The JOIN algorithm is typically used in the data preprocessing stage to consolidate relevant information from different data sources into a single data table by matching records on one or more fields. This operation is similar to the JOIN statement in SQL and aims to ensure that the merged data is accurate in terms of integrity and consistency, providing a reliable data foundation for subsequent training and analysis.
Configure the component
You can configure the JOIN component on the pipeline page of Machine Learning Designer. The following table describes the parameters.
Parameter | Description |
Join Type | The join type. Valid values: Left Join, Inner Join, Right Join, and Full Join. |
MapJoin Optimization | Specifies whether to load data in the small table to the memory to accelerate the execution of the JOIN operation. Valid values:
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Join Condition | The join conditions, which are in the format of equations. You can manually add or remove join conditions. |
Select Output Columns from the Left Table | The output columns from the left table. |
Select Output Columns from the Right Table | The output columns from the right table. |