RegexClean Transformation

by Darren Green 25 Jul 2017 16:03
Use the power of regular expressions to cleanse your data right there inside the Data Flow. This transformation includes a full user interface for simple configuration, as well as advanced features such as error output configuration. Two regular expressions are used, a match expression and a replace expression. The transformation is designed around the named capture groups or match groups, and even supports multiple expressions. This allows for rich and complex expressions to be built, all through an easy to reuse transformation where a bespoke Script Component was previously the only alternat... [More]

Regular Expression Transformation

by Darren Green 25 Jul 2017 16:01
The regular expression transformation exposes the power of regular expression matching within the pipeline. One or more columns can be selected, and for each column an individual expression can be applied. The way multiple columns are handled can be set on the options page. The AND option means all columns must match, whilst the OR option means only one column has to match. If rows pass their tests then rows are passed down the successful match output. Rows that fail are directed down the alternate output. This transformation is ideal for validating data through the use of regular expressions.... [More]