Overview
Who should take this course?
This course is for Mule 4 developers or architects who want to advance their DataWeave skills beyond those taught in the Anypoint Platform Development: Fundamentals course so that they can build complex transformations.
Prerequisites
When you complete this course, you will be able to:
- Write generalized and reusable transformations using variables, functions, DataWeave modules.
- Use the DataWeave Playground to test and author DataWeave code.
- Build complex transformations from smaller testable steps.
- Build more robust and testable functions and expressions using strong typing, match operators, error handling, and logging.
- Create, transform, filter, combine, shuffle, select from, and reduce complex data structures that include nested arrays, objects, and arrays of objects.
- Recursively replace or format every element or a list of elements in a nested schema.
- Reduce arrays to other data structures or data types and calculate key performance indicators.
Lessons and Topics
Applying DataWeave fundamentals
- Review and apply DataWeave fundamentals as learned in the Anypoint Platform Development: Fundamentals course
- Set example input to preview DataWeave results in Anypoint Studio
- Chain together two argument functions by using infix notation
- Filter, order, and group elements of an object or array
- Retype DataWeave expressions in the middle of code execution
Organizing an reusing DataWeave code
- Organize DataWeave code into variables and functions
- Enclose variables and functions in do statement scopes
- Pass functions and lambda expressions as arguments to other DataWeave functions
- Create and use reusable DataWeave modules
Writing more defensive and more robust DataWeave
- Write more defensive DataWeave expressions that filter and route data based upon conditions
- Write more robust functions using a match operator to test for data types
- Handle and raise errors
- Log from inside DataWeave expressions
Constructing arrays and objects
- Add components to and remove elements from arrays and objects
- Construct objects from lists of DataWeave expressions by using object constructor curly braces { }
- Troubleshoot common issues when using object constructor curly braces { }
Iteratively mapping and joining arrays and objects
- Combine objects and arrays into nested data structures by using map and mapObject functions
- Extract an array of keys and/or values from an object by using the pluck function
- Extract an array of keys and/or values from an object by using the DataWeave core Arrays module functions
- Conditionally join together two nested schema by using the join function
Updating inside nested data structures
- Conditionally update and mask parts of nested data structures
- Format and transform nested data structures by using recursive functions
Reducing data from arrays
- Conditionally test, count, and sum up elements of an array by using the DataWeave core Arrays module
- Reduce and accumulate array elements to other output types by using the reduce function
- Calculate key performance indicators from input collections by using the reduce function
Setup requirements
- A computer with at least 8-16 GB available RAM (16 highly recommended), 2GHz CPU, and 10GB available storage
- Internet access to port 80 (with > 5Mbps download and > 2Mbps upload)
- Anypoint Studio 7.14.0 or later with embedded Mule runtime
- Advanced REST Client (or any other REST client application)