Dataflows

Link table-like datasets using joins

When it comes to manipulating columns, we’ve already shown you how to add a new column and fill it by Look Up. However, to link two or even more table-like datasets together, we recommend using joins - a technique you may be familiar with from relational databases, such as SQL or Oracle. First, start by creating a dataflow. Then click on [add step] in the dataflow and select [join]. In the dialog you can now select the data source you want to connect to the dataflow (2). Via [+] you add the key attributes (3) - these are the attributes that can be found in both tables and are used as identifiers for the join. In the following article we will show you which join type (4) is suitable for your use case.

Join Data

Inner Join

The result of an inner join contains exactly those rows that have a counterpart in both tables. If there are rows in the left or right table that have no equivalent in the other table, these rows will be lost.

Dataflow SQL Inner Join

Full Join

No rows are lost during the full join. If there are rows without correspondence in the left or right table, these rows will be empty in the result.

Dataflow SQL Full Join

Left Join

During the left join, the rows of the left table remain fully intact. The rows of the right table are only included in the result if they have a counterpart on the left side.

Dataflow SQL Left Join

Right Join

During the right join, the rows of the right table remain fully intact. The rows of the left table are only included in the result if they have a counterpart on the right side.

Dataflow SQL Right Join

Note

By the way, you can not only connect data sources, but of course also other dataflows in this way.

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