Categories: EAIESBSOA

Enterprise Integration Patterns (EIP) Revisited in 2014

Today, I had a talk about “Enterprise Integration Patterns (EIP) Revisited in 2014” at Java Forum Stuttgart 2014, a great conference for developers and architects with 1600 attendees.

Enterprise Integration Patterns

Data exchanges between companies increase a lot. Hence, the number of applications which must be integrated increases, too. The emergence of service-oriented architectures and cloud computing boost this even more. The realization of these integration scenarios is a complex and time-consuming task because different applications and services do not use the same concepts, interfaces, data formats and technologies.

Originated and published over ten years ago by Gregor Hohpe and Bobby Woolf,  Enteprise Integration Patterns (EIP) became the world wide de facto standard for describing integration problems. They offer a standardized way to split huge, complex integration scenarios into smaller recurring problems. These patterns appear in almost every integration project. Most developers already have used some of these patterns such as the filter, splitter or content-based-router – some of them without being aware of using EIPs. Today, EIPs are still used to reduce efforts and complexity a lot. This session revisits EIPs and gives an overview about the status quo.

Open Source, Apache Camel, Talend ESB, JBoss, WSO2, TIBCO BusinessWorks, StreamBase, IBM WebSphere, Oracle, …

Fortunately, EIPs offer more possibilities than just be used for modelling integration problems in a standardized way. Several frameworks and tools already implement these patterns. The developer does not have to implement EIPs on his own. Therefore, the end of the session shows different frameworks and tools available, which can be used for modelling and implementing complex integration scenarios by using the EIPs.

Slides

http://www.slideshare.net/KaiWaehner/enterprise-integration-patterns-revisited-in-2014

Kai Waehner

bridging the gap between technical innovation and business value for real-time data streaming and applied AI.

Recent Posts

dbt Meets Apache Flink: One Workflow for Data Engineers on Snowflake, BigQuery, Databricks, and Confluent

Two toolchains, two skill sets, two CI/CD pipelines — that has been the reality for…

3 weeks ago

The Shift Left Architecture 2.0: Operational, Analytical and AI Interfaces for Real-Time Data Products

The Shift Left Architecture moves data integration logic into an event-driven architecture where governed data…

3 weeks ago

UFC VIP Experience Worth the Price? Fan Review. Business Perspective. Tech Vision.

The Ultimate Fighting Championship (UFC) held Fight Night London on March 21, 2026, at The…

4 weeks ago

Dashboards and Queries for Apache Kafka: Operational, Explorative, and the Role of the Context Engine

Dashboards are a popular way to make streaming data visible and useful, but they are…

1 month ago

Data Streaming at MWC 2026: How Apache Kafka, Flink and Agentic AI Power Telecom Trends

Mobile World Congress (MWC) 2026 highlights the shift from batch systems to real time data…

1 month ago

From Takeoff to Touchdown: Real-Time Aviation with Data Streaming at Qantas

This blog post explores how data streaming transforms airline operations by enabling real-time visibility, faster…

2 months ago