Transformers are elements in Mule flows that prepare messages for further processing by altering message properties, variables, or payloads. Mule provides standard transformers to handle common data transformation scenarios with minimal configuration. Transformers can be categorized as Java object, content, SAP, script, or properties/variables/attachments depending on what types of changes they make to messages. DataWeave is also a transformer that can both convert data formats and map fields, as well as perform conditional routing and expressions.

1. Mule Concepts
Elements in a Mule Flow :
Transformers

2. • Transformer prepares a message for further
processing by enhancing or altering the contents of
the message properties, variables, or payload.
• Data transformation is one of the most powerful
functionalities of Mule.
• A pre-built transformer to perform a standard data
conversion can be used instead of writing custom
logic.
Elements in a Mule Flow :Transformers

3. • Mule provides a set of standard transformers to
handle the most common data transformation
scenarios.
• These elements require minimal configuration so as
to facilitate quick construction of applications that
must juggle different data formats between
resources and processors.
• You can arrange several transformers in a sequence
to achieve the output you need, if a particular
transformer is unavailable.
Elements in a Mule Flow :Transformers

4. • The Transformers can be broadly categorized into the
following :
– Java Object
– Content
– SAP
– Script
– Properties, Variables, and Attachments
Elements in a Mule Flow :Transformers

5. • Java Object :
– This category contains the bulk of the Mule
transformers.
– Each Transformer from this category can change a
Java Object into :
• another Java object.
• a non-Java data type e.g. an HTTP request.
• Or from a non-java data type to a Java Object.
– Examples :
• JSON to Object
• XML to Object
Elements in a Mule Flow :Transformers

6. • Content :
– This group of transformers modifies messages by
• adding to
• deleting from
• Or by converting a message payload.
– Example :
• Append String
• Expression
• Parse Template
• XSLT
Elements in a Mule Flow :Transformers

7. • SAP :
– These transformers change SAP objects (JCo functions or
IDoc documents) into XML representations, or vice versa.
– Examples :
• SAP Object to XML
• XML to SAP Function (BAPI)
• XML to SAP IDoc
Elements in a Mule Flow :Transformers

8. • Script:
– This type of transformer utilizes a custom script to perform
the transformation.
– Use one of these transformers to implement a custom
script to perform the transformation, If you can’t find the
transformer you need.
– Add one of these to the flow, then write a script in your
favorite language to convert data as needed.
– Examples :
• Groovy
• JavaScript
• Python
• Ruby
• Script
Elements in a Mule Flow :Transformers

9. • Properties, Variables, and Attachments :
– These transformers add, remove, or copy properties,
variables, and attachments on the message, rather than
acting on the message payload.
– This group of message processors doesn’t so much
transform as manipulate or enrich the contents of the
message header, hence also called as message enrichers.
– Examples :
• Attachment
• Property
• Session Variable
• Variable
Elements in a Mule Flow :Transformers

10. • DataWeave Transformer:
– This message processor both converts and maps data.
– It is labeled as Transform Message in the palette.
– In addition to transforming data from one format to
another, DataWeave can map an input field, such as
last_name, to a different output field, such as
family_name.
– It can map multiple fields, such as title, first_name, and
last_name, to a composite output field such as full_name.
– It can retrieve session state information in a message to
facilitate conditional message routing.
– it can use Mule expression evaluation to facilitate
conditional value recalculation.
– it can even look up information in tables or other flows
Elements in a Mule Flow :Transformers

11. Mule Concepts
Thank You.