engineers can make changes in the ELT process, while ETL changes require mid/senior backend developers.

Whether it's ETL or ELT, Improvado Has You Covered

No matter your approach, Improvado can help your data flow where it needs to go with its wide range of data source connectors and destinations. Improvado's team of experienced data engineers can help design and implement a solution tailored specifically to your internal and external data regulations and needs. 

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