ETL vs. ELT: What’s the Difference?

By |2021-09-21T12:49:10+00:00August 23rd, 2020|

ETL stands for Extract, Transform, and Load. It’s been a conventional process to manage data pipelines for years. However, the increasing popularity of contemporary cloud-based data warehouses is shifting the customary 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 ETL vs. ELT in depth.

Everything to know about ETL vs. ELT

This article will solve the ETL vs. ELT dilemma and discuss the pros and cons of each approach to find out which one would be more suitable for your business. Furthermore, we will outline the basic features of the ETL vs. ELT architecture and get a basic understanding of these two concepts.

What is ETL?

The ETL process includes three important steps, including Extraction, Transformation, and Loading. ETL tools fetch data from one database and put it into another one after transformation and quality checks.

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

During Transformation, the data extracted is then converted into a format that is acceptable for another database. Data transformation is done using expressions, rules, lookup tables, or by merging two or more data sets in this stage.

The last step is Loading, which is the process of writing or stacking the data into the targeted database or data warehouse.

ETL vs. ELT: The ETL process flow diagram explained

The ETL process flow diagram

ETL makes an essential component in modern business intelligence processes. It makes it possible to integrate structured or unstructured data from dissimilar sources in one location to extract business insights. Some people often ask the question, “is ETL outdated?” The answer to this widely asked question is that it depends on an organization’s needs. However, ETL has a place in the data warehouse legacy and is still popularly used by businesses that do not need to move to the cloud.

What is ELT Approach?

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

Unlike ETL, where data transformation occurs on a staging area before being loaded into the target system, ELT loads raw data directly into the target system and converts it over there.

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

ETL vs ELT: The ELT process

As compared to the ETL process, ELT considerably reduces the load time. In addition, as compared to ETL, ELT is a more resource-efficient method as it leverages the processing capability developed into a data warehousing setup, decreasing the time spent in data transfer. After understanding what ETL and ELT are, let’s move on to see which of the two approaches are right for you.

ETL vs. ELT: Finding the Right Approach

Whether you should use ETL vs. ELT for a data management use-case depends primarily on three things; the fundamental storage technologies, your data storage architecture, and the application of a data warehouse for your business.

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 data integration architecture, one by one.

Advantages of the ETL Process

  • ETL can balance the capacity and share the amount of work with the relational database management system (RDBMS).
  • It can execute intricate operations in a single data flow diagram using data maps.
  • It can handle segregating and parallelism irrespective of the data model, database design, and source data model infrastructure.
  • It can process data while being 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 co-location of data sets.
  • The ETL process extracts huge 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 for implementing 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 cluster, cloud, or data appliances.
  • As compared to ETL, ELT needs lesser 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 itself performs this work.
  • Given that source and target data are in the same database in ELT, it retains all data in the RDBMS permanently.

Drawbacks of the ELT Process

  • There are limited tools available that offer complete support for ELT processes.
  • In the case of ELT for data warehouses, there’s 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 resulting from it.
etl vs etl

 ETL vs. ELT

ETL vs. ELT: Key Takeaway

Though there are differences between ETL and ELT processes, they are used to fulfill the same requirement, i.e., preparing data to be analyzed and used for superior business decision-making.

The simplest way to solve the ETL vs. ELT dilemma and understand the difference between ETL and ELT is by comprehending the ‘T’ in both approaches. The key factor that differentiates the two is when and where the transformation process is executed.

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Implementing an ELT process is more intricate as compared to ETL. However, ELT is now being favored over ETL. The design and execution of ELT may necessitate some more exertions, but it offers more benefits than ETL in the long run.

Overall, ELT is an economical process as it requires fewer resources and takes a smaller amount of time. However, if the target system is not robust enough for ELT, ETL might be a more suited choice.

It wouldn’t be wrong to call ELT the new monarch in data management, which is gradually superseding the ETL process. Replacing ETL with ELT, businesses can analyze greater volumes of data with less upkeep and time to fuel data-driven innovation.