The DataWeave Language is a simple, powerful tool used to query and transform data inside of Mule. It can be implemented to:
graphically map fields by dragging one attribute to another, just like you were able to with the now deprecated DataMapper, or
leverage its powerful object-oriented language that’s specially designed to make writing transformations quick, without compromising maintainability.
DataWeave supports a variety of transformations: simple one-to-one, one-to-many or many-to-one mappings from an assortment of data structures, and can complete more elaborate mappings including normalization, grouping, joins, partitioning, pivoting and filtering. With DataWeave and Mule Expression Language (MEL), you can take your application’s data transformation ability to the next level.
You can also call upon the power of DataWeave language within other components by using Mule Expression Language DataWeave Functions.
The language is tightly integrated with Mule and Anypoint Studio. Use the Transform Message component, which allows you to use the language to query and transform data through DataWeave. Any mappings you perform through the graphical interface will be expressed in DataWeave code in real-time.
DataWeave is a functional programming language designed for transforming data. It is MuleSoft’s primary language for data transformation, as well as the expression language used to configure components and connectors. However, DataWeave is also available in other contexts, like as a command-line tool. These tutorials will largely treat DataWeave as a standalone language, with Mule-specific info designated with (M).
DataWeave allows users to easily perform a common use case for integration developers: read and parse data from one format, transform it, and write it out as a different format. For example, a DataWeave script could take in a simple CSV file and transform it into an array of complex JSON objects. It could take in XML and write the data out to a flat file format. DataWeave allows the developer to focus on the transformation logic instead of worrying about the specifics of reading, parsing, and writing specific data formats in a performant way.
