Developing with Data Format Description Language (DFDL), a standard of the Open Grid Forum, enables you to model and parse text and binary data.

In this age of big data, the bulk of data needing to be analyzed is not XML, but rather it is other structured and semi-structured formats, both text and binary. The DFDL standard was developed to describe a wide variety of such data formats. A DFDL description allows any text or binary data to be read from its native format and to be presented as an instance of an information set. DFDL also allows data to be taken from an instance of an information set and written out to its native format. DFDL achieves this by leveraging W3C XML Schema Definition Language (XSDL) 1.0. It is therefore very easy to use DFDL to convert text and binary data to a corresponding XML document.

Introduction to DFDL video (10:29)

Learn what DFDL is, why it can be useful, and look at a simple example of how to get started using DFDL with IBM Integration Bus.

First steps with DFDL video (10:28)

Explore some components of the DFDL schema and how you can create a DFDL schema to model pipe-delimited and fixed-length text data formats.

Self-study labs

[iib10] Lab 91: Message Modeling with DFDL Lab 1: Modeling CSV Files

Create a message model for a CSV file that has a header record, and several detail records, but no trailer record. Using the new Shared Library function that was introduced in IBM Integration Bus Version 10.

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[iib10] Lab 92: Message Modeling with DFDL Lab 2: Modeling fixed-length data using a COBOL copybook

Create a message model from a COBOL Copybook, test parse the model against a valid data file, and then use the trace facility to test the message model against a malformed message.

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[iib10] Lab 93: Message Modeling with DFDL Lab 3: Record-oriented, tagged, delimited text

Create a message model capable of parsing a tagged, delimited file (such as for messages that conform to the ACORD AL3, EDIFACT, HL7, SWIFT, or X12 standards).

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[iib10] Lab 94: Message Modeling with DFDL Lab 4: Record-oriented, tagged, delimited text (advanced)

Extend the DFDL structure in the message model from Lab 3, to parse a data file that contains multiple Company records, and then test the model against a data file.

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[iib10] Lab 95: Message Modeling with DFDL Lab 5: Using DFDL length prefixes

Extend the tagged-delimited message model from Lab 3, to create two message models that use length prefixes. Such use of length prefixes is a particular requirement for some industry standard models; for example, the ISO8583 standard used in credit card processing.

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[iib10] Lab 96: Message Modeling with DFDL Lab 6: Resolving Choices using Discriminators

Create a message model that uses discriminators to assert that an object is ‘known to exist’. This enables the DFDL parser to parse data without incorrect back-tracking for processing errors related to that object.

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Other DFDL resources

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