by CData Arc Marketing | July 12, 2021

Enterprise Application Integration (EAI) vs. ETL/ELT Data Integration

IT teams have long relied on the enterprise data warehouse as the central data infrastructure for their business workflows. Everything runs to and through the warehouse, and ETL/ELT tools are the tried-and-true workhorse for data integrations, extracting data from source applications and systems, loading it into the target warehouse, and transforming it to be accessible.

But ETL tools alone are not enough. As enterprises leverage more data systems that require greater data volume and accessibility, IT teams are faced with more demands for complex data transformations that ETL cannot deliver. These include scenarios that require:

  • Complex business logic and validation
  • B2B systems integration
  • Real-time integration

This article discusses why an ETL tool might be insufficient for meeting these business requirements, and how an iPaaS integration solution can address these complex demands.

1. Complex Business Logic and Application Integration

In some cases, when you move data from one application to another, it may not be possible to perform a simple 1:1 data integration between your source and target systems. The data might have multiple sources or destinations that require business logic to determine where to send data and what to do with it based on pre-determined variables.

For example, let's look at a typical e-commerce transaction. When a customer places an order, the system can trigger a lookup in an inventory database to check if an item is present, mark an item as sold, add the sale value to an accounting system, and route a shipping order to the logistics system. The process involves logic. If the item is available in inventory, you can kick off a workflow that sends a ship notification and triggers an order shipment. If the item is not in the warehouse, a different workflow might need to direct your ERP system to restock the item and then kick off a shipment.

You might also need business logic is to validate data. For example, a workflow could determine whether an order contains items you no longer carry. Or you might want to call up the USPS address validation service to make sure a ship-to address is correct.

An ETL solution that simply moves data will be unable to provide the business logic necessary to create these complex workflows or perform the requisite data validations.

2. B2B Systems Integration

Your business partners generate vast quantities of data each day, all stored in their own databases, ERPs, CRMs, and other enterprise systems. To make use of this data, you'll need to connect these external applications and databases to your own data warehouse and applications. The process not only involves data transformations, but also secure data movement between enterprise networks.

One real-life example involves Michelin, a leading global tire manufacturer. They rely heavily on a fleet of logistics partners to distribute their tires around the world and connect their supply chain. To centralize and leverage the data tucked away in their partners' individual data stores and applications, Michelin needed to pipe that data into their central PostgreSQL database. They also required secure, high-volume enterprise file transfer and highly complex data mappings to transform data from a multitude of systems into a common data model for their PostgreSQL system.

Michelin ultimately turned to CData Arc's message-based application integration software to handle this complex B2B integration.

See How Michelin Integrated with Supply Chain Partners

3. Real-Time Integration

In some cases, data might be time sensitive. For example, imagine a hotel needs to update its booking database as soon as a customer makes a reservation to avoid the risk of double-booking rooms. The hotel can't tolerate time lags between booking a room and updating the system.

Most ETL tools use a scheduled or polling approach to moving data. At designated intervals of time, ETL solutions ask the source application if anything has changed, and if so, push the updated data to the destination database.

If you pull the data too frequently, you burn resources unnecessarily. If you don't pull it frequently enough, you risk having inaccurate data.

Instead, you need a workflow triggered by a specific event. An application integration solution that incorporates webhooks can constantly listen for changes in the source system. When a request comes in that requires a particular data set, webhooks immediately integrate the necessary data to eliminate polling intervals and provide data in real-time.

The iPaaS Solution

While ETL can't address the scenarios outlined above, an integration platform as a service (iPaaS) solution can fulfill your modern enterprise data needs.

CData Arc, a lightweight, low-code iPaaS application, allows you to perform complex, live integrations and fill in the gaps where ETL falls short.

CData Arc provides an intuitive, drag-and-drop workflow canvas that gives you the power to quickly build application integration processes – without coding. It can power all your complex integrations in one place with capabilities for B2B systems integration, advanced business logic, data validation, and real-time integration through webhooks.

It also simplifies workflow management, providing you a central location to orchestrate your integrations, complete with:

  • Active, 24-7 process monitoring
  • Logs for easy auditing
  • Military-grade security and encryption
  • User management
  • Drag and drop data mapping for XML-driven data transformations
  • Built-in scripting engine for advanced mappings and integrations
  • Developer-friendly admin API for remote management

CData Arc is available on-premise, in the cloud, and on AWS and Azure. To learn more, explore CData Arc integration or request a personalized product demo.