ETL vs. ELT: What’s the Difference?

One of the most common debates in the data industry is ETL vs. ELT. ETL, or Extract, Transform, and Load process, is a conventional data management method that makes insights accessible to the user. However, the increasing popularity of databases and data warehouses is shifting the idea of ETL in the direction of ELT.

So, what is ELT? What’s the difference between ETL and ELT? Does the only difference lie in the order in which you perform the steps? Of course, not! Let’s explore the differences between ETL and ELT in depth.

Everything You Need to Know About ETL vs. ELT

Let’s start with understanding the basics of both concepts:

What is ETL?

The ETL process includes three critical steps: Extraction, Transformation, and Loading. ETL tools fetch data from one system, such as a file, database, data warehouse, or application, and put it into the destination system after transformation and quality checks.

The first step in the ETL architecture framework is Extraction, which involves pulling out raw data from a source system. The data is read and gathered during this phase, often from numerous sources, such as on-premise and cloud databases, enterprise applications, file systems, and more.

During Transformation, the extracted data is converted into an acceptable format for the destination system. Data transformation is done using expressions, rules, lookup tables, or merging two or more data sets in this stage.

The last step is Loading, which is writing the data into a data store, such as a database, warehouse, or data lake. Below is a diagram that displays the ETL process.

ETL is essential in modern business intelligence processes. It makes it possible to integrate raw structured or unstructured data from different sources into one location to extract business insights. Some people often ask the question, “is ETL outdated?” The answer to this depends on an organization’s needs, such as how many data systems they have in place, whether they need to transform this data, whether they need timely access to the compiled data, etc.

What is ELT?

ELT is an acronym for Extract, Load, and Transform. It’s a process that extracts raw data from a source system to a target system, and the information is then transformed into the source or destination system for downstream applications.

Unlike ETL, where data transformation processes occur on a staging area before being loaded into the target system, in ELT, data is loaded directly into the target system and converted there.

In this way, ELT is most useful for handling enormous datasets and using them for business intelligence and data analytics.

As compared to the ETL process, ELT considerably reduces the load time. In addition, ELT is a more resource-efficient method as it leverages the processing capability developed into a data warehousing setup, decreasing the time spent on data transfer.

The next question arises, which approach is right for your business? To figure that out for your organization, you should keep a few things in mind.

ETL vs. ELT: Finding the Right Approach

Whether you should use ETL vs. ELT for a data management use case depends mostly on the fundamental storage technologies, your data storage architecture, and the performance requirements.

To help you choose between the two, let’s get into the difference between ETL vs. ELT by discussing the advantages and drawbacks of each technique.

Advantages of the ETL Process

• ETL can balance the capacity and share work with the relational database management system (RDBMS).
• Using data maps, it can execute complex operations in a single data flow diagram.
• It can handle segregating and parallelism irrespective of the data model, database design, and source data model infrastructure.
• It can process data transmitted from the source and load data to target even in batches.
• You can preserve current data source platforms without worrying about data synchronization, as ETL doesn’t necessitate the co-location of data sets.
• The ETL process extracts vast amounts of metadata and can run on SMP or MPP hardware that can be managed and used more efficiently without performance conflict with the database.
• In the Business Intelligence (BI) ETL process, the information is processed one row at a time. So, it performs well with data integration into 3rd party systems.
• Owing to parallel processing, the ETL process offers remarkable performance and scalability.

Drawbacks of the ETL Process

• ETL requires extra hardware outlay unless you run it on the database server.
• Due to the row-based approach, there’s a possibility of reduced performance in the ETL process.
• You’ll need expert skills and experience to implement a proprietary ETL tool.
• There’s a possibility of reduced flexibility because of dependence on ETL tool vendors.
• Data has to transfer across an additional layer before it reaches the data mart unless it is only an output of the ETL process.
• There’s no programmed error control or retrieval mechanism in traditional ETL processes.

Advantages of the ELT Process

• For better scalability, the DWH ELT process uses an RDBMS engine.
• There’s better performance and data safety as it operates with high-end data devices like Hadoop clusters, cloud, or data appliances.
• When comparing ETL vs. ELT, the latter needs less time and resources as the data is transformed and loaded in parallel. The data size can also be enormous.
• The ELT process doesn’t need a discrete transformation block as the target system performs this work.
• Since source and target data are in the same database in ELT, it retains all data in the RDBMS permanently.

Drawbacks of the ELT Process

• Limited tools offer complete support for ELT.
• In ETL vs. ELT, the former risks a loss of comprehensive run-time monitoring statistics and information.
• There’s also a lack of modularity because of set-based design for optimal performance and the lack of functionality and flexibility.

The Key Takeaway

Though there are differences between ETL and ELT, they are used to fulfill the exact requirement, i.e., preparing data for analysis and using it for superior business decision-making.

The simplest way to solve the ETL vs. ELT dilemma and understand their differences is by comprehending the ‘T’ in both approaches. The critical factor differentiating the two is when and where the execution of the transformation takes place.

Implementing an ELT process is more difficult as compared to ETL. However, now businesses favor ELT over ETL due to faster performance. The design and execution of ELT may need more exertion, but it offers more benefits than ETL in the long run.

Overall, ELT is economical as it requires fewer resources and less time. However, if the target system is not robust enough for ELT, ETL might be a more appropriate choice.

Ultimately, the right choice depends on your organization’s resources and requirements. It’s best to opt for a solution that offers both capabilities within the same platform. Data integration tools, such as Astera Centerprise, allow you to execute ETL and ELT based on your requirements. Try the free version and experience code-free ETL and ELT for yourself.