CaseTranform.scala

def transformSchemaFieldNames(
    s0: StructType,
    transformSchemaFieldName0: (String) => String
  ): StructType = {
    def loop(f0: StructField): StructField = {
      f0.dataType match {
        case s1: ArrayType =>
          s1.elementType match {
            case f1: StructType =>
              f0.copy(
                name = transformSchemaFieldName0(f0.name),
                dataType = s1.copy(
                  elementType =
                    transformSchemaFieldNames(f1, transformSchemaFieldName0)
                )
              )
            case _ =>
              f0.copy(name = transformSchemaFieldName0(f0.name))
          }
        case s1: StructType =>
          f0.copy(
            name = transformSchemaFieldName0(f0.name),
            dataType = transformSchemaFieldNames(s1, transformSchemaFieldName0)
          )
        case _ =>
          f0.copy(name = transformSchemaFieldName0(f0.name))
      }
    }
    s0.copy(fields = s0.map(loop).toArray)
  }

  def castNestedDataframe(df0: DataFrame, schema: StructType): DataFrame = {
    def loop0(schema: List[StructField],
              prefixes: Vector[String] = Vector()): List[(String, DataType)] = {
      schema match {
        case h :: tail =>
          val newPrefixes = prefixes :+ h.name
          h.dataType match {
            case f: ArrayType =>
              f.elementType match {
                case f0: StructType =>
                  (newPrefixes.mkString("."), f) :: loop0(tail, prefixes)
                case _ => loop0(tail, prefixes)
              }
            case f: StructType =>
              (newPrefixes.mkString("."), f) :: loop0(tail, prefixes)
            case _ => loop0(tail, Vector())
          }
        case Nil => List()
      }
    }

    val casts = loop0(
      transformSchemaFieldNames(
        df0.schema,
        transformNames.transformDatasetFieldName
      ).toList
    )

    casts.foldLeft(
      df0.toDF(df0.columns.map(transformNames.transformDatasetFieldName): _*)
    )({
      case (df1, (fieldName, cast)) =>
        df1.withColumn(fieldName, col(fieldName).cast(cast))
    })
  }