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ETL vs. ELT: Top Ten Differences

ETL vs. ELT: Top Ten Differences

With the rise of cloud technology, a major shift is happening in data integration practices: more and more businesses and government software developers are shifting their data pipelines from “ETL” to “ELT.” But what do these acronyms mean, and what is the impact of choosing one over another? Here’s everything you need to know about the different characteristics of these two data pipeline models.

First, let’s define our terms.

  • Extract: The data is pulled in its raw format out of its source, such as an ATM or a Twitter account.
  • Transform: The data is converted to a structure that is useful for analytics tools to visualize or otherwise gain actionable information; often, but not always, this means putting it in a spreadsheet format. Data points that are considered irrelevant may be stripped away in the process.
  • Load: The data is loaded into its final destination—usually a data warehouse.

The acronyms “ETL” and “ELT” represent the processes of performing these actions in a different order.

ETL: Extract, Transform, Load.

Data is Extracted from its source, Transformed on a processing server, and Loaded into the data warehouse, where it’s ready to be used in analytics.

ELT: Extract, Load, Transform.

Data is Extracted from its source, Loaded into the data warehouse, and can be Transformed within the warehouse while preparing analytics.

The difference between the two may seem negligible at first glance, but it actually represents two drastically different approaches to data integration and storage. To illustrate how, we’ve created a list of the top ten differences between ETL and ELT.

Top ten differences: ETL vs ELT

#1: Security

In ETL, when the data is transformed on a processing server, this is a critical step for any database that contains personally identifiable information. ETL data pipelines are ideal choices for organizations that are working with sensitive data or have to meet compliance standards, such as HIPAA or GDPA. It’s not that compliance can’t also be met in an ELT pipeline, but the security requirements have to be much steeper in order to compensate for the lack of a data cleansing step in the process.

#2: Maintenance

ETL pipelines are much more high-maintenance than their ELT counterparts. Transforming the data before loading it into the data warehouse means that you have to be very calculated in how you design the process. Even when many of the steps are automated, the system is complex enough that those automated processes require routine updates. More often than not, ETL pipelines require a lot of oversight. ELT, on the other hand, can, with enough expertise, become fully automated with very little oversight needed to continuously run.

#3: Transformations—Where, When, and How Often?

An ETL pipeline requires a dedicated processing server in order to perform the data transformation before loading it, but the data only gets transformed once, and then it’s done. In an ELT pipeline, the data is kept in its raw form in the data warehouse and can get transformed right where it is. In fact, by maintaining the raw data in its final storage, it can be transformed again and again to perform different analytics.

#4: Data Structure

In an ETL pipeline, the data always enters the data warehouse as structured data. In an ELT pipeline, since data is mostly raw, it can be structured, unstructured, or semi-structured. Read more about data structures in our blog on the subject.

#5: Pipeline Speed

An ETL pipeline is slow. Data transformations can be labor-intensive and time-consuming for either ETL or ELT, but in ETL’s case, the transformation happens in the pipeline, meaning the transportation of data takes a while. ELT, on the other hand, can be near-instantaneous thanks to pushing off the data transformation to later.

#6: Output Speed

Conversely, an ETL pipeline’s analytics output is rapid because the data is already neatly structured. In an ELT pipeline, the data is often unstructured, and therefore needs to be transformed before it can be analyzed. Essentially, somewhere between extracting the data and analyzing it, there’s going to be a waiting period somewhere; ELT places that waiting period later in the process.

#7: On-Site or On the Cloud

ETL was designed in the era of on-site server rooms and is best suited to those circumstances, although it can also occur on the cloud. ELT has only become possible in the first place due to the explosion of massive cloud storage solutions, meaning it mostly exists on the cloud.

#8: Maturity

ETL has been around since the 1970s, and that comes with significant benefits. There are well-established and tightly-refined tools to aid in the process. Since ELT only came about because of the cloud, that means it’s relatively new—however, the technology is catching up quickly, and it looks likely that ELT is going to have major staying power down the line.

#9: Storage Types

In this blog, we’ve been using the term “data warehouse” to describe where the data gets loaded, and in the case of ETL, that’s accurate; however, in the case of ELT, a data lake is also an option. Read our blog to learn more about data storage options.

#10: Cost

This is more complicated of an issue than you might expect. ETL requires more manpower and a whole extra processing server, so that can drive the cost up higher than ELT. On the other hand, ELT involves moving and storing much larger quantities of data, and there are many cases where that can drive the cost up higher than ETL. So, which is pricier? It depends on the pipeline you ultimately build.

Which version of data integration does your organization use?

Whether you use ETL or ELT, and whether you plan to stick with it or want to make a change, PVM has the expertise to help you make the very most of your data. Our partnerships with Palantir, Vertica, and Amazon Web Services are all the proof you need that when it comes to managing big data, PVM has what it takes and is up for the challenge. Contact us to discuss your big data needs today!