Over recent years, business enterprises relying on accurate and consistent data to make informed decisions have been gravitating towards integration technologies. The subject of Enterprise Application Integration (EAI) and Extraction, Transformation & Loading (ETL) lately seems to pop up in most Enterprise Information Management conversations.
From an architectural perspective, both techniques share a striking similarity. However, they essentially serve different purposes when it comes to information management. We’ve decided to do a little bit of research and establish the differences between the two integration technologies.
Enterprise Application Integration
EAI is an integration framework that consists of technologies and services, allowing for seamless coordination of vital systems, processes, as well as databases across an enterprise.
Simply put, this integration technique simplifies and automates your business processes to a whole new level without necessarily having to make major changes to your existing data structures or applications.
With EAI, your business can integrate essential systems like supply chain management, customer relationship management, business intelligence, enterprise resource planning, and payroll. Well, the linking of these apps can be done at the back end via APIs or the front end GUI.
The systems in question might use different databases, computer languages, exist on different operating systems or older systems that might not be supported by the vendor anymore.
The objective of EAI is to develop a single, unified view of enterprise data and information, as well as ensure the information is correctly stored, transmitted, and reflected. It enables existing applications to communicate and share data in real-time.
Extraction, Transformation & Loading
The general purpose of an ETL system is to extract data out of one or more source databases and then transfer it to a target destination system for better user decision making. Data in the target system is usually presented differently from the sources.
The extracted data goes through the transformation phase, which involves checking for data integrity and converting the data into a proper storage format or structure. It is then moved into other systems for analysis or querying function.
With data loading, it typically involves writing data into the target database destination like data warehouse and operational data store.
ETL can integrate data from multiple systems. The systems we’re talking about in this case are often hosted on separate computer hardware or supported by different vendors.
Differences between ETL and EAI
- Retrieves small amounts of data in one operation and is characterized by a high number of transactions
- EAI system is utilized for process optimization and workflow
- The system does not require user involvement after it’s implemented
- Ensures a bi-directional data flow between the source and target applications
- Ideal for real-time business data needs
- Limited data validation
- Integrating operations is pull, push, and event-driven.
- It is a one-way process of creating a historical record from homogeneous or heterogeneous sources
- Mainly designed to process large batches of data from source systems
- Requires extensive user involvement
- Meta-data driven complex transformations
- Integrating operation is a pull, query-driven
- Supports proper profiling and data cleaning
- Limited messaging capabilities
Both integration technologies are an essential part of EIM, as they provide strong capabilities for business intelligence initiatives and reporting. They can be used differently and sometimes in mutual consolidation.