Uplatz provides this in-depth course on DataWeave. It is a self-paced course consisting of video lectures. You will be awarded Course Completion Certificate at the end of the course.
DataWeave is a rich and extensive functional programming language designed for transforming data. It is a 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.
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. As an instance, 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.
When DataWeave receives data, it puts it through the reader. The reader’s job is to parse the input data into a canonical model. It then passes that model to the DataWeave script where it is used to generate the output, which is another canonical model. That last canonical model is passed into a writer. The writer is responsible for serializing the canonical model into the desired output data format.
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.
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. DataWeave also leverages its powerful object-oriented language features 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.
Course Objectives
- Write generalized and reusable transformations using variables, functions, DataWeave modules and mappings, and dynamic evaluation components.
- 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.
- Understand how to write transformation logic using mule DataWeave easily and efficiently
- Learn to transform for various regularly used scenarios
- Understand the best practices in writing DataWeave transformations
Course Overview
In this DataWeave course by Uplatz, you will learn DataWeave which is MuleSoft’s expression language for advanced data transformation and data integration. This course starts with the basic constructs and start writing your first DataWeave program. You will learn how to work with selectors to traverse through complex arrays and objects. With a practical application of DataWeave module functions like map, mapObject, filter, filterObject and reduce, you will then move to more advanced topics like writing our own mappings and modules, calling Java methods from DataWeave, handling exceptions and retrying on failures etc. We will also work between XML/JSON/CSV transformations so you will get a sense of various formats and functions available in DataWeave to handle these different formats.
