Updated Sample ETL program to compile

docs/spark-etl/spark-etl-intro.md

@@ -20,23 +20,21 @@ This affords an opportunity to modify the dataset using any of the GeoTrellis op
 import geotrellis.raster.Tile
 import geotrellis.spark._
 import geotrellis.spark.etl.Etl
-import geotrellis.spark.io.index.ZCurveKeyIndexMethod
+import geotrellis.spark.etl.config.EtlConf
 import geotrellis.spark.util.SparkUtils
 import geotrellis.vector.ProjectedExtent
-
 import org.apache.spark.SparkConf
 
 object GeoTrellisETL {
+  type I = ProjectedExtent // or TemporalProjectedExtent for temporal ingest
+  type K = SpatialKey // or SpaceTimeKey for temporal ingest
+  type V = Tile // or MultibandTile to ingest multiband tile
   def main(args: Array[String]): Unit = {
     implicit val sc = SparkUtils.createSparkContext("GeoTrellis ETL", new SparkConf(true))
-    type I = ProjectedExtent // or TemporalProjectedExtent for temporal ingest
-    type K = SpatialKey // or SpaceTimeKey for temporal ingest
-    type V = Tile // or MultibandTile to ingest multiband tile
-
     try {
       EtlConf(args) foreach { conf =>
         /* parse command line arguments */
-        val etl = Etl(conf, modules)
+        val etl = Etl(conf)
         /* load source tiles using input module specified */
         val sourceTiles = etl.load[I, V]
         /* perform the reprojection and mosaicing step to fit tiles to LayoutScheme specified */