When a data flow component applies a transformation to column data, extracts data from sources, or loads data into destinations, errors can occur. Errors frequently occur because of unexpected data values.
For example, a data conversion fails because a column contains a string instead of a number, an insertion into a database column fails because the data is a date and the column has a numeric data type, or an expression fails to evaluate because a column value is zero, resulting in a mathematical operation that is not valid.
Errors typically fall into one the following categories:
- Data conversion errors, which occur if a conversion results in loss of significant digits, the loss of insignificant digits, and the truncation of strings. Data conversion errors also occur if the requested conversion is not supported.
-Expression evaluation errors, which occur if expressions that are evaluated at run time perform invalid operations or become syntactically incorrect because of missing or incorrect data values.
-Lookup errors, which occur if a lookup operation fails to locate a match in the lookup table.
Many data flow components support error outputs, which let you control how the component handles row-level errors in both incoming and outgoing data. You specify how the component behaves when truncation or an error occurs by setting options on individual columns in the input or output.
For example, you can specify that the component should fail if customer name data is truncated, but ignore errors on another column that contains less important data.
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