select efficiently specific columns from multiple dataframes join

I'm joining multiple dataframe and selecting specific columns to create desired output

For example

    df1.join(df2, condition).join(df3, some condition).join(df4, some condition)
    .map {x =>
       
       val someData_1 = x._1._1._1
       val someData_2 = x._1._1._2
       val someData_3 = x._1._2
       val someData_4 = x._2

       Output(
       someData_1.getAs("colName1").asInstanceOf[String],
       .
       .
       .
       someData_2.getAs("colName2").asInstanceOf[String],
       .
       .
       .
       someData_3.getAs("colName3").asInstanceOf[String],
       .
       .
       .
       someData_4.getAs("colName4").asInstanceOf[String],
       .
       .
       .
)
}

As of now that's how I'm selecting specific columns from output of multiple DatFrames join and using asInstanceOf[String] as well to get String type.

Is there any other efficient way of selecting specific cols from multiple joins in DataFrame ? And secondly, how can I avoid using asInstanceOf[String] while creating new Instance Output using different columns ?

Please suggest ideas.

Thanks