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.
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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.
- Get complete information about InfoSphere Classic in the IBM InfoSphere Classic Information Center.
- Learn more about InfoSphere Federation Server in the IBM InfoSphere Classic Federation Information Center.