Hi all, I'm fairly new to spark and scala so bear with me.
I'm working with a dataset containing a set of column / fields. The data is stored in hdfs as parquet and is sourced from a postgres box so fields and values are reasonably well formed. We are in the process of trying out a switch from pentaho and various sql databases to pulling data into hdfs and applying transforms / new datasets with processing being done in spark ( and other tools - evaluation ) A rough version of the code I'm running so far: val sample_data = spark.read.parquet("my_data_input") val example_row = spark.sql("select * from parquet.my_data_input where id = 123").head I want to apply a trim operation on a set of fields - lets call them field1, field2, field3 and field4. What is the best way to go about applying those trims and creating a new dataset? Can I apply the trip to all fields in a single map? or do I need to apply multiple map functions? When I try the map ( even with a single ) scala> val transformed_data = sample_data.map( | _.trim(col("field1")) | .trim(col("field2")) | .trim(col("field3")) | .trim(col("field4")) | ) I end up with the following error: <console>:26: error: value trim is not a member of org.apache.spark.sql.Row _.trim(col("field1")) ^ Any ideas / guidance would be appreciated!