Welcome to the first entry in my new series dedicated to mastering DataWeave!
If you have spent any time in the "integration trenches" working with MuleSoft, you know that practice is the only way to truly get comfortable with data transformation. Reading documentation is great, but writing code is where the magic happens.
This post brings you a collection of DataWeave practice exercises directly inspired by extensive experience in the field. We aren't just looking at random syntax here; we are focusing on the "heavy hitters"—the language features and functions developers use every single day: map, filter, mapObject, pluck, groupBy, and reduce.
I have organized these exercises by function, starting with the simpler tasks to build your confidence and gradually increasing the complexity. Consider this a living document; it will be updated regularly with new challenges.
Before We Begin
If you are brand new to DataWeave, I highly recommend building a solid foundation first. Check out the basics of DataWeave structure and syntax before diving into these drills. However, if you are ready to get your hands dirty, let's go!
The "Map" Function
Let's start with the bread and butter of arrays: the map function.
Think of map as a personal assembly line for your data. It takes an input array, processes every single item through a transformation rule (a lambda function), and returns a brand new array with the changes applied.
Your lambda function generally has access to two specific things:
- The Value ($): The current element being processed.
- The Index ($$): The numeric position of that element in the array.
The following exercises will test your ability to effectively use map in real-world scenarios.
Practice Exercise 1: The Warm-Up
The Challenge:
You have a simple list of numbers: [1, 2, 3, 4, 5]. Your task is to increment each value in this array by 1.
The Solution:
Here is the DataWeave script to solve the exercise. Note how we use the dollar sign ($) to represent the current value of the array during the iteration.
%dw 2.0
output application/json
var myPayload = [1, 2, 3, 4, 5]
---
myPayload map(
$ + 1
)How it Works:
%dw 2.0: Specifies the DataWeave version.output application/json: Tells the script to produce a JSON result.var myPayload: Defines our input array.map ($ + 1): Loops through every number (1, 2, 3...) and adds 1 to it.
Conclusion
This was a quick introduction to the power of the map function. While this example is simple, understanding how to iterate over arrays is crucial for complex data transformations. Stay tuned for more complex exercises in the next part of this series!