All things Anypoint Templates

September 17 2014

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motif

Over the last few months we’ve been actively building and releasing new Anypoint Templates. Anypoint Templates are designed to make it easier and faster to go from a blank canvas to a production application.They’re bit for bit Mule applications requiring only Anypoint Studio to build and design, and are deployable both on-premises and in the cloud.

Anypoint Templates are based on five common data integration patterns and can be customized and extended to fit your integration needs.

Intro: Salesforce to Database Anypoint Templates

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I’d like to announce and introduce you to our second set of Anypoint TemplatesSalesforce to Database. This set leverages the newly improved Database connector which allows you to connect with almost any JDBC relational database, consistently using the same interface for every case. Our first set of templates, Salesforce Org to Org integration, and is a good base for any “Salesforce to X”,

Intro to Data Integration Patterns – Aggregation

In this post I want to close the loop on introducing you to the last of the five initial patterns that we are basing our Anypoint Templates on. I’m sure we’ll continue creating templates and we’re going to continue discovering new data integration patterns. If you are just entering at this post, I would recommend that you look through the previous four posts to understand the other patterns.

Intro to Data Integration Patterns – Correlation

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So far, in this series, we have covered Migration, Broadcast, Bi-Directional Sync, and today we are going to cover a new integration pattern: Correlation. In an effort to avoid repeating myself, for those who are reading through the whole series, I will omit a lot of relevant information which is shared between the patterns that I have previously covered. I urge you to read at least the previous post about bi-directional sync as correlation can be viewed as a variation of bi-directional sync.

Intro to Data Integration Patterns – Bi-Directional Sync

In this post I will continue talking about the various integration patterns that we used as the basis for our Anypoint Templates. The next pattern to discuss is bi-directional sync. Since bi-directional sync can be also accomplished as two, 1:1 broadcast applications combined and pointed in opposite directions, I would recommend reading my last post on the broadcast pattern before digging into this one since I will omit a lot of the same content.

Intro to Data Integration Patterns – Broadcast

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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.

APIs, Connectors and Integration Applications

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In this post, I wanted to give an analogy around how to think about API’s, connectors, and integration applications. This is something that can be confusing when you first start working with or building integrations since the definitions of applications and connectors are relative terms which means that they differ in the application space vs the integration space. Let’s say that you want to connect your laptop to your TV so that you can watch some YouTube videos.

Chasing the bottleneck: True story about fighting thread contention in your code

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Today I’m going to share some valuables lessons learned about developing highly concurrent software. These are real life lessons that come straight from the development of the Mule ESB. This is a story about deadlocks, context switches, CPU usage and profiling, focusing in how to diagnose this issues which is often the hardest part of the solution.

So the story begins a couple of weeks ago when I started working on a new feature for Mule 3.5.0.

Aggregation with Mule – “Fork and join pattern”

September 12 2013

1 comment.
motif

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,