Docs on raster write: samples, parquet

Signed-off-by: Jason T. Brown <[email protected]>

Author: vpipkt

pyrasterframes/src/main/python/docs/raster-io.md

@@ -2,19 +2,18 @@
 
 The standard mechanism by which any data is brought in and out of a Spark Dataframe is the [Spark SQL DataSource][DS]. RasterFrames provides specialized DataSources for geospatial raster data and maintains compatibility with existing general purpose DataSources, such as Parquet.
 
-Three types of DataSources will be introduced: 
-
 * @ref:[Catalog Readers](raster-catalogs.md)
     - `aws-pds-l8-catalog`: built-in catalog over [Landsat on AWS][Landsat]
     - `aws-pds-modis-catalog`: built-in catalog over [MODIS on AWS][MODIS]
     - `geotrellis-catalog`: for enumerating [GeoTrellis layers][GTLayer]
 * @ref:[Raster Readers](raster-read.md)
-    - `raster`: the standard reader for most raster data
+    - `raster`: the standard reader for most raster data, including single raster files or catalogs
     - `geotiff`: a simplified reader for reading a single GeoTIFF file
     - `geotrellis`: for reading a [GeoTrellis layer][GTLayer])
 * @ref:[Raster Writers](raster-write.md)
-    - `geotrellis`: for creating a [GeoTrellis layer][GTLayer]
+    - @ref:[Tile](raster-write.md#tile-samples) and @ref:[DataFrame](raster-write.md#dataframe-samples) samples
     - `geotiff`: beta writer to GeoTiff file
+    - `geotrellis`: creating a [GeoTrellis layer][GTLayer]
     - [`parquet`][Parquet]: general purpose writer 
 
 There is also support for @ref:[vector data](vector-data.md) for masking and data labeling.

pyrasterframes/src/main/python/docs/raster-read.pymd

@@ -12,13 +12,15 @@ RasterFrames registers a DataSource named `raster` that enables reading of GeoTI
 
 ## Single Raster
 
-The simplest form is reading a single raster from a single URI:
+The simplest form is reading a single raster from a single URI.
 
 ```python read_one_uri
 rf = spark.read.raster('https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif')
 rf.printSchema()
 ```
 
+The file at the address above is a valid [Cloud Optimized GeoTIFF (COG)](https://www.cogeo.org/), which RasterFrames fully supports. RasterFrames will take advantage of the optimizations in the COG format to enable more efficient reading compared to vanilla GeoTIFFs.  
+
 Let's unpack the `proj_raster` column and look at the contents in more detail. It contains a [_CRS_][CRS], a spatial _extent_ measured in that CRS, and a two-dimensional array of numeric values called a _tile_. 
 
 ```python unpack_schema