ETL vs ELT—which should you use? Both are data processes that allow internal and external business users to access data.
For example, internal users like product managers, data analysts, business analysts, data scientists, and machine learning engineers can benefit from a properly employed ETL or ELT process. With ELT or ETL in place, all of these users can analyze data, make insights, and build models.
External users (like your customers or clients) can also reap the benefits of this data because it’s what composes a well-managed product. For example, having that data readily available on the backend in a database allows customers to search for specific products in a company on their website quickly and correctly.
Both processes obtain data from outside sources, restructure it to fit the necessary data requirements of the company like column naming and column type, and insert the data into a location for users to access.
The main difference between ETL and ELT is the order in which each step occurs. ETL stands for extract, transform, and load—which is the process reflected above.
On the other hand, ELT stands for extract, load, and then transform, which looks like this: obtain data from outside sources, insert the data into a data lake, and then transform the data as it’s extracted and used.
Next, we’ll dive deeper into the specifics of each process, cover some benefits and drawbacks, and discuss which is better and why—depending on your company’s needs.
What is ETL?
The users who tend to prefer the ETL process include data engineers and database developers. These are the people responsible for extracting raw data and then transforming it (also known as data cleaning) so it can be queried for end users.
Many times, this step includes additional business rules applied to the data transformation, such as the creation of a Net Profit column, for example.
The last step is to load the transformed data into a data warehouse where business users can access it.
- Extraction: Retrieve data from your data sources such as traditional databases, XML, JSON, or non-relational database structures and validate it so that records are correct.
- Transformation: Data values may need to be processed differently to match the expectations of your data warehouse. Focus on ordering or sorting the data based on business requirements—similar to cleaning the data. As part of the transformation process, all data must align with the structure of your final table.
- Load: The data is finally loaded to its end location. If the existing data is old, it may be erased and replaced with new data (depending on if your company retains historical data or not).
What is ELT?
The ELT process is the ETL process, but with the final two steps switched. This change means that the raw data is transformed after storage instead of before.
Similarly to ETL, ELT follows three main steps—just in a different order:
ELT is a newer process that allows your data to be transformed in its target location. It’s favored by those with data lakes and large amounts of unstructured, non-relational data.
ETL vs ELT
ETL vs ELT—both have certain benefits and drawbacks. Knowing these can help you determine which process is better for your organization.
ETL Pros and Cons
|ETL Benefits||ETL Drawbacks|
|Easy to manage data privacy before loading into a data warehouse||Because you have to transform data before loading, it may not be in the desired format after it’s loaded|
|ETL has been around for several years, so there’s plenty of documentation and tutorials to reference||May have to spend time editing to get your data in the desired format if it was transformed improperly or differently than expected before loading|
|Does not use a data lake|
|Works best for smaller data sets|
|Loading time can be slower|
ELT Pros and Cons
|ELT Benefits||ELT Drawbacks|
|Faster loading from the original, raw data format (as it’s the second step of the process)||Because this process is fairly new, there is not as much documentation|
|You can transform the data at query runtime in the way that you want, meaning you will not have to start a new process or project to change the data, reducing administrative and collaboration time||The overall process may be more expensive, which might not be suitable for all companies|
|Can be easier to automate|
|Uses the popular data lake structure|
|Eliminates the main data staging part of the process|
As you can see, there are plenty of benefits and drawbacks to both ETL and ELT.
Before you decide which is better for you, first understand your data goals, when you need data to be available, and the resources and tools you can plan to utilize to accompany either process.
Over to You
Whether you prefer the ETL vs ELT process , Kloudio can help you extract, load, and transform your data.
From Amazon Redshift, Google BigQuery, and Microsoft Azure integrations (plus dozens more), to easy SQL querying in your Google Sheets document, Kloudio complements whatever data process you’ve established at your organization.
Additionally, Kloudio can produce automatically generated reports for ad-hoc analysis. Business users and data engineers alike can navigate data pulled from a SaaS tool or database.
The main difference between ETL and ELT is the process order and transformation stage—as well as the time it takes to perform both of these aspects. It’s up to you to decide what process is best for your data needs.
Learn more about how to craft a modern data stack that fits your organization’s needs.