InfoSphere Classic products: Troubleshooting and repairing badly formed data

This article provides practical concepts, tips, and examples to help you troubleshoot and fix problems with badly formed data on the mainframe, enabling your Classic data server to convert legacy data seamlessly to the relational SQL formats that your business requires.

Mike Cavanaugh (mgcavana@us.ibm.com), Support Team Lead, IBM

Mike Cavanaugh photoMike Cavanaugh is the Support Team Lead for IBM Classic federation, data event publishing, and replication. He joined IBM in 2004 to support Classic products as they went to market. As a customer-facing support specialist, Mike was in a unique position to develop the Classic features that handle badly-formed data. Customer reports from the field about problems with mainframe data inspired him to become a co-author of this white paper. Mike is currently focused on managing the Classic Level 3 Support Team, and providing the best support available to users of Classic products.



Thomas Atwood (taatwood@us.ibm.com), Information Developer, IBM

Photo: Thomas AtwoodThomas Atwood is an Information Developer at the IBM Silicon Valley Laboratory in San Jose, California. He joined IBM in 2006 as a writer for Information Management products, including Classic federation, data event publishing, and replication. He designed the information center for IMS replication and authored the documentation for the first release of Classic change data capture. Thomas is currently focused on documentation for DB2 z/OS Tools, and is dedicated to providing the best possible information experience to users.



29 October 2012

If you are a DBA, developer, or architect who is accustomed to viewing data in relational format, you can use the information presented here to help you locate and analyze data-related errors. Even experienced mainframe professionals can build troubleshooting skills by mastering the concepts, reference material, and concrete examples presented here.

In this article

IBM® InfoSphere® Classic products provide direct access to and near-real-time delivery of data that is stored in non-relational databases or file systems on the mainframe. You can read, write, transport, or transform z/OS® data to support mission-critical information management initiatives, and the business value is great.

However, mainframe data can be badly formed or corrupted, especially data that is decades old. Relational data processing models have more stringent requirements for data types and data quality. When you try to integrate non-relational data with relational applications, errors can occur that are difficult to diagnose and fix.

For a variety of reasons, badly formed data can cause query or capture processing failures while converting a value from the source data definition to the expected SQL format. At the source database or file, a value might not be valid for the defined data type. The data type of the source field might not match the column definition in the mapped table, as defined in the USE AS statement. This paper is a one-stop resource for resolving data-related problems.

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