April 29, 2022
ELT vs ETL – The difference in the acronym is so minute. It can cause a typo. And yet, both ETL and ELT processes are important in today's data processing. So, if you're looking for their stark differences, you're in the right place.
Maybe you heard that ETL is much more mature. But ELT is the newer kid on the block. So, is it ETL or ELT? If you're confused, you're not alone. That's why this article will discuss the differences, the pros and cons, and the use cases of these two data pipelines.
To know the difference between ETL and ELT, here's what we are going to cover:
Let's start the comparison by introducing each of them. Let's begin.
So, what is ETL? ETL stands for Extract, Transform, Load. Let's extend the ETL meaning further. It is a data pipeline that copies data from various data sources. Then, this copy is transformed by cleansing, summarizing, filtering, and more. And finally, the transformed data is loaded to a target database.
There are a few ways ETL can behave too. It can be by batch by updating chunks of data on a regular schedule. Batching can be full load or incremental load. ETL can also be streamed. Streaming ETL or real-time ETL is a way to copy source data to a target when a minute of processing is too long. And last is Reverse ETL, where the source and targets are reversed. Instead of the data warehouse being the target, it becomes the source. Then, after some transformations, insights are copied back to operational systems.
It is an ETL data pipeline, but of course, reversed. Instead of having the data warehouse as the target, it becomes the source. And the insights coming from it will be formatted and pushed to the target third-party apps. Figure 1 below illustrates that point.
ETL is in use since the '70s for data warehousing. So, it's already a traditional method or pattern in processing data. It's also mature to the point that various ETL tools exist, and a lot of people with data pipeline skills know it.
What is ETL used for?
Here are some of the common ETL use cases:
In a later section, you will learn more about when to use ETL.
So, what is ELT? ELT stands for Extract, Load, Transform. Let's extend the ELT meaning further. ELT data pipeline works by copying the data source to the destination. And then, the destination's computing power will handle the transformations.
While ELT history shows it has recently gained popularity, the concept is not new. With the wider adoption of the cloud and data lakes, ELT adoption also accelerated. It makes sense because of these factors: the growing data size, cheaper cloud storage, and faster internet.
When we talk about ELT, we generally mean cloud-driven ELT. Well-known ELT tools harness the power of the cloud. ELT is the answer to an ever-increasing size of data where gigabytes are too small. To make this work, the autoscaling cloud infrastructure and near-infinite storage are necessary. So, this scenario will only make sense in the cloud.
So, why use ELT?
Here are some ELT use cases:
Note that some use-cases in ELT can also be done using ETL. One example is data warehousing. And note that both ELT and ETL can also work with structured data. The difference is in the approach used.
In a later section, you will learn more about when to use ELT.
Let's explain the ELT vs ETL key differences further.
The main difference between ETL and ELT processes lies in the transformation. With this, the difference is like night and day between ELT and ETL workflows. This is shown in the figure below.
It illustrates the ETL meaning we had earlier. Meanwhile, ELT delays the transformation until everything is loaded to the destination. This difference affects the pipeline's maintainability, data security, and compliance.
Because of ETL's approach, errors during transformation will stop the loading to the destination. This doesn't happen in ELT, as shown in the figure below.
Moreover, fixing the ETL pipeline for bugs requires restarting the whole ETL pipeline. So, the transformed data will reach the destination.
Another key difference between ELT and ETL lies in the data it can process. Notice in Figure 1 that ELT sources can also be unstructured like images and videos. ETL only allows structured data. The size of these data is a differentiating factor too. ELT can handle big data when ETL performs badly with it.
Finally, another key difference is where the pipeline lives. ETL pipelines can either be on-premise or in the cloud. Meanwhile, ELT pipelines are mostly cloud-based.
Below is the ETL and ELT comparison table.
From here, let's examine the ETL and ELT pros and cons.
Let's start with ETL.
Though ETL has its drawbacks, there’s a place for it in your data integration efforts. So, when can you opt for ETL as the better option?
The following are the common reasons when ETL should be used:
Now, let’s check out the ELT camp. Here are the ELT pros and cons.
So, when to use ELT?
ELT should be used when:
Have you decided yet on ELT vs ETL?
Both ETL and ELT are important in today’s data-driven organizations. You may want to use both depending on the need.
Remember: ELT is for faster loading and on-demand transformation. It deals mostly with big data that is structured, unstructured, or semi-structured on the cloud. ETL is for a few terabytes or less of structured data that can be batch or real-time. ETL is also for on-premise, legacy data.
There is a number of cloud tools, supporting ELT or ETL scenarios. Here are some of the examples:
Skyvia is a powerful data platform that offers both ELT and ETL tools. For ELT scenarios it offers Replication tool which allows copying cloud data to cloud and on-premise databases and data warehouses with little to no configuration efforts. You only need to create connections to corresponding data sources, select what data to replicate, and then schedule replication for automatic execution. Everything can be done in under 5 minutes.
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