ETL vs ELT: 5 Critical Differences

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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 method moves data from the source to staging, then into the data warehouse, allowing for intricate data transformations and more cost-effectiveness. On the other hand, ELT uses the capabilities of the data warehouse for transformations, eliminating the need for data staging and facilitating a potentially faster data processing.

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.

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, Chartio, 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.

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 new ETL platform providing businesses across multiple industries with a range of data integration options, including super-fast change data capture (CDC) and reverse ETL capabilities.

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.

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 has more specific use cases 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 generally more difficult to find than ETL developers.

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 Nike 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 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 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.	As a new technology, the tools to implement an ELT solution are still evolving. Moreover, in-house experts with the requisite ELT knowledge and skills can be difficult to find, which is what makes platforms like Integrate.io so useful.
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	Cloud-based SaaS ETL platforms that bill with a pay-per-session pricing model (such as Integrate.io) offer flexible, scalable plans that can adjust as data ingestion grows or reduces. Enterprise-level on-site ETL solutions are much more expensive.	Cloud-based SaaS ELT platforms that bill with a pay-per-session pricing model also offer flexible plans with low-cost options for smaller-scale transformations. 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	With ETL, transformations happen within a staging area outside the data warehouse.	With ETL, 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 can perform sophisticated data transformations and can be more cost-effective than ELT

How Integrate.io Can Fuel Your Data Mining

If you're suffering from any kind of data integration bottleneck, Integrate.io's automated ETL platform offers a cloud-based, visual, and low-code interface that makes data integration and transformation simple. Use the super-fast CDC capabilities for real-time data replication, or take advantage of advanced reverse ETL capabilities to ensure your business data is exactly where it needs to be.

Try Integrate.io for yourself with a 14-day trial: schedule your prep call with our expert team here, and find out how you can power business growth with our award-winning data integration solution.

ETL

ETL vs ELT:
5 Critical Differences

Overview of ETL and ELT

ETL Process in Detail: What is ETL?

The Biggest Advantages of ETL