Intro to Data Integration Patterns – Broadcast


In my post yesterday, we did a brief introduction to the migration pattern. Today we are going to do a similar overview of the broadcast pattern which is a kinetic version of the migration pattern.

Pattern 2: Broadcast

What is it?

Broadcast can also be called “one way sync from one to many”, and it is the act of moving data from a single source system to many destination systems in an ongoing,

Intro to Data Integration Patterns – Migration

Hi all, in this post I wanted to introduce you to how we are thinking about integration patterns at MuleSoft. Patterns are the most logical sequences of steps to solving a generic problem. Like a hiking trail, patterns are discovered and established based on use. Patterns always come in degrees of perfection with much room to optimize or adopt based on the needs to solve business needs. An integration application is comprised of a pattern and business use case.

Aggregation with Mule – “Fork and join pattern”

September 12 2013

1 comment.

In your daily work as an integration developer you’re working with different kinds of patterns, even if you’re not aware of it.

Since Mule is based on EIP (Enterprise Integration Patterns) you’re most definitely using patterns when using Mule.

One of those patterns that seems to raise a lot of questions is the “fork and join pattern”. The purpose of the fork and join pattern is to send a request to different targets,

Synchronous and Asynchronous Throttling


One of the most common use cases while building flows/applications in mule is to be able to communicate to external systems. The performance of that external system is often beyond the user’s control. It could be possible where the rate at which mule flow sends the messages outbound is faster than the rate at which that external system could process the message. In such scenarios, there is a need to be able to perform some kind of throttling so that we don’t burden/break the external system.