ETL vs. ELT: A Detailed Comparison

Businesses rely significantly on effective data processing techniques like ETL (extract, transform, load) and ELT (extract, load, transform) to handle and analyze massive volumes of information in today’s data-driven world. These procedures are essential for turning unprocessed data into insightful knowledge that influences choices.

ETL is perfect for structured data, since it processes the data on a different server before loading it into the data warehouse. In contrast, ELT inserts unstructured data into the data warehouse first, enabling faster processing and more flexibility.

The primary distinction is in the timing of the transformation: ETL loads data after transformation, whereas ELT loads data before transformation. This essential distinction is crucial in helping you choose the best strategy for your company.

Throughout this article, you’ll learn about the key differences between ETL and ELT. You’ll also learn the pros and cons of each approach, and how to choose the best method for your needs. We’ll also provide real-world examples and explore how modern tools like Workato can help you leverage both processes effectively.

What Is ETL?

ETL stands for extract, transform, and load. It refers to a specific method for formatting and organizing data in a consistent manner. This way, it can be analyzed for business intelligence. ETL is one way to answer the question, “How do we transport the right data from all our software to our data warehouse?” The three steps within the ETL process are detailed below.

Extract

Structured data is pulled from one or more sources in batches. The data could come from a variety of different sources. Additionally, it could be in several different formats, including JSON or XML.

Transform

In this step, the data is cleaned to match a specific set of predefined instructions that align with the needs of the business. Transformation might include filtering the data to remove duplicate records and applying rules to prevent bad data from being added to the company’s data warehouse.

Load

In this final stage, the data is delivered to its ultimate destination, where it can be viewed and analyzed by team members with the right permissions.

ETL Example

Imagine a logistics company with 100 vehicles spread across the country that specializes in the cold storage of perishable items. They utilize Internet of Things (IoT) temperature monitors in their trucks. These monitors feed data to a central gateway every fifteen minutes, to ensure temperature management.

These sensors may provide the organization with temperature data and driving information. After that, this data is converted into a standard format to guarantee accuracy and interoperability. Eventually, the data is cleaned and stored in the company’s data warehouse, where authorized staff can view it.

In this case, analysts in the headquarters may track and enhance delivery performance by associating cargo temperatures with certain drivers. To ensure timely insights, the ETL process would be set up to run at regular intervals and process data in batches.

Now, let’s answer some of the most common questions about the ETL process.

How has the ETL process evolved since the 1970s?

Since the 1970s, ETL (extract, transform, load) has been a fundamental component of data processing. With the introduction of contemporary computing, ETL—which was originally intended for batch processing in mainframe environments—has undergone significant evolution. To keep up with the growing volume and diversity of data, the process has evolved from straightforward data transfers to intricate processes requiring numerous data sources and sophisticated transformations.

What is the role of data mapping in ETL?

To ensure that data from diverse sources is correctly processed and loaded into the target system, data mapping is an essential part of the ETL process. To guarantee correctness, consistency, and compatibility throughout the transformation process, it specifies how data fields from the source are matched to fields in the destination.

What is the OLAP data warehouse in the ETL process?

OLAP (online analytical processing) data warehouses are essential to the ETL process because they provide sophisticated searches and data processing. Typically, ETL procedures load transformed data into an OLAP data warehouse, where it’s organized for multidimensional analysis. This enables businesses to run complex analytical queries on historical and aggregated data and produce insightful findings.

Related: The differences between an iPaaS and ETL

What Is ELT?

By this point, careful readers may have noted that if ETL stands for “extract, transform, load,” then ELT must refer to “extract, load, transform.” Though it’s only a subtle change in the name, ELT is drastically different. The three steps within the ELT process are detailed below.

Extract

Any type of data, whether structured or raw, is pulled from any source. It could be on-prem software, a SaaS solution, a private data cloud, or anything else.

Load

This data is loaded directly into a data lake without any type of filtering. Whatever data existed in the source programs, you get it all in this stage: the good, the bad, and the ugly.

Transform

Once all this data is loaded into the target destination, it can be transformed into a consistent format and analyzed in real-time. These transformations are only limited by the ingenuity of the people looking at the data. This type of flexibility is one of the big differences between ELT and ETL.

ELT Example

Consider a national grocery chain that oversees tens of thousands of distinct food products. This company handles enormous volumes of data, including vendor history, real-time inventory, best-by dates, and sales performance, in contrast to the logistics company from the ETL example. The company would first collect all pertinent data and load it into a data lake to make sense of this data.

Several teams can alter the data on demand after it’s in the data lake. For example, the finance team might compare vendor performance, while a category manager might examine the sales success of a particular item. The marketing team may then utilize the information to compare sales across various stores to plan a regional campaign.

Because of its flexibility and the ability to process large amounts of data in parallel, which modern cloud data warehouses provide, ELT is the best option for the supermarket chain’s complicated data needs.

Some of the most asked questions for ELT are answered below.

What security features are provided directly within the data warehouse for the ELT process?

The data warehouse, which often has robust built-in capabilities, is directly responsible for maintaining data security in the ELT process. Common examples of these characteristics are encryption both in transit and at rest, audit logging, and role-based access control (RBAC). By retaining the data inside the warehouse, ELT minimizes exposure risk, minimizes data migration, and ensures that sensitive data is protected throughout the processing pipeline.

How does ELT utilize the processing power and parallelization that cloud data warehouses offer?

ELT makes full use of the processing power and parallelization available in modern cloud data warehouses. These warehouses can process large volumes of data concurrently across multiple nodes, allowing for rapid transformation and analysis. By making use of this parallel processing, ELT can handle complex data transformations more skillfully. This results in quicker insights and reduced processing and analysis times for big datasets.

Related: What is reverse ETL?

ELT vs. ETL: Pros

Now that you know a bit about ELT and ETL, it’s time to look at the pros of each technique to better understand how they compare.

ELT Pros

• Real-time data analysis. With ELT, you don’t have to wait for your IT teams to extract a new batch of data. You can run experiments on all the data in your system whenever you want.
• Much more flexibility in how you analyze data. Easily change your transformation parameters every time you have a new query.
• Work with all types of data, whether it’s structured or raw.
• Easier to scale as you load more and more data.
• Store huge quantities of data with no problems.
• Load data as soon as it’s created.

ETL Pros

• Easier to manage your data storage costs, since data is transformed and filtered before it’s loaded into your data warehouse. You don’t pay for data that you don’t store: duplicates, bad data, and anything else excluded by your initial criteria.
• Supports your data privacy and compliance goals in alignment with GDPR, CCPA, HIPAA, and similar regulations. Within ETL processes, sensitive data is masked or encrypted during the transformation stage.
• Safe, simple, straightforward way to transform and load data over time.
• Good choice when complex transformations are required.
• ETL has been around for decades, so it’s easy to find technology solutions and experts who can help you set up the best ETL process for your business needs.

ELT vs. ETL: Cons

It’s also worth taking a look at a more detailed list of cons for both techniques.

ELT Cons

• Since you’re storing all types of data, storage requirements may be much higher.
• Sensitive private data must be loaded into a data lake before it’s transformed. This makes it visible to sysadmins and potentially more exposed in the event of a data breach. Extra security measures are required for ELT processes to support data compliance with GDPR, CCPA, HIPAA, and similar regulations.
• ELT often requires custom security solutions, such as encryption and role-based access controls, to protect sensitive data during the transformation process, especially when dealing with regulated industries.

ETL Cons

• Higher ongoing maintenance costs, since changing input sources will require constant updates to basic steps that define your ETL process.
• Offers less flexibility in how you analyze data, since the transformation step is baked into the entire process from the beginning.
• Doesn’t support projects that rely on machine learning or real-time analysis.
• Can only integrate your data, not the systems responsible for that data.
• Typically can’t move data across systems in real time.
• Works best for smaller amounts of data.

Related: The key differences between an iPaaS and an enterprise automation platform

Conclusion

ETL evolved to address companies’ rapidly growing data sets. As this trend accelerated and the amount of data exploded exponentially, ELT was developed to give businesses more flexibility and agility with their data analysis.

Both methods are used to achieve similar goals: transforming large amounts of data into formats that can be more easily understood so that you and your team can perform meaningful business intelligence. Without ETL or ELT, we’d be doomed to endure the consequences of disconnected data silos, the ultimate example of our left hand not knowing what our right hand is doing.

In general, ETL is faster when dealing with smaller, structured data sets that require significant transformations before loading into a data warehouse. Conversely, ELT is faster for processing large volumes of data because it leverages the processing power of modern cloud data warehouses, allowing raw data to be loaded first and transformed later on demand.

With Workato, the leader in enterprise automation, you don’t have to choose between ETL and ELT. It offers hundreds of pre-built connectors and thousands of automation templates. This allows you to easily connect your data warehouse or data lake with the rest of your tech stack and implement either process quickly. In addition, the platform offers enterprise-grade security through features like role-based access controls, audit logs, and life cycle management, ensuring that you stay in compliance with data privacy regulations.