In this section we talk about concatenating datasets, about the operation itself and the issues that might need solving, such as dealing with duplicate non-unique RowIDs in the output dataset or whether to include data columns that are not present in both (or more) datasets.
Reference workflow is on the EXAMPLES server under:
02_ETL_Data_Manipulation/03_Joining_and_Concatenating/01_Concatenate02_ETL_Data_Manipulation/03_Joining_and_Concatenating/01_Concatenate*
Exercise
- Read adult.csv data set. Then extract people with age between 20 and 40 and working in a work group starting with "S" and people with age between 40 and 60 and working in the Private sector (workclass starts with "P"). Put both groups in a single data table.
The final table has 9620 data rows: 2024 people age in (20, 40) and with a job starting with “S” and 7596 people with age in (40, 60) and working as ”Private”.
A possible solution can be found inside the workflow on EXAMPLES Server:
02_ETL_Data_Manipulation/03_Joining_and_Concatenating/03_JoinConcatenate_Examples02_ETL_Data_Manipulation/03_Joining_and_Concatenating/03_JoinConcatenate_Examples*
* The link will open the workflow directly in KNIME Analytics Platform (requirements: Windows; KNIME Analytics Platform must be installed with the Installer version 3.2.0 or higher)
