Mule Dev Tricks: Transform Dates Using Datawave Functions

Introduction

Processing dates is very common practice in data transformation. Dataweave's function makes this task very easy. This short blog demonstrate the procedures to do so.

Normalizing Dates

As shown in the following Dataweave code. The function normalize replace "/" and "." with "-". Note: we can use replace in a concatenation style.

%dw 1.0
%output application/json
%function normalize(date) date replace "/" with "-" replace "." with "-"
---
{
 dates : {
  date1: normalize("26-JUL-16") as :date {format: "dd-MMM-yy"} as :string {format: "yyyy-MM-dd"} as :date,
  date2: normalize("26/JUL/16") as :date {format: "dd-MMM-yy"} as :string {format: "yyyy-MM-dd"} as :date,
  date3: normalize("26.JUL.16") as :date {format: "dd-MMM-yy"} as :date {format: "yyyy-MM-dd"}
   }
}

In the above code, I provide two flavors for transforming the dates to the format of "yyyy-MM-dd". Apparently, date3 is a bit cleaner. The above code can be simplified by adding the data format in the function body:

%dw 1.0
%output application/json
%function normalize(date) date replace "/" with "-" replace "." with "-" as :date {format: "dd-MMM-yy"} as :date {format: "yyyy-MM-dd"}
---
{
 dates : {
  date1: normalize("26-JUL-16"),
  date2: normalize("26/JUL/16"),
  date3: normalize("26.JUL.16") 
   }
}

As you can see, now the Data-weave code is much cleaner.