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s_605 - use the stored rules and lists to actually prepare the data

Spark H2o Data Pereparation Machine learning
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s_605 - apply the label encoding and other transformations stored in SQL code and the selected final column as RegEx string Get the results back and export them to .parquet files so you could use them in a powerful R or Python environment. (or leave them on the big data system). Of course you could also do the model building in Spark with a genuine Spark-ML model or H2O.ai Sparkling Water. All that matters is that the result is a MOJO file KNIME would be able to read and apply to Sparkling Water.

External resources

  • Combine Big Data, Spark and H2O.ai Sparkling Water

Used extensions & nodes

Created with KNIME Analytics Platform version 4.6.0
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

    knime
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    KNIME Database Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

    knime
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    KNIME Extension for Apache Spark Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

    knime
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    KNIME Extension for Big Data File Formats Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

    knime
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    KNIME Extension for Local Big Data Environments Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

    knime
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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.6.0

    knime
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