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Mulesoft DataWeave Online Training

Mulesoft Dataweave Online course is intended for developers interested in improving dataWeave skills who are taught more in the Anypoint Platform Development (Fundamental or Self-Paced MuleSoft Development course). With the help of MuleSoft DataWeave 2.0 Skills one can build complex transformations.

DataWeave is basically a MuleSoft expression language. It is mainly used for accessing and transforming the data received through a Mule application. Mule runtime is responsible for running the script and expressions in our Mule application, DataWeave is strongly integrated with Mule runtime.

What is the objective of any point platform development: Mulesoft Data Weave online training?

At the end of this course, students should be able to obtain the following information:
Use variables, functions, and DataWeave modules to write common reusable transformations.
mapping, dynamic evaluation components.
Build complex transformations from testable small steps.
Use powerful tydes, matchoperators to build more robust, testable functions and expressions.
Error handling and logging.
Create, transform, filter, merge, shuffle, select, and reduce complex data structures.
An array of nested arrays, objects, and objects.
Recursively replaceors or sets a list of all elements or elements in a nested schema.
Transform between complex data structures that contain nested arrays and objects or XML
Write generic transformations using functions, variables, and operators.
Localize and Format the dates and numbers.
Convert Excel spreadsheets, flat files, complex CSV files, and fixed-length files.

Prerequisites To Mulesoft Dataweave

Should have Experience in developing Mule 4 applications, as shown in one of the following:

MuleSoft Certified Developer – Passed the Level 1 (Mule 4) exam.
Point Platform Development: Complete the Basic (Mule 4) course.
Completed anypoint platform development: Mule 4, mule 3 for user course.

Set the requirements To Learn Mulesoft Dataweave

A computer with at least 4 GB of free RAM, 2GHz CPU, and 10 GB of free hard disk space.
Unlimited Internet access port 80 (with 5Mbps downloads and 2 Mbps uploads)
JDK 1.8 http://www.oracle.com/technetwork/java/javase/downloads/index.html.
Anypoint Studio 6.4 or later https://www.mulesoft.com/lp/dl/dl/studio/previous, with embedded Mule 3.9 or later.
You can download the detailed setup documentation here: https://training.mulesoft.com/downloads.

Module 1: Transforming Data using Metadata

Review DataWeave fundamentals
Configure input and output metadata for DataWeave transformation
Apply schema and example input for DataWeave transformations
Identify Dataweave Error/ problems
Best ways to define DataWeave file structure

Module 2: Organizing DataWeave Code with Variables and Functions

Organize DataWeave code into variables and functions
Pass functions and lambda expressions as parameters to other DataWeave functions
Chain DataWeave functions together
Create and use reusable DataWeave modules
Write more robust functions using the match operator to test for data types

Module 3:Enriching and Formatting Data

Enrich DataWeave transformations with external functions, flows, and properties
Create reference data for use in other DataWeave expressions
Coerce between and format various data types - including dates, times, and numbers
Change character encodings for DataWeave input and output

Module 4: Constructing Arrays and Objects

Add components to and remove elements from arrays and objects
Construct objects from lists of DataWeave expressions using object constructor curly braces { }
Troubleshoot common issues when using object constructor curly braces { }

Module 5: Iteratively Transforming Data using Mapping Operators

Transform elements of arrays into a new array using the map operator
Transform elements of objects into a new object using the mapObject operator
Combine map and mapObject operators to transform complex schema
Extract an array of keys and/or values from an object using the pluck operator
Reduce and accumulate array elements to other output types using the reduce operator

Module 6: Recursively Transforming Complex Structures

Write recursive functions to transform complex schema
Replace keys or values at any level of a nested data structure using a lookup object
Combine match and mapping operators to recursively transform every element of a complex schema

Module 7: Effective DataWeave code

Read legacy formatted file data into DataWeave - including Excel, fixed-width, and complex flat-file formatted files
Transform legacy file formatted input to JSON, XML, and CSV type output
Output complex schema to legacy formatted files - including fixed-width and complex flat-file formatted files
Build up 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.