In this blog, we’ll look at how a regular expression (regex) can give you the power to transform text in your DataWeave programming. When you need to select, replace, remove, or transform text, you can define a regex pattern to define what you want to match, and perhaps one that defines what you’d like to provide as a substitute.
Contrary to what most developers believe, there are different ways to achieve concatenation in DataWeave 2.0 for several data types. While the most popular function to achieve this is by using the plus plus (++) function, it is not the only way to concatenate data types. Before taking the Anypoint Platform Development: DataWeave (Mule 4) training, I used the ++ (plus plus) function to concatenate data types like arrays, strings,
It’s rare for developers to work with flat data structures — instead we often work with multilevel data structures. Normally, XML uses multiple layers of hierarchy. We need to perform changes on all the levels, without knowing how deep into the hierarchy we need to process the entire structure.
Developers typically use recursive calls to solve these types of problems. In this blog post, we will see how to implement simple recursive calls using DataWeave.
Tyler Haigh, DevOps Engineer at New South Wales Health Pathology (NSWHP), spoke at our MuleSoft CONNECT Digital event in APAC. He has more than four years of experience using Anypoint Platform. Recently, his projects focused on creating reusable DataWeave scripts for healthcare acceleration at NSWHP. In this blog, he shares how he uses DataWeave and MUnit testing to improve NSWHP healthcare messaging systems and patient experience.
One of the most valuable characteristics of DataWeave is that it is a functional programming language. This means it is dynamically able to solve problems with various approaches — one being currying, which is a common feature of functional programming languages like Haskel (from where it derives), and JavaScript.
In this post, I’ll explain what currying is and how to write curried functions in DataWeave.
Sometimes developers face optimization challenges in their code — regardless of the programming language they use. One of the most common optimization challenges is the complexity (or performance) of an algorithm that increases and grows infinitely when the number of arguments it has to process increases. This is called big-O complexity or big-O notation.
In this blog post, you’ll learn how to perform DataWeave code optimization to improve performance by following the big-O notation principles.
Veera Gopalakrishnan, a System Integration Engineer, and Swarnim Ranjitkar, a Sr. Software Engineer, at University of California, San Francisco (UCSF) both have more than five years of MuleSoft experience. Veera presented a UCSF and MuleSoft use case during our Developer Meetup at MuleSoft CONNECT Digital Americas. Here, Veera and Swarnim provide an in-depth explanation of how UCSF uses dynamic DataWeave scripts and the Salesforce platform for secure access to Electronic Health Record (EHR) data through APIs.
Have you ever used DataWeave? In this three-part tutorial series, you will be guided through different DataWeave operators that you can use directly in your Anypoint Studio projects.
At some point while developing a Mule application, it’s almost inevitable that you’ll need to manipulate XML.
In this article, I will teach you how to read XML using DataWeave scripts and how to modify its encoding, namespaces, fields, and attributes with the goal to generate a JSON document output.
In this article, you will learn how to use method chaining in DataWeave with reference to Java 8 lambda expressions and how to convert from Java Streams into DataWeave chaining.
Java 8 introduced the Stream API, which is used to process a stream of objects. Various methods can be chained together such as filter, limit, map, and reduce,
