Anypoint Platform Development: DataWeave 2.0 (Mule 4) Course Overview

Target Audience for Anypoint Platform Development: DataWeave 2.0 (Mule 4)

The Anypoint Platform Development: DataWeave 2.0 (Mule 4) course is designed for IT professionals seeking to master data transformation in MuleSoft's Anypoint Platform.

Target audience for the course includes:

• MuleSoft Developers
• Data Integration Specialists
• Software Engineers focusing on Integration Solutions
• API Developers
• Systems Architects
• IT Professionals working with ETL tools
• Technical Leads overseeing integration projects
• DevOps Engineers involved in Continuous Integration/Continuous Deployment (CI/CD) processes for Mule applications
• Data Analysts needing to manipulate and transform data within Mule flows
• IT Consultants specializing in enterprise integration solutions
• Technical Project Managers responsible for delivering MuleSoft integration projects

Learning Objectives - What you will Learn in this Anypoint Platform Development: DataWeave 2.0 (Mule 4)?

Introduction to Learning Outcomes and Concepts

In the Anypoint Platform Development: DataWeave 2.0 (Mule 4) course, learners will master advanced data transformation techniques using DataWeave, enhancing their MuleSoft development skills to handle complex data integration challenges.

Learning Objectives and Outcomes

• Understand and apply DataWeave fundamentals for data transformation, building upon skills acquired in the Development Fundamentals course.
• Configure and utilize metadata effectively to inform DataWeave transformations, ensuring accurate input and output data structures.
• Create and manage example input data for DataWeave scripts, facilitating testing and validation of transformations.
• Organize DataWeave scripts efficiently using variables and functions, promoting code reusability and maintainability.
• Implement functions and lambda expressions, passing them as parameters to enhance the modularity of DataWeave code.
• Chain multiple DataWeave functions together to perform complex data manipulation tasks.
• Develop and incorporate reusable DataWeave modules, streamlining the development process across multiple integration projects.
• Utilize the match operator to write robust functions that handle different data types and patterns effectively.
• Manipulate arrays and objects, adding or removing elements, and constructing dynamic objects using DataWeave expressions.
• Apply mapping operators such as map, mapObject, and pluck to iteratively transform data, and use the reduce operator to process and accumulate array elements.
• Write recursive functions to process complex and nested data structures, and manipulate data at any level using lookup objects.