Just like ETL, ELT is a data integration process found in most data management processes.
When building data infrastructure and selecting a tool stack, data engineers and even software developers often find themselves at a crossroads: what data integration process would work best? And what tools are best for the job?
On one hand, there is the familiar and reliable ETL process that has been a staple of most data management systems since the 1990s. On the other, there is the more modern ELT process that has several advantages over ETL, yet it doesn’t have quite the name recognition.
Here, we’ll settle the dispute between ELT and ETL once and for all by:
ELT means Extract, Load, Transform. It is a method of data replication and transformation used to perform data integration at any scale. The purpose of ELT is to extract specific data, such as customer information or billing records, from its source (a CRM system or an SQL database, for instance), and deliver it to its end point in the fastest, most reliable way possible. As you can imagine, the name ELT means that this process consists of three steps:
ELT is often used interchangeably with ETL. However, the two approaches have a few notable differences which directly influence their benefits and use cases.
The ultimate goal of both the ETL and ELT process is to transform raw, unprocessed data into valuable and comprehensible insights, such as analytics reports. Both methods achieve this but the latter is preferable for the following reasons.
While the two processes have their similarities, ELT has a few clear advantages over ETL, thanks to the inherent efficiency, reliability and scalability built into the flow. Let’s look at four key points that make ELT the superior process between the two.
You don’t want that to be a one-time or destructive process that can’t be repeated or that can’t be iterated upon.
The transformation is where you want to apply your business logic you’re cleansing. And the important thing to think about is that transforms your business logic and your cleansing is going to improve constantly.
When you do the transformation in the middle, then you’re in, you’re imposing your business logic on the data. While it’s in flight, as your business laws changes, you have no way to rerun what you ran at that point in time. You’re also going to have a lot more failures.
What is helpful about ELT is that you can have a kind of virtuous downstream cycle. The example being, I extract and load transform and publish. But my teammate also needs to extract from me and they can have the same process and so on and so on and so on. So you can have a core team doing ELT. They can also have subscribing teams which can have subscribing teams and so on and so on. The benefit of this ETL process here is that they have an official record of whatever we published on that day. You are able to restate everything to your downstream consumer who will have some record of what I published on that day. And that’s one of the reasons why we have a separate EL step vs including the transformation in with the replication.
Because of ELT’s unique features and advantages, it is superior to ETL as a data processing protocol in a variety of use scenarios and projects. But when would ETL benefit you the most?
Here are a few scenarios when ELT would work best for you:
In contrast to many ETL solutions, ELT is cloud-based and does not require as much human input for updates and maintenance.
Meltano is an open-source ELT platform powered by:
Aimed at data consultants, engineers and developers of data products, Meltano is a self-hosted solution that can be integrated into any local machine or production environment and managed via a built-in command line interface (CLI). True to our open-source roots, Meltano is supported by a global network of 1,900+ data specialists at startups and Fortune 500 companies alike. Our goal is to become the leading DataOps Ops and enable our users to build next-generation data stacks.
At the heart of every data management infrastructure lies a process that transforms the data from an unorganized collection of entries into comprehensible reports and insights. This process usually goes by one of the following two names — ETL which means Extract, Transform, Load or ELT which means Extract, Load, Transform. And while the two names and the mechanics behind them are similar, the two processes couldn’t be more different. While ETL is the older and better-known method of the two, it’s not without its drawbacks. It doesn’t rely on the power of cloud computing as much as ELT, takes more effort and resources to maintain and can be very difficult to scale. By comparison, ELT is more streamlined and follows the key principle of a data pipeline — to process and organize your data as quickly, accurately and seamlessly as possible.
In other words, ELT means cost-effectiveness, flexibility and limitless collaboration.
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