Docs: use proper URIs (subpackages of com.datastax.driver.spark)

doc/2_loading.md

@@ -25,7 +25,7 @@ Load data into the table:
 Now you can read that table as `RDD`:
 
     val rdd = sc.cassandraTable("test", "words")
-    // rdd: com.datastax.driver.spark.CassandraRDD[com.datastax.driver.spark.CassandraRow] = CassandraRDD[0] at RDD at CassandraRDD.scala:41
+    // rdd: com.datastax.driver.spark.rdd.CassandraRDD[com.datastax.driver.spark.rdd.reader.CassandraRow] = CassandraRDD[0] at RDD at CassandraRDD.scala:41
 
     rdd.toArray.foreach(println)
     // CassandraRow{word: bar, count: 20}
@@ -41,7 +41,7 @@ Continuing with the previous example, follow these steps to access individual co
 Store the first item of the rdd in the firstRow value.
 
     val firstRow = rdd.first
-    // firstRow: com.datastax.driver.spark.CassandraRow = CassandraRow{word: bar, count: 20}
+    // firstRow: com.datastax.driver.spark.rdd.reader.CassandraRow = CassandraRow{word: bar, count: 20}
 
 Get the number of columns and column names:
 
@@ -85,7 +85,7 @@ In the test keyspace, set up a collection set using cqlsh:
 Then in your application, retrieve the first row: 
 
     val row = sc.cassandraTable("test", "users").first
-    // row: com.datastax.driver.spark.CassandraRow = CassandraRow{username: someone, emails: [[email protected], [email protected]]}
+    // row: com.datastax.driver.spark.rdd.reader.CassandraRow = CassandraRow{username: someone, emails: [[email protected], [email protected]]}
 
 Query the collection set in Cassandra from Spark:
 

doc/4_mapper.md

@@ -54,4 +54,4 @@ Property values might be also set by Scala-style setters. The following class is
     }
 
 
-[Next - Saving data](5_saving.md)  
+[Next - Saving data](5_saving.md)

doc/5_saving.md

@@ -56,4 +56,4 @@ The following properties set in `SparkConf` can be used to fine-tune the saving
   - `cassandra.output.batch.size.bytes`: maximum total size of the batch in bytes; defaults to 64 kB.
   - `cassandra.output.concurrent.writes`: maximum number of batches executed in parallel by a single Spark task; defaults to 5
 
-[Next - Customizing the object mapping](6_advanced_mapper.md)  
+[Next - Customizing the object mapping](6_advanced_mapper.md)