The five critical differences of ETL vs ELT:

  1. ETL is the Extract, Transform, and Load process for data. ELT is Extract, Load, and Transform process for data.
  2. In ETL, data moves from the data source to staging into the data warehouse.
  3. ELT leverages the data warehouse to do basic transformations. There is no need for data staging.
  4. ETL can help with data privacy and compliance by cleaning sensitive and secure data even before loading into the data warehouse.
  5. ETL can perform sophisticated data transformations and can be more cost-effective than ELT. 

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ETL vs ELT is easy to explain, but understanding the big picture—i.e., the potential advantages of ETL vs. ELT—requires a deeper knowledge of how ETL works with data warehouses, and how ELT works with data lakes.

Table of Contents


Overview of ETL and ELT

The ETL and ELT are necessary in data science because information sources—whether they use a structured SQL database or an unstructured NoSQL database—will rarely use the same or compatible formats. Therefore, you have to clean, enrich, and transform your data sources before integrating them into an analyzable whole. That way, your business intelligence platform (like LookerChartio, Tableau, or QuickSight) can understand the data to derive insights.

Regardless of whether it's ETL or ELT, the data transformation/integration process involves the following three steps:

  • Extract: Extraction refers to pulling the source data from the original database or data source. With ETL, the data goes into a temporary staging area. With ELT, it goes immediately into a data lake storage system.
  • Transform: Transformation refers to the process of changing the structure of the information, so it integrates with the target data system and the rest of the data in that system.
  • Load: Loading refers to the process of depositing the information into a data storage system.

As we’ve already established, ETL and ELT perform these steps in a different order. So the question is: Should you transform your data before or after loading it into the data repository? To answer that, you need to understand ETL and ELT separately.

ETL Process in Detail


Online Analytical Processing (OLAP) data warehouses—whether they are cloud-based or onsite—need to work with relational SQL-based data structures. Therefore, any data you load into your OLAP data warehouse must transform into a relational format before the data warehouse can ingest it. As a part of this data transformation process, data mapping may also be necessary to combine multiple data sources based on correlating information (so your business intelligence platform can analyze the information as a single, integrated unit).

That’s why data warehouses require ETL—because the transformations must happens before the loading. Here are some details to understand about ETL:

  • A continuous, ongoing process with a well-defined workflow: ETL first extracts data from homogeneous or heterogeneous data sources. Next, it deposits the data into a staging area. From there, the data goes through a cleansing process, gets enriched and transformed, and is finally stored in a data warehouse.
  • Used to required detailed planning, supervision, and coding by data engineers and developers: The old-school methods of hand-coding ETL transformations in data warehousing took an enormous amount of time. Even after designing the process, it took time for the data to go through each stage when updating the data warehouse with new information.
  • Modern ETL solutions are easier and faster: Modern ETL, especially for cloud-based data warehouses and cloud-based SaaS platforms, happens a lot faster. By using a cloud-based ETL solution, like, users can instantly extract, transform, and load their data from diverse sources without having programming expertise.

The Biggest Advantages of ETL

One of the biggest advantages of ETL over ELT relates to the pre-structured nature of the OLAP data warehouse. After structuring/transforming the data, ETL allows for speedier, more efficient, more stable data analysis. In contrast, ELT isn't ideal when the task requires speedy analysis.

Another significant advantage for ETL over ELT relates to compliance. Often companies regulated by GDPRHIPAA, or CCPA need to remove, mask, or encrypt specific data fields to protect the privacy of their clients. This could involve transforming emails to just the domain or removing the last part of an IP address. ETL provides a more secure way to perform these transformations because it transforms the data before putting it into the data warehouse.

In contrast, ELT requires you to upload the sensitive data first. That causes it to show up in logs where SysAdmins can access to it. Also, using ELT to transform data could inadvertently violate the EU's GDPR compliance standards if non-compliant data leaves the EU when uploading it to a data lake. Ultimately, ETL reduces the risk of compliance violations because non-compliant data will never accidentally find its way into a data warehouse or reports. 

Finally, as a data integration/transformation process, ETL has existed for over two decades, which means that there are many well-developed ETL tools and platforms available to assist with data extraction, transformation, and loading needs. Also, data engineers skilled and experienced at setting up ETL pipelines are easy to find.

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ELT Process in Detail


What is ELT?

ELT stands for "Extract, Load, and Transform." In this process, data gets leveraged via a data warehouse in order to do basic transformations. That means there's no need for data staging. ELT uses cloud-based data warehousing solutions for all different types of data - including structured, unstructured, semi-structured, and even raw data types.

The ELT process also works hand-in-hand with data lakes. "Data Lakes" are special kinds of data stores that—unlike OLAP data warehouses—accept any kind of structured or unstructured data. Data lakes don't require you to transform your data before loading it. You can immediately load any type of raw information into a data lake, no matter the format or lack thereof.

Data transformation is still necessary before analyzing the data with a business intelligence platform. However, data cleansing, enrichment, and transformation occur after loading the data into the data lake. Here are some details to understand about ELT and data lakes:

  • A new technology made possible by high-speed, cloud-based servers: ELT is a relatively new technology, made possible because of modern, cloud-based server technologies. Cloud-based data warehouses offer near-endless storage capabilities and scalable processing power. For example, platforms like Amazon Redshift and Google BigQuery make ELT pipelines possible because of their incredible processing capabilities.
  • Ingest anything and everything as the data becomes available: ELT paired with a data lake lets you ingest an ever-expanding pool of raw data immediately, as it becomes available. There's no requirement to transform the data into a special format before saving it in the data lake.
  • Transforms only the data you need: ELT transforms only the data required for a particular analysis. Although it can slow down the process of analyzing the data, it offers more flexibility—because you can transform the data in different ways on the fly to produce different types of metrics, forecasts, and reports. Conversely, with ETL, the entire ETL pipeline—and the structure of the data in the OLAP warehouse—may require modification if the previously-decided structure doesn't allow for a new type of analysis.
  • ELT is less-reliable than ETL: It’s important to note that the tools and systems of ELT are still evolving, so they're not as reliable as ETL paired with an OLAP database. Although it takes more effort to set up, ETL provides more accurate insights when dealing with massive pools of data. Also, ELT developers who know how to use ELT technology are more difficult to find than ETL developers.

The Biggest Advantages of ELT

The primary advantage of ELT over ETL relates to flexibility and ease of storing new, unstructured data. With ELT, you can save any type of information—even if you don’t have the time or ability to transform and structure it first—providing immediate access to all of your information whenever you want it. Furthermore, you don’t have to develop complex ETL processes before data ingests, and saves developers and BI analysts time when dealing with new information. 

Here are some other benefits of ELT:

BENEFIT #1: High Speed

When it comes to data availability, ELT is the faster option. ELT allows for all of the data to go into the system immediately, and from there, users can determine the exact data they need to both transform and analyze.

BENEFIT #2: Low-Maintenance

With ELT, users generally won't have to have a "high-touch' maintenance plan. Since ELT is cloud-based, it utilizes automated solutions instead of relying on the user to initiate manual updates. 

BENEFIT 3#: Quicker Loading

Because the transformation step doesn't occur until after the data has entered the warehouse, it cuts down on the time it takes to load the data into its final location. There's no need to wait for the data to be cleansed or otherwise modified, and it only needs to go into the target system once.

Best Ways to Use ELT

As outlined in this article, ETL vs. ELT is an ongoing debate. So, in what circumstances might you consider using ELT instead of ETL? Here are some of them:


A company with massive amounts of data. ELT works best with huge quantities of data, both structured and unstructured. As long as the target system is cloud-based, you will likely be able to process those huge amounts of data more quickly with an ELT solution.


An organization with the resources to handle the processing power needed. With ETL, the majority of the processing takes place while the data is still in pipeline before it gets to your warehouse. ELT does its work once the data has already arrived in the data lake. Depending on what needs to be done to the data to suit your purposes, smaller companies may not have the financial flexibility to develop or explore the extensive technology needed to get the full benefits of a data lake.


A company that needs all it's data in one place as soon as possible. When the transformations take place at the end of the process, ELT prioritizes the speed of transfer over almost everything else, which means that all data - good, bad, and otherwise - ends up in the data lake for later transformation.

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ETL vs. ELT Comparison

Adoption of the technology and availability of tools and experts ETL is a well-developed process used for over 20 years, and ETL experts are readily available.
ELT is a new technology, so it can be difficult to locate experts and more challenging to develop an ELT pipeline compared to an ETL pipeline.
Availability of data in the system ETL only transforms and loads the data that you decide is necessary when creating the data warehouse and ETL process. Therefore, only this information will be available.
ELT can load all data immediately, and users can determine later which data to transform and analyze.
Can you add calculations? Calculations will either replace existing columns, or you can append the dataset to push the calculation result to the target data system.
ELT adds calculated columns directly to the existing dataset.
Compatible with data lakes? ETL is not normally a solution for data lakes. It transforms data for integration with a structured relational data warehouse system.
ELT offers a pipeline for data lakes to ingest unstructured data. Then it transforms the data on an as-needed basis for analysis.
Compliance ETL can redact and remove sensitive information before putting it into the data warehouse or cloud server. This makes it easier to satisfy GDPR, HIPAA, and CCPA compliance standards. It also protects data from hacks and inadvertent exposure.
ELT requires you to upload the data before redacting/removing sensitive information. This could violate GDPR, HIPAA, and CCPA standards. Sensitive information will be more vulnerable to hacks and inadvertent exposure. You could also violate some compliance standards if the cloud-server is in another country.
Data size vs. complexity of transformations ETL is best suited for dealing with smaller data sets that require complex transformations.
ELT is best when dealing with massive amounts of structured and unstructured data.
Data warehousing support ETL works with cloud-based and onsite data warehouses. It requires a relational or structured data format.
ELT works with cloud-based data warehousing solutions to support structured, unstructured, semi-structured, and raw data types.
Hardware requirements Cloud-based ETL platforms (like don't require special hardware. Legacy, onsite ETL processes have extensive and costly hardware requirements, but they are not as popular today.
ELT processes are cloud-based and don't require special hardware.
How are aggregations different? Aggregation becomes more complicated as the dataset increases in size.
As long as you have a powerful, cloud-based target data system, you can quickly process massive amounts of data.
Implementation Complexity ETL experts are easy to procure when building an ETL pipeline. Highly evolved ETL tools are also available to facilitate this process.
As a new technology, the tools to implement an ELT solution are still evolving. Moreover, experts with the requisite ELT knowledge and skills can be difficult to find.
Maintenance requirement Automated, cloud-based ETL solutions, like, require little maintenance. However, an onsite ETL solution that uses a physical server will require frequent maintenance.
ELT is cloud-based and generally incorporates automated solutions, so very little maintenance is required.
Order of the extract, transform, load process Data transformations happen immediately after extraction within a staging area. After transformation, the data is loaded into the data warehouse.
Data is extracted, then loaded into the target data system first. Only later is some of the data transformed on an “as-needed” basis for analytical purposes.
Costs Cloud-based SaaS ETL platforms that bill with a pay-per-session pricing model (such as offer flexible plans that start at approximately $100 and go up from there, depending on usage requirements. Meanwhile, an enterprise-level onsite ETL solution like Informatica could cost over $1 million a year!
Cloud-based SaaS ELT platforms that bill with a pay-per-session pricing model offer flexible plans that start at approximately $100 and go up from there. One cost advantage of ELT is that you can load and save your data without incurring large fees, then apply transformations as needed. This can save money on initial costs if you just want to load and save information. However, financially strapped businesses may never be able to afford the processing power required to reap the full benefits of their data lake.
Transformation process Transformations happen within a staging area outside the data warehouse.
Transformations happen inside the data system itself, and no staging area is required.
Unstructured data support ETL can be used to structure unstructured data, but it can’t be used to pass unstructured data into the target system.
ELT is a solution for uploading unstructured data into a data lake and make unstructured data available to business intelligence systems.
Waiting time to load information ETL load times are longer than ELT because it's a multi-stage process: (1) data loads into the staging area, (2) transformations take place, (3) data loads into the data warehouse. Once the data is loaded, analysis of the information is faster than ELT.
Data loading happens faster because there's no waiting for transformations and the data only loads one time into the target data system. However, analysis of the information is slower than ETL.
Waiting time to perform transformations Data transformations take more time initially because every piece of data requires transformation before loading. Also, as the size of the data system increases, transformations take longer. However, once transformed and in the system, analysis happens quickly and efficiently.
Since transformations happen after loading, on an as-needed basis—and you transform only the data you need to analyze at the time—transformations happen a lot of faster. However, the need to continually transform data slows down the total time it takes for querying/analysis.

In Summary:

  • ETL stands for Extract, Transform, and Load, while ELT stands for Extract, Load, and Transform.
  • In ETL, data flow from the data source to staging to the data destination.
  • ELT lets the data destination do the transformation, eliminating the need for data staging.
  • ETL can help with data privacy and compliance, cleansing sensitive data before loading into the data destination, while ELT is simpler and for companies with minor data needs.

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