In Mule 4, DataWeave is everywhere: every listener and processor can be configured with it. Because most Mule users already know Java well, this article will help Java developers to easily use DataWeave by rewriting their lambdas expressions.
This post will examine the remaining additions to the DataWeave Arrays module that weren’t covered in part 1: the join functions.
MuleSoft recently released runtime version 4.2.0, and along with it, DataWeave 2.2! DataWeave 2.2 has a ton of new features that I won’t be able to cover in a single blog post, so I’ll be covering them over the course of a few blog posts.
In this post, I will discuss how I format my DataWeave code to improve its readability.
I have been asked so many times about DataWeave Performance during my time in the field. This is because developers try to find arguments to not use it when they realize that a new and proprietary programming language is introduced. Most of the time they have the same “natural response” of resolving the problem by going to the known and comfortable zone called “Java.”
Here’s how NVIDIA leveraged an API-led approach to build system APIs for backend applications and stitch data together using a process API that leverages MuleSoft’s Message Processors.
This year, we published over 215 blog posts spanning a wide variety of topics––from why messaging queues suck to why ESB-led integration is no longer an adequate approach for organizations.
Check out the top 7 most popular blog posts for this year!
When building DataWeave transformations for your Mule application, you will run into situations in which you will need to invoke external logic that may be encapsulated in a Java POJO, Groovy, Python, Ruby script, or really any lookup that uses a CSV file or database table as part of the transformation.
When integration involves different applications, systems, or databases, we face a common challenge: how do we bridge between data formats and how can we provide interoperability for fields that store dates and date/time values?
Logic handling using DataWeave is essential for simple mediums and highly complex transformations, in which the mapping requirements necessitate generating outputs based on values provided in the input payload.
