Functional Programming with DataWeave

Functional Programming with DataWeave, available at $39.99, has an average rating of 2.42, with 87 lectures, 1 quizzes, based on 6 reviews, and has 81 subscribers.

You will learn about Learn DataWeave (DW) functional programming language Understand examples of DataWeave use-cases Learn DW operators, variables, conditions, functions Understand DW selectors and multi selectors Learn DW flow control and pattern matching Deep-dive into Map and Map Objects Learn to build generalized and reusable transformations with DataWeave Understand how to write transformation logic using Mule DataWeave Apply useful Core, String, Array, Object, and Runtime module functions Write reusable custom mappings and custom modules Advanced concepts with XML/JSON/CSV transformations Design data organization architecture using DataWeave This course is ideal for individuals who are Mulesoft Developers or Software Engineers or Mule Engineers or Anyone interested in Functional Programming or Beginners & newbies aspiring for a career in software engineering & product development or Platform Architects or Data Architects or Data Engineers or Mule 4 Developers/Architects or Application Developers It is particularly useful for Mulesoft Developers or Software Engineers or Mule Engineers or Anyone interested in Functional Programming or Beginners & newbies aspiring for a career in software engineering & product development or Platform Architects or Data Architects or Data Engineers or Mule 4 Developers/Architects or Application Developers.

Enroll now: Functional Programming with DataWeave

Summary

Title: Functional Programming with DataWeave

Price: $39.99

Average Rating: 2.42

Number of Lectures: 87

Number of Quizzes: 1

Number of Published Lectures: 87

Number of Published Quizzes: 1

Number of Curriculum Items: 88

Number of Published Curriculum Objects: 88

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn DataWeave (DW) functional programming language
Understand examples of DataWeave use-cases
Learn DW operators, variables, conditions, functions
Understand DW selectors and multi selectors
Learn DW flow control and pattern matching
Deep-dive into Map and Map Objects
Learn to build generalized and reusable transformations with DataWeave
Understand how to write transformation logic using Mule DataWeave
Apply useful Core, String, Array, Object, and Runtime module functions
Write reusable custom mappings and custom modules
Advanced concepts with XML/JSON/CSV transformations
Design data organization architecture using DataWeave

Who Should Attend

Mulesoft Developers
Software Engineers
Mule Engineers
Anyone interested in Functional Programming
Beginners & newbies aspiring for a career in software engineering & product development
Platform Architects
Data Architects
Data Engineers
Mule 4 Developers/Architects
Application Developers

Target Audiences

Mulesoft Developers
Software Engineers
Mule Engineers
Anyone interested in Functional Programming
Beginners & newbies aspiring for a career in software engineering & product development
Platform Architects
Data Architects
Data Engineers
Mule 4 Developers/Architects
Application Developers

A warm welcome to the Functional Programming with DataWeavecourse by Uplatz.

DataWeaveis one of the easy-to-use and efficient functional programming languages. DataWeave provides features and functionality for transforming data. DataWeave also serves as an expression language to configure components and connectors. In DataWeave there are multiple modules that can be imported and applied and can perform a huge variety of operations such as string manipulation, URL encoding, date formatting, etc. DataWeave facilitates performing use cases where data needs to be read and parsed, to be formatted and transformed, finally to be sent as an output in an entirely different format. Examples include transforming CSVs into JSONs, XML into JSON or CSV, and the like. The beauty of DataWeave functional programming is that the developers can actually focus on building the transformation logic rather than devoting time on syntax, formats and parsing efforts.

What is Functional Programming?

Functional Programming is essentially the process of building software by composing pure functions, avoiding shared state, mutable data, and side-effects. Functional programming is declarative rather than imperative, and application state flows through pure functions. Functional programming is based on mathematical functions. A few of the popular functional programming languages include: DataWeave, Lisp, Python (not in strict sense), Erlang, Haskell, Clojure, etc.

DataWeave – Course Syllabus

DATAWEAVE (DW) INTRODUCTION
EXAMPLES OF DATAWEAVE
DW OPERATORS
MERGE FIELD FROM DIFFERENT OBJECT OPERATORS AND UPDATE OPERATOR
DW VARIABLES CONDITIONS AND FUNTIONSVARS
FLOW CONTROL
DW PATTERN MATCHING
DW FUNCTIONS
MAP-AND-PLUCK-OPERATOR
MAP-AND-MAPOBJECT
FLATTEN FUNCTION
PLUCK OPERATOR
DW SELECTORS
JOINBY AND SPLITBY
DATAWEAVE FUNCTION CHEAT SHEET
DATAWEAVE OPERATORS
TYPES OF ERRORS AND ESC CHARACTERS
++ on DATES
DATAWEAVE BASIC TRANSFORMATION
DW DATA TYPES
SINGLE MULTI SELECTORS EXAMPLE
DW FILTER OPERATOR
ATTRIBUTE AND DESCENDANT SELECTOR
REDUCE AND ORDERBY FUNCTIONS
LOGICAL AND APPEND AND PREPEND OPERATORS
INDEXOF, KEYSOF, MINBY, MAXBY, REPLACE IN DW
SPLITAT, SPLITWHERE, TAKEWHILE, SUMOF
GEOMETRIC AND TIME OPERATIONS
GLOBAL VARIABLES AND DW FUNCTIONS
FUNCTIONS OVERLOADING AND LAMBDA EXPRESSIONS
HANDLING TRANSFORMATIONS IN DW
DEFAULT VALUES IN DATAWEAVE
RENAME JSON KEYS
TRANSFORM NESTED STRUCTURES TO FLAT
DATA ORGANIZATION USING DATAWEAVE

Course Curriculum

Chapter 1: Introduction to DataWeave with Examples

Lecture 1: Introduction to DataWeave with Examples

Chapter 2: DataWeave Basics

Lecture 1: DataWeave Basics – part 1

Lecture 2: DataWeave Basics – part 2

Lecture 3: DataWeave Basics – part 3

Chapter 3: Brackets in DataWeave

Lecture 1: Brackets in DataWeave

Chapter 4: Data Types in DataWeave

Lecture 1: Data Types in DataWeave

Chapter 5: Operators in DataWeave

Lecture 1: DataWeave Operators

Lecture 2: Pluck Operator

Lecture 3: Descendent Operator

Chapter 6: orderBy

Lecture 1: orderBy

Chapter 7: Rename Keys in DataWeave

Lecture 1: Rename Keys in DataWeave

Chapter 8: DataWeave Selectors

Lecture 1: DataWeave Selectors – part 1

Lecture 2: DataWeave Selectors – part 2

Lecture 3: DataWeave Selectors – part 3

Lecture 4: DataWeave Selectors – part 4

Lecture 5: DataWeave Selectors – part 5

Lecture 6: DataWeave Selectors – part 6

Lecture 7: DataWeave Selectors – part 7

Lecture 8: Attribute Selector

Chapter 9: splitBy

Lecture 1: splitBy

Chapter 10: Try in DataWeave

Lecture 1: Try in DataWeave

Chapter 11: DataWeave Functions

Lecture 1: DataWeave Functions – part 1

Lecture 2: DataWeave Functions – part 2

Lecture 3: DataWeave Functions – part 3

Lecture 4: DataWeave Functions – part 4

Lecture 5: Useful Functions

Lecture 6: Ten Quick DataWeave Functions

Chapter 12: keysOf etc

Lecture 1: keysOf etc

Chapter 13: () and If-Else to Print Specific Data Items

Lecture 1: () and If-Else to Print Specific Data Items

Chapter 14: {} and () in DataWeave

Lecture 1: {} and () in DataWeave

Chapter 15: Array and Object Manipulation

Lecture 1: Array and Object Manipulation

Lecture 2: Arrays deep-dive

Lecture 3: Array Functions

Chapter 16: Do

Lecture 1: Do

Chapter 17: If-Else

Lecture 1: If-Else – part 1

Lecture 2: If-Else – part 2

Chapter 18: Iteration

Lecture 1: Iteration

Chapter 19: Map in DataWeave

Lecture 1: Map

Lecture 2: mapObject

Chapter 20: Update

Lecture 1: Simple Update

Lecture 2: New Update – part 1

Lecture 3: New Update – part 2

Lecture 4: New Update – part 3

Lecture 5: DW Update

Lecture 6: Conditional Update – part 1

Lecture 7: Conditional Update – part 2

Lecture 8: Update deep dive – part 1

Lecture 9: Update deep dive – part 2

Chapter 21: Filter

Lecture 1: Filter

Chapter 22: joinBy

Lecture 1: joinBy

Chapter 23: Match Operator

Lecture 1: Match Operator – part 1

Lecture 2: Match Operator – part 2

Lecture 3: Match Operator – part 3

Chapter 24: Logical Operators

Lecture 1: Logical Operators

Lecture 2: Logical and Append & Prepend Operators

Chapter 25: Mathematical Operators

Lecture 1: Mathematical Operators

Chapter 26: Upserting

Lecture 1: Upserting – part 1

Lecture 2: Upserting – part 2

Lecture 3: Upserting – part 3

Chapter 27: Recursion

Lecture 1: Recursion – part 1

Lecture 2: Recursion – part 2

Chapter 28: Reduce in DW

Lecture 1: Reduce – part 1

Lecture 2: Reduce – part 2

Chapter 29: XML Processing

Lecture 1: XML Processing

Chapter 30: Array to Numbers Function

Lecture 1: Array to Numbers Function

Chapter 31: Attribute and Descendent Selector

Lecture 1: Attribute and Descendent Selector

Chapter 32: Single and Multi Selectors Example

Lecture 1: Single and Multi Selectors Example

Chapter 33: Flatten

Instructors

Uplatz Training
Fastest growing global Technology & Cloud Training Provider

Rating Distribution

1 stars: 3 votes
2 stars: 1 votes
3 stars: 0 votes
4 stars: 2 votes
5 stars: 0 votes

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