select transformation - Mapping data flow select transformation
Use this transformation for column selectivity (reducing number of columns), alias columns and stream names, and reorder columns.
Columns Patterns -Mapping data flows column patterns
Several Azure Data Factory Data Flow transformations support the idea of "Columns Patterns" so that you can create template columns based on patterns instead of hard-coded column names. You can use this feature within the Expression Builder to define patterns to match columns for transformation instead of requiring exact, specific field names. Patterns are useful if incoming source fields change often, particularly in the case of changing columns in text files or NoSQL databases. This condition is sometimes referred to as "Schema Drift". Column patterns Column patterns are useful for handling both Schema Drift scenarios as well as general scenarios. It is good for conditions where you are not able to fully know each column name. You can pattern match on column name and column data type and build an expression for transformation that will perform that operation against any field in the data stream that matches your name & type patterns.
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