ETL (Extract, Transform, Load) transforms data before it enters the warehouse; ELT (Extract, Load, Transform) loads data first and transforms it inside the warehouse using the warehouse's own compute. ETL is generally the better choice for compliance-sensitive data (GDPR, HIPAA, CCPA) since sensitive fields can be masked before loading, and it's often more cost-effective for smaller, well-defined datasets. ELT suits large volumes of structured or unstructured data where speed of ingestion matters more than upfront structure.
In the world of data management, the debate between Extract-Transform-Load (ETL) and Extract-Load-Transform (ELT) is an increasingly relevant topic. The essential difference lies in the sequence of operations: ETL processes data before it enters the data warehouse, while ELT leverages the power of the data warehouse to transform data after it's loaded. As the digital landscape continues to evolve, understanding the critical differences between these two methodologies becomes essential in optimizing data transformation strategies.
Both ETL and ELT are integral processes in data integration, with a distinctive variation in their approach. ETL transforms data in a separate processing engine before it reaches the warehouse, which suits complex transformations and sensitive data. ELT loads raw data first and uses the warehouse’s own compute to transform it, which gets data in faster but shifts transformation costs onto your warehouse bill.
However, the complexity of this topic extends beyond simple sequencing. From considerations on data privacy and compliance to cost-effectiveness, this article delves into the five critical differences between ETL and ELT, providing you with a comprehensive guide to make an informed decision tailored to your data needs.
ETL vs. ELT at glance
| ETL | ELT | |
|---|---|---|
| Order | Extract → Transform → Load | Extract → Load → Transform |
| Where transformation runs | A separate processing engine, before the warehouse | Inside the destination warehouse or lake |
| Compute you pay for | The pipeline platform’s | Your warehouse’s (Snowflake, BigQuery, Redshift, Databricks) |
| Sensitive data | Can be masked or removed before it lands | Lands raw first, then gets transformed |
| Speed to load | Slower, because data is transformed first | Faster, because raw data loads immediately |
| Data types | Mostly structured | Structured, semi-structured and unstructured |
| Best for | Compliance-heavy data, complex logic, predictable warehouse costs | Large volumes, exploratory analysis, flexible schemas |
Use ETL when:
- You handle regulated data (HIPAA, GDPR, PCI) and need to mask or remove fields before they reach the warehouse.
- Transformations are complex, such as joining many sources, deduplicating or applying business rules.
- You want to keep warehouse compute costs predictable.
- The destination isn’t a cloud warehouse, for example an operational database or a SaaS app.
Use ELT when:
- You load large volumes into a cloud warehouse and want data available quickly.
- Analysts want raw data to model in SQL, often with dbt.
- Schemas change often and you don’t want to rebuild pipelines each time.
- Warehouse compute costs are acceptable, or already budgeted.
Use both (sometimes called ETLT) when: you need light cleanup or masking in the pipeline, then heavier modelling in the warehouse. Many teams run ETL for sensitive or operational data and ELT for analytics in the same stack.
Overview of ETL and ELT
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.
Both ETL and ELT are necessary integration methods 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 Looker, Tableau, or QuickSight) can understand the data properly and derive actionable insights that drive business success.
As we’ve touched on, regardless of whether your data managers use ETL or ELT, the data transformation and 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 or data warehouse storage system.
- Transform: Transformation refers to the process of changing the structure and format 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.
We’ve already established that ETL and ELT perform these steps in a different order from one another. 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.
Read more: ETL Methodologies: A Guide to Our Data Warehouse Integration Platform
ETL Process in Detail: What is ETL?
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 be transformed 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. This is so that your business intelligence platform can analyze the information as a single, integrated unit.
That’s why some types of data warehouses require ETL—because the transformations must happen before the data is loaded. Here are some details to understand about ETL:
- ETL requires 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.
- It used to require 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, users can instantly extract, transform, and load their data from diverse sources without having programming expertise.
Integrate.io is a data pipeline platform, founded in 2012, that supports ETL, ELT, change data capture (CDC) and Reverse ETL in one place.
Read more: Reverse ETL: What You Need to Know
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, and more stable data analysis. In contrast, ELT isn't ideal when the task requires speedy analysis of data that has just been loaded.
Another significant advantage of ETL over ELT relates to compliance. Often, companies that are regulated by GDPR, HIPAA, 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 changes the data before putting it into the data warehouse. Our platform has a huge focus on security, protecting personally identifiable information (PII) via highly secure transformations, minimizing the risk to data in transit and reducing the chance of data breaches.
In contrast, ELT requires you to upload sensitive data first. That causes it to show up in logs where SysAdmins could gain 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 to a data lake. This is a particularly important consideration for companies that operate globally. 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, although they can be expensive as a full-time hire, which is why modern, cloud-based ETL platforms are vital for businesses that need to deal with scalable volumes of big data.
ELT Process in Detail: What Is ELT?
What is ELT?
ELT stands for "Extract, Load, and Transform." In this process, the transformation of data occurs after it is loaded into the data storage solution. That means there's no need for data staging. ELT can use cloud-based data warehousing solutions such as Snowflake 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 never 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.
Read more: Data Lake vs Data Warehouse: 7 Critical Differences
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 more recent 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 immediately ingest an ever-expanding pool of raw data 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 upon structure doesn't allow for a new type of analysis.
- ELT shifts cost and risk to the warehouse: ELT is now mainstream, with mature tools and plenty of experienced engineers. The trade-off is that every transformation runs on warehouse compute you pay for, and raw data, including sensitive fields, lands before it’s cleaned.
Talk to our team to find out how easy ELT (or CDC) is with our intuitive platform.
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 ingest, and it 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 all the data to go into the system immediately, and from there, users can determine the exact data they need to transform and analyze.
Benefit #2: Low Maintenance
With ELT, users generally won't need 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:
Use Case #1:
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. However, you may gain more accurate insights with ETL.
Use Case #2:
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 the 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.
Use Case #3:
A company that needs all its data in one place as soon as possible. Because 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.
Discover how businesses like Caterpillar and Boston Red Sox take the pain out of data integration with Integrate.io: Try it for yourself with a 14-day trial here.
ETL vs. ELT Comparison
|
ETL |
ELT |
|
|
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 became mainstream with cloud warehouses like Snowflake, BigQuery and Redshift. Tools are mature and experienced engineers are widely available. |
|
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 Integrate.io) 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 generally easy to procure when building an ETL pipeline, although they can be expensive. A more cost-effective solution is highly evolved ETL tools created to facilitate this process. |
ELT tools are mature and widely used, but teams need SQL skills (often with dbt) to model data inside the warehouse. |
|
Maintenance requirements |
Automated, cloud-based ETL solutions, like Integrate.io, 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, and 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 |
Fixed-fee platforms such as Integrate.io ($1,999/month, unlimited data) keep costs flat as volume grows. Usage-based tools bill by rows or compute. Enterprise on-premise ETL is much more expensive. |
Loading is cheap, but every transformation runs on warehouse compute, billed separately by your warehouse provider, so costs rise with transformation volume. |
|
Transformation process |
With ETL, transformations happen within a staging area outside the data warehouse. |
With ELT, 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 with ELT because there's no waiting for transformations and the data only loads one time into the target data system. However, the analysis of the information is slower than ETL. |
|
Waiting time to perform transformations |
Data transformations take more time initially with ETL 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 with ELT, on an as-needed basis—and you transform only the data you need to analyze at the time—transformations happen a lot faster. However, the need to continually transform data slows down the total time it takes for queries/analysis. |
In Summary:
Some of the top five critical differences between ETL vs. ELT are:
- ETL stands for Extract, Transform, and Load. ELT means Extract, Load, and Transform. Both are processes for data integration.
- Using the ETL method, data moves from the data source to staging, then into the data warehouse.
- ELT leverages the data warehouse to do basic transformations. There is no need for data staging.
- ETL can help with data privacy and compliance by cleaning sensitive and secure data before loading it into the data warehouse.
- ETL handles sophisticated transformations outside the warehouse, which can be more cost-effective than ELT when warehouse compute is expensive
How Integrate.io Supports ETL and ELT
Integrate.io supports both approaches, so you can choose per pipeline rather than per platform:
- ETL: build pipelines in a drag-and-drop interface with 220+ built-in transformations. Transformations run on Integrate.io’s own engine, so they don’t add to your warehouse bill.
- ELT and CDC: replicate databases to your warehouse with log-based CDC every 60 seconds, then transform in the warehouse with your own SQL or dbt.
- Both in one stack: mask sensitive fields in an ETL pipeline and send everything else via ELT, on the same platform.
Pricing is a fixed fee from $1,999/month with unlimited data volumes, pipelines and connectors, so switching between ETL and ELT doesn’t change your bill. See pricing or talk to a Solution Engineer.
FAQs
Is ELT replacing ETL?
No. ELT is the default for cloud analytics, but ETL is still preferred for regulated data, operational destinations and complex transformations. Most data teams use both.
Which is cheaper, ETL or ELT?
It depends on where you pay for compute. ELT tools often look cheaper, but transformations run on your warehouse bill. ETL on a fixed-fee platform keeps transformation costs separate and predictable.
Is ETL or ELT better for compliance?
ETL, because sensitive fields can be masked or removed before data reaches the warehouse. With ELT, raw data lands first.
Can I use ETL and ELT together?
Yes. A common pattern is light cleanup or masking in the pipeline, then modelling in the warehouse.
