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

Click on the button to load the content from www.slideshare.net.

Load content

Kai Waehner

bridging the gap between technical innovation and business value for real-time data streaming, processing and analytics

Recent Posts

Tesla Energy Platform – The Power of Data Streaming with Apache Kafka

Tesla’s Virtual Power Plant (VPP) turns thousands of home batteries, solar panels, and energy storage…

1 week ago

How Data Streaming with Apache Kafka and Flink Drives the Top 10 Innovations in FinServ

The financial industry is rapidly shifting toward real-time, intelligent, and seamlessly integrated services. From IoT…

2 weeks ago

Free Ebook: Data Streaming Use Cases and Industry Success Stories Featuring Apache Kafka and Flink

Real-time data is no longer optional—it’s essential. Businesses across industries use data streaming to power…

2 weeks ago

Why Generative AI and Data Streaming Are Replacing Visual Coding with Low-Code / No-Code Platforms

Low-code/no-code tools have revolutionized software development and data engineering by providing visual interfaces that empower…

3 weeks ago

The Role of Data Streaming in McAfee’s Cybersecurity Evolution

In today’s digital landscape, cybersecurity faces mounting challenges from sophisticated threats like ransomware, phishing, and…

4 weeks ago

Fully Managed (SaaS) vs. Partially Managed (PaaS) Cloud Services for Data Streaming with Kafka and Flink

The cloud revolution has reshaped how businesses deploy and manage data streaming with solutions like…

1 month ago