School of duplicates - and how to deal with them - H2 version

Dealing with duplicates is a constant theme with data scientist. And a lot of things can go wrong. The easienst ways to deal with them is GROUP BY or DISTINCT. Just get rid of them and be done. But as this examples might demonstrate this might not always be the best option. Even if your data provider swears your combined IDs are unique especially in Big Data scenarios there might still be lurking some muddy duplicates and you shoudl still be able to deal with them. And you should be able to bring a messy dataset into a meaningful table with a nice unique ID without loosing too much information. And this workflow would like to encourage you to think about what to do with your duplicates and not to get caught off guard but to take control :-)

XML - Large Dataset Import

External resources

Used extensions & nodes

KNIME Base nodesTrusted extension

KNIME Column Expressions (legacy)Trusted extension

KNIME JavasnippetTrusted extension

KNIME Python IntegrationTrusted extension

KNIME XML-ProcessingTrusted extension

Legal

KNIME Base nodes

KNIME Column Expressions (legacy)

KNIME Javasnippet

KNIME Python Integration

KNIME XML-Processing