March 12th, 2021

In today's era, data has become the core of many companies' information systems. Extracting, manipulating, structuring, and exchanging data have become crucial processes. With the accumulation of data and the high number of business processes that flow from it, many solutions have been developed to meet process optimization and automation needs.

There are countless IT terms related to data. Although many are similar in appearance, they are indeed different and address various issues. Here you’ll find the differences between ETL and EAI, but you can also read about many other solution types here.

ETL Defined

ETL stands for Extract, Transform, Load. It’s a computer procedure that consists of copying data from one or more sources to a destination system that will reproduce the data differently, or in a different context from the source(s). One of the benefits is that this allows for information synchronization between one data source and another on a massive scale.

The ETL procedure is based on three key processes. First, an extraction of data from a source. Second, data transformation, which involves validation and conversion to the appropriate storage format. Third, data loading to the final, target database. An ETL solution therefore relies on connectors to export and import data into applications, as well as transformers to manipulate the data and convert it to the required format.

The most common use for an ETL solution is to collect data from multiple sources, restructure it, and then transfer it to a data warehouse. An ETL is often used in batch mode to transfer data in bulk.

EAI Defined

Enterprise Application Integration, or EAI, refers to an information system architecture that allows several software applications (ERP, CRM, WMS, and so on) to communicate with one another by exchanging data.

Despite the fact that it is often necessary for systems to share data and adopt certain business rules, it’s normal that different types of systems cannot communicate with each other natively. This lack of inter-application communication leads to inefficiencies. For example, if and when identical data is stored in several places, it is highly prone to inconsistencies. Another downside is that simple processes cannot be automated.

EAI software links applications within an organization to simplify and automate business processes as much as possible, all while avoiding the need to make radical changes to existing applications or data structures. Unlike ETL, EAI operates continuously (near real-time) according to business rules. Enterprise application integration systems can also handle bi-directional data flows. In general, EAI is suitable for moderate volumes.

Enterprise application integration offers many advantages, including the centralization of data flows, cost reduction, data security, process harmonization, and more. Although an EAI solution may involve many internal systems and applications, a typical enterprise application integration scenario involves purchasing a connector to link individual systems and applications.


Just to provide a little context, EDI is most often used for data exchange between trading partners and requires integration with external companies. However, it’s also important that your EDI system seamlessly connects with other internal systems and applications to ensure data coherence. This is important to consider when evaluating EDI service providers while also keeping in mind that your internal systems are subject to change. Make sure your EDI system won’t be limiting in terms of connectivity in either instance—internal or external.

Enterprise application integration (EAI) is generally used for internal purposes. In theory, an EAI solution can be used to connect your EDI system with your ERP or any other internal system. However, this should be a standard inclusion from your EDI provider; it’s not common to have to figure out a solution for this type of integration on your own. That being said, most businesses turn to an EAI solution to gain better insights into certain business areas by combining data that would be much too challenging to pull together manually.